The Neural Basis of Spatial Decision Making
1 Max Planck Institute of Animal Behavior
2 University of Oxford
Understanding the neural basis of animal behavior is a central challenge in biology. In this work, we establish a connection between the spatio-temporal decision-making processes of animals and the ring attractor dynamics of neurons, which determine the organism’s heading direction. We formulate a neural field model within the mean field approximation to provide a microscopic description of the spontaneous formation of bifurcations along their movement trajectories towards multiple targets. These bifurcations have been observed in recent animal experiments. The direction of movement corresponds to the position of the activity bump on the ring. Beyond neuronal dynamics triggered by external stimuli, our formulation of the neural field model also predicts the spontaneous formation of activity bumps. Thus, we observe not only patterns of movement resulting from external stimuli but also additional movement patterns arising from spontaneous bumps. By exploring the patterns produced by both induced and spontaneous activity bumps, we gain a comprehensive understanding of how neural dynamics on the ring interact with the organism’s movement in space within this neural field model. We identify fundamentally different regimes of animal behavior as coming from distinct regimes of neural dynamics. Our mean field analysis is further supported by corresponding stochastic simulations of neuronal dynamics.