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.

翻译：在人工智能时代，卷积神经网络等深度神经网络已成为前沿技术，其性能甚至超越人类能力。这些深度网络常被视为解决所有挑战的万能方案。然而，这些网络普遍存在"黑箱"特性，其运作方式并不一定反映生物神经系统的机制。有些网络甚至拥有数百万/数十亿个可学习（可调）参数，其训练需要大量数据和时间。本文在卷积神经网络的特定层中融入生物神经元原理。具体而言，我们探索基于神经科学的计算模型——外侧膝状体（LGN）和初级视皮层简单细胞的建模应用。通过利用此类模型，我们旨在提取图像特征作为卷积神经网络的输入，以期提升训练效率并实现更优精度。我们希望以简单细胞的推拉组合感受野（PP-CORF）模型作为网络基础层，使浅层网络增强学习过程与性能。为此，我们提出双塔卷积神经网络架构：一个浅层塔与ResNet-18深层塔。该系统并非盲目提取特征，而是模拟大脑感知与特征提取机制。在CIFAR-10、CIFAR-100和ImageNet-100数据集上，所提系统相较于ResNet-18展现出显著性能提升（平均5%-10%）。此外，我们还验证了网络中仅推拉塔部分的运行效率。