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Pairwise analysis can account for network structures arising from spike-timing dependent plasticity. | LitMetric

Pairwise analysis can account for network structures arising from spike-timing dependent plasticity.

PLoS Comput Biol

Center for Theoretical Neuroscience, Department of Neuroscience, Columbia University, New York, New York, United States of America.

Published: October 2013

AI Article Synopsis

  • Spike timing-dependent plasticity (STDP) adjusts the strengths of synapses based on the timing of input signals and can create global patterns in a network of connected neurons.
  • Our study shows that STDP can eliminate feedback loops in neuron connections and categorizes neurons into groups known as in- and out-hubs, where the balance between depression and potentiation determines the network's loop structure.
  • By analyzing pairs of neurons, we gain key insights into how STDP leads to different organizational structures in large networks, particularly through mechanisms that buffer firing rates and alter loop generation based on temporal shifts.

Article 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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3578766PMC
http://dx.doi.org/10.1371/journal.pcbi.1002906DOI Listing

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