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UCLA

1. Jami, Shekib Ahmad. Muscarinic Modulation of Pyramidal Cell Excitability and Long Term Potentiation Across Dorsal-Ventral Axis of Mouse Hippocampus.

Degree: Molec, Cell, & Integ Physiology, 2018, UCLA

Behavioral, physiological, and anatomical evidence indicates that the dorsal and ventral zones of the hippocampus have distinct roles in cognition. How the unique functions of these zones might depend on differences in synaptic and neuronal function arising from the strikingly different gene expression profiles exhibited by dorsal and ventral CA1 pyramidal cells is unclear. To begin to address this question, I investigated the mechanisms underlying differences in synaptic transmission and plasticity at dorsal and ventral Schaffer collateral (SC) synapses.Strikingly, although basal synaptic transmission is similar, SC synapses in the dorsal and ventral hippocampus exhibit markedly different responses to theta-frequency patterns of stimulation. In contrast to dorsal hippocampus, theta-frequency stimulation fails to elicit postsynaptic complex-spike bursting and does not induce LTP at ventral SC synapses. Moreover, EPSP-spike coupling, a process that strongly influences information transfer at synapses, is weaker in ventral pyramidal cells. All of these differences in postsynaptic function are due to an enhanced activation of SK-type K+ channels that suppresses NMDA receptor (NMDAR)-dependent EPSP amplification at ventral SC synapses. Consistent with this, mRNA levels for the SK3 subunit of SK channels are significantly higher in ventral CA1 pyramidal cells. Together, my findings indicated that a dorsal-ventral difference in SK channel regulation of NMDAR activation has a profound effect on the transmission, processing and storage of information at SC synapses and thus likely contributes to the distinct roles of the dorsal and ventral hippocampus in different behaviors. SK channel activity at dendritic spines is strongly down-regulated by -adrenergic (Carter et al. 2012) and muscarinic receptor activation (Buchanan et al. 2010; Giessel and Sabatini 2010). Thus, in addition to coincident pre- and postsynaptic activity, the induction of LTP at SC synapses in the ventral hippocampus may be highly state-dependent and require the release of modulatory neurotransmitters, such as norepinephrine or acetylcholine, to overcome the SK channel inhibition of NMDAR activation. Experiments in this dissertation focus on muscarinic neuromodulation of dorsal and ventral hippocampus and its effect on pyramidal cell excitability and LTP across dorsal-ventral axis of mouse hippocampus.

Subjects/Keywords: Neurosciences; Hippocampus; Long Term Portentiation; Muscarinic Neuromodulation; NMDA; SK Type K Channel; Synaptic Plasticity

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

APA (6th Edition):

Jami, S. A. (2018). Muscarinic Modulation of Pyramidal Cell Excitability and Long Term Potentiation Across Dorsal-Ventral Axis of Mouse Hippocampus. (Thesis). UCLA. Retrieved from http://www.escholarship.org/uc/item/5b8661zs

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

Chicago Manual of Style (16th Edition):

Jami, Shekib Ahmad. “Muscarinic Modulation of Pyramidal Cell Excitability and Long Term Potentiation Across Dorsal-Ventral Axis of Mouse Hippocampus.” 2018. Thesis, UCLA. Accessed July 07, 2020. http://www.escholarship.org/uc/item/5b8661zs.

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

MLA Handbook (7th Edition):

Jami, Shekib Ahmad. “Muscarinic Modulation of Pyramidal Cell Excitability and Long Term Potentiation Across Dorsal-Ventral Axis of Mouse Hippocampus.” 2018. Web. 07 Jul 2020.

Vancouver:

Jami SA. Muscarinic Modulation of Pyramidal Cell Excitability and Long Term Potentiation Across Dorsal-Ventral Axis of Mouse Hippocampus. [Internet] [Thesis]. UCLA; 2018. [cited 2020 Jul 07]. Available from: http://www.escholarship.org/uc/item/5b8661zs.

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

Council of Science Editors:

Jami SA. Muscarinic Modulation of Pyramidal Cell Excitability and Long Term Potentiation Across Dorsal-Ventral Axis of Mouse Hippocampus. [Thesis]. UCLA; 2018. Available from: http://www.escholarship.org/uc/item/5b8661zs

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

2. Björk, Gabriella. Evaluation of system design strategies and supervised classification methods for fruit recognition in harvesting robots.

