Emergence of slow collective oscillations in neural networks with spike timing dependent plasticity

TL;DR

This paper investigates how spike timing dependent plasticity (STDP) induces slow, irregular collective oscillations in neural networks, resembling brain activity during sleep, through a feedback mechanism called the Sisyphus Effect.

Contribution

It introduces the Sisyphus Effect as a mechanism explaining persistent oscillations caused by STDP in neural networks, highlighting a novel dynamic behavior.

Findings

STDP induces irregular oscillations between synchronized states

Oscillations are explained by the Sisyphus Effect mechanism

Synaptic weights oscillate, preventing system from reaching equilibrium

Abstract

The collective dynamics of excitatory pulse coupled neurons with spike timing dependent plasticity (STDP) is studied. The introduction of STDP induces persistent irregular oscillations between strongly and weakly synchronized states, reminiscent of brain activity during slow-wave sleep. We explain the oscillations by a mechanism, the Sisyphus Effect, caused by a continuous feedback between the synaptic adjustments and the coherence in the neural firing. Due to this effect, the synaptic weights have oscillating equilibrium values, and this prevents the system from relaxing into a stationary macroscopic state.