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Signatures of criticality in efficient coding networks.

Shervin Safavi1,2, Matthew Chalk3, Nikos K Logothetis2,4

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Neural networks optimized for efficient coding exhibit critical brain dynamics. This suggests that criticality and efficient coding theories are linked, potentially enhancing neural processing and behavior.

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Area of Science:

  • Computational neuroscience
  • Theoretical neuroscience
  • Complex systems

Background:

  • The critical brain hypothesis posits that neural computation is optimized near a second-order phase transition.
  • Previous research linked criticality to enhanced sensory processing, but its role in behaviorally relevant tasks remained unclear.

Purpose of the Study:

  • To investigate signatures of criticality in neural networks optimized for efficient coding.
  • To explore the relationship between efficient coding and critical dynamics in neural systems.

Main Methods:

  • Simulated a spike-coding network of leaky integrate-and-fire neurons with synaptic delays.
  • Analyzed network dynamics across varying noise amplitudes to identify criticality signatures.
  • Examined scale-free spiking dynamics and crackling noise relations.

Main Results:

  • Networks optimized for efficient coding displayed signatures of criticality near optimal noise levels.
  • Scale-free dynamics in spiking activity and crackling noise were observed.
  • Performance varied non-monotonically with noise amplitude, peaking near critical regimes.

Conclusions:

  • Efficient coding and criticality are potentially intertwined mechanisms in neural processing.
  • Critical dynamics may support behaviorally relevant computations in optimized neural networks.
  • This research bridges two major theories of neural optimization.