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Self-Organization of Microcircuits in Networks of Spiking Neurons with Plastic Synapses

Ocker, GK and Litwin-Kumar, A and Doiron, B (2015) Self-Organization of Microcircuits in Networks of Spiking Neurons with Plastic Synapses. PLoS Computational Biology, 11 (8). ISSN 1553-734X

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The synaptic connectivity of cortical networks features an overrepresentation of certain wiring motifs compared to simple random-network models. This structure is shaped, in part, by synaptic plasticity that promotes or suppresses connections between neurons depending on their joint spiking activity. Frequently, theoretical studies focus on how feedforward inputs drive plasticity to create this network structure. We study the complementary scenario of self-organized structure in a recurrent network, with spike timing-dependent plasticity driven by spontaneous dynamics. We develop a self-consistent theory for the evolution of network structure by combining fast spiking covariance with a slow evolution of synaptic weights. Through a finite-size expansion of network dynamics we obtain a low-dimensional set of nonlinear differential equations for the evolution of two-synapse connectivity motifs. With this theory in hand, we explore how the form of the plasticity rule drives the evolution of microcircuits in cortical networks. When potentiation and depression are in approximate balance, synaptic dynamics depend on weighted divergent, convergent, and chain motifs. For additive, Hebbian STDP these motif interactions create instabilities in synaptic dynamics that either promote or suppress the initial network structure. Our work provides a consistent theoretical framework for studying how spiking activity in recurrent networks interacts with synaptic plasticity to determine network structure.


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Item Type: Article
Status: Published
CreatorsEmailPitt UsernameORCID
Ocker, GKgko1@pitt.eduGKO1
Litwin-Kumar, A
Doiron, Bbdoiron@pitt.eduBDOIRON
ContributionContributors NameEmailPitt UsernameORCID
Date: 1 August 2015
Date Type: Publication
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Journal or Publication Title: PLoS Computational Biology
Volume: 11
Number: 8
DOI or Unique Handle: 10.1371/journal.pcbi.1004458
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > Mathematics
Dietrich School of Arts and Sciences > Neuroscience
Refereed: Yes
ISSN: 1553-734X
Date Deposited: 23 Aug 2016 13:43
Last Modified: 30 Mar 2021 12:56


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