Neural fields, spectral responses and lateral connections.

Area of Science:

• Computational neuroscience
• Theoretical neuroscience
• Neurodynamics

Background:

• Cortical microcircuits exhibit sparse intrinsic connections.
• Neural field models are used to study mesoscopic dynamics on the cortical surface.
• Understanding connectivity's role in neural dynamics is crucial for brain modeling.

Purpose of the Study:

• To develop a neural field model for local cortical dynamics with sparse connectivity.
• To analyze the impact of connectivity architecture and synaptic gain on spatiotemporal dynamics.
• To investigate the induction of Turing instabilities and phase transitions.

Main Methods:

• Modeling sparse intrinsic connections using radial connectivity functions with non-central peaks.
• Analyzing spectral responses to exogenous input and random fluctuations.
• Characterizing the effects of connectivity range, dispersion, propagation speed, and synaptic gain.

Main Results:

• Spatial deployment and speed of lateral connections influence spatial modes across scales.
• Only synaptic gain was found to induce phase transitions (Turing instabilities).
• The model predicts spectral responses based on connectivity and input.

Conclusions:

• Synaptic gain is the key factor for inducing phase transitions in this neural field model.
• Findings have implications for using neural fields as generative models in dynamic causal modeling (DCM).
• The model provides a framework for understanding how sparse connectivity shapes cortical dynamics.