Towards a Self-Organized Agent-Based Simulation Model for Exploration of Human Synaptic Connections

TL;DR

This paper presents an early-stage agent-based simulation model inspired by neuroscience, aiming to emulate human nervous system learning to explore synaptic connections without claiming biological accuracy.

Contribution

It introduces a novel self-organized agent-based framework for simulating human-like learning in neural connection exploration, distinct from biological modeling.

Findings

Initial design of the simulation model

Model learns in a human-like manner

Potential to estimate unknown synaptic connections

Abstract

In this paper, the early design of our self-organized agent-based simulation model for exploration of synaptic connections that faithfully generates what is observed in natural situation is given. While we take inspiration from neuroscience, our intent is not to create a veridical model of processes in neurodevelopmental biology, nor to represent a real biological system. Instead, our goal is to design a simulation model that learns acting in the same way of human nervous system by using findings on human subjects using reflex methodologies in order to estimate unknown connections.