subject:(STDP)

Virginia Tech

1. Zhao, Chenyuan. Spike Processing Circuit Design for Neuromorphic Computing.

Degree: PhD, Electrical Engineering, 2019, Virginia Tech

► Neuromorphic computing is a kind of specific electronic system that could mimic biological bodies’ behavior. In most cases, neuromorphic computing system is built with analog… (more)

Subjects/Keywords: Neuromorphic computing; neuron; LIF; temporal encoding; spiking neural network; Inter-spike interval; encoder; decoder; STDP; latency

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APA (6^th Edition):

Zhao, C. (2019). Spike Processing Circuit Design for Neuromorphic Computing. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/93591

Chicago Manual of Style (16^th Edition):

Zhao, Chenyuan. “Spike Processing Circuit Design for Neuromorphic Computing.” 2019. Doctoral Dissertation, Virginia Tech. Accessed December 05, 2019. http://hdl.handle.net/10919/93591.

MLA Handbook (7^th Edition):

Zhao, Chenyuan. “Spike Processing Circuit Design for Neuromorphic Computing.” 2019. Web. 05 Dec 2019.

Vancouver:

Zhao C. Spike Processing Circuit Design for Neuromorphic Computing. [Internet] [Doctoral dissertation]. Virginia Tech; 2019. [cited 2019 Dec 05]. Available from: http://hdl.handle.net/10919/93591.

Council of Science Editors:

Zhao C. Spike Processing Circuit Design for Neuromorphic Computing. [Doctoral Dissertation]. Virginia Tech; 2019. Available from: http://hdl.handle.net/10919/93591

University of California – San Diego

2. Umbria Pedroni, Bruno. Boltzmann Energetics and Temporal Dynamics of Learning Neuromorphic Systems.

Degree: Bioengineering, 2019, University of California – San Diego

URL: http://www.escholarship.org/uc/item/5d83n1x2

► The brain's cognitive power does not arise on exacting digital precision in high-performance computing, but emerges from an extremely efficient and resilient collective form of… (more)

▼ The brain's cognitive power does not arise on exacting digital precision in high-performance computing, but emerges from an extremely efficient and resilient collective form of computation extending over very large ensembles of sluggish, imprecise, and unreliable analog components. In contrast to the reliable spike generation mechanism of cortical neurons, synapses are regarded as the primary source of this probabilistic behavior owing to release failures and quantal fluctuations. It has been speculated that the overall power efficiency and noise tolerance of the brain is a result of this unreliability in communication between neurons. Inspired by the stochastic nature of brain dynamics, we present methods of exploiting these concepts in order to produce more efficient algorithms and systems in the realm of neuromorphic computing, offering links between two traditionally disjoint scientific disciplines: computational neuroscience concerned with constructing models of brain function, and machine learning concerned with realizing adaptive computational intelligence.The first part of the dissertation investigates extensions on the Boltzmann machine, a stochastic recurrent artificial neural network capable of learning probability distributions over its inputs. Boltzmann machines are interesting from a neuromorphic perspective due to the local and Hebbian nature of their learning rule, along with parallel processing between network layers with which biological neural networks also operate. Additionally, the neurons in these networks present probabilistic activation functions and communicate with binary events, similar to what has been observed in experimental recordings of neural data. In search of more biological plausibility in inference and learning, we present conditions for significant equivalence between Boltzmann machines with contrastive divergence machine learning and integrate-and-fire neuronal networks with spike-timing-dependent plasticity (STDP). Next, we extend our methods to networks whose sole source of stochasticity pertains to the synapse, showing that synaptic noise can produce an efficient means of sampling. As the hallmark learning rule in spiking neural networks, we then investigate how STDP can be readily performed using simply forward connectivity access, and compare different data structures for organizing synaptic weights for memory efficiency.In the second part of the dissertation, we focus on learning neuromorphic systems and applications, including a methodology and an automation tool for implementing generative models of Boltzmann machines with digital spiking neurons. Next, we demonstrate how sparsely active neurons are capable of producing efficient results in a small-footprint keyword spotting application. Lastly, we present our ongoing work in designing a very large-scale reconfigurable digital neuromorphic system, tailored for both the machine learning as well as the computational neuroscience communities, which exploits the stochastic and temporal coding strategies developed in the…

Subjects/Keywords: Artificial intelligence; Neurosciences; Computer engineering; artificial neural network; Boltzmann; Neuromorphic; spiking neural network; STDP; synaptic connectivity

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APA (6^th Edition):

Umbria Pedroni, B. (2019). Boltzmann Energetics and Temporal Dynamics of Learning Neuromorphic Systems. (Thesis). University of California – San Diego. Retrieved from http://www.escholarship.org/uc/item/5d83n1x2

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Umbria Pedroni, Bruno. “Boltzmann Energetics and Temporal Dynamics of Learning Neuromorphic Systems.” 2019. Thesis, University of California – San Diego. Accessed December 05, 2019. http://www.escholarship.org/uc/item/5d83n1x2.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Umbria Pedroni, Bruno. “Boltzmann Energetics and Temporal Dynamics of Learning Neuromorphic Systems.” 2019. Web. 05 Dec 2019.

Vancouver:

Umbria Pedroni B. Boltzmann Energetics and Temporal Dynamics of Learning Neuromorphic Systems. [Internet] [Thesis]. University of California – San Diego; 2019. [cited 2019 Dec 05]. Available from: http://www.escholarship.org/uc/item/5d83n1x2.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Umbria Pedroni B. Boltzmann Energetics and Temporal Dynamics of Learning Neuromorphic Systems. [Thesis]. University of California – San Diego; 2019. Available from: http://www.escholarship.org/uc/item/5d83n1x2

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Adelaide

3. Lightheart, Toby Asher. Constructive spiking neural networks for simulations of neuroplasticity.

Degree: 2018, University of Adelaide

URL: http://hdl.handle.net/2440/115481

► Artificial neural networks are important tools in machine learning and neuroscience; however, a difficult step in their implementation is the selection of the neural network… (more)

▼ Artificial neural networks are important tools in machine learning and neuroscience; however, a difficult step in their implementation is the selection of the neural network size and structure. This thesis develops fundamental theory on algorithms for constructing neurons in spiking neural networks and simulations of neuroplasticity. This theory is applied in the development of a constructive algorithm based on spike-timing- dependent plasticity (STDP) that achieves continual one-shot learning of hidden spike patterns through neuron construction. The theoretical developments in this thesis begin with the proposal of a set of definitions of the fundamental components of constructive neural networks. Disagreement in terminology across the literature and a lack of clear definitions and requirements for constructive neural networks is a factor in the poor visibility and fragmentation of research. The proposed definitions are used as the basis for a generalised methodology for decomposing constructive neural networks into components to perform comparisons, design and analysis. Spiking neuron models are uncommon in constructive neural network literature; however, spiking neurons are common in simulated studies in neuroscience. Spike- timing-dependent construction is proposed as a distinct class of constructive algorithm for spiking neural networks. Past algorithms that perform spike-timing-dependent construction are decomposed into defined components for a detailed critical comparison and found to have limited applicability in simulations of biological neural networks. This thesis develops concepts and principles for designing constructive algorithms that are compatible with simulations of biological neural networks. Simulations often have orders of magnitude fewer neurons than related biological neural systems; there- fore, the neurons in a simulation may be assumed to be a selection or subset of a larger neural system with many neurons not simulated. Neuron construction and pruning may therefore be reinterpreted as the transfer of neurons between sets of simulated neurons and hypothetical neurons in the neural system. Constructive algorithms with a functional equivalence to transferring neurons between sets allow simulated neural networks to maintain biological plausibility while changing size. The components of a novel constructive algorithm are incrementally developed from the principles for biological plausibility. First, processes for calculating new synapse weights from observed simulation activity and estimates of past STDP are developed and analysed. Second, a method for predicting postsynaptic spike times for synapse weight calculations through the simulation of a proxy for hypothetical neurons is developed. Finally, spike-dependent conditions for neuron construction and pruning are developed and the processes are combined in a constructive algorithm for simulations of STDP. Repeating hidden spike patterns can be detected by neurons tuned through STDP; this… Advisors/Committee Members: Grainger, Steven Drummond (advisor), Lu, Tien-Fu (advisor), School of Mechanical Engineering (school).

Subjects/Keywords: constructive neural networks; spiking neurons; neural simulation; neuroplasticity; STDP; pattern detection; one-shot learning; continual learning

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APA (6^th Edition):

Lightheart, T. A. (2018). Constructive spiking neural networks for simulations of neuroplasticity. (Thesis). University of Adelaide. Retrieved from http://hdl.handle.net/2440/115481

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Lightheart, Toby Asher. “Constructive spiking neural networks for simulations of neuroplasticity.” 2018. Thesis, University of Adelaide. Accessed December 05, 2019. http://hdl.handle.net/2440/115481.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Lightheart, Toby Asher. “Constructive spiking neural networks for simulations of neuroplasticity.” 2018. Web. 05 Dec 2019.

Vancouver:

Lightheart TA. Constructive spiking neural networks for simulations of neuroplasticity. [Internet] [Thesis]. University of Adelaide; 2018. [cited 2019 Dec 05]. Available from: http://hdl.handle.net/2440/115481.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Lightheart TA. Constructive spiking neural networks for simulations of neuroplasticity. [Thesis]. University of Adelaide; 2018. Available from: http://hdl.handle.net/2440/115481

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

EPFL

4. Wozniak, Stanislaw Andrzej. Unsupervised Learning of Phase-Change-Based Neuromorphic Systems.

Degree: 2017, EPFL

URL: http://infoscience.epfl.ch/record/232675

► Neuromorphic systems provide brain-inspired methods of computing. In a neuromorphic architecture, inputs are processed by a network of neurons receiving operands through synaptic interconnections, tuned… (more)

▼ Neuromorphic systems provide brain-inspired methods of computing. In a neuromorphic architecture, inputs are processed by a network of neurons receiving operands through synaptic interconnections, tuned in the process of learning. Neurons act simultaneously as asynchronous computational and memory units, which leads to a high degree of parallelism. Furthermore, owing to developments in novel materials, memristive devices were proposed for area- and energy-efficient mixed digital-analog implementation of neurons and synapses. In this dissertation, we propose neuromorphic architectures based on phase-change memristors combined with biologically-inspired synaptic learning rules, and we experimentally demonstrate their pattern- and feature-learning capabilities. Firstly, by exploiting the physical properties of phase-change devices, we propose neuromorphic building blocks comprising phase-change-based neurons and synapses operating according to an unsupervised local learning rule. At the same time, we introduce multiple enhancements for pattern learning: an integration threshold for the phase-change soma to ensure noise-robust operation; selective synaptic depression mechanism to limit negative impact of asymmetric conductance response of phase-change synapses during the learning; WTA (Winner-Take-All) mechanism with level-tuned neurons that decreases power consumption in comparison to the classic lateral inhibition WTA; and learning WTA that enhances the quality of pattern visualization. Experimental results demonstrate the capabilities of the proposed architectures. In particular, a neuron with phase-change synapses was shown to learn and re-learn patterns of correlated activity. Furthermore, an all-phase-change neuron with a record number of 1M synapses successfully detected and visualized weakly-correlated patterns. Lastly, a network of all-phase-change neurons operating with level-tuned neurons accurately learned multiple patterns. Secondly, to scale-up the proposed architectures, we identify the need to improve the knowledge representation to learn features rather than patterns. We determine the key role of the feedback links for controlling the learning process, and combine intraneuronal with interneuronal feedback. Intraneuronal feedback determines what each neuron learns, whereas interneuronal feedback determines how information is distributed between the neurons. We propose two feature-learning architectures: an architecture with interneuronal feedback to the learning rule, and an architecture inspired by the biological observation of synaptic competition for learning-related proteins. Furthermore, we introduce a model of synaptic competition that guides the learning as well as detects novelty in the input, which is then used to dynamically adjust the size of the network. In a series of benchmarks for different feature types, synaptic competition outperformed other common methods, simultaneously adjusting the network to the optimal size. Finally, it was the only method that succeeded for a challenging dataset… Advisors/Committee Members: Leblebici, Yusuf, Pantazi, Angeliki.

