Analytical condition for synchrony in a neural network with two periodic inputs

Area of Science:

• Computational Neuroscience
• Theoretical Neuroscience
• Complex Systems

Background:

• Neural networks exhibit complex dynamics influenced by synaptic connections and external inputs.
• Understanding synchronization in neural systems is crucial for deciphering information processing.
• Previous models often simplify the interplay between network connectivity and input characteristics.

Purpose of the Study:

• To investigate the conditions governing neural synchrony in networks with multiple periodic inputs.
• To elucidate the impact of synaptic connection strength and input timing on neural synchronization.
• To develop a theoretical framework for predicting synchronization behavior based on network parameters.

Main Methods:

• Application of mean-field theory to a neural network model.
• Derivation of a self-consistent condition for neural synchrony.
• Analysis of the influence of synaptic weights and input train properties on synchrony cycles.

Main Results:

• Stronger synaptic connections lead to shorter neural synchrony cycles.
• Longer external input cycles result in longer synchrony cycles.
• A single equation unifies synaptic weights, input properties, and synchrony cycles, identifying regions of synchrony and asynchrony.

Conclusions:

• Mean-field theory provides a powerful tool for understanding neural synchrony.
• Synaptic strength and input periodicity are key determinants of neural network synchronization patterns.
• The derived equation offers a computationally efficient method for predicting synchronization feasibility in neural networks.