Biologically Plausible Observer Neural Network Models of Brain Areas Involved in Spatial Navigation

Adrianna R. Loback, University of Cambridge


Many higher-order brain areas – including the hippocampus and posterior parietal cortex (PPC), which are involved in spatial navigation and sensorimotor control, respectively – have access to only indirect information about the environmental variables they represent, and are hence observers at the system theoretic level.  Motivated by recent experimental neuroscience results, and by the observer framework from control engineering, we seek in this work to develop a data-driven theoretical framework for biologically plausible observer neural network models of the PPC and hippocampus.  We show that a general observer neural network model can reconcile two key experimental findings. To incorporate biological plausibility constraints, we focus on recurrent neural network architectures, and plan to incorporate biologically relevant plasticity rules.