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Examining heterogeneous weight perturbations in neural networks with spike-timing-dependent plasticity

Bredenberg, Colin (2017) Examining heterogeneous weight perturbations in neural networks with spike-timing-dependent plasticity. Undergraduate Thesis, University of Pittsburgh. (Unpublished)

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Large-scale cortical networks employing homeostatic mechanisms and synaptic plasticity rules have been shown to differentiate into neural ensembles when common stimuli are applied in tandem to selected subsets of neurons. These ensembles were found to be stable in response to small perturbations to synaptic strengths—such ensemble stability is a critical feature for network-based memory. Previous studies applied relatively simple perturbations to probe the stability of the network—all synapses within a given population were lowered by a uniform percentage. The goal of this work has been to analyze whether more complex perturbations can reveal more information about network stability. Towards this aim, we constructed a reduced stochastic Wilson-Cowan model, which captures the same perturbation phenomenon observed in spiking simulations, but which is analytically much simpler. We found that when the mean self-excitatory synaptic weight for a population was preserved, perturbations that were distributed more evenly among synapses would lead to a more stable response than focused perturbations, and that this was caused by quantization of neural activity levels within a population.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Bredenberg, Colincolin.bredenberg@gmail.comcjb107
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairDoiron,
Date: 26 April 2017
Date Type: Publication
Defense Date: 14 April 2017
Approval Date: 26 April 2017
Submission Date: 20 April 2017
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 35
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > Mathematics
David C. Frederick Honors College
Degree: BPhil - Bachelor of Philosophy
Thesis Type: Undergraduate Thesis
Refereed: Yes
Uncontrolled Keywords: STDP, spike-timing-dependent plasticity, perturbation, memory, neuroscience, neural networks, mathematics, math
Date Deposited: 26 Apr 2017 14:34
Last Modified: 27 Apr 2017 05:15


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