Path integration based on a recurrent spiking neural network

Path integration refers to the process by which animals continuously update spatial position during movement by integrating locomotor stimuli such as speed and head direction, thereby enabling real-time localization in continuous space. This process relies on the cooperation of spatial neurons, including grid cells and place cells. In recent years, continuous attractor networks and recurrent neural networks have provided useful insights into the mechanisms of path integration; however, they often rely on fixed-weight connections or exhibit a lack of biological plausibility. To explore more biologically plausible mechanisms, we propose a path integrator based on the recurrent spiking neural network (RSNN). Employing recurrently connected leaky integrate and fire (LIF) neurons, the RSNN encodes spatial positions as spike sequences via membrane potential reset mechanisms, enabling robust long-term path integration. Analysis reveals the spontaneous emergence of spatial and locomotor units, with some units exhibiting spatial-locomotor conjunctive properties, indicating synergistic computations underlying path integration. Ablation experiments confirm that stripe and border units have a larger effect on performance than other unit types under our experimental conditions. Under sparse spiking conditions, the network naturally develops diverse biologically inspired representations. The RSNN's performance provides novel insights into neuronal synergistic mechanisms in biological navigation, offering a biologically grounded framework for path integration modeling and contributing to the development of brain-inspired navigation algorithms.