Subjects/Keywords: neuromorphic systems; phase-change memristors; spiking neural networks; WTA; STDP; correlation detection; unsupervised online learning; feature extraction; independent components; synaptic competition

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Wozniak, S. A. (2017). Unsupervised Learning of Phase-Change-Based Neuromorphic Systems. (Thesis). EPFL. Retrieved from http://infoscience.epfl.ch/record/232675

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Wozniak, Stanislaw Andrzej. “Unsupervised Learning of Phase-Change-Based Neuromorphic Systems.” 2017. Thesis, EPFL. Accessed December 05, 2019. http://infoscience.epfl.ch/record/232675.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Wozniak, Stanislaw Andrzej. “Unsupervised Learning of Phase-Change-Based Neuromorphic Systems.” 2017. Web. 05 Dec 2019.

Vancouver:

Wozniak SA. Unsupervised Learning of Phase-Change-Based Neuromorphic Systems. [Internet] [Thesis]. EPFL; 2017. [cited 2019 Dec 05]. Available from: http://infoscience.epfl.ch/record/232675.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wozniak SA. Unsupervised Learning of Phase-Change-Based Neuromorphic Systems. [Thesis]. EPFL; 2017. Available from: http://infoscience.epfl.ch/record/232675

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

5. Zhang, Xu. Indirect Training Algorithms for Spiking Neural Networks based on Spiking Timing Dependent Plasticity and Their Applications .

Degree: 2017, Duke University

URL: http://hdl.handle.net/10161/14362

► Spiking neural networks have been used to investigate the mechanisms of processing in biological neural circuits or to propose hypotheses that can be tested… (more)

▼ Spiking neural networks have been used to investigate the mechanisms of processing in biological neural circuits or to propose hypotheses that can be tested in exper- iments. Because of their biological plausibility and event-based information trans- mission, Spiking Neural Networks (SNNs) have suggested as alternatives to Articial Neural Networks for pattern recognition, classication and function approximation problems with fewer neurons. In machine learning, SNNs has been shown to be able to solve pattern and robotic control. For SNNs to be used for such problems, they must incorporate some mechanism for learning. Current methods to train SNNs use learning algorithms which adjust the synaptic weights according to an update rule. In most cases the weights are modied directly. In potential applications such as driving plasticity in neural culture (in-vitro) and training neuromorphic chips the directly manipulation of synaptic weights is not possible. Therefore, indirect algo- rithms, which cause the SNNs to learn based on some biological learning mechanisms using stimulation of neurons oer signicant advantages over the existing algorithm for these real world applications. Indirect algorithms train the neural network by using external stimuli to modulate the synaptic strengths of a neural network according to synapses intrinsic mechanisms for plasticity. The training algorithms have been demonstrated in both Integrate and Fire neurons and more biologically realistic neural networks. In this thesis, four indi- rect methods to drive the synaptic weights to its desired value in a network through Spike Time Dependent Plasticity (STDP) are developed: Indirect Perturbation, In- direct Stochastic Gradient, Indirect ReSuMe, and Indirect Training with Supervised Teaching Signals. These algorithms are used to solve the temporal and spatial input- output mapping problem using temporal coding. The other type of problem is to mapping input output ring rates using rate coding. To test the algorithms, SNNs are used to control both virtual and real world robots. For the real world robots with SNNs, known and Neurorobots, two types of robot localization techniques are used: Optitrack, using ceiling mounted cameras and onboard markers, and embedded cameras. Both small and large SNNs with biologically realistic neurons are used to drive the neurorobots are modeled with input coming from Optitrack or the cameras with GPU accelerated SNN simulator. The results show that the indirect perturbation and indirect stochastic gradient algorithms can train an SNN to control the robot to nd targets and avoid obstacles even in the presence of sensor noise. The results also show that indirect training with supervised training signals algorithm can train a feedforward network with 1000s of neurons to process and output the correct movement… Advisors/Committee Members: Henriquez, Craig S (advisor).

Subjects/Keywords: Artificial intelligence; Neurosciences; Robotics; Indirect Training; Learning Mechanism; Neurorobotics; Robotics; Spiking Neural Networks; STDP

…Spike Timing-Dependent Plasticity (STDP) . . . . . . . . . . . . . . 28 2.4.1… …Implement STDP using Local Variables . . . . . . . . . . . . . 31 Discussion… …respectively. . . . . . . . . . . . . . . . . . . . . . . . . . 30 2.11 STDP learning scheme… …indirect ReSuMe under different STDP parameters… …101 5.9 Time cost analysis of indirect ReSuMe under different STDP parameters.102 xv…

11 more images…

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APA (6^th Edition):

Zhang, X. (2017). Indirect Training Algorithms for Spiking Neural Networks based on Spiking Timing Dependent Plasticity and Their Applications . (Thesis). Duke University. Retrieved from http://hdl.handle.net/10161/14362

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Zhang, Xu. “Indirect Training Algorithms for Spiking Neural Networks based on Spiking Timing Dependent Plasticity and Their Applications .” 2017. Thesis, Duke University. Accessed December 05, 2019. http://hdl.handle.net/10161/14362.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Zhang, Xu. “Indirect Training Algorithms for Spiking Neural Networks based on Spiking Timing Dependent Plasticity and Their Applications .” 2017. Web. 05 Dec 2019.

Vancouver:

Zhang X. Indirect Training Algorithms for Spiking Neural Networks based on Spiking Timing Dependent Plasticity and Their Applications . [Internet] [Thesis]. Duke University; 2017. [cited 2019 Dec 05]. Available from: http://hdl.handle.net/10161/14362.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Zhang X. Indirect Training Algorithms for Spiking Neural Networks based on Spiking Timing Dependent Plasticity and Their Applications . [Thesis]. Duke University; 2017. Available from: http://hdl.handle.net/10161/14362

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Sydney

6. Palmer, John H.C. Propagating spike waves in neural circuits: formation mechanisms and functional roles .

Degree: 2016, University of Sydney

URL: http://hdl.handle.net/2123/16758

► Propagating spike waves are a ubiquitous type of activity within the brain. However, both their formation mechanisms and functional roles remain largely unknown. In this… (more)

Subjects/Keywords: spiking neural circuits; plasticity; STDP; associative learning; spike waves

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APA (6^th Edition):

Palmer, J. H. C. (2016). Propagating spike waves in neural circuits: formation mechanisms and functional roles . (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/16758

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Palmer, John H C. “Propagating spike waves in neural circuits: formation mechanisms and functional roles .” 2016. Thesis, University of Sydney. Accessed December 05, 2019. http://hdl.handle.net/2123/16758.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Palmer, John H C. “Propagating spike waves in neural circuits: formation mechanisms and functional roles .” 2016. Web. 05 Dec 2019.

Vancouver:

Palmer JHC. Propagating spike waves in neural circuits: formation mechanisms and functional roles . [Internet] [Thesis]. University of Sydney; 2016. [cited 2019 Dec 05]. Available from: http://hdl.handle.net/2123/16758.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Palmer JHC. Propagating spike waves in neural circuits: formation mechanisms and functional roles . [Thesis]. University of Sydney; 2016. Available from: http://hdl.handle.net/2123/16758

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Universidade do Rio Grande do Sul

7. Susin, Eduarda Demori. Plasticidade sináptica e homeostase intrínseca em uma rede neural in silico : propriedades globais e de resposta a estímulos.

Degree: 2016, Universidade do Rio Grande do Sul

URL: http://hdl.handle.net/10183/143172

►

Recentemente observou-se experimentalmente, Johnson et al. (2010), que fatias organotípicas corticais de rato são capazes de completar padrões espaço-temporais, após serem treinadas. Embora se especule… (more)

Subjects/Keywords: Biofísica; Integrate-and-fire; Redes neurais; STDP; Homeostase; Intrinsic homeostasis; Signal propagation; Simulação computacional; Local connections; Random connections; Learning; Spatiotemporal processing

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APA (6^th Edition):

Susin, E. D. (2016). Plasticidade sináptica e homeostase intrínseca em uma rede neural in silico : propriedades globais e de resposta a estímulos. (Thesis). Universidade do Rio Grande do Sul. Retrieved from http://hdl.handle.net/10183/143172

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Susin, Eduarda Demori. “Plasticidade sináptica e homeostase intrínseca em uma rede neural in silico : propriedades globais e de resposta a estímulos.” 2016. Thesis, Universidade do Rio Grande do Sul. Accessed December 05, 2019. http://hdl.handle.net/10183/143172.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Susin, Eduarda Demori. “Plasticidade sináptica e homeostase intrínseca em uma rede neural in silico : propriedades globais e de resposta a estímulos.” 2016. Web. 05 Dec 2019.

Vancouver:

Susin ED. Plasticidade sináptica e homeostase intrínseca em uma rede neural in silico : propriedades globais e de resposta a estímulos. [Internet] [Thesis]. Universidade do Rio Grande do Sul; 2016. [cited 2019 Dec 05]. Available from: http://hdl.handle.net/10183/143172.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Susin ED. Plasticidade sináptica e homeostase intrínseca em uma rede neural in silico : propriedades globais e de resposta a estímulos. [Thesis]. Universidade do Rio Grande do Sul; 2016. Available from: http://hdl.handle.net/10183/143172

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

8. Verhoog, M.B. Information processing and storage by the human pyramidal neuron .

Degree: 2016, Vrije Universiteit Amsterdam

URL: http://hdl.handle.net/1871/54242

Subjects/Keywords: human brain slice electrophysiology neocortex dendritic morphology synaptic plasticity STDP nicotine

…stimulation induces bidirectional STDP-like plasticity in human motor cortex. Front. Hum. Neurosci… …Kerr, J.N.D. (2010). Timing is not everything: Neuromodulation opens the STDP gate… …sensitivity and loss in temporal contrast of STDP by dopaminergic modulation at hippocampal synapses…

1 more image…

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APA (6^th Edition):

Verhoog, M. B. (2016). Information processing and storage by the human pyramidal neuron . (Doctoral Dissertation). Vrije Universiteit Amsterdam. Retrieved from http://hdl.handle.net/1871/54242

Chicago Manual of Style (16^th Edition):

Verhoog, M B. “Information processing and storage by the human pyramidal neuron .” 2016. Doctoral Dissertation, Vrije Universiteit Amsterdam. Accessed December 05, 2019. http://hdl.handle.net/1871/54242.

MLA Handbook (7^th Edition):

Verhoog, M B. “Information processing and storage by the human pyramidal neuron .” 2016. Web. 05 Dec 2019.

Vancouver:

Verhoog MB. Information processing and storage by the human pyramidal neuron . [Internet] [Doctoral dissertation]. Vrije Universiteit Amsterdam; 2016. [cited 2019 Dec 05]. Available from: http://hdl.handle.net/1871/54242.

Council of Science Editors:

Verhoog MB. Information processing and storage by the human pyramidal neuron . [Doctoral Dissertation]. Vrije Universiteit Amsterdam; 2016. Available from: http://hdl.handle.net/1871/54242

University of Toronto

9. Himberger, Kevin D. A Spiking Circuit Model of Sequence Learning.

Degree: 2015, University of Toronto

URL: http://hdl.handle.net/1807/70418

►

The ability to integrate information over time is fundamental to cognition. Any sensory experience, from object recognition to speech comprehension, utilizes this ability. Contrary to… (more)

Subjects/Keywords: model; plasticity; process memory; sequence learning; spiking; STDP; 0317

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APA (6^th Edition):

Himberger, K. D. (2015). A Spiking Circuit Model of Sequence Learning. (Masters Thesis). University of Toronto. Retrieved from http://hdl.handle.net/1807/70418

Chicago Manual of Style (16^th Edition):

Himberger, Kevin D. “A Spiking Circuit Model of Sequence Learning.” 2015. Masters Thesis, University of Toronto. Accessed December 05, 2019. http://hdl.handle.net/1807/70418.

MLA Handbook (7^th Edition):

Himberger, Kevin D. “A Spiking Circuit Model of Sequence Learning.” 2015. Web. 05 Dec 2019.

