This paper addresses the challenges in training spiking neural networks (SNNs) by proposing a spatio-temporal backpropagation (STBP) framework. SNNs are promising for exploring brain-like behaviors due to their ability to encode rich spatio-temporal information, but existing training methods often overlook the temporal domain and rely on complex techniques. The authors introduce an iterative leaky integrate-and-fire (LIF) model that facilitates gradient descent training and simultaneously considers both the spatial domain (SD) and the timing-dependent temporal domain (TD). They design a loss function that minimizes errors over time windows and derive the necessary derivatives for gradient descent. To handle the non-differentiable spike activity, they approximate its derivative using four different curves. The proposed method is evaluated on static MNIST, a custom object detection dataset, and the dynamic N-MNIST dataset, achieving the best accuracy compared to state-of-the-art algorithms. The results demonstrate the effectiveness of the STBP framework in leveraging the spatio-temporal dynamics of SNNs, making it a promising approach for future brain-like computing paradigms.This paper addresses the challenges in training spiking neural networks (SNNs) by proposing a spatio-temporal backpropagation (STBP) framework. SNNs are promising for exploring brain-like behaviors due to their ability to encode rich spatio-temporal information, but existing training methods often overlook the temporal domain and rely on complex techniques. The authors introduce an iterative leaky integrate-and-fire (LIF) model that facilitates gradient descent training and simultaneously considers both the spatial domain (SD) and the timing-dependent temporal domain (TD). They design a loss function that minimizes errors over time windows and derive the necessary derivatives for gradient descent. To handle the non-differentiable spike activity, they approximate its derivative using four different curves. The proposed method is evaluated on static MNIST, a custom object detection dataset, and the dynamic N-MNIST dataset, achieving the best accuracy compared to state-of-the-art algorithms. The results demonstrate the effectiveness of the STBP framework in leveraging the spatio-temporal dynamics of SNNs, making it a promising approach for future brain-like computing paradigms.