STDP-driven networks and the \emph{C. elegans} neuronal network

TL;DR

This paper investigates how spike-timing-dependent plasticity (STDP) influences neural network structure, demonstrating that STDP-driven networks exhibit similar motif profiles to the legans" neural network and are robust to parameter changes.

Contribution

It introduces a model of neural network dynamics driven by STDP and compares its motif profile to the legans" neural network, highlighting qualitative similarities and robustness.

Findings

STDP-driven networks develop residual structures similar to legans".

The resulting network's motif profile matches that of the legans" neural network.

Network structure remains stable under parameter variations.

Abstract

We study the dynamics of the structure of a formal neural network wherein the strengths of the synapses are governed by spike-timing-dependent plasticity (STDP). For properly chosen input signals, there exists a steady state with a residual network. We compare the motif profile of such a network with that of a real neural network of \emph{C. elegans} and identify robust qualitative similarities. In particular, our extensive numerical simulations show that this STDP-driven resulting network is robust under variations of the model parameters.