Topological Effects of Synaptic Time Dependent Plasticity

TL;DR

This paper demonstrates that spike timing-dependent plasticity (STDP) influences the formation and elimination of neural loops, predicting a predominantly feed-forward network structure in the brain due to STDP's loop-regulating effects.

Contribution

It provides a theoretical and simulation-based analysis showing how STDP acts as a loop-regulating plasticity, shaping neural network topology across scales.

Findings

STDP can form or eliminate functional loops depending on its polarity.

In linear network models, STDP acts as a loop regulator.

Predicted neural architecture favors feed-forward connections under normal conditions.

Abstract

We show that the local Spike Timing-Dependent Plasticity (STDP) rule has the effect of regulating the trans-synaptic weights of loops of any length within a simulated network of neurons. We show that depending on STDP's polarity, functional loops are formed or eliminated in networks driven to normal spiking conditions by random, partially correlated inputs, where functional loops comprise weights that exceed a non-zero threshold. We further prove that STDP is a form of loop-regulating plasticity for the case of a linear network comprising random weights drawn from certain distributions. Thus a notable local synaptic learning rule makes a specific prediction about synapses in the brain in which standard STDP is present: that under normal spiking conditions, they should participate in predominantly feed-forward connections at all scales. Our model implies that any deviations from this prediction would require a substantial modification to the hypothesized role for standard STDP. Given its widespread occurrence in the brain, we predict that STDP could also regulate long range synaptic loops among individual neurons across all brain scales, up to, and including, the scale of global brain network topology.