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You searched for +publisher:"ETH Zürich" +contributor:("de Arcangelis, Lucilla"). Showing records 1 – 3 of 3 total matches.

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ETH Zürich

1. Michiels Van Kessenich, Laurens. Critical Neural Networks and Pattern Recognition.

Degree: 2018, ETH Zürich

How cortical systems self-organise and function is one of the great unresolved questions science currently faces. The combination of billions of neurons, each, by themselves, relatively simple units, can bring forth rich collective dynamics which we witness and experience on a day to day basis. The observation of neuronal avalanches measured in vitro and in vivo in different cortical systems provided the first evidence of scale-free neuronal dynamics, suggesting that the observed complexity in neuronal systems could originate from the same principles which are described by the physics of second order phase transitions in classical statistical physics. The mechanism of self-organised criticality provides an explanation how such scale-invariant dynamics, which reveals itself by the observation of power-law distributed neuronal avalanches, can be observed consistently in cortical systems. The experimentally measured critical exponents are typical for a mean field self-organised branching process. These results have been reproduced by neuronal models based on self-organised criticality which, interestingly, let to the observation of mean-field exponents on different network topologies, including regular lattices, which is not expected for conventional critical systems. This raises the question about the origin of the mean field behaviour observed experimentally. This thesis provides an answer to this open question by investigating the effect of activity-dependent plasticity in combination with the neuronal refractory time in a neuronal network. Our results show that the refractory time hinders backward avalanches forcing a directed propagation of neuronal activity. Hebbian plastic adaptation then plays the role of sculpting these directed avalanche patterns into the topology of the network and slowly changes it into a branched structure where loops are marginal. The observed mean field behaviour of neuronal systems can, therefore, be considered a robust feature of spontaneous neuronal activity. Many additional neurobiological ingredients, such as inhibitory neurons, enrich the dynamics. They play a crucial role in the functioning of cortical systems. The neurotransmitter of inhibitory synapses, such as Gamma-Aminobutyric acid (GABA), reduce the membrane potential in post-synaptic neurons and lower their probability to activate, therefore, reducing the overall activity in the network. The introduction of inhibitory neurons into a self-organised critical neuronal model modifies the dynamics and one can observe that neuronal avalanches do not scale with the system size. Since experimental measurements show scaling avalanches with inhibition, we introduce the concept of dynamical synapses. Dynamical synapses are a form of short-term plasticity and allow the direct modelling of neurotransmitter at the synapses. This provides a neuronal model which features non-conserving dynamics and enables the modelling of synaptic plasticity on two time-scales. The results show that with both short-term and long-term plasticity the… Advisors/Committee Members: Herrmann, Hans J., de Arcangelis, Lucilla.

Subjects/Keywords: info:eu-repo/classification/ddc/4; info:eu-repo/classification/ddc/530; info:eu-repo/classification/ddc/570; Data processing, computer science; Physics; Life sciences

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APA (6th Edition):

Michiels Van Kessenich, L. (2018). Critical Neural Networks and Pattern Recognition. (Doctoral Dissertation). ETH Zürich. Retrieved from http://hdl.handle.net/20.500.11850/277641

Chicago Manual of Style (16th Edition):

Michiels Van Kessenich, Laurens. “Critical Neural Networks and Pattern Recognition.” 2018. Doctoral Dissertation, ETH Zürich. Accessed April 13, 2021. http://hdl.handle.net/20.500.11850/277641.

MLA Handbook (7th Edition):

Michiels Van Kessenich, Laurens. “Critical Neural Networks and Pattern Recognition.” 2018. Web. 13 Apr 2021.

Vancouver:

Michiels Van Kessenich L. Critical Neural Networks and Pattern Recognition. [Internet] [Doctoral dissertation]. ETH Zürich; 2018. [cited 2021 Apr 13]. Available from: http://hdl.handle.net/20.500.11850/277641.