Vancouver:

Himberger KD. A Spiking Circuit Model of Sequence Learning. [Internet] [Masters thesis]. University of Toronto; 2015. [cited 2019 Dec 05]. Available from: http://hdl.handle.net/1807/70418.

Council of Science Editors:

Himberger KD. A Spiking Circuit Model of Sequence Learning. [Masters Thesis]. University of Toronto; 2015. Available from: http://hdl.handle.net/1807/70418

Texas A&M University

10. Thulasiraman, Kumaran. Enhanced Reinforcement Learning with Attentional Feedback and Temporally Attenuated Distal Rewards.

Degree: 2015, Texas A&M University

URL: http://hdl.handle.net/1969.1/155519

► This thesis presents a new reinforcement learning mechanism suitable to be employed in artificial spiking neural networks of leaky integrate-and-fire (LIF) or Izhikevich neurons. The… (more)

▼ This thesis presents a new reinforcement learning mechanism suitable to be employed in artificial spiking neural networks of leaky integrate-and-fire (LIF) or Izhikevich neurons. The proposed mechanism is upgraded from, and closely built upon the learning algorithm introduced by Florian, in which local synaptic plasticity is based on the relative spike-timing of the pre and post-synaptic neurons (STDP), and is modulated by a global reinforcement signal. This work introduces and deals with multiple challenges identified in existing reinforcement learning schemes, that includes the distal reward problem, the spatial credit assignment problem and the response numbness problem. A number of improvements, that are inspired either from the biological elements or from similar implementations in non-spiking neural networks, are suggested to handle these challenges, and are validated through biologically-inspired experiments. The notion and implementation of attentional feedback that handles the spatial credit assignment problem during synaptic reinforcement are introduced. The effects of attenuated rewards, which gate network learning after satisfactory reinforcement is achieved, are also demonstrated. This aids in the exploration of the agent to discover other rewardable behaviors during learning. A spike-rate based input encoding scheme termed as balanced-pair binary state (BPBS) encoding, and a corresponding methodology for response selection are also introduced to improve network stability and confidence in response selection. The proposed techniques are validated using multiple biologically-inspired single agent as well as multi-agent game-theoretic experimental tasks. The single-agent tasks include exclusive OR (XOR) function reproduction and a bot walking task. The multi-agent interactive and cooperative tasks demonstrated include the general-sum iterated prisoners' dilemma (IPD) game problem and the distributed SensorNetwork problem from the NIPS '05 reinforcement learning benchmarks. The results and findings discussed in this work validate that the proposed improvements to existing implementations of reinforcement learning could, in fact, lead to better brain-like learning and behavior in artificial agents. Advisors/Committee Members: Li, Peng (advisor), Sprintson, Alex (committee member), Choe, Yoonsuck (committee member).

Subjects/Keywords: reinforcement learning; spiking neural networks; dopamine-modulated; STDP

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APA (6^th Edition):

Thulasiraman, K. (2015). Enhanced Reinforcement Learning with Attentional Feedback and Temporally Attenuated Distal Rewards. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/155519

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Thulasiraman, Kumaran. “Enhanced Reinforcement Learning with Attentional Feedback and Temporally Attenuated Distal Rewards.” 2015. Thesis, Texas A&M University. Accessed December 05, 2019. http://hdl.handle.net/1969.1/155519.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Thulasiraman, Kumaran. “Enhanced Reinforcement Learning with Attentional Feedback and Temporally Attenuated Distal Rewards.” 2015. Web. 05 Dec 2019.

Vancouver:

Thulasiraman K. Enhanced Reinforcement Learning with Attentional Feedback and Temporally Attenuated Distal Rewards. [Internet] [Thesis]. Texas A&M University; 2015. [cited 2019 Dec 05]. Available from: http://hdl.handle.net/1969.1/155519.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Thulasiraman K. Enhanced Reinforcement Learning with Attentional Feedback and Temporally Attenuated Distal Rewards. [Thesis]. Texas A&M University; 2015. Available from: http://hdl.handle.net/1969.1/155519

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

NSYSU

11. Su, Wan-ching. Study on the Bionic Synapse Application of Lithium aluminum oxide Non-Volatile Resistance Random Access Memory.

Degree: Master, Mechanical and Electro-Mechanical Engineering, 2015, NSYSU

URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0317115-123644

► Since long time ago, the nature being has been the source of humanâs senses of invention principles productions, in which bionic technology has been well… (more)

▼ Since long time ago, the nature being has been the source of humanâs senses of invention principles productions, in which bionic technology has been well developed in recent years. Lithium aluminum oxide (LiAlO) thin film Resistance Random Access memory (RRAM) reveals an excellent multi-bites memory. It is considered for application of artificial neural network and analog storage to emulating memory rules in the brain. In this thesis, two different RRAMs, investigated are that in one the insulation layer is lithium aluminum oxide, and the other one is aluminum oxide, fabricated by Multi-target magnetron sputtering. The RRAM upper and lower electrodes are platinum and titanium nitride, respectively. In operating process of this LiAlO RRAM the reset process reveals two reset stages. Applying the ionic diffusion and the titanium nitride attraction an ion model of lithium-ion, is discussed about the phenomenon of the two-phase wide resistance value. And utilizing oxygen ions model the two-stage redox Reset resistance of lithium wires are explained. The experiment using sputtering platinum on the titanium nitride electrodes can prevent lithium ions diffusing into the TiN electrode, that HRS distribution is reduced to one Order. However, the distribution of LRS on titanium nitride becomes wider than that one without Pt layer on titanium nitride electrode. Since the Pt electrode blocks oxygen ions and lithium ions diffusing into the titanium nitride electrode, causes preventing of the instability of LRS. Both DC and Pulse Voltage have been applied to perform lithium aluminum oxide RRAM, and reveal the HRS resistance changing continuously, but alumina RRAM does not. Applied with Pulse voltage on lithium aluminum oxide RRAM can induce a bionic brain behavior of Spike-Timing-Dependent Plasticity (STDP), which is one of the most important characteristics of biological brain and memories. the LRS of LiAlO RRAM changes gradually after applying a continuous DC voltage. From the fitting of their I-V curves the conductive mechanism is identified. From its resistance of Ohmic conduction region changes after numbers of I-V scans, a Multi-filament model is proposed to explain the Multi-Reset electrical mechanism. Advisors/Committee Members: Ting-Chang Chang (chair), Tsung-Ming Tsai (chair), Tai-Fa Young (committee member), Che-Hsin Lin (chair).

Subjects/Keywords: Neural Networks; Lithium Aluminum Oxide; STDP; Multi-Bits Access; Multi-Filament; RRAM

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APA (6^th Edition):

Su, W. (2015). Study on the Bionic Synapse Application of Lithium aluminum oxide Non-Volatile Resistance Random Access Memory. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0317115-123644

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Su, Wan-ching. “Study on the Bionic Synapse Application of Lithium aluminum oxide Non-Volatile Resistance Random Access Memory.” 2015. Thesis, NSYSU. Accessed December 05, 2019. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0317115-123644.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Su, Wan-ching. “Study on the Bionic Synapse Application of Lithium aluminum oxide Non-Volatile Resistance Random Access Memory.” 2015. Web. 05 Dec 2019.

Vancouver:

Su W. Study on the Bionic Synapse Application of Lithium aluminum oxide Non-Volatile Resistance Random Access Memory. [Internet] [Thesis]. NSYSU; 2015. [cited 2019 Dec 05]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0317115-123644.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Su W. Study on the Bionic Synapse Application of Lithium aluminum oxide Non-Volatile Resistance Random Access Memory. [Thesis]. NSYSU; 2015. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0317115-123644

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

12. Drake, Kolton T. Biomimetic Application of Ion-Conducting-Based Memristive Devices in Spike-Timing-Dependent-Plasticity.

Degree: 2015, Boise State University

URL: https://scholarworks.boisestate.edu/td/1001

► The design and synthesis of artificial learning systems has been aided by the study of biological learning systems. Classic biological learning is driven by the… (more)

Subjects/Keywords: memristor; neuromorphic; STDP; biomimetics; Electrical and Computer Engineering

11 more images…

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Drake, K. T. (2015). Biomimetic Application of Ion-Conducting-Based Memristive Devices in Spike-Timing-Dependent-Plasticity. (Thesis). Boise State University. Retrieved from https://scholarworks.boisestate.edu/td/1001

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Drake, Kolton T. “Biomimetic Application of Ion-Conducting-Based Memristive Devices in Spike-Timing-Dependent-Plasticity.” 2015. Thesis, Boise State University. Accessed December 05, 2019. https://scholarworks.boisestate.edu/td/1001.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Drake, Kolton T. “Biomimetic Application of Ion-Conducting-Based Memristive Devices in Spike-Timing-Dependent-Plasticity.” 2015. Web. 05 Dec 2019.

Vancouver:

Drake KT. Biomimetic Application of Ion-Conducting-Based Memristive Devices in Spike-Timing-Dependent-Plasticity. [Internet] [Thesis]. Boise State University; 2015. [cited 2019 Dec 05]. Available from: https://scholarworks.boisestate.edu/td/1001.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Drake KT. Biomimetic Application of Ion-Conducting-Based Memristive Devices in Spike-Timing-Dependent-Plasticity. [Thesis]. Boise State University; 2015. Available from: https://scholarworks.boisestate.edu/td/1001

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Illinois – Urbana-Champaign

13. Duda, Alexander Michael. Towards a neocortically-inspired ab initio cellular model of associative memory.

Degree: PhD, Electrical & Computer Engr, 2015, University of Illinois – Urbana-Champaign

URL: http://hdl.handle.net/2142/78735

► We are interested in self-organization and adaptation in intelligent systems that are robustly coupled with the real world. Such systems have a variety of sensory… (more)

Subjects/Keywords: Ab Initio Cellular Models; Associative Memory; Attractors; Complex Networks; Emergence; Information-Preserving; Multi-Scale Modeling; Neurorobotics; Nonlinear Dynamics; Real-World Coupling; Spiking Neural Networks; Spike-timing Dependent Plasticity; Spike-timing Dependent Plasticity (STDP) Learning; Topological Adaptation

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Duda, A. M. (2015). Towards a neocortically-inspired ab initio cellular model of associative memory. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/78735

Chicago Manual of Style (16^th Edition):

Duda, Alexander Michael. “Towards a neocortically-inspired ab initio cellular model of associative memory.” 2015. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed December 05, 2019. http://hdl.handle.net/2142/78735.

MLA Handbook (7^th Edition):

Duda, Alexander Michael. “Towards a neocortically-inspired ab initio cellular model of associative memory.” 2015. Web. 05 Dec 2019.

Vancouver:

Duda AM. Towards a neocortically-inspired ab initio cellular model of associative memory. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2015. [cited 2019 Dec 05]. Available from: http://hdl.handle.net/2142/78735.

Council of Science Editors:

Duda AM. Towards a neocortically-inspired ab initio cellular model of associative memory. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2015. Available from: http://hdl.handle.net/2142/78735

Arizona State University

14. Sivaraj, Mahraj. STDP Implementation Using CBRAM Devices in CMOS.

Degree: Electrical Engineering, 2015, Arizona State University

URL: http://repository.asu.edu/items/34779

Subjects/Keywords: Electrical engineering; Adaptive systems; Conductive Bridge RAM (CBRAM); Learning systems; Memristor; Spike-Timing Dependent Plasticity (STDP); Spiking Neural Networks

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Sivaraj, M. (2015). STDP Implementation Using CBRAM Devices in CMOS. (Masters Thesis). Arizona State University. Retrieved from http://repository.asu.edu/items/34779

Chicago Manual of Style (16^th Edition):

Sivaraj, Mahraj. “STDP Implementation Using CBRAM Devices in CMOS.” 2015. Masters Thesis, Arizona State University. Accessed December 05, 2019. http://repository.asu.edu/items/34779.

MLA Handbook (7^th Edition):

Sivaraj, Mahraj. “STDP Implementation Using CBRAM Devices in CMOS.” 2015. Web. 05 Dec 2019.

