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Living systems adapt using recurrent networks to process environmental information for survival. This review explores how simple, evolutionarily plausible recurrent networks enable complex computations in biology.

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Area of Science:

  • Computational Neuroscience
  • Systems Biology
  • Evolutionary Biology

Background:

  • Living organisms exhibit adaptability to environmental changes.
  • This adaptability relies on processing temporal and spatial environmental cues.
  • Recurrent neural networks are increasingly recognized for their role in complex information processing.

Purpose of the Study:

  • To review the role of recurrent networks in biological information processing.
  • To focus on evolutionarily plausible, simpler network structures.
  • To discuss the evidence and evolutionary aspects of natural recurrent computation.

Main Methods:

  • Review of existing literature from machine learning and computational neuroscience.
  • Analysis of studies on naturally occurring recurrent networks in biological systems.
  • Exploration of evolutionary principles related to natural computation.

Main Results:

  • Recurrent networks, through the interplay of stimuli and internal states, perform temporal computations.
  • Simpler recurrent network structures and learning algorithms are relevant to biological systems.
  • Evidence suggests the existence of naturally occurring recurrent networks across biological scales.

Conclusions:

  • Recurrent networks are fundamental to biological adaptability and information processing.
  • Evolution has likely favored simpler, robust recurrent network architectures.
  • Understanding natural computation paradigms offers insights into biological intelligence and evolution.