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Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
05:19

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Published on: November 12, 2019

A spiking neuron as information bottleneck.

Lars Buesing1, Wolfgang Maass

  • 1Institute for Theoretical Computer Science, Graz University of Technology, Graz, Austria. lars@igi.tugraz.at

Neural Computation
|March 27, 2010
PubMed
Summary
This summary is machine-generated.

We propose a novel learning rule for spiking neurons based on the information bottleneck (IB) framework. This rule minimizes information loss and models three-factor synaptic plasticity, enabling predictive coding.

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Area of Science:

  • Computational Neuroscience
  • Machine Learning
  • Information Theory

Background:

  • Neurons process numerous inputs but produce a single output, acting as information bottlenecks.
  • Understanding how neuronal networks transmit information efficiently is crucial for neuroscience.
  • Existing models of synaptic plasticity often involve multiple factors.

Purpose of the Study:

  • To propose a novel, theoretically motivated learning rule for spiking neurons.
  • To leverage the information bottleneck (IB) framework to minimize information loss in neuronal output.
  • To model synaptic plasticity involving three factors and enable predictive coding in spiking neurons.

Main Methods:

  • Derived a learning rule for spiking neuron weights from the information bottleneck (IB) framework.
  • Incorporated contextual information as a 'third factor' alongside pre- and postsynaptic activity.
  • Analyzed the rule's ability to minimize information loss and learn predictive codes.

Main Results:

  • The proposed IB learning rule effectively minimizes the loss of relevant information in the output spike train.
  • The rule naturally incorporates a 'third factor' (contextual information), aligning with experimental observations of synaptic plasticity.
  • Spiking neurons trained with the IB rule learn to extract input features predictive of future input, demonstrating predictive coding.

Conclusions:

  • The information bottleneck (IB) framework provides a powerful basis for developing biologically plausible learning rules for spiking neurons.
  • The proposed IB learning rule offers a unified explanation for three-factor synaptic plasticity and predictive coding.
  • This framework advances our understanding of efficient information processing and learning in neural systems.