# From biological vision to unsupervised hierarchical sparse coding

## TL;DR

This paper presents a biologically inspired model of visual cortex development using an unsupervised hierarchical sparse coding approach, replicating the ML-CSC algorithm to explain neural connection formation.

## Contribution

It introduces a new biological model of visual cortex formation based on hierarchical sparse coding, replicating the ML-CSC algorithm to bridge neuroscience and machine learning.

## Key findings

- Emergence of feature-selective cells in the model
- Development of contour-based visual representations
- Replication of ML-CSC algorithm in a biological context

## Abstract

The formation of connections between neural cells is emerging essentially from an unsupervised learning process. For instance, during the development of the primary visual cortex of mammals (V1), we observe the emergence of cells selective to localized and oriented features. This leads to the development of a rough contour-based representation of the retinal image in area V1. We propose a biological model of the formation of this representation along the thalamo-cortical pathway. To achieve this goal, we replicated the Multi-Layer Convolutional Sparse Coding (ML-CSC) algorithm developed by Michael Elad's group.