Natural Neuron Networks Modeling

Abstract

This article presents Jneopallium, a robust framework designed for modeling natural neuron networks with varying levels of detail. Drawing inspiration from historical advancements in neuropsychology and artificial neural networks, Jneopallium offers a modular and flexible approach to simulate neural structures. It allows for the definition of multiple signal types, neuron types, and processing logic, enabling detailed replication of natural cognitive processes. Utilizing Java for implementation, Jneopallium provides an intuitive interface for researchers to define neural architectures, processing rules, and inputoutput logic. This framework aims to bridge the gap between neurobiology and computer science, supporting applications in robotics, AI development, and neuroscience research. The paper details the functional, structural, and IO logic definition processes, showcasing the frameworks versatility and potential for advancing neural network modeling.