Vancouver:

Sivaraj M. STDP Implementation Using CBRAM Devices in CMOS. [Internet] [Masters thesis]. Arizona State University; 2015. [cited 2019 Dec 05]. Available from: http://repository.asu.edu/items/34779.

Council of Science Editors:

Sivaraj M. STDP Implementation Using CBRAM Devices in CMOS. [Masters Thesis]. Arizona State University; 2015. Available from: http://repository.asu.edu/items/34779

Arizona State University

15. Mahalanabis, Debayan. Multilevel Resistance Programming in Conductive Bridge Resistive Memory.

Degree: Electrical Engineering, 2015, Arizona State University

URL: http://repository.asu.edu/items/36417

► This work focuses on the existence of multiple resistance states in a type of emerging non-volatile resistive memory device known commonly as Programmable Metallization Cell… (more)

▼ This work focuses on the existence of multiple resistance states in a type of emerging non-volatile resistive memory device known commonly as Programmable Metallization Cell (PMC) or Conductive Bridge Random Access Memory (CBRAM), which can be important for applications such as multi-bit memory as well as non-volatile logic and neuromorphic computing. First, experimental data from small signal, quasi-static and pulsed mode electrical characterization of such devices are presented which clearly demonstrate the inherent multi-level resistance programmability property in CBRAM devices. A physics based analytical CBRAM compact model is then presented which simulates the ion-transport dynamics and filamentary growth mechanism that causes resistance change in such devices. Simulation results from the model are fitted to experimental dynamic resistance switching characteristics. The model designed using Verilog-a language is computation-efficient and can be integrated with industry standard circuit simulation tools for design and analysis of hybrid circuits involving both CMOS and CBRAM devices. Three main circuit applications for CBRAM devices are explored in this work. Firstly, the susceptibility of CBRAM memory arrays to single event induced upsets is analyzed via compact model simulation and experimental heavy ion testing data that show possibility of both high resistance to low resistance and low resistance to high resistance transitions due to ion strikes. Next, a non-volatile sense amplifier based flip-flop architecture is proposed which can help make leakage power consumption negligible by allowing complete shutdown of power supply while retaining its output data in CBRAM devices. Reliability and energy consumption of the flip-flop circuit for different CBRAM low resistance levels and supply voltage values are analyzed and compared to CMOS designs. Possible extension of this architecture for threshold logic function computation using the CBRAM devices as re-configurable resistive weights is also discussed. Lastly, Spike timing dependent plasticity (STDP) based gradual resistance change behavior in CBRAM device fabricated in back-end-of-line on a CMOS die containing integrate and fire CMOS neuron circuits is demonstrated for the first time which indicates the feasibility of using CBRAM devices as electronic synapses in spiking neural network hardware implementations for non-Boolean neuromorphic computing.

Subjects/Keywords: Electrical engineering; Nanotechnology; CBRAM; Compact Model; Neuromorphic Computing; Non-volatile Memory; Resistive Memory; STDP

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Mahalanabis, D. (2015). Multilevel Resistance Programming in Conductive Bridge Resistive Memory. (Doctoral Dissertation). Arizona State University. Retrieved from http://repository.asu.edu/items/36417

Chicago Manual of Style (16^th Edition):

Mahalanabis, Debayan. “Multilevel Resistance Programming in Conductive Bridge Resistive Memory.” 2015. Doctoral Dissertation, Arizona State University. Accessed December 05, 2019. http://repository.asu.edu/items/36417.

MLA Handbook (7^th Edition):

Mahalanabis, Debayan. “Multilevel Resistance Programming in Conductive Bridge Resistive Memory.” 2015. Web. 05 Dec 2019.

Vancouver:

Mahalanabis D. Multilevel Resistance Programming in Conductive Bridge Resistive Memory. [Internet] [Doctoral dissertation]. Arizona State University; 2015. [cited 2019 Dec 05]. Available from: http://repository.asu.edu/items/36417.

Council of Science Editors:

Mahalanabis D. Multilevel Resistance Programming in Conductive Bridge Resistive Memory. [Doctoral Dissertation]. Arizona State University; 2015. Available from: http://repository.asu.edu/items/36417

Vilnius University

16. Feiza, Vidmantas. Neurono sinapsių plastiškumo modeliavimas.

Degree: Master, 2014, Vilnius University

URL: http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2011~D_20141223_175459-67418 ;

►

Tobulėjanti pavienių nervinių ląstelių parametrų matavimo technika suteikia milžiniškus kiekius informacijos. Kad apdoroti tokias gausias duomenų apimtis, būtina taikyti sudėtingus duomenų apdorojimo metodus pasitelkiant galingas… (more)

Subjects/Keywords: Synapses; Synaptic plasticity; STDP; Temporal window; Input discrimination; Modeling; Sinapsių plastiškumas; Modeliavimas; Sinapsės; Piramidinis neuronas

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Feiza, Vidmantas. (2014). Neurono sinapsių plastiškumo modeliavimas. (Masters Thesis). Vilnius University. Retrieved from http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2011~D_20141223_175459-67418 ;

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Chicago Manual of Style (16^th Edition):

Feiza, Vidmantas. “Neurono sinapsių plastiškumo modeliavimas.” 2014. Masters Thesis, Vilnius University. Accessed December 05, 2019. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2011~D_20141223_175459-67418 ;.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

MLA Handbook (7^th Edition):

Feiza, Vidmantas. “Neurono sinapsių plastiškumo modeliavimas.” 2014. Web. 05 Dec 2019.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

Feiza, Vidmantas. Neurono sinapsių plastiškumo modeliavimas. [Internet] [Masters thesis]. Vilnius University; 2014. [cited 2019 Dec 05]. Available from: http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2011~D_20141223_175459-67418 ;.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

Feiza, Vidmantas. Neurono sinapsių plastiškumo modeliavimas. [Masters Thesis]. Vilnius University; 2014. Available from: http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2011~D_20141223_175459-67418 ;

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

17. Lecerf, Gwendal. Développement d'un réseau de neurones impulsionnels sur silicium à synapses memristives : Development of a silicon spiking neural network with memristives synapses.

Degree: Docteur es, Electronique, 2014, Bordeaux

URL: http://www.theses.fr/2014BORD0219

►

Durant ces trois années de doctorat, financées par le projet ANR MHANN (MemristiveHardware Analog Neural Network), nous nous sommes intéressés au développement d’une nouvelle architecture… (more)

▼

Durant ces trois années de doctorat, financées par le projet ANR MHANN (MemristiveHardware Analog Neural Network), nous nous sommes intéressés au développement d’une nouvelle architecture de calculateur à l’aide de réseaux de neurones. Les réseaux de neurones artificiels sont particulièrement bien adaptés à la reconnaissance d’images et peuvent être utilisés en complément des processeurs séquentiels. En 2008, une nouvelle technologie de composant a vu le jour : le memristor. Classé comme étant le quatrième élément passif, il est possible de modifier sa résistance en fonction de la densité de courant qui le traverse et de garder en mémoire ces changements. Grâce à leurs propriétés, les composants memristifs sont des candidats idéaux pour jouer le rôle des synapses au sein des réseaux de neurones artificiels. En effectuant des mesures sur la technologie des memristors ferroélectriques de l’UMjCNRS/Thalès de l’équipe de Julie Grollier, nous avons pu démontrer qu’il était possible d’obtenir un apprentissage de type STDP (Spike Timing Dependant Plasticity) classiquement utilisé avec les réseaux de neurones impulsionnels. Cette forme d’apprentissage, inspirée de la biologie, impose une variation des poids synaptiques en fonction des évènements neuronaux. En s’appuyant sur les mesures réalisées sur ces memristors et sur des simulations provenant d’un programme élaboré avec nos partenaires de l’INRIA Saclay, nous avons conçu successivement deux puces en silicium pour deux technologies de memristors ferroélectriques. La première technologie (BTO), moins performante, a été mise de côté au profit d’une seconde technologie (BFO). La seconde puce a été élaborée avec les retours d’expérience de la première puce. Elle contient deux couches d’un réseau de neurones impulsionnels dédié à l’apprentissage d’images de 81 pixels. En la connectant à un boitier contenant un crossbar de memristors, nous pourrons réaliser un démonstrateur d’un réseau de neurones hybride réalisé avec des synapses memristives ferroélectriques.

Supported financially by ANR MHANN project, this work proposes an architecture ofspiking neural network in order to recognize pictures, where traditional processing units are inefficient regarding this. In 2008, a new passive electrical component had been discovered : the memristor. Its resistance can be adjusted by applying a potential between its terminals. Behaving intrinsically as artificial synapses, memristives devices can be used inside artificial neural networks.We measure the variation in resistance of a ferroelectric memristor (obtained from UMjCNRS/Thalès) similar to the biological law STDP (Spike Timing Dependant Plasticity) used with spiking neurons. With our measurements on the memristor and our network simulation (aided by INRIASaclay) we designed successively two versions of the IC. The second IC design is driven by specifications of the first IC with additional functionalists. The second IC contains two layers of a spiking neural network dedicated to learn a picture of 81 pixels. A demonstrator of…

Advisors/Committee Members: Saïghi, Sylvain (thesis director), Tomas, Jean (thesis director).

Subjects/Keywords: Conception Analogique; Réseau de Neurones impulsionnels; Memristor; Résistance Memristive; Apprentissage; STDP; Analog Circuit Conception; Spiking Neural Network; Memristor,; Memristive Resistor; Learning; STDP

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Lecerf, G. (2014). Développement d'un réseau de neurones impulsionnels sur silicium à synapses memristives : Development of a silicon spiking neural network with memristives synapses. (Doctoral Dissertation). Bordeaux. Retrieved from http://www.theses.fr/2014BORD0219

Chicago Manual of Style (16^th Edition):

Lecerf, Gwendal. “Développement d'un réseau de neurones impulsionnels sur silicium à synapses memristives : Development of a silicon spiking neural network with memristives synapses.” 2014. Doctoral Dissertation, Bordeaux. Accessed December 05, 2019. http://www.theses.fr/2014BORD0219.

MLA Handbook (7^th Edition):

Lecerf, Gwendal. “Développement d'un réseau de neurones impulsionnels sur silicium à synapses memristives : Development of a silicon spiking neural network with memristives synapses.” 2014. Web. 05 Dec 2019.

Vancouver:

Lecerf G. Développement d'un réseau de neurones impulsionnels sur silicium à synapses memristives : Development of a silicon spiking neural network with memristives synapses. [Internet] [Doctoral dissertation]. Bordeaux; 2014. [cited 2019 Dec 05]. Available from: http://www.theses.fr/2014BORD0219.

Council of Science Editors:

Lecerf G. Développement d'un réseau de neurones impulsionnels sur silicium à synapses memristives : Development of a silicon spiking neural network with memristives synapses. [Doctoral Dissertation]. Bordeaux; 2014. Available from: http://www.theses.fr/2014BORD0219

Université Paris-Sud – Paris XI

18. Cabaret, Théo. Etude, réalisation et caractérisation de memristors organiques électro-greffés en tant que nanosynapses de circuits neuro-inspirés : Study, fabrication and characterization of electro-grafted organic memristors as nanosynapses for neuro inspired circuits.

Degree: Docteur es, Physique‎, 2014, Université Paris-Sud – Paris XI

URL: http://www.theses.fr/2014PA112168

►

Cette thèse s'inscrit dans le contexte de l'étude des circuits neuromorphiques utilisant des dispositifs memristifs comme synapses. Son objectif principal est d'évaluer les mérites d'une… (more)

▼

Cette thèse s'inscrit dans le contexte de l'étude des circuits neuromorphiques utilisant des dispositifs memristifs comme synapses. Son objectif principal est d'évaluer les mérites d'une nouvelle classe de mémoires organiques développées au LICSEN (CEA Saclay/IRAMIS) et, plus particulièrement, leur adéquation avec les propositions d'implémentation et les règles d'apprentissage proposées par l'équipe NanoArchi de l'IEF (Univ. Paris-Sud, Orsay). Les memristors étudiés sont basés sur l'electro-greffage en films minces de complexes organiques redox pour la formation de jonctions métal/molécules/métal robustes et scalables. Outre la fabrication de memristors, le travail inclut d'importants efforts de caractérisation électrique (vitesse, non-volatilité, scalabilité, robustesse, etc.) visant d'une part à étudier les mécanismes de commutation dans ces nouveaux matériaux memristifs organiques, et d'autres part, à évaluer leur potentiel en tant que synapses. Cette thèse présente également une étude préparatoire à la réalisation d'un démonstrateur de circuit mixte de type réseaux de neurones combinant nano-memristors et électronique conventionnelle (programmabilité des dispositifs en mode impulsionnel, réalisation d'assemblées de dispositifs, variabilité). De plus, la démonstration de la compatibilité de ces memristors avec la propriété STDP (Spike Timing Dependent Plasticity) ainsi que de l’apprentissage d’un « réflexe conditionné » ouvrent la voie aux apprentissages non-supervisés.