Degree: Industrial Engineering and Management (ITM), 2017, KTH

This master thesis project is carried out by one student at the Royal Institute of Technology in collaboration with Cybercom Group. The aim was to evaluate and compare system design strategies for fruit recognition in harvesting robots and the performance of supervised machine learning classification methods when applied to this specific task. The thesis covers the basics of these systems; to which parameters, constraints, requirements, and design decisions have been investigated. The framework is used as a foundation for the implementation of both sensing system, and processing and classification algorithms. A plastic tomato plant with fruit of varying maturity was used as a basis for training and testing, and a Kinect v2 for Windows including sensors for high resolution color-, depth, and IR data was used for image acquisition. The obtained data were processed and features of objects of interest extracted using MATLAB and a SDK for Kinect provided by Microsoft. Multiple views of the plant were acquired by having the plant rotate on a platform controlled by a stepper motor and an Ardunio Uno. The algorithms tested were binary classifiers, including Support Vector Machine, Decision Tree, and k-Nearest Neighbor. The models were trained and validated using a five fold cross validation in MATLABs Classification Learner application. Peformance metrics such as precision, recall, and the F1-score, used for accuracy comparison, were calculated. The statistical models k-NN and SVM achieved the best scores. The method considered most promising for fruit recognition purposes was the SVM.

Det här masterexamensarbetet har utförts av en student från Kungliga Tekniska Högskolan i samarbete med Cybercom Group. Målet var att utvärdera och jämföra designstrategier för igenkänning av frukt i en skörderobot och prestandan av klassificerande maskininlärningsalgoritmer när de appliceras på det specifika problemet. Arbetet omfattar grunderna av dessa system; till vilket parametrar, begränsningar, krav och designbeslut har undersökts. Ramverket användes sedan som grund för implementationen av sensorsystemet, processerings- och klassifikationsalgoritmerna. En tomatplanta i pplast med frukter av varierande mognasgrad användes som bas för träning och validering av systemet, och en Kinect för Windows v2 utrustad med sensorer för högupplöst färg, djup, och infraröd data anvöndes för att erhålla bilder. Datan processerades i MATLAB med hjälp av mjukvaruutvecklingskit för Kinect tillhandahållandet av Windows, i syfte att extrahera egenskaper ifrån objekt på bilderna. Multipla vyer erhölls genom att låta tomatplantan rotera på en plattform, driven av en stegmotor Arduino Uno. De binära klassifikationsalgoritmer som testades var Support Vector MAchine, Decision Tree och k-Nearest Neighbor. Modellerna tränades och valideras med hjälp av en five fold cross validation i MATLABs Classification Learner applikation. Prestationsindikatorer som precision, återkallelse och F1poäng beräknades för de olika…

Subjects/Keywords: Supervised Machine Learning; Classification; Image Processing; Computer Vision; Support Vector Machine; k-Nearest Neighbor; Decision Tree; Harvesting Robot; Recognition System; Kinect v2; Maskininl¨arning; Klassifikation; Bildprocessering; Dataseende; Support Vector Machine; k-Nearest Neighbor; Decision Tree; Sk¨orderobot; Igenk¨anningssystem; Kinect v2; Engineering and Technology; Teknik och teknologier

…5.1.4 k-NN . . . . . . . . . . . . . . 5.2 Performance Evaluation… …learning . . . . . . . . . . . . . . . An illustration of a k-NN for point p with k = 3. The… …subsets using cross-validation with k=5 . . The Kinect for Windows v2 Sensor, including color… …Decision Tree FN False Negative FP False Positive ICP Iterative Closest Point IR Infrared k-NN K… 

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

APA (6th Edition):

Björk, G. (2017). Evaluation of system design strategies and supervised classification methods for fruit recognition in harvesting robots. (Thesis). KTH. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-217859

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

Chicago Manual of Style (16th Edition):

Björk, Gabriella. “Evaluation of system design strategies and supervised classification methods for fruit recognition in harvesting robots.” 2017. Thesis, KTH. Accessed July 07, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-217859.

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

MLA Handbook (7th Edition):

Björk, Gabriella. “Evaluation of system design strategies and supervised classification methods for fruit recognition in harvesting robots.” 2017. Web. 07 Jul 2020.

Vancouver:

Björk G. Evaluation of system design strategies and supervised classification methods for fruit recognition in harvesting robots. [Internet] [Thesis]. KTH; 2017. [cited 2020 Jul 07]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-217859.

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

Council of Science Editors:

Björk G. Evaluation of system design strategies and supervised classification methods for fruit recognition in harvesting robots. [Thesis]. KTH; 2017. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-217859

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

.