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Stabilizing sequence learning in stochastic spiking networks with GABA-Modulated STDP.

Marius Vieth1, Jochen Triesch1

  • 1Frankfurt Institute for Advanced Studies, Frankfurt am Main, Germany.

Neural Networks : the Official Journal of the International Neural Network Society
|December 12, 2024
PubMed
Summary
This summary is machine-generated.

We introduce GABA-Modulated Spike Timing-Dependent Plasticity (GMS), a novel unsupervised learning method for artificial neural networks. GMS enables stable learning and replay of complex sequences, inspired by biological brain functions.

Keywords:
PymoNNtoSTDPSpontaneous replayStochastic spiking neuronsText- and bar-sequencesUnsupervised training

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Area of Science:

  • Computational neuroscience
  • Artificial intelligence
  • Machine learning

Background:

  • Cortical networks exhibit unsupervised learning and sequence replay.
  • Artificial spiking neural networks (SNNs) face challenges in replicating these abilities.
  • The role of plasticity rules in SNN learning stability is not fully understood.

Purpose of the Study:

  • To introduce a biologically inspired plasticity rule for stable unsupervised learning in SNNs.
  • To demonstrate the efficacy of this rule in learning complex temporal sequences.
  • To investigate the impact of inhibition on synaptic plasticity and network stability.

Main Methods:

  • Developed GABA-Modulated Spike Timing-Dependent Plasticity (GMS), a novel STDP variant.
  • Utilized GMS in recurrent spiking neural networks for sequence learning tasks.
  • Investigated the effect of inhibition levels on synaptic plasticity (depression/potentiation).
  • Tested the model with character-based, token-based text, and visual sequences.

Main Results:

  • GMS enabled stable learning of complex temporal sequences, including natural language.
  • The GMS rule effectively modulated synaptic plasticity based on network inhibition levels.
  • Networks demonstrated stable spontaneous replay of learned sequences.
  • Hierarchical and clustered representations of input sequences were formed.

Conclusions:

  • GMS provides a biologically plausible mechanism for unsupervised sequence learning in SNNs.
  • This plasticity rule contributes to both learning and network stability.
  • The findings offer insights into brain-inspired AI and computational neuroscience.