This PhD project takes place in the context of the study of neuromorphic circuits using memristor devices as synapses. The main objective is to evaluate a new class of organic memories developed at LICSEN (CEA Saclay/IRAMIS) and particularly their compatibility with the learning rules and the implementation strategy proposed by the Nanoarchi group at IEF (Univ. Paris-Sud, Orsay). These new memristors are based on the electro-grafting of organic redox complexes thin films to form robust and scalable metal/molecules/metal junctions. In addition to memristor fabrication, this work includes detailed electrical characterization studies (speed, retention property, scalability, robustness, etc.) aiming at, on the one hand, establishing the commutation mechanism in these new memristors and, on the other hand, evaluating their potential as synapses. This work also proposes a preparatory study of a neural-network type mixed-circuit demonstrator combining nano-memristors and conventional electronic (programmability of devices by spikes, fabrication of assemblies of memristors, variability). Moreover the demonstration of the compatibility of such memristors with the STDP (Spike Timing Dependent Plasticity) property and of the learning of a “conditioned reflex” opens the way to future unsupervised learning studies.

Advisors/Committee Members: Klein, Jacques-Olivier (thesis director).

Subjects/Keywords: Memristors organiques; Mémoires résistives; Circuit neuro-inspiré; STDP; Nanotubes de carbone; Organic memristors; Resistive memory; Neuro inspired circuit; STDP; Carbon nanotubes

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Cabaret, T. (2014). Etude, réalisation et caractérisation de memristors organiques électro-greffés en tant que nanosynapses de circuits neuro-inspirés : Study, fabrication and characterization of electro-grafted organic memristors as nanosynapses for neuro inspired circuits. (Doctoral Dissertation). Université Paris-Sud – Paris XI. Retrieved from http://www.theses.fr/2014PA112168

Chicago Manual of Style (16^th Edition):

Cabaret, Théo. “Etude, réalisation et caractérisation de memristors organiques électro-greffés en tant que nanosynapses de circuits neuro-inspirés : Study, fabrication and characterization of electro-grafted organic memristors as nanosynapses for neuro inspired circuits.” 2014. Doctoral Dissertation, Université Paris-Sud – Paris XI. Accessed December 05, 2019. http://www.theses.fr/2014PA112168.

MLA Handbook (7^th Edition):

Cabaret, Théo. “Etude, réalisation et caractérisation de memristors organiques électro-greffés en tant que nanosynapses de circuits neuro-inspirés : Study, fabrication and characterization of electro-grafted organic memristors as nanosynapses for neuro inspired circuits.” 2014. Web. 05 Dec 2019.

Vancouver:

Cabaret T. Etude, réalisation et caractérisation de memristors organiques électro-greffés en tant que nanosynapses de circuits neuro-inspirés : Study, fabrication and characterization of electro-grafted organic memristors as nanosynapses for neuro inspired circuits. [Internet] [Doctoral dissertation]. Université Paris-Sud – Paris XI; 2014. [cited 2019 Dec 05]. Available from: http://www.theses.fr/2014PA112168.

Council of Science Editors:

Cabaret T. Etude, réalisation et caractérisation de memristors organiques électro-greffés en tant que nanosynapses de circuits neuro-inspirés : Study, fabrication and characterization of electro-grafted organic memristors as nanosynapses for neuro inspired circuits. [Doctoral Dissertation]. Université Paris-Sud – Paris XI; 2014. Available from: http://www.theses.fr/2014PA112168

NSYSU

19. Wang, Zhi-yang. Study on Applications of LiSiOx Thin-Film Resistance Random Access Memory as Synapse in Neuromorphic Systems.

Degree: Master, Materials and Optoelectronic Science, 2014, NSYSU

URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0609114-133231

► The information stored in human brain is different from computer, it storages and transmits messages through analog signal instead of digital signal. In this study,… (more)

Subjects/Keywords: RRAM; CRS; synaptic plasticity; STDP; STM; LTM; analog storage; lithium silicate

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Wang, Z. (2014). Study on Applications of LiSiOx Thin-Film Resistance Random Access Memory as Synapse in Neuromorphic Systems. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0609114-133231

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Wang, Zhi-yang. “Study on Applications of LiSiOx Thin-Film Resistance Random Access Memory as Synapse in Neuromorphic Systems.” 2014. Thesis, NSYSU. Accessed December 05, 2019. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0609114-133231.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Wang, Zhi-yang. “Study on Applications of LiSiOx Thin-Film Resistance Random Access Memory as Synapse in Neuromorphic Systems.” 2014. Web. 05 Dec 2019.

Vancouver:

Wang Z. Study on Applications of LiSiOx Thin-Film Resistance Random Access Memory as Synapse in Neuromorphic Systems. [Internet] [Thesis]. NSYSU; 2014. [cited 2019 Dec 05]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0609114-133231.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wang Z. Study on Applications of LiSiOx Thin-Film Resistance Random Access Memory as Synapse in Neuromorphic Systems. [Thesis]. NSYSU; 2014. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0609114-133231

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Tokyo Institute of Technology / 東京工業大学

20. 宮田, 龍太. 連想記憶に最適な神経実装の探索: 海馬のスパイクタイミング依存可塑性と位相応答曲線 : Optimal Design for Associative Memory: Hippocampal Spike-Timing-Dependent Plasticity and Phase Response Curve; 連想記憶に最適な神経実装の探索: 海馬のスパイクタイミング依存可塑性と位相応答曲線.

Degree: 博士(工学), 2015, Tokyo Institute of Technology / 東京工業大学

URL: http://t2r2.star.titech.ac.jp/cgi-bin/publicationinfo.cgi?q_publication_content_number=CTT100691071

►

Recently, some researchers have tried to specify optimal pairs of the synaptic plasticity rule for memory storage and the form of neural interactions for memory… (more)

▼

Recently, some researchers have tried to specify optimal pairs of the synaptic plasticity rule for memory storage and the form of neural interactions for memory retrieval in a neural network model. Lengyel et al. developed a normative theory for auto-associative memory networks recalling spatio-temporal spike patterns on the basis of Bayesian theory. Under the speculation that a phase response curve (PRC) is an appropriate way to formulate the neural interactions if memories are stored by a spike-timing-dependent plasticity (STDP) rule, they derived the optimal pairs of STDP window functions and PRCs for executing the associative memory. In this doctoral thesis, we propose a synthetic approach of optimal design for associative memory networks, which consists of bottom-up and top-down steps: In the bottom-up step, under the assumption of regular spiking and weak coupling, we formulate an associative memory network recalling spatio-temporal patterns as a phase oscillator model consisting of an STDP window function and a PRC. Next, we analytically derive the mutual information between a stored pattern and the retrieval one, and use it to evaluate memory retrieval performance. In the top-down steps, by maximizing the objective function given by the mutual information, we derive optimal pairs of STDP window functions and PRCs. We firstly verify whether the optimal pair of STDP window functions and PRCs for executing the auto-associative memory derived from our approach are identical those derived from Lengyel's approach. With a zero noise limit, we obtain the same relation between the STDP window function and the PRC as that which Lengyel et al. obtained. Furthermore, recently reported experimental findings suggest that the hippocampal CA1 network stores spatio-temporal spike patterns and retrieves temporally reversed and spread-out patterns. We secondly applied to our approach to derive optimal pairs of STDP window functions and PRCs for executing such memory retrievals.

　近年、記憶を記銘するためのシナプス可塑性則と想起のための神経相互作用則の最適な組合せを探索する試みがある。Lengyelら（2005）は、スパイクタイミング依存可塑性(spike-timing-dependent plasticity, STDP) に着目した場合、その記憶想起には位相応答曲線（phase response curve, PRC）で記述された神経相互作用を用いることが適切であると仮定し、ベイズ理論の枠組みから最適なSTDPとPRCの関係を導いた。本研究では、神経細胞の高次元非線形動力学系の位相縮約モデルを求め、このモデルの物理的制約下で最適な記銘と想起を実現するSTDPとPRCの組合せを導出し、Lengyelらとの対応を明らかにした。また、最近の研究から、海馬ではスパイク時空間パターンの記銘とその時間反転パターンや引き延ばしパターンの想起が行われていることが示唆されている。そこで本手法をこれらの記憶想起の場合に適用し、最適なSTDPとPRCの組合せを探索した。

Subjects/Keywords: associative memory; 連想記憶; STDP; スパイクタイミング依存可塑性; PRC; 位相応答曲線; reversed pattern retrieval; 反転パターン想起; spread-out pattern retrieval; 引き延ばしパターン想起; mutual information maximization; 相互情報量最大化

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

宮田, �. (2015). 連想記憶に最適な神経実装の探索: 海馬のスパイクタイミング依存可塑性と位相応答曲線 : Optimal Design for Associative Memory: Hippocampal Spike-Timing-Dependent Plasticity and Phase Response Curve; 連想記憶に最適な神経実装の探索: 海馬のスパイクタイミング依存可塑性と位相応答曲線. (Thesis). Tokyo Institute of Technology / 東京工業大学. Retrieved from http://t2r2.star.titech.ac.jp/cgi-bin/publicationinfo.cgi?q_publication_content_number=CTT100691071

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

宮田, 龍太. “連想記憶に最適な神経実装の探索: 海馬のスパイクタイミング依存可塑性と位相応答曲線 : Optimal Design for Associative Memory: Hippocampal Spike-Timing-Dependent Plasticity and Phase Response Curve; 連想記憶に最適な神経実装の探索: 海馬のスパイクタイミング依存可塑性と位相応答曲線.” 2015. Thesis, Tokyo Institute of Technology / 東京工業大学. Accessed December 05, 2019. http://t2r2.star.titech.ac.jp/cgi-bin/publicationinfo.cgi?q_publication_content_number=CTT100691071.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

宮田, 龍太. “連想記憶に最適な神経実装の探索: 海馬のスパイクタイミング依存可塑性と位相応答曲線 : Optimal Design for Associative Memory: Hippocampal Spike-Timing-Dependent Plasticity and Phase Response Curve; 連想記憶に最適な神経実装の探索: 海馬のスパイクタイミング依存可塑性と位相応答曲線.” 2015. Web. 05 Dec 2019.

Vancouver:

宮田 �. 連想記憶に最適な神経実装の探索: 海馬のスパイクタイミング依存可塑性と位相応答曲線 : Optimal Design for Associative Memory: Hippocampal Spike-Timing-Dependent Plasticity and Phase Response Curve; 連想記憶に最適な神経実装の探索: 海馬のスパイクタイミング依存可塑性と位相応答曲線. [Internet] [Thesis]. Tokyo Institute of Technology / 東京工業大学; 2015. [cited 2019 Dec 05]. Available from: http://t2r2.star.titech.ac.jp/cgi-bin/publicationinfo.cgi?q_publication_content_number=CTT100691071.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

宮田 �. 連想記憶に最適な神経実装の探索: 海馬のスパイクタイミング依存可塑性と位相応答曲線 : Optimal Design for Associative Memory: Hippocampal Spike-Timing-Dependent Plasticity and Phase Response Curve; 連想記憶に最適な神経実装の探索: 海馬のスパイクタイミング依存可塑性と位相応答曲線. [Thesis]. Tokyo Institute of Technology / 東京工業大学; 2015. Available from: http://t2r2.star.titech.ac.jp/cgi-bin/publicationinfo.cgi?q_publication_content_number=CTT100691071

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Tokyo Institute of Technology / 東京工業大学

21. 宮田, 龍太. 連想記憶に最適な神経実装の探索: 海馬のスパイクタイミング依存可塑性と位相応答曲線 : Optimal Design for Associative Memory: Hippocampal Spike-Timing-Dependent Plasticity and Phase Response Curve; 連想記憶に最適な神経実装の探索: 海馬のスパイクタイミング依存可塑性と位相応答曲線.

