Towards a Self-Organized Agent-Based Simulation Model for Exploration of Human Synaptic Connections
This work addresses the challenge of modeling synaptic connections for researchers in computational neuroscience or AI, but it is incremental as it presents an early design without validation.
The paper tackles the problem of exploring human synaptic connections by proposing a self-organized agent-based simulation model that learns to act similarly to the human nervous system, using reflex methodologies on human subjects to estimate unknown connections, but no concrete results or numbers are provided.
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.