Perspectives and constraints on neural network models of neurobiological processes

TL;DR

This paper reviews neural network models of brain functions, discussing their biological realism, recent advances, and the constraints needed to improve their neurobiological credibility.

Contribution

It provides a comprehensive discussion of different neural models and identifies key aspects for enhancing their biological plausibility.

Findings

Highlighting recent biologically inspired neural network models

Identifying constraints for improving neurobiological realism

Discussing various neural network architectures and mechanisms

Abstract

Artificial and natural neural network models are a new toolkit which could be potentially have been used for clarifying of complex brain functions. To attend this goal, such models need to be neurobiologically realistic. However, although neural networks have advanced keenly in recent decades their strict similarity in aspects of brain anatomy and physiology is imperfect. In this work we discuss different types of neural models, including localist, attractor and deep network models, and also identify aspects under which their biological credibility can be improved. These conditions range from the choice of neuron models and of mechanisms of synaptic plasticity and learning to implementation of inhibition and control, along with network architectures (modularity, connectivity). We highlight recent advances in biologically inspired neural network models and their constraints.