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Learning Universal Computations with Spikes.

Dominik Thalmeier1, Marvin Uhlmann2,3, Hilbert J Kappen1

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Summary
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Spiking neural networks can perform complex computations by learning world models. This study shows how these networks, with specific constraints and learning rules, can achieve advanced tasks like generating chaotic dynamics and controlling external systems.

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Area of Science:

  • Computational neuroscience
  • Artificial intelligence

Background:

  • Spiking neural networks (SNNs) are crucial for understanding information processing in animals.
  • Complex computations, like delayed reactions and self-sustained activity, are essential for SNNs.
  • Many SNN computations necessitate the internal modeling of the external world.

Purpose of the Study:

  • To demonstrate how SNNs can perform complex computations and generate internal world models.
  • To identify constraints enabling SNNs as substrates for general-purpose computing.
  • To explore learning rules for SNNs to tackle challenging benchmark tasks.

Main Methods:

  • Deriving constraints for SNNs, including dendritic/synaptic nonlinearities and constrained connectivity.
  • Combining SNNs with learning rules for output and recurrent connections.
  • Applying these SNNs to benchmark tasks like chaotic dynamics generation and memory-dependent computations.

Main Results:

  • Identified network constraints that enable powerful, general-purpose computation in SNNs.
  • Demonstrated successful learning of complex tasks, including self-sustained chaotic dynamics.
  • Showcased SNNs' ability to build world models and utilize them for system control.

Conclusions:

  • Constrained SNNs with nonlinearities and learning rules can perform sophisticated computations.
  • These networks can generate internal world models for advanced cognitive functions.
  • The findings provide a neurobiological basis for complex information processing and control in SNNs.