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Stable recurrent dynamics in heterogeneous neuromorphic computing systems using excitatory and inhibitory plasticity.

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Researchers developed a cross-homeostatic rule for neuromorphic spiking recurrent networks. This rule ensures robust, self-sustained brain-like activity despite component variability, enabling stable memory and low-power computing.

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Area of Science:

  • Neuroscience
  • Neuromorphic Engineering
  • Computational Neuroscience

Background:

  • Neural computations rely on balanced excitation and inhibition in recurrent circuits.
  • Neuromorphic circuits mimic brain functions but face challenges with analog component variability.
  • Robustness of biological networks is difficult to replicate in current neuromorphic systems.

Purpose of the Study:

  • To apply a biologically-plausible cross-homeostatic rule to neuromorphic spiking recurrent networks.
  • To achieve robust, self-sustained network dynamics despite device mismatch.
  • To enable automatic configuration of ultra-low power neuromorphic technologies.

Main Methods:

  • Implemented a cross-homeostatic rule in neuromorphic spiking recurrent networks.
  • Simulated network behavior under conditions of device variability.
  • Analyzed emergent network dynamics, including memory storage and 'paradoxical effect'.

Main Results:

  • The rule autonomously tuned the network to produce robust, self-sustained dynamics in an inhibition-stabilized regime.
  • Networks demonstrated stability even with significant device mismatch.
  • Multiple co-existing stable memories and emergent soft-winner-take-all dynamics were observed.
  • The 'paradoxical effect' seen in cortical circuits was reproduced.

Conclusions:

  • Biologically-inspired homeostatic rules can overcome variability in neuromorphic hardware.
  • This approach enables the creation of robust, efficient neuromorphic computing systems.
  • The findings validate neuroscience models on hardware with biological-like limitations.