An Agent-Based Model to Reproduce the Boolean Logic Behaviour of Neuronal Self-Organised Communities through Pulse...

Area of Science:

• Computational Neuroscience
• Mathematical Biology
• Artificial Intelligence

Background:

• The human brain's complexity presents significant challenges for understanding and modeling its neurological processes.
• Both signal processing and biophysical aspects influence brain structure, function, and adaptation.
• Existing approaches utilize information theory, graph theory, and AI frameworks inspired by brain function.

Purpose of the Study:

• To present a novel computational model for simulating brain learning and signal processing.
• To incorporate key brain mechanisms like metastability, backpropagation, and neuroplasticity.
• To demonstrate the impact of dynamic neuroplasticity on neural connectivity and signal synchronization.

Main Methods:

• Development of a computational model integrating mathematical concepts (agents, graph theory, topology, backpropagation) with biological principles (metastability, neuroplasticity, neuron migration).
• Simulation of dynamic neuroplasticity, neural inhibition, and neuron migration to observe effects on logical connectivity.
• Analysis of signal processing synchronization and achievement of target latencies within the model.

Main Results:

• The model successfully mimics brain metastability and backpropagation, incorporating neuroplasticity.
• Simulations demonstrated that dynamic neuroplasticity, neural inhibition, and neuron migration can reshape neural connectivity.
• These modifications led to synchronized signal processing and the attainment of specific target latencies.

Conclusions:

• Dynamic logical and biophysical remodeling are crucial for brain plasticity.
• Complex brain phenomena, including learning and signal processing, can be reproduced through simplified computational models.
• This work provides a foundation for more sophisticated and accurate brain simulations.