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Emergence of input selective recurrent dynamics via information transfer maximization.

Itsuki Kanemura1, Katsunori Kitano2

  • 1Graduate School of Information Science and Engineering, Ritsumeikan University, 2-150, Iwakuracho, Ibaraki, Osaka, 5670871, Japan. is0404sk@ed.ritsumei.ac.jp.

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Summary

Brain network wiring optimizes information flow by balancing transmission efficiency and reduced maintenance costs. This leads to sparse, modular circuits with adaptable dynamics, mimicking biological systems and improving computing.

Keywords:
Genetic algorithmInformation transferMulti-objective optimizationReservoir computingVision system

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Area of Science:

  • Computational Neuroscience
  • Systems Neuroscience
  • Evolutionary Computing

Background:

  • Brain network structures exhibit specialized wiring patterns crucial for function, influenced by genetics and evolution.
  • The organizational principles underlying these efficient wiring patterns for information processing remain incompletely understood.
  • Understanding these principles is key to deciphering biological information processing and advancing artificial systems.

Purpose of the Study:

  • To investigate the underlying principles of neural circuit formation and information processing.
  • To frame the optimization of information transmission and maintenance costs as a multi-objective challenge.
  • To explore wiring patterns in the visual system using computational models.

Main Methods:

  • Utilized information theory to quantify information transmission.
  • Employed evolutionary computing algorithms to explore multi-objective optimization.
  • Focused on the visual system as a model for circuit formation and information processing.

Main Results:

  • Efficient information transmission requires sparse circuits with modular structures and distinct wiring patterns.
  • Significant trade-offs exist, emphasizing the need for balance in wiring pattern development.
  • Effective circuits demonstrate moderate flexibility in response to stimuli, aligning with visual system studies.

Conclusions:

  • Maximizing information transfer can lead to self-organization of information processing functions, similar to biological circuits.
  • These findings offer insights into neuroscience and the potential for improving reservoir computing.
  • The study highlights the importance of balancing efficiency and cost in neural network design.