Delay-Induced Multistability and Loop Formation in Neuronal Networks with Spike-Timing-Dependent Plasticity

Area of Science:

• Computational Neuroscience
• Neuroplasticity
• Network Dynamics

Background:

• Spike-timing-dependent plasticity (STDP) is a fundamental mechanism for synaptic plasticity.
• STDP influences the structural and dynamical states of neuronal populations.
• Previous studies explored STDP's role in theoretical and computational models.

Purpose of the Study:

• Investigate how dendritic and axonal delays in recurrent networks affect emergent connectivity.
• Determine the influence of initial synaptic weight distribution on network structure.
• Analyze the impact of neuronal firing rates on network evolution and synapse symmetry.

Main Methods:

• Simulated recurrent networks of oscillatory neurons with STDP.
• Incorporated dendritic and axonal propagation delays.
• Analyzed asymptotic connectivity based on initial synaptic weight distributions.
• Utilized a two-neuron framework for theoretical justification.

Main Results:

• Network connectivity exhibits multistability, dependent on initial synaptic strength distribution.
• Initial synaptic weight standard deviation impacts final synaptic weight, loop counts, and structural symmetry.
• Higher neuronal firing rates promote more symmetric synaptic configurations.
• Results were validated using a two-neuron model and scaled to large networks.

Conclusions:

• Dendritic and axonal delays introduce multistability in STDP-based network connectivity.
• Initial synaptic weight distribution is a critical factor shaping emergent network structures.
• Neuronal firing rates modulate network evolution towards more symmetric synaptic arrangements.