Multilayer adaptive networks in neuronal processing

TL;DR

This paper proposes multilayer adaptive networks as a framework to understand neuronal processing, emphasizing the role of neuromodulation in reconfiguring neural connectomes and exploring their computational capabilities.

Contribution

It introduces a simplified multilayer adaptive network model that incorporates neuromodulatory layers, advancing the understanding of complex neuronal interactions.

Findings

Multilayer adaptive networks can model neuromodulation effects.

The model shows emergent computational capabilities.

Neuromodulatory layers reconfigure connectome dynamics.

Abstract

The connectome is a wiring diagram mapping all the neural connections in the brain. At the cellular level, it provides a map of the neurons and synapses within a part or all of the brain of an organism. In recent years, significant advances have been made in the study of the connectome via network science and graph theory. This analysis is fundamental to understand neurotransmission (fast synaptic transmission) networks. However, neurons use other forms of communication as neuromodulation that, instead of conveying excitation or inhibition, change neuronal and synaptic properties. This additional neuromodulatory layers condition and reconfigure the connectome. In this paper, we propose that multilayer adaptive networks, in which different synaptic and neurochemical layers interact, are the appropriate framework to explain neuronal processing. Then, we describe a simplified multilayer adaptive network model that accounts for these extra-layers of interaction and analyse the emergence of interesting computational capabilities.