A deep oscillatory neural network with graph convolution to model spatial cells in hippocampal formation

Navigation and memory are vital for survival, with organisms relying on landmarks and objects for orientation and foraging. The hippocampal formation constructs a spatial cognitive map using spatial cells like Place, Grid, and Head Direction cells. Sensory processing guides this map's construction, with the Medial Entorhinal Cortex (MEC) encoding self-motion cues (Path Integration) analogous to the 'where' pathway, while the Lateral Entorhinal Cortex (LEC) integrates sensory inputs analogous to the 'what' pathway. Object-dependent spatial cells, including Object Vector cells, Object Sensitive cells, Border cells, Landmark Vector cells, and Object Trace cells, have been identified, shedding light on how organisms navigate and remember their surroundings.Many computational models address these representations individually or in subsets, but only some comprehensively explain them within a single model. We propose a deep neural network with two separate pipelines for Path Integration and vision. A Graph Convolution Neural Network (GNN) integrates these inputs, followed by a feed-forward network. A virtual agent traverses a 3D environment; its locomotion cues are the Path Integration input, and POV (Point of View) is the vision input for the model. The model's output is the Heading Direction, Reward and Agent's current position. The choice of environment and learning rate for different outputs leads to the emergence of all aforementioned spatial cells. This comprehensive modelling approach is novel as it propagates a minimalist theory of multisensory integration using GNN, explains all observed spatial representations and can be extended as a complete navigation model using a reinforcement learning agent in future.