Spike timing-dependent plasticity induces non-trivial topology in the brain

TL;DR

This paper investigates how spike timing-dependent plasticity (STDP) influences the topology of neural networks, showing that initial configurations evolve into complex structures with diverse synchronous behaviors, driven by Hebbian learning rules.

Contribution

It demonstrates that STDP can induce complex, non-trivial topologies in neural networks, depending on initial conditions and external perturbations, revealing a link between Hebbian learning and network structure.

Findings

Initial all-to-all networks evolve into complex topologies.

External perturbations induce cluster coexistence.

Hebbian rules cause preferential attachment between neuron groups.

Abstract

We study the capacity of Hodgkin-Huxley neuron in a network to change temporarily or permanently their connections and behavior, the so called spike timing-dependent plasticity (STDP), as a function of their synchronous behavior. We consider STDP of excitatory and inhibitory synapses driven by Hebbian rules. We show that the final state of networks evolved by a STDP depend on the initial network configuration. Specifically, an initial all-to-all topology envolves to a complex topology. Moreover, external perturbations can induce co-existence of clusters, those whose neurons are synchronous and those whose neurons are desynchronous. This work reveals that STDP based on Hebbian rules leads to a change in the direction of the synapses between high and low frequency neurons, and therefore, Hebbian learning can be explained in terms of preferential attachment between these two diverse communities of neurons, those with low-frequency spiking neurons, and those with higher-frequency spiking neurons.