Human eye inspired log-polar pre-processing for neural networks

In this paper we draw inspiration from the human visual system, and present a bio-inspired pre-processing stage for neural networks. We implement this by applying a log-polar transformation as a pre-processing step, and to demonstrate, we have used a naive convolutional neural network (CNN). We demonstrate that a bio-inspired pre-processing stage can achieve rotation and scale robustness in CNNs. A key point in this paper is that the CNN does not need to be trained to identify rotation or scaling permutations; rather it is the log-polar pre-processing step that converts the image into a format that allows the CNN to handle rotation and scaling permutations. In addition we demonstrate how adding a log-polar transformation as a pre-processing step can reduce the image size to ~20\% of the Euclidean image size, without significantly compromising classification accuracy of the CNN. The pre-processing stage presented in this paper is modelled after the retina and therefore is only tested against an image dataset. Note: This paper has been submitted for SAUPEC/RobMech/PRASA 2020

Combination of diffuse and local connections in the cortex

We present a simple neural network model that combines local and diffuse connections. It represents the interaction between ascending or monamine systems, and the cortex in the human brain. We show that this simple model allows the use of salience (cognitive significance) as an input and response in the neural network. Results for single and multiple-trial learning show that salience can drive the learning process to a high degree