Degree: 博士(工学), 2014, Tokyo Institute of Technology / 東京工業大学

URL: http://t2r2.star.titech.ac.jp/cgi-bin/publicationinfo.cgi?q_publication_content_number=CTT100667348

►

Recently, some researchers have tried to specify optimal pairs of the synaptic plasticity rule for memory storage and the form of neural interactions for memory… (more)

▼

Recently, some researchers have tried to specify optimal pairs of the synaptic plasticity rule for memory storage and the form of neural interactions for memory retrieval in a neural network model. Lengyel et al. developed a normative theory for auto-associative memory networks recalling spatio-temporal spike patterns on the basis of Bayesian theory. Under the speculation that a phase response curve (PRC) is an appropriate way to formulate the neural interactions if memories are stored by a spike-timing-dependent plasticity (STDP) rule, they derived the optimal pairs of STDP window functions and PRCs for executing the associative memory. In this doctoral thesis, we propose a synthetic approach of optimal design for associative memory networks, which consists of bottom-up and top-down steps: In the bottom-up step, under the assumption of regular spiking and weak coupling, we formulate an associative memory network recalling spatio-temporal patterns as a phase oscillator model consisting of an STDP window function and a PRC. Next, we analytically derive the mutual information between a stored pattern and the retrieval one, and use it to evaluate memory retrieval performance. In the top-down steps, by maximizing the objective function given by the mutual information, we derive optimal pairs of STDP window functions and PRCs. We firstly verify whether the optimal pair of STDP window functions and PRCs for executing the auto-associative memory derived from our approach are identical those derived from Lengyel's approach. With a zero noise limit, we obtain the same relation between the STDP window function and the PRC as that which Lengyel et al. obtained. Furthermore, recently reported experimental findings suggest that the hippocampal CA1 network stores spatio-temporal spike patterns and retrieves temporally reversed and spread-out patterns. We secondly applied to our approach to derive optimal pairs of STDP window functions and PRCs for executing such memory retrievals.

　近年、記憶を記銘するためのシナプス可塑性則と想起のための神経相互作用則の最適な組合せを探索する試みがある。Lengyelら（2005）は、スパイクタイミング依存可塑性(spike-timing-dependent plasticity, STDP) に着目した場合、その記憶想起には位相応答曲線（phase response curve, PRC）で記述された神経相互作用を用いることが適切であると仮定し、ベイズ理論の枠組みから最適なSTDPとPRCの関係を導いた。本研究では、神経細胞の高次元非線形動力学系の位相縮約モデルを求め、このモデルの物理的制約下で最適な記銘と想起を実現するSTDPとPRCの組合せを導出し、Lengyelらとの対応を明らかにした。また、最近の研究から、海馬ではスパイク時空間パターンの記銘とその時間反転パターンや引き延ばしパターンの想起が行われていることが示唆されている。そこで本手法をこれらの記憶想起の場合に適用し、最適なSTDPとPRCの組合せを探索した。

Subjects/Keywords: associative memory; 連想記憶; STDP; スパイクタイミング依存可塑性; PRC; 位相応答曲線; reversed pattern retrieval; 反転パターン想起; spread-out pattern retrieval; 引き延ばしパターン想起; mutual information maximization; 相互情報量最大化

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

宮田, �. (2014). 連想記憶に最適な神経実装の探索: 海馬のスパイクタイミング依存可塑性と位相応答曲線 : Optimal Design for Associative Memory: Hippocampal Spike-Timing-Dependent Plasticity and Phase Response Curve; 連想記憶に最適な神経実装の探索: 海馬のスパイクタイミング依存可塑性と位相応答曲線. (Thesis). Tokyo Institute of Technology / 東京工業大学. Retrieved from http://t2r2.star.titech.ac.jp/cgi-bin/publicationinfo.cgi?q_publication_content_number=CTT100667348

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

宮田, 龍太. “連想記憶に最適な神経実装の探索: 海馬のスパイクタイミング依存可塑性と位相応答曲線 : Optimal Design for Associative Memory: Hippocampal Spike-Timing-Dependent Plasticity and Phase Response Curve; 連想記憶に最適な神経実装の探索: 海馬のスパイクタイミング依存可塑性と位相応答曲線.” 2014. Thesis, Tokyo Institute of Technology / 東京工業大学. Accessed December 05, 2019. http://t2r2.star.titech.ac.jp/cgi-bin/publicationinfo.cgi?q_publication_content_number=CTT100667348.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

宮田, 龍太. “連想記憶に最適な神経実装の探索: 海馬のスパイクタイミング依存可塑性と位相応答曲線 : Optimal Design for Associative Memory: Hippocampal Spike-Timing-Dependent Plasticity and Phase Response Curve; 連想記憶に最適な神経実装の探索: 海馬のスパイクタイミング依存可塑性と位相応答曲線.” 2014. Web. 05 Dec 2019.

Vancouver:

宮田 �. 連想記憶に最適な神経実装の探索: 海馬のスパイクタイミング依存可塑性と位相応答曲線 : Optimal Design for Associative Memory: Hippocampal Spike-Timing-Dependent Plasticity and Phase Response Curve; 連想記憶に最適な神経実装の探索: 海馬のスパイクタイミング依存可塑性と位相応答曲線. [Internet] [Thesis]. Tokyo Institute of Technology / 東京工業大学; 2014. [cited 2019 Dec 05]. Available from: http://t2r2.star.titech.ac.jp/cgi-bin/publicationinfo.cgi?q_publication_content_number=CTT100667348.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

宮田 �. 連想記憶に最適な神経実装の探索: 海馬のスパイクタイミング依存可塑性と位相応答曲線 : Optimal Design for Associative Memory: Hippocampal Spike-Timing-Dependent Plasticity and Phase Response Curve; 連想記憶に最適な神経実装の探索: 海馬のスパイクタイミング依存可塑性と位相応答曲線. [Thesis]. Tokyo Institute of Technology / 東京工業大学; 2014. Available from: http://t2r2.star.titech.ac.jp/cgi-bin/publicationinfo.cgi?q_publication_content_number=CTT100667348

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

EPFL

22. Zenke, Friedemann. Memory formation and recall in recurrent spiking neural networks.

Degree: 2014, EPFL

URL: http://infoscience.epfl.ch/record/203260

► Our brain has the capacity to analyze a visual scene in a split second, to learn how to play an instrument, and to remember events,… (more)

▼ Our brain has the capacity to analyze a visual scene in a split second, to learn how to play an instrument, and to remember events, faces and concepts. Neurons underlie all of these diverse functions. Neurons, cells within the brain that generate and transmit electrical activity, communicate with each other through chemical synapses. These synaptic connections dynamically change with experience, a process referred to as synaptic plasticity, which is thought to be at the core of the brain's ability to learn and process the world in sophisticated ways. Our understanding of the rules of synaptic plasticity remains quite limited. To enable efficient computations among neurons or to serve as a trace of memory, synapses must create stable connectivity patterns between neurons. However there remains an insufficient theoretical explanation as to how stable connectivity patterns can form in the presence of synaptic plasticity. Since the dynamics of recurrently connected neurons depend upon their connections, which themselves change in response to the network dynamics, synaptic plasticity and network dynamics have to be treated as a compound system. Due to the nonlinear nature of the system this can be analytically challenging. Utilizing network simulations that model the interplay between the network connectivity and synaptic plasticity can provide valuable insights. However, many existing network models that implement biologically relevant forms of plasticity become unstable. This suggests that current models do not accurately describe the biological networks, which have no difficulty functioning without succumbing to exploding network activity. The instability in these network simulations could originate from the fact that theoretical studies have, almost exclusively, focused on Hebbian plasticity at excitatory synapses. Hebbian plasticity causes connected neurons that are active together to increase the connection strength between them. Biological networks, however, display a large variety of different forms of synaptic plasticity and homeostatic mechanisms, beyond Hebbian plasticity. Furthermore, inhibitory cells can undergo synaptic plasticity as well. These diverse forms of plasticity are active at the same time, and our understanding of the computational role of most of these synaptic dynamics remains elusive. This raises the important question as to whether forms of plasticity that have not been previously considered could -in combination with Hebbian plasticity- lead to stable network dynamics. Here we illustrate that by combining multiple forms of plasticity with distinct roles, a recurrently connected spiking network model self-organizes to distinguish and extract multiple overlapping external stimuli. Moreover we show that the acquired network structures remain stable over hours while plasticity is active. This long-term stability allows the network to function as an associative memory and to correctly classify distorted or partially cued stimuli. During intervals in which no stimulus is shown the network… Advisors/Committee Members: Gerstner, Wulfram.

Subjects/Keywords: spiking neural networks; synaptic plasticity; homeostasis; Hebbian learning; cell assembly; inhibitory plasticity; heterosynaptic plasticity; STDP; secreted factors; selective delay activity

Record Details Similar Records Cite « Share

❌

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Zenke, F. (2014). Memory formation and recall in recurrent spiking neural networks. (Thesis). EPFL. Retrieved from http://infoscience.epfl.ch/record/203260

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Zenke, Friedemann. “Memory formation and recall in recurrent spiking neural networks.” 2014. Thesis, EPFL. Accessed December 05, 2019. http://infoscience.epfl.ch/record/203260.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Zenke, Friedemann. “Memory formation and recall in recurrent spiking neural networks.” 2014. Web. 05 Dec 2019.

Vancouver:

Zenke F. Memory formation and recall in recurrent spiking neural networks. [Internet] [Thesis]. EPFL; 2014. [cited 2019 Dec 05]. Available from: http://infoscience.epfl.ch/record/203260.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Zenke F. Memory formation and recall in recurrent spiking neural networks. [Thesis]. EPFL; 2014. Available from: http://infoscience.epfl.ch/record/203260

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

23. Skorheim, Steven W. An Exploration of the Role of Cellular Neuroplasticity in Large Scale Models of Biological Neural Networks.

Degree: Neuroscience, 2014, University of California – Riverside

URL: http://www.escholarship.org/uc/item/24z3f4g1

► Cellular level learning is vital to almost all brain function, and extensive homeostatic plasticity is required to maintain brain functionality. While much has been learned… (more)

▼ Cellular level learning is vital to almost all brain function, and extensive homeostatic plasticity is required to maintain brain functionality. While much has been learned about cellular level plasticity in vivo, how these mechanisms affect higher level functionality is not readily apparent. The cellular level circuitry of most networks that process information is unknown. A variety of models were developed to better understand plasticity in both learning and homeostasis. Spike time dependent plasticity (STDP) and reward-modulated plasticity may be the primary methods through which neurons record information. We implemented rewarded STDP to model foraging behavior in a virtual environment. When appropriate homeostatic mechanisms were in place, the network of spiking neurons developed the capability of producing highly successful decision-making. The networks used in the foraging model used a very simple initial configuration to avoid assumptions about network organization. More realistic network configurations can help to show how plasticity interacts with genetically determined network. We developed three network models of synaptic mechanisms of FM sweep processing based on published experimental data. One of these, the `inhibitory sideband' model, used frequency selective inputs to a network of excitatory and inhibitory cells. The strength and asymmetry of these connections resulted in neurons responsive to sweeps in a single direction and of sufficient rate. STDP was shown to be capable of causing to become selective for sweeps in the same direction as a repeatedly presented training sweep.The experience dependent plasticity, occurs primarily during the waking state, however, sleep is essential for learning. Slow wave sleep activity may be essential for memory consolidation and homeostasis. We developed a model of slow wave sleep that included methods to calculate the electrical field in the space around the network. We show here that a network model of up and down states displays this CSD profile only if a frequency-filtering extracellular medium is assumed. While initiation of the active cortical states during sleep slow oscillation has been intensively studied, the it's termination remains poorly understood. We explored the impact of intrinsic and synaptic inhibition on the state transition. We found that synaptic inhibition controls the duration and the synchrony of active state termination. Together these models set the stage for a model network that can learn through input driven processes in a waking state then explore the consolidation of memory in a sleeping state. This will allow us to explore in greater detail how plasticity on the level of a single cell contributes to learning and stability on the level of the whole brain.

