Abstract:
A multilayer perceptron network is a very effective tool for both classification and regression type problems, which has been successfully used in many areas. In this era of artificial
intelligence, deep neural networks such as Convolutional Neural Networks (CNNs) have
been found to be extremely successful in solving many difficult problems, often defeating
human performance. Often deep networks are viewed as ”all-cure” solutions. Unfortunately, most of these networks are generally of ”black-box” nature and their functioning
usually is not related to the way biological neural network works. Some of these networks
have millions of free parameters! Moreover, training deep networks often demands a huge
volume of training data.
In this study we intend to incorporate knowledge of biological neurons in some of the
layers of convolutional neural networks. In particular, we study the computational models of some of the cells like Lateral Geniculate Nucleus (LGN) cells and Retinal Ganglion
Cells and make use of such models to extract features from images to be used as input
with the intention that if such features help in improving performance, such computational models will be built into the deep neural network. We also hope that this will
enable us to reduce the size of the network because instead of blindly extracting features,
it will try to mimic, to some extent, the way the brain extracts features. In this context,
first we use the Combination of Receptive Fields (CORF) model. But our experiments do
not exhibit the expected results. Then we propose another CNN model that uses the Difference of Gaussians (DoG) filters in some of the layers of the network because the CORF
model makes all computations on DoG. This has resulted in noticeable improvement in
performance with fewer trainable parameters than the ResNet-18 (we use ResNet-18 as
the base network due to limited computing resources).