Council of Science Editors:

Michiels Van Kessenich L. Critical Neural Networks and Pattern Recognition. [Doctoral Dissertation]. ETH Zürich; 2018. Available from: http://hdl.handle.net/20.500.11850/277641


ETH Zürich

2. Berger, Damian. Learning Capabilities and Robustness of Critical Neural Networks.

Degree: 2019, ETH Zürich

Subjects/Keywords: info:eu-repo/classification/ddc/4; info:eu-repo/classification/ddc/610; Data processing, computer science; Medical sciences, medicine

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

APA (6th Edition):

Berger, D. (2019). Learning Capabilities and Robustness of Critical Neural Networks. (Doctoral Dissertation). ETH Zürich. Retrieved from http://hdl.handle.net/20.500.11850/341903

Chicago Manual of Style (16th Edition):

Berger, Damian. “Learning Capabilities and Robustness of Critical Neural Networks.” 2019. Doctoral Dissertation, ETH Zürich. Accessed April 13, 2021. http://hdl.handle.net/20.500.11850/341903.

MLA Handbook (7th Edition):

Berger, Damian. “Learning Capabilities and Robustness of Critical Neural Networks.” 2019. Web. 13 Apr 2021.

Vancouver:

Berger D. Learning Capabilities and Robustness of Critical Neural Networks. [Internet] [Doctoral dissertation]. ETH Zürich; 2019. [cited 2021 Apr 13]. Available from: http://hdl.handle.net/20.500.11850/341903.

Council of Science Editors:

Berger D. Learning Capabilities and Robustness of Critical Neural Networks. [Doctoral Dissertation]. ETH Zürich; 2019. Available from: http://hdl.handle.net/20.500.11850/341903


ETH Zürich

3. Lombardi, Fabrizio. Temporal correlations in spontaneous brain activity.

Degree: 2014, ETH Zürich

Subjects/Keywords: HIRNAKTIVIT√ĄT (NEUROLOGIE); GROSSHIRNRINDE + HIRNWINDUNGEN (NEUROLOGIE); SIGNALTRANSDUKTION (NEUROLOGIE); NEURONALE NETZWERKE + NEUROMORPHE SYSTEME (NEUROLOGIE); STOCHASTISCHE PROZESSE (WAHRSCHEINLICHKEITSRECHNUNG); BRAIN ACTIVITY (NEUROLOGY); CEREBRAL CORTEX + CEREBRAL CONVULSIONS (NEUROLOGY); SIGNAL TRANSDUCTION (NEUROLOGY); NEURAL NETWORKS + NEUROMORPHIC SYSTEMS (NEUROLOGY); STOCHASTIC PROCESSES (PROBABILITY THEORY); info:eu-repo/classification/ddc/610; Medical sciences, medicine

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

APA (6th Edition):

Lombardi, F. (2014). Temporal correlations in spontaneous brain activity. (Doctoral Dissertation). ETH Zürich. Retrieved from http://hdl.handle.net/20.500.11850/155121

Chicago Manual of Style (16th Edition):

Lombardi, Fabrizio. “Temporal correlations in spontaneous brain activity.” 2014. Doctoral Dissertation, ETH Zürich. Accessed April 13, 2021. http://hdl.handle.net/20.500.11850/155121.

MLA Handbook (7th Edition):

Lombardi, Fabrizio. “Temporal correlations in spontaneous brain activity.” 2014. Web. 13 Apr 2021.

Vancouver:

Lombardi F. Temporal correlations in spontaneous brain activity. [Internet] [Doctoral dissertation]. ETH Zürich; 2014. [cited 2021 Apr 13]. Available from: http://hdl.handle.net/20.500.11850/155121.

Council of Science Editors:

Lombardi F. Temporal correlations in spontaneous brain activity. [Doctoral Dissertation]. ETH Zürich; 2014. Available from: http://hdl.handle.net/20.500.11850/155121

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