An Emergent Model for Mimicking Human Neuronal Pathways in Silico

Abstract

In this study, our aim is to mimick human neuronal pathways without assuming the transition from microscopic to macroscopic scales depend upon mathematical arguments. Human neuronal pathways are natural complex systems in which large sets of neurons interact locally and give bottomup rise to collective macroscopic behaviors. In this sense, correct knowledge of the synaptic effective connections between neurons is a key prerequisite for relating them to the operation of their central nervous system (CNS). However, estimating these effective connections between neurons in the human CNS poses a great challenge since direct recordings are impossible. Consequently, the network between human neurons is often expressed as a black box and the properties of connections between neurons are estimated using indirect methods (Türker and Powers, 2005). In indirect methods a particular receptor system is stimulated and the responses of neurons that are affected by the stimulus recorded to estimate the properties of the circuit. However, these neuronal circuits in human subjects are only estimations and their existence cannot be directly proven. Furthermore, there is no satisfactory theory on how these unknown parts of the CNS operate.