  Neuromorphic engineering has emerged as a promising avenue for developing
brain-inspired computational systems. However, conventional electronic AI-based
processors often encounter challenges related to processing speed and thermal
dissipation. As an alternative, optical implementations of such processors have
been proposed, capitalizing on the intrinsic information-processing
capabilities of light. Within the realm of optical neuromorphic engineering,
various optical neural networks (ONNs) have been explored. Among these, Spiking
Neural Networks (SNNs) have exhibited notable success in emulating the
computational principles of the human brain. Nevertheless, the integration of
optical SNN processors has presented formidable obstacles, mainly when dealing
with the computational demands of large datasets. In response to these
challenges, we introduce a pioneering concept: the Free-space Optical deep
Spiking Convolutional Neural Network (OSCNN). This novel approach draws
inspiration from computational models of the human eye. We have meticulously
designed various optical components within the OSCNN to tackle object detection
tasks across prominent benchmark datasets, including MNIST, ETH 80, and
Caltech. Our results demonstrate promising performance with minimal latency and
power consumption compared to their electronic ONN counterparts. Additionally,
we conducted several pertinent simulations, such as optical intensity
to-latency conversion and synchronization. Of particular significance is the
evaluation of the feature extraction layer, employing a Gabor filter bank,
which stands to impact the practical deployment of diverse ONN architectures
significantly.
