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Related Experiment Video

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Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

Published on: June 21, 2022

Defying Expectations: How Neurons Compute Prediction Errors in Visual Cortex.

Zahid Padamsey¹, Nathalie L Rochefort²

¹Centre for Discovery Brain Sciences, School of Biomedical Sciences Edinburgh, Edinburgh EH8 9XD, UK.

|December 28, 2020

Summary

Neurons in mouse visual cortex compute prediction errors during locomotion. Layer 2/3 neurons subtract predicted from actual visual flow, unlike deeper layers, revealing subthreshold computation mechanisms in predictive coding.

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Using Looming Visual Stimuli to Evaluate Mouse Vision

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Published on: June 13, 2019

Area of Science:

Neuroscience
Computational Neuroscience
Visual Processing

Background:

Predictive coding models suggest the brain anticipates sensory input.
Understanding the neural computations underlying these predictions is crucial for explaining perception.
Subthreshold neuronal activity plays a key role in integrating and processing information.

Purpose of the Study:

To investigate the subthreshold computations involved in predictive coding within the mouse visual cortex.
To determine which neuronal layers are involved in computing prediction errors.
To elucidate the mechanisms by which predicted and actual sensory inputs are compared.

Main Methods:

Whole-cell recordings were performed in vivo in the mouse visual cortex.
Neuronal activity was recorded during locomotion to generate visual flow stimuli.
Analysis focused on subthreshold membrane potential fluctuations to assess neuronal computations.

Main Results:

Layer 2/3 neurons in the visual cortex exhibit computations consistent with prediction error signaling.
These neurons appear to subtract predicted visual flow from actual visual flow.
Layer 5/6 neurons do not show evidence of computing prediction errors in this manner.
Subthreshold activity in layer 2/3 neurons reflects the comparison of expected and unexpected visual motion.

Conclusions:

Layer 2/3 neurons are critical for computing prediction errors related to locomotion-generated visual flow.
Subthreshold computations in these neurons implement a key aspect of predictive coding.
These findings provide a cellular-level mechanism for how the brain updates its predictions based on sensory evidence.