Subjects/Keywords: Neurosciences; Audio; Learning; Model; STDP

…a small subset. Spike time dependent plasticity (STDP) [4], as… …sparsely active networks. STDP on its own faces a number of limitations. It is by its nature an… …result of the network activity. STDP is prone to run away synaptic dynamics when stronger… …in 1 chapter 2, it is still possible to use experience dependent non-rewarded STDP to… …events, rewarded STDP stores the changes as traces that are activated when and if a reward…

11 more images…

Record Details Similar Records Cite « Share

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Skorheim, S. W. (2014). An Exploration of the Role of Cellular Neuroplasticity in Large Scale Models of Biological Neural Networks. (Thesis). University of California – Riverside. Retrieved from http://www.escholarship.org/uc/item/24z3f4g1

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Skorheim, Steven W. “An Exploration of the Role of Cellular Neuroplasticity in Large Scale Models of Biological Neural Networks.” 2014. Thesis, University of California – Riverside. Accessed December 05, 2019. http://www.escholarship.org/uc/item/24z3f4g1.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Skorheim, Steven W. “An Exploration of the Role of Cellular Neuroplasticity in Large Scale Models of Biological Neural Networks.” 2014. Web. 05 Dec 2019.

Vancouver:

Skorheim SW. An Exploration of the Role of Cellular Neuroplasticity in Large Scale Models of Biological Neural Networks. [Internet] [Thesis]. University of California – Riverside; 2014. [cited 2019 Dec 05]. Available from: http://www.escholarship.org/uc/item/24z3f4g1.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Skorheim SW. An Exploration of the Role of Cellular Neuroplasticity in Large Scale Models of Biological Neural Networks. [Thesis]. University of California – Riverside; 2014. Available from: http://www.escholarship.org/uc/item/24z3f4g1

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

24. Rahimi Azghadi, Seyed Mostafa. Neuromorphic VLSI designs for spike timing and rate-based synaptic plasticity with application in pattern classification.

Degree: 2014, University of Adelaide

URL: http://hdl.handle.net/2440/84732

► This thesis presents a versatile study on the design and Very Large Scale Integration(VLSI) implementation of various synaptic plasticity rules ranging from phenomenological rules, to… (more)

▼ This thesis presents a versatile study on the design and Very Large Scale Integration(VLSI) implementation of various synaptic plasticity rules ranging from phenomenological rules, to biophysically realistic ones. In particular, the thesis aims at developing novel spike timing-based learning circuits that advance the current neuromorphic systems, in terms of power consumption, compactness and synaptic modification (learning) abilities. Furthermore, the thesis investigates the usefulness of the developed designs and algorithms in specific engineering tasks such as pattern classification. To follow the mentioned goals, this thesis makes several original contributions to the field of neuromorphic engineering, which are briefed in the following. First, a programmable multi-neuron neuromorphic chip is utilised to implement a number of desired rate- and timing-based synaptic plasticity rules. Specific software programs are developed to set up and program the neuromorphic chip, in a way to show the required neuronal behaviour for implementing various synaptic plasticity rules. The classical version of Spike Timing Dependent Plasticity (STDP), as well as the triplet-based STDP and the rate-based Bienenstock-Cooper-Munro (BCM) rules are implemented and successfully tested on this neuromorphic device. In addition, the implemented triplet STDP learning mechanism is utilised to train a feedforward spiking neural network to classify complex rate-based patterns, with a high classification performance. In the next stage, VLSI designs and implementations of a variety of synaptic plasticity rules are studied and weaknesses and strengths of these implementations are highlighted. In addition, the applications of these VLSI learning networks, which build upon various synaptic plasticity rules are discussed. Furthermore, challenges in the way of implementing these rules are investigated and effective ways to address those challenges are proposed and reviewed. This review provides us with deep insight into the design and application of synaptic plasticity rules in VLSI. Next, the first VLSI designs for the triplet STDP learning rule are developed, which significantly outperform all their pair-based STDP counterparts, in terms of learning capabilities. It is shown that a rate-based learning feature is also an emergent property of the new proposed designs. These primary designs are further developed to generate two different VLSI circuits with various design goals. One of these circuits that has been fabricated in VLSI as a proof of principle chip, aimed at maximising the learning performance—but this results in high power consumption and silicon real estate. The second design, however, slightly sacrifices the learning performance, while remarkably improves the silicon area, as well as the power consumption of the design, in comparison to all previous triplet STDP circuits, as well as many pair-based STDP circuits. Besides, it significantly outperforms other neuromorphic learning circuits with various biophysical as well as phenomenological… Advisors/Committee Members: Al-Sarawi, Said Fares Khalil (advisor), Iannella, Nicolangelo (advisor), Abbott, Derek (advisor), School of Electrical and Electronic Engineering (school).

Subjects/Keywords: Neuromorphic Engineering; VLSI; neural systems; synapse; Neuron; Synaptic Plasticity; STDP; BCM; pattern classification

…Contents 6.3.1 6.3.2 Voltage-mode Triplet-based STDP Circuit Model… …139 6.3.3 6.4 Triplet-based STDP . . . . . . . . . . . . . . . . . . . . . . . . . . . 138… …Current-mode Triplet-based STDP Circuit Model . . . . . . . . . 140 Experimental Setup… …158 7.2 A Different Arrangement of Triplet-based STDP . . . . . . . . . . . . . . 159 7.3… …Page viii Contents Chapter 8. Compact Low Energy Neuromorphic Circuit for Triplet STDP 191…

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Rahimi Azghadi, S. M. (2014). Neuromorphic VLSI designs for spike timing and rate-based synaptic plasticity with application in pattern classification. (Thesis). University of Adelaide. Retrieved from http://hdl.handle.net/2440/84732

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Rahimi Azghadi, Seyed Mostafa. “Neuromorphic VLSI designs for spike timing and rate-based synaptic plasticity with application in pattern classification.” 2014. Thesis, University of Adelaide. Accessed December 05, 2019. http://hdl.handle.net/2440/84732.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Rahimi Azghadi, Seyed Mostafa. “Neuromorphic VLSI designs for spike timing and rate-based synaptic plasticity with application in pattern classification.” 2014. Web. 05 Dec 2019.

Vancouver:

Rahimi Azghadi SM. Neuromorphic VLSI designs for spike timing and rate-based synaptic plasticity with application in pattern classification. [Internet] [Thesis]. University of Adelaide; 2014. [cited 2019 Dec 05]. Available from: http://hdl.handle.net/2440/84732.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Rahimi Azghadi SM. Neuromorphic VLSI designs for spike timing and rate-based synaptic plasticity with application in pattern classification. [Thesis]. University of Adelaide; 2014. Available from: http://hdl.handle.net/2440/84732

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

25. Vitanza, Alessandra. Methodologies and Tools for the Emergence of Cooperation in Biorobotics.

Degree: 2013, Università degli Studi di Catania

URL: http://hdl.handle.net/10761/1310

► One of the main purposes of the Ph.D. activities was the investigation of the swarm aspects in order to formulate new strategies for the emergence… (more)

Subjects/Keywords: Area 09 - Ingegneria industriale e dell'informazione; Swarm Intelligence,collaborative algorithms,labor division,swarm robotic applications,task partitioning,bio-robotics,spike-frequency adaptation,STDP learning,robotic framework,dynamic simulator

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Vitanza, A. (2013). Methodologies and Tools for the Emergence of Cooperation in Biorobotics. (Thesis). Università degli Studi di Catania. Retrieved from http://hdl.handle.net/10761/1310

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

Vitanza, Alessandra. “Methodologies and Tools for the Emergence of Cooperation in Biorobotics.” 2013. Thesis, Università degli Studi di Catania. Accessed December 05, 2019. http://hdl.handle.net/10761/1310.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

Vitanza, Alessandra. “Methodologies and Tools for the Emergence of Cooperation in Biorobotics.” 2013. Web. 05 Dec 2019.

Vancouver:

Vitanza A. Methodologies and Tools for the Emergence of Cooperation in Biorobotics. [Internet] [Thesis]. Università degli Studi di Catania; 2013. [cited 2019 Dec 05]. Available from: http://hdl.handle.net/10761/1310.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Vitanza A. Methodologies and Tools for the Emergence of Cooperation in Biorobotics. [Thesis]. Università degli Studi di Catania; 2013. Available from: http://hdl.handle.net/10761/1310

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

26. Davies, Sergio. Learning in Spiking Neural Networks.

Degree: 2013, University of Manchester

URL: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:186210

► Artificial neural network simulators are a research field which attracts the interest of researchers from various fields, from biology to computer science. The final objectives… (more)

▼ Artificial neural network simulators are a research field which attracts the interest of researchers from various fields, from biology to computer science. The final objectives are the understanding of the mechanisms underlying the human brain, how to reproduce them in an artificial environment, and how drugs interact with them. Multiple neural models have been proposed, each with their peculiarities, from the very complex and biologically realistic Hodgkin-Huxley neuron model to the very simple ``leaky integrate-and-fire'' neuron. However, despite numerous attempts to understand the learning behaviour of the synapses, few models have been proposed. Spike-Timing-Dependent Plasticity (STDP) is one of the most relevant and biologically plausible models, and some variants (such as the triplet-based STDP rule) have been proposed to accommodate all biological observations. The research presented in this thesis focuses on a novel learning rule, based on the spike-pair STDP algorithm, which provides a statistical approach with the advantage of being less computationally expensive than the standard STDP rule, and is therefore suitable for its implementation on stand-alone computational units. The environment in which this research work has been carried out is the SpiNNaker project, which aims to provide a massively parallel computational substrate for neural simulation. To support such research, two other topics have been addressed: the first is a way to inject spikes into the SpiNNaker system through a non-real-time channel such as the Ethernet link, synchronising with the timing of the SpiNNaker system. The second research topic is focused on a way to route spikes in the SpiNNaker system based on populations of neurons. The three topics are presented in sequence after a brief introduction to the SpiNNaker project. Future work could include structural plasticity (also known as synaptic rewiring); here, during the simulation of neural networks on the SpiNNaker system, axons, dendrites and synapses may be grown or pruned according to biological observations. Advisors/Committee Members: GARSIDE, JAMES J, Garside, James, Furber, Stephen.

Subjects/Keywords: Learning; Spiking neural networks; Neural network simulators; Neuromimetic hardware; Neuromorphic hardware; SpiNNaker; Population-based routing; STDP; TTS; Real-time software; Asyncronous software execution

…6.7 6.8 6.9 6.10 6.11 6.12 6.13 S ERGIO DAVIES STDP curve. The horizontal axis… …Implementation of the standard STDP learning rule. . . . . . . . . . . Example of computation of the… …for STDP with TTS forecast - −66mV ≤ L ≤ −63mV . Set 1 has synaptic weights in the interval… …Scatter plots for STDP with TTS forecast - −70mV ≤ L ≤ −67mV . Set 1 has synaptic weights in the… …Scatter plots for STDP with TTS forecast - −66mV ≤ L ≤ −63mV . Set 3 has synaptic weights in the…

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Davies, S. (2013). Learning in Spiking Neural Networks. (Doctoral Dissertation). University of Manchester. Retrieved from http://www.manchester.ac.uk/escholar/uk-ac-man-scw:186210

Chicago Manual of Style (16^th Edition):

Davies, Sergio. “Learning in Spiking Neural Networks.” 2013. Doctoral Dissertation, University of Manchester. Accessed December 05, 2019. http://www.manchester.ac.uk/escholar/uk-ac-man-scw:186210.

MLA Handbook (7^th Edition):

Davies, Sergio. “Learning in Spiking Neural Networks.” 2013. Web. 05 Dec 2019.

