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Related Experiment Video

Updated: Jun 27, 2025

Using Neuron Spiking Activity to Trigger Closed-Loop Stimuli in Neurophysiological Experiments
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Predictive coding with spiking neurons and feedforward gist signaling.

Kwangjun Lee1, Shirin Dora1,2, Jorge F Mejias1

  • 1Cognitive and Systems Neuroscience Group, Swammerdam Institute for Life Sciences, Faculty of Science, University of Amsterdam, Amsterdam, Netherlands.

Frontiers in Computational Neuroscience
|April 29, 2024
PubMed
Summary
This summary is machine-generated.

We developed a spiking neural network for predictive coding (SNN-PC) that uses event-driven spikes for biologically plausible perception and learning. This model demonstrates unsupervised learning capabilities and potential for neuromorphic applications.

Keywords:
Hebbian learningpredictive processingrecurrent processingrepresentation learningsensory processingspiking neural networkunsupervised learningvisual cortex

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Artificial Intelligence

Background:

  • Predictive coding (PC) is a key neuroscience theory for perception.
  • Existing PC models lack biological plausibility due to artificial neurons and synchronous signaling.
  • A need exists for more biologically realistic computational models of predictive coding.

Purpose of the Study:

  • To develop a biologically plausible spiking neural network for predictive coding (SNN-PC).
  • To introduce novel mechanisms for efficient information processing and learning in neural networks.
  • To explore unsupervised learning and perceptual inference in a brain-inspired model.

Main Methods:

  • Developed a spiking neural network (SNN-PC) using event-driven, asynchronous spikes.
  • Implemented hierarchical structure and Hebbian learning algorithms.
  • Introduced a fast feedforward sweep for gist representation and separated positive/negative error neurons.

Main Results:

  • SNN-PC successfully learned hierarchical internal representations from the MNIST dataset.
  • The model demonstrated the ability to reconstruct unseen handwritten digits.
  • The network exhibited biologically plausible mechanisms for perceptual inference and unsupervised learning.

Conclusions:

  • SNN-PC offers a biologically plausible framework for predictive coding and perceptual inference.
  • The model's architecture supports efficient, event-driven, and local learning.
  • SNN-PC holds promise for advancing neuromorphic computing applications.