Neural networks with dynamical synapses: from mixed-mode oscillations and spindles to chaos

TL;DR

This study investigates how short-term synaptic depression influences complex brain rhythm patterns in neural networks, revealing stable oscillations, chaos, and potential implications for neurological disorders.

Contribution

It demonstrates that synaptic plasticity parameters can switch neural network dynamics between regular, oscillatory, and chaotic states, advancing understanding of brain rhythm formation.

Findings

Presence of STSD leads to complex oscillations and chaos.

Parameters of synaptic plasticity act as control switches.

Chaotic activity has a collective neural origin.

Abstract

Understanding of short-term synaptic depression (STSD) and other forms of synaptic plasticity is a topical problem in neuroscience. Here we study the role of STSD in the formation of complex patterns of brain rhythms. We use a cortical circuit model of neural networks composed of irregular spiking excitatory and inhibitory neurons having type 1 and 2 excitability and stochastic dynamics. In the model, neurons form a sparsely connected network and their spontaneous activity is driven by random spikes representing synaptic noise. Using simulations and analytical calculations, we found that if the STSD is absent, the neural network shows either asynchronous behavior or regular network oscillations depending on the noise level. In networks with STSD, changing parameters of synaptic plasticity and the noise level, we observed transitions to complex patters of collective activity: mixed-mode and spindle oscillations, bursts of collective activity, and chaotic behaviour. Interestingly, these patterns are stable in a certain range of the parameters and separated by critical boundaries. Thus, the parameters of synaptic plasticity can play a role of control parameters or switchers between different network states. However, changes of the parameters caused by a disease may lead to dramatic impairment of ongoing neural activity. We analyze the chaotic neural activity by use of the 0-1 test for chaos (Gottwald, G. & Melbourne, I., 2004) and show that it has a collective nature.