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Measuring Connectivity in the Primary Visual Pathway in Human Albinism Using Diffusion Tensor Imaging and Tractography

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Toward a Biologically Plausible Model of LGN-V1 Pathways Based on Efficient Coding.

Yanbo Lian¹, David B Grayden^1,2, Tatiana Kameneva^1,3

¹Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia.

Frontiers in Neural Circuits

|April 2, 2019

Summary

Sparse coding efficiently represents visual information using minimal neural activity. A new model explains how primary visual cortex simple cells achieve this, incorporating biological constraints for realistic receptive fields.

Keywords:

LGN-V1 pathways biological plausibility contrast invariance efficient coding phase-reversed feedback push-pull effect receptive fields separated ON and OFF sub-regions

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Area of Science:

Computational Neuroscience
Neuroscience
Computer Vision

Background:

The visual system utilizes sparse coding for efficient stimulus representation.
Simple cells in the primary visual cortex (V1) exhibit linear spatial summation.
Existing sparse coding models often oversimplify V1 inputs, ignoring biological non-linearities.

Purpose of the Study:

To develop a biologically constrained sparse coding model for the visual pathway.
To explain the emergence of linear summation and receptive field properties in V1 simple cells.
To account for phenomena like ON/OFF segregation and contrast-invariant tuning.

Main Methods:

A two-layer neural network model simulating the lateral geniculate nucleus to V1 pathway.
Incorporation of biological constraints: ON/OFF pathway separation, excitation/inhibition.
Training the model on natural images to learn sparse representations.

Main Results:

The model successfully reproduces the emergence of linear spatial summation from non-linear inputs.
Observed phenomena include ON/OFF sub-region segregation and push-pull effects.
The model accounts for diverse receptive field shapes and contrast-invariant orientation tuning.

Conclusions:

Sparse coding can be implemented in V1 simple cells via biologically plausible neural circuits.
The model provides a unified explanation for several key V1 simple cell properties.
This work bridges computational models and biological realities of visual processing.