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Energy optimization induces predictive-coding properties in a multi-compartment spiking neural network model.

Mingfang Zhang1, Raluca Chitic2, Sander M Bohté1,3

  • 1Centrum Wiskunde & Informatica, Amsterdam, The Netherlands.

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Summary
This summary is machine-generated.

Energy optimization in spiking neural networks can lead to emergent predictive coding properties. This suggests a self-organizing principle for how the brain processes sensory information efficiently.

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Area of Science:

  • Computational neuroscience
  • Artificial intelligence
  • Neuroscience

Background:

  • Predictive coding is a key theory for brain sensory processing.
  • Implementing predictive coding in cortical neuron networks remains challenging.
  • Previous work focused on hand-wired circuits or rate-based networks.

Purpose of the Study:

  • Investigate if predictive coding emerges from energy optimization in spiking neural networks.
  • Explore self-organization principles for cortical connectivity.
  • Understand emergent properties in biologically plausible neural models.

Main Methods:

  • Developed a multi-compartment spiking neural network model.
  • Trained the network with both a task-relevant objective and an energy optimization objective.
  • Analyzed network responses to expected and unexpected stimuli.

Main Results:

  • The energy-optimized model reconstructed internal representations using top-down expectations.
  • Neurons showed distinct responses to expected versus unexpected stimuli.
  • Behavior qualitatively matched experimental evidence for predictive coding.

Conclusions:

  • Predictive coding-like behavior can emerge from energy optimization principles.
  • Energy efficiency may drive self-organization in cortical connectivity.
  • Provides a novel perspective on neural implementation of predictive coding.