MULTISCALE MOTION AND DEFORMATION OF BUMPS IN STOCHASTIC NEURAL FIELDS WITH DYNAMIC CONNECTIVITY

Area of Science:

• Computational neuroscience
• Neural dynamics modeling
• Synaptic plasticity mechanisms

Background:

• Neural activity dynamics and synaptic plasticity are crucial for brain learning and memory.
• Activity-dependent plasticity modifies neural circuits, influencing neural activity patterns.
• Neural activity 'bumps' in models with excitation/inhibition store short-term memories.

Purpose of the Study:

• To extend previous models of neural activity bumps by incorporating short-term synaptic plasticity.
• To analyze how synaptic plasticity affects the stability and dynamics of neural activity bumps.
• To develop accurate mathematical descriptions of bump dynamics under plasticity.

Main Methods:

• Developed nonlinear Langevin equations using an interface method for neural field models.
• Incorporated short-term plasticity modifying connectivity via an integral kernel.
• Applied linear stability analysis and multiscale approximations to stochastic dynamics.

Main Results:

• Synaptic facilitation/depression impacts bump stability, with effects depending on whether plasticity acts on excitatory or inhibitory synapses.
• Plasticity variables evolve into slowly diffusing, 'blurred' profiles.
• Smoothed synaptic efficacy profiles can tether or repel neural activity bumps.

Conclusions:

• Short-term synaptic plasticity significantly shapes neural activity bump dynamics.
• The developed nonlinear Langevin equations accurately capture the interplay between plasticity and bump behavior.
• This work provides insights into how dynamic synaptic changes contribute to neural computation and memory.