Synchrony and asynchrony in a fully stochastic neural network

Area of Science:

• Computational neuroscience
• Theoretical neuroscience
• Complex systems

Background:

• Stochastic pulse-coupled neural networks (SPCNNs) are crucial for modeling brain function.
• Synaptic failures and external inputs introduce randomness, impacting network dynamics.
• Understanding transitions between network states (synchrony/asynchrony) is key to neural computation.

Purpose of the Study:

• To develop and analyze a model of SPCNNs incorporating synaptic failure and random external input.
• To investigate the conditions under which these networks exhibit synchronous and asynchronous behavior.
• To explore the phenomenon of spontaneous switching between these states.

Main Methods:

• Development of a mathematical model for stochastic pulse-coupled neural networks.
• Analysis of network behavior, including synchronous and asynchronous states.
• Investigation of mean-field models to understand underlying dynamics.
• Characterization of spontaneous switching as rare events.

Main Results:

• The SPCNN model demonstrates both synchronous and asynchronous activity patterns.
• A specific parameter range was identified where the network spontaneously switches between synchrony and asynchrony.
• Mean-field analysis revealed bistability in the switching parameter regime.
• The observed switches were characterized as rare events within the stochastic system.

Conclusions:

• Stochasticity, arising from synaptic failure and external input, can drive complex dynamics in neural networks.
• The spontaneous switching between synchronous and asynchronous states is a robust phenomenon explained by mean-field bistability.
• This switching behavior represents rare events, providing a novel perspective on neural state transitions.