Vancouver:

Davies S. Learning in Spiking Neural Networks. [Internet] [Doctoral dissertation]. University of Manchester; 2013. [cited 2019 Dec 05]. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:186210.

Council of Science Editors:

Davies S. Learning in Spiking Neural Networks. [Doctoral Dissertation]. University of Manchester; 2013. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:186210

University of Plymouth

27. Humble, James. Learning, self-organisation and homeostasis in spiking neuron networks using spike-timing dependent plasticity.

Degree: PhD, 2013, University of Plymouth

URL: http://hdl.handle.net/10026.1/1499

► Spike-timing dependent plasticity is a learning mechanism used extensively within neural modelling. The learning rule has been shown to allow a neuron to find the… (more)

Subjects/Keywords: 006.3; spike-timing dependent plasticity; STDP; learning; spiking neuron; neural network; homeostasis; self-organisation

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Humble, J. (2013). Learning, self-organisation and homeostasis in spiking neuron networks using spike-timing dependent plasticity. (Doctoral Dissertation). University of Plymouth. Retrieved from http://hdl.handle.net/10026.1/1499

Chicago Manual of Style (16^th Edition):

Humble, James. “Learning, self-organisation and homeostasis in spiking neuron networks using spike-timing dependent plasticity.” 2013. Doctoral Dissertation, University of Plymouth. Accessed December 05, 2019. http://hdl.handle.net/10026.1/1499.

MLA Handbook (7^th Edition):

Humble, James. “Learning, self-organisation and homeostasis in spiking neuron networks using spike-timing dependent plasticity.” 2013. Web. 05 Dec 2019.

Vancouver:

Humble J. Learning, self-organisation and homeostasis in spiking neuron networks using spike-timing dependent plasticity. [Internet] [Doctoral dissertation]. University of Plymouth; 2013. [cited 2019 Dec 05]. Available from: http://hdl.handle.net/10026.1/1499.

Council of Science Editors:

Humble J. Learning, self-organisation and homeostasis in spiking neuron networks using spike-timing dependent plasticity. [Doctoral Dissertation]. University of Plymouth; 2013. Available from: http://hdl.handle.net/10026.1/1499

University of Manchester

28. Davies, Sergio. Learning in spiking neural networks.

Degree: PhD, 2013, University of Manchester

URL: https://www.research.manchester.ac.uk/portal/en/theses/learning-in-spiking-neural-networks(2d2be0d7-9557-481e-b9f1-3889a5ca2447).html ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.568611

► Artificial neural network simulators are a research field which attracts the interest of researchers from various fields, from biology to computer science. The final objectives… (more)

▼ Artificial neural network simulators are a research field which attracts the interest of researchers from various fields, from biology to computer science. The final objectives are the understanding of the mechanisms underlying the human brain, how to reproduce them in an artificial environment, and how drugs interact with them. Multiple neural models have been proposed, each with their peculiarities, from the very complex and biologically realistic Hodgkin-Huxley neuron model to the very simple 'leaky integrate-and-fire' neuron. However, despite numerous attempts to understand the learning behaviour of the synapses, few models have been proposed. Spike-Timing-Dependent Plasticity (STDP) is one of the most relevant and biologically plausible models, and some variants (such as the triplet-based STDP rule) have been proposed to accommodate all biological observations. The research presented in this thesis focuses on a novel learning rule, based on the spike-pair STDP algorithm, which provides a statistical approach with the advantage of being less computationally expensive than the standard STDP rule, and is therefore suitable for its implementation on stand-alone computational units. The environment in which this research work has been carried out is the SpiNNaker project, which aims to provide a massively parallel computational substrate for neural simulation. To support such research, two other topics have been addressed: the first is a way to inject spikes into the SpiNNaker system through a non-real-time channel such as the Ethernet link, synchronising with the timing of the SpiNNaker system. The second research topic is focused on a way to route spikes in the SpiNNaker system based on populations of neurons. The three topics are presented in sequence after a brief introduction to the SpiNNaker project. Future work could include structural plasticity (also known as synaptic rewiring); here, during the simulation of neural networks on the SpiNNaker system, axons, dendrites and synapses may be grown or pruned according to biological observations.

Subjects/Keywords: 006.3; Learning; Spiking neural networks; Neural network simulators; Neuromimetic hardware; Neuromorphic hardware; SpiNNaker; Population-based routing; STDP; TTS; Real-time software; Asyncronous software execution

Record Details Similar Records Cite « Share

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Davies, S. (2013). Learning in spiking neural networks. (Doctoral Dissertation). University of Manchester. Retrieved from https://www.research.manchester.ac.uk/portal/en/theses/learning-in-spiking-neural-networks(2d2be0d7-9557-481e-b9f1-3889a5ca2447).html ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.568611

Chicago Manual of Style (16^th Edition):

Davies, Sergio. “Learning in spiking neural networks.” 2013. Doctoral Dissertation, University of Manchester. Accessed December 05, 2019. https://www.research.manchester.ac.uk/portal/en/theses/learning-in-spiking-neural-networks(2d2be0d7-9557-481e-b9f1-3889a5ca2447).html ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.568611.

MLA Handbook (7^th Edition):

Davies, Sergio. “Learning in spiking neural networks.” 2013. Web. 05 Dec 2019.

Vancouver:

Davies S. Learning in spiking neural networks. [Internet] [Doctoral dissertation]. University of Manchester; 2013. [cited 2019 Dec 05]. Available from: https://www.research.manchester.ac.uk/portal/en/theses/learning-in-spiking-neural-networks(2d2be0d7-9557-481e-b9f1-3889a5ca2447).html ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.568611.

Council of Science Editors:

Davies S. Learning in spiking neural networks. [Doctoral Dissertation]. University of Manchester; 2013. Available from: https://www.research.manchester.ac.uk/portal/en/theses/learning-in-spiking-neural-networks(2d2be0d7-9557-481e-b9f1-3889a5ca2447).html ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.568611

29. O'Brien, Michael John. The Role of Short-Term Synaptic Plasticity in Neural Network Spiking Dynamics and in the Learning of Multiple Distal Rewards.

Degree: Mathematics, 2013, UCLA

URL: http://www.escholarship.org/uc/item/63r8s0br

► In this thesis, we assess the role of short-term synaptic plasticity in an artificial neuralnetwork constructed to emulate two important brain functions: self-sustained activity andsignal… (more)

Subjects/Keywords: Mathematics; Neurosciences; ARG-STDP; Distal Rewards; Reinforcement Learning; R-STDP; STDP; STP

…50 4.3.1 Reward Modulated STDP . . . . . . . . . . . . . . . . . . . . . . . . 51 4.3.2… …R-STDP with Attenuated Reward Gating . . . . . . . . . . . . . . . 52 4.4 Single… …to Multiple Synapse Learning . . . . . . . . . . . . . . . . . . 57 4.5.1 R-STDP with… …STP Learns Multiple r-Patterns . . . . . . . . . . . . . 59 4.5.2 ARG-STDP Learns… …Multiple r-Patterns . . . . . . . . . . . . . . . . . 60 4.5.3 STP Stabilizes ARG-STDP Network…

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

O'Brien, M. J. (2013). The Role of Short-Term Synaptic Plasticity in Neural Network Spiking Dynamics and in the Learning of Multiple Distal Rewards. (Thesis). UCLA. Retrieved from http://www.escholarship.org/uc/item/63r8s0br

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^th Edition):

O'Brien, Michael John. “The Role of Short-Term Synaptic Plasticity in Neural Network Spiking Dynamics and in the Learning of Multiple Distal Rewards.” 2013. Thesis, UCLA. Accessed December 05, 2019. http://www.escholarship.org/uc/item/63r8s0br.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^th Edition):

O'Brien, Michael John. “The Role of Short-Term Synaptic Plasticity in Neural Network Spiking Dynamics and in the Learning of Multiple Distal Rewards.” 2013. Web. 05 Dec 2019.

Vancouver:

O'Brien MJ. The Role of Short-Term Synaptic Plasticity in Neural Network Spiking Dynamics and in the Learning of Multiple Distal Rewards. [Internet] [Thesis]. UCLA; 2013. [cited 2019 Dec 05]. Available from: http://www.escholarship.org/uc/item/63r8s0br.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

O'Brien MJ. The Role of Short-Term Synaptic Plasticity in Neural Network Spiking Dynamics and in the Learning of Multiple Distal Rewards. [Thesis]. UCLA; 2013. Available from: http://www.escholarship.org/uc/item/63r8s0br

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

30. Arizumi, Nana. Emergence and stability of complex structures from stochastic neuronal networks.

Degree: PhD, 0112, 2012, University of Illinois – Urbana-Champaign

URL: http://hdl.handle.net/2142/30961

► A single neuron’s connectivity is the key to understanding the network of neurons in the brain. However, it is already a complicated system and many… (more)

▼ A single neuron’s connectivity is the key to understanding the network of neurons in the brain. However, it is already a complicated system and many different approaches to understanding it have been taken over the years. One way is from anatomical study, which is to observe the morphology of each neuron and the organization of the neuronal connections. Another way is from physiological study, which describes the specific electrical outputs of the cells. Computational studies have been developed to fill the gaps between these studies. There are several stochastic computational models, but none of them is easy to analyze quantitatively and typically, the analysis resorts to simulations. Many of the previous studies were focused on physiological structures through Monte Carlo simulations, not on the model itself. This thesis introduces a general purpose stochastic model with mathematically rigorous assumptions, so that analysis of the model itself using a Markov chain is applicable. With specific input stimuli and parameters, the model demonstrates rich properties, such as selectivity of input structures and competition between input neurons. This method provides a well-positioned balance between neuro-biological relevance and theoretical tractability. The model is first studied quantitatively to prove theorems about the existence of a controlled Markov chain over an appropriate time scale. Using the Markov chain makes it possible to show the existence of an invariant measure with some convergence rates. In this context, other theorems are introduced to shed insight beyond the simple phenomenological approaches with simulations that others have developed. Then the system is studied qualitatively by simulating the neuronal physiology of visual neurons, which uses more complicated assumptions. This shows the emergence of direction and orientation selectivity, as the visual neuron’s properties. Hence, this selectivity could be an epiphenomenon of the assumptions chosen for the models. The key insight here is that the model shows a robust phenomenon to the initial condition, but not to the input stimulus, which implies the importance of the initial condition and the noisy inputs. These dynamics may explain learning and reinforcement of the visual neurons and could predict results in future experiments. Advisors/Committee Members: Coleman, Todd P. (advisor), Coleman, Todd P. (Committee Chair), DeVille, Lee (committee member), Olson, Luke N. (committee member), Heath, Michael T. (committee member), Gropp, William D. (committee member), Nelson, Mark E. (committee member).

Subjects/Keywords: computational neuroscience; spike-timing- dependent plasticity (STDP); neuronal network

…Hebbian plasticity (b) STDP plastiticity Figure 1.2: Window function The plasticity… …to a temporally asymmetric spike-timingdependent plasticity (STDP) model. The… …main difference between Hebb’s learning and the STDP is the time-plasticity window function… …presynaptic spikes happen after a postsynaptic spike, the STDP window function returns a negative… …synaptic weight as shown in Fig. 1.2b. The STDP window function is different for each neuron and…

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6^th Edition):

Arizumi, N. (2012). Emergence and stability of complex structures from stochastic neuronal networks. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/30961

Chicago Manual of Style (16^th Edition):

Arizumi, Nana. “Emergence and stability of complex structures from stochastic neuronal networks.” 2012. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed December 05, 2019. http://hdl.handle.net/2142/30961.

MLA Handbook (7^th Edition):

Arizumi, Nana. “Emergence and stability of complex structures from stochastic neuronal networks.” 2012. Web. 05 Dec 2019.

Vancouver:

Arizumi N. Emergence and stability of complex structures from stochastic neuronal networks. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2012. [cited 2019 Dec 05]. Available from: http://hdl.handle.net/2142/30961.

Council of Science Editors:

Arizumi N. Emergence and stability of complex structures from stochastic neuronal networks. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2012. Available from: http://hdl.handle.net/2142/30961

Open Access Theses and Dissertations