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Pairwise Analysis Can Account for Network Structures Arising from Spike-Timing Dependent Plasticity

Baktash Babadi; Larry Abbott

Title:
Pairwise Analysis Can Account for Network Structures Arising from Spike-Timing Dependent Plasticity
Author(s):
Babadi, Baktash
Abbott, Larry
Date:
Type:
Articles
Department(s):
Neuroscience
Volume:
9
Persistent URL:
Book/Journal Title:
PLOS Computational Biology
Abstract:
Spike timing-dependent plasticity (STDP) modifies synaptic strengths based on timing information available locally at each synapse. Despite this, it induces global structures within a recurrently connected network. We study such structures both through simulations and by analyzing the effects of STDP on pair-wise interactions of neurons. We show how conventional STDP acts as a loop-eliminating mechanism and organizes neurons into in- and out-hubs. Loop-elimination increases when depression dominates and turns into loop-generation when potentiation dominates. STDP with a shifted temporal window such that coincident spikes cause depression enhances recurrent connections and functions as a strict buffering mechanism that maintains a roughly constant average firing rate. STDP with the opposite temporal shift functions as a loop eliminator at low rates and as a potent loop generator at higher rates. In general, studying pairwise interactions of neurons provides important insights about the structures that STDP can produce in large networks.
Subject(s):
Biology
Biology--Classification
Neurosciences
Publisher DOI:
https://doi.org/10.1371/journal.pcbi.1002906
Item views
213
Metadata:
text | xml
Suggested Citation:
Baktash Babadi, Larry Abbott, , Pairwise Analysis Can Account for Network Structures Arising from Spike-Timing Dependent Plasticity, Columbia University Academic Commons, .

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