sowole-aims/IMBIZO-Project

This repository contains my mini-project that I carried out at the IBRO-Simons Computational Neuroscience Summer School 2019 at Cape Town, South Africa between January 6th and 27th. # Part-Implementation of 'Using Deep Learning to Reveal the Neural code for images in primary visual cortex'. This repository contains my mini-project that I carried out at the IBRO-Simons Computational Neuroscience Summer School 2019 at Cape Town, South Africa between January 6th and 27th. ### Abstract Primary visual cortex (V1) is known to be the first stage of cortical image processing, and a significant effort in systems neuroscience is being devoted to understanding how it encodes information about visual stimuli. Within V1, many neurons respond selectively to edges of a given preferred orientation: these are known as simple or complex cells, and they are well-studied. Other neurons respond to localized center-surround image features. Still others respond selectively to certain image stimuli, but the specific features that excite them are unknown. Moreover, even for the simple and complex cells– the best-understood V1 neurons– it is challenging to predict how they will respond to natural image stimuli. Thus, there are important gaps in our understanding of how V1 encodes images. To fill this gap, we train deep convolutional neural networks to predict the firing rates of V1 neurons in response to natural image stimuli, and find that 15% of these neurons are within 10% of their theoretical limit of predictability.