Self-organization of microcircuits in networks of neurons with plastic synapses

TL;DR

This paper develops a theoretical framework to understand how spike timing-dependent plasticity influences the self-organization of microcircuits in recurrent neural networks, revealing how motif interactions can promote or suppress network structures.

Contribution

It introduces a low-dimensional nonlinear differential equations model that links synaptic plasticity rules with the evolution of network motifs in cortical circuits.

Findings

Motif interactions depend on the balance of potentiation and depression.

Plasticity rules can induce instabilities that alter network structure.

The theory explains how spontaneous activity shapes microcircuit organization.

Abstract

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 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 that describes the evolution of network structure by combining fast spiking covariance with a fast-slow theory for synaptic weight dynamics. 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 the frequency of 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.