Co-existent activity patterns in inhibitory neuronal networks with short-term synaptic depression

Area of Science:

• Computational Neuroscience
• Dynamical Systems Theory

Background:

• Neuronal networks exhibit complex dynamics influenced by synaptic properties.
• Short-term synaptic plasticity, like depression, can significantly alter network behavior.

Purpose of the Study:

• To investigate the impact of short-term synaptic depression on the activity patterns of a two-neuron network.
• To determine if synaptic depression promotes the emergence and coexistence of diverse firing patterns.

Main Methods:

• Analysis of a mutually coupled inhibitory two-neuron network model with depressing synapses.
• Application of geometric singular perturbation methods to derive analytical conditions for firing patterns.
• Numerical simulations using Hodgkin-Huxley and Morris-Lecar neuronal models to validate findings.

Main Results:

• Synaptic depression expands the repertoire of network activity patterns, including various n-m anti-phase firing patterns.
• Coexistence of different n-m anti-phase firing patterns is observed, particularly n-n patterns when conductances are identical.
• These phenomena are absent in networks with non-depressing synapses.

Conclusions:

• Short-term synaptic depression is crucial for generating a richer variety of neuronal network dynamics.
• The study provides a theoretical framework and numerical evidence for pattern coexistence driven by synaptic depression.
• Findings highlight the role of synaptic plasticity in neuronal computation and network function.