The brain's spatial orientation system relies on specific neuron groups for navigation, such as head direction cells for orientation and grid cells for mapping environments. These neurons work together in patterns, firing simultaneously to create directional and positional signals. To better understand and decode these patterns, we developed a new topological deep learning model that goes beyond traditional graph-based approaches. Our model, a simplicial convolutional recurrent neural network, uses topological structures to capture complex neural relationships. This method allows us to predict head direction and location from neural data without needing prior similarity measures, proving effective in head direction and trajectory prediction.