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Statistical physics of liquid brains.

Jordi Piñero1,2, Ricard Solé1,2,3

  • 11 ICREA-Complex Systems Lab, Universitat Pompeu Fabra , 08003 Barcelona , Spain.

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Summary
This summary is machine-generated.

Liquid brains, like ant colonies and immune systems, exhibit cognitive abilities through collective dynamics rather than fixed connections. These systems leverage population dynamics and fluctuations for learning and decision-making, offering new insights into distributed cognition.

Keywords:
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Area of Science:

  • Complex Systems
  • Statistical Physics
  • Cognitive Science

Background:

  • Liquid neural networks feature mobile agents (e.g., ants, immune cells) without fixed connections, unlike standard neural networks.
  • Understanding how these 'liquid brains' achieve cognition (learning, decision-making) is a key challenge.

Purpose of the Study:

  • To explore the collective dynamics, memory, and learning properties of liquid brains.
  • To compare liquid brains with standard neural networks ('solid brains'), ant colonies, and the immune system using a statistical physics perspective.

Main Methods:

  • Comparative analysis of three classes of systems: standard neural networks, ant colonies, and the immune system.
  • Application of statistical physics principles to understand collective dynamics and information processing.

Main Results:

  • Liquid brains share formal descriptive properties with standard neural systems but differ significantly in their mechanisms.
  • Attractors in liquid brains can be based on population abundances, not solely connection weights.
  • Some liquid systems utilize fluctuations similarly to cortical networks, suggesting a role for criticality in rapid signal response.

Conclusions:

  • Liquid brains demonstrate cognitive functions through emergent properties of collective dynamics and population interactions.
  • The study highlights parallels and divergences between biological and artificial neural systems, informing the design of distributed cognitive architectures.