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Related Experiment Video

Updated: Jan 18, 2026

Optogenetic Entrainment of Hippocampal Theta Oscillations in Behaving Mice

Optogenetic Entrainment of Hippocampal Theta Oscillations in Behaving Mice

Published on: June 29, 2018

Cortical networks with multiple interneuron types generate oscillatory patterns during predictive coding.

Kwangjun Lee¹, Cyriel M A Pennartz^1,2, Jorge F Mejias^1,2

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

Plos Computational Biology

|September 10, 2025

Summary

This summary is machine-generated.

This study introduces a computational model of predictive coding (PC) that details neural circuits for brain inference. The model demonstrates how specific cell types and Hebbian learning enable efficient prediction error minimization and generate neural oscillations.

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Related Experiment Videos

Last Updated: Jan 18, 2026

Optogenetic Entrainment of Hippocampal Theta Oscillations in Behaving Mice

Optogenetic Entrainment of Hippocampal Theta Oscillations in Behaving Mice

Published on: June 29, 2018

Generation of Local CA1 γ Oscillations by Tetanic Stimulation

Generation of Local CA1 γ Oscillations by Tetanic Stimulation

Published on: August 14, 2015

Large-scale Three-dimensional Imaging of Cellular Organization in the Mouse Neocortex

Large-scale Three-dimensional Imaging of Cellular Organization in the Mouse Neocortex

Published on: September 5, 2018

Area of Science:

Computational Neuroscience
Systems Neuroscience
Cognitive Neuroscience

Background:

Predictive coding (PC) theory posits the brain as an inference engine minimizing prediction errors.
Existing PC models lack detailed neuroanatomical implementations and biological circuit validation.

Purpose of the Study:

To develop a neurobiologically plausible computational model of predictive coding.
To investigate the role of specific cortical cell types (excitatory, PV, SST, VIP) in PC.
To explore emergent neural dynamics and responses to stimuli within the PC framework.

Main Methods:

Constructed a computational model integrating a two-area cortical hierarchy with simplified laminar organization and cell-type-specific connectivity.
Implemented Hebbian learning for forming latent representations and minimizing prediction errors on visual datasets (MNIST, fashion-MNIST, CIFAR-10).
Simulated neural oscillatory activity and used optogenetics-inspired inactivation to assess cell-type roles; analyzed responses to deviant stimuli.

Main Results:

The model efficiently performed PC, robust to noise, by minimizing prediction errors using learned latent representations.
Stereotypical excitatory-PV-SST-VIP circuits were shown to compute both positive and negative prediction errors.
Emergent neural oscillations and differentiated cell-type roles (PV, SST, VIP) in dynamics were observed.
Model exhibited anomalous responses to deviant stimuli, consistent with mismatch negativity and oddball paradigms.

Conclusions:

The proposed model provides a neuroanatomically informed framework for understanding predictive coding circuits in the brain.
It highlights the functional significance of specific cell types and their connectivity in mediating prediction error computation and neural dynamics.
This work advances the understanding of how the brain performs inference and processes sensory information, offering a bridge between theory and biological implementation.