  In this era of artificial intelligence, deep neural networks like
Convolutional Neural Networks (CNNs) have emerged as front-runners, often
surpassing human capabilities. These deep networks are often perceived as the
panacea for all challenges. Unfortunately, a common downside of these networks
is their ''black-box'' character, which does not necessarily mirror the
operation of biological neural systems. Some even have millions/billions of
learnable (tunable) parameters, and their training demands extensive data and
time.
  Here, we integrate the principles of biological neurons in certain layer(s)
of CNNs. Specifically, we explore the use of neuro-science-inspired
computational models of the Lateral Geniculate Nucleus (LGN) and simple cells
of the primary visual cortex. By leveraging such models, we aim to extract
image features to use as input to CNNs, hoping to enhance training efficiency
and achieve better accuracy. We aspire to enable shallow networks with a
Push-Pull Combination of Receptive Fields (PP-CORF) model of simple cells as
the foundation layer of CNNs to enhance their learning process and performance.
To achieve this, we propose a two-tower CNN, one shallow tower and the other as
ResNet 18. Rather than extracting the features blindly, it seeks to mimic how
the brain perceives and extracts features. The proposed system exhibits a
noticeable improvement in the performance (on an average of $5\%-10\%$) on
CIFAR-10, CIFAR-100, and ImageNet-100 datasets compared to ResNet-18. We also
check the efficiency of only the Push-Pull tower of the network.
