This repository contains the code for our paper 'How Convolutional Neural Network Architecture Biases Learned Opponency and Spectral Tuning'
and a previous version entitled 'Spatial and Colour Opponency in Anatomically Constrained Deep Networks', accepted to the NeurIPS 2019 workshop on Shared Visual Representations in Humans and Machines (SVRHM).
Notebooks
Spectral Opponency: Generating the spectral opponency figures from the paper.
Spatial Opponency: Generating the spatial opponency figures from the paper.
Double Opponency: Generating the double opponency figures from the paper.
CIELAB: Experiments in CIELAB colour space.
Channel Shuffled: Experiments in with random channel shuffling.
Classification Performance: Accuracy plots from trained models.
Gratings: Generate example grating images.
Ventral Depth - Spectral: Plot spectral opponency as a function of ventral depth.
Ventral Depth - Spatial: Plot spatial opponency as a function of ventral depth.
Colour Distribution: Plot of most excitatory and inhibitory colours.
Characterising a Single Cell: Experiments showing the characterisation of a single cell.
Colour Perception: Experiments on colour sensitivity in Humans and Machines.
Mouse Spatial Tuning: Spatial tuning curves for cells in the Mouse LGN from Zhao et al.