DOI: 10.1101/478412Nov 26, 2018Paper

Statistical physics of liquid brains

BioRxiv : the Preprint Server for Biology
Jordi Pinero, Ricard Sole

Abstract

Liquid neural networks (or ''liquid brains'') are a widespread class of cognitive living networks characterised by a common feature: the agents (ants or immune cells, for example) move in space. Thus, no fixed, long-term agent-agent connections are maintained, in contrast with standard neural systems. How is this class of systems capable of displaying cognitive abilities, from learning to decision-making? In this paper, the collective dynamics, memory and learning properties of liquid brains is explored under the perspective of statistical physics. Using a comparative approach, we review the generic properties of three large classes of systems, namely: standard neural networks (''solid brains''), ant colonies and the immune system. It is shown that, despite their intrinsic physical differences, these systems share key properties with standard neural systems in terms of formal descriptions, but strongly depart in other ways. On one hand, the attractors found in liquid brains are not always based on connection weights but instead on population abundances. However, some liquid systems use fluctuations in ways similar to those found in cortical networks, suggesting a relevant role of criticality as a way of rapidly reacting to exte...Continue Reading

Related Concepts

Ants
Brain
Cerebral Cortex
Cognition
Decision Making
Memory
Anatomical Space Structure
Immune Effector Cell
Ability to Perform Cognitive Activity
Description

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