  Animals ranging from rats to humans can demonstrate cognitive map
capabilities. We evolved weights in a biologically plausible recurrent neural
network (RNN) using an evolutionary algorithm to replicate the behavior and
neural activity observed in rats during a spatial and working memory task in a
triple T-maze. The rat was simulated in the Webots robot simulator and used
vision, distance and accelerometer sensors to navigate a virtual maze. After
evolving weights from sensory inputs to the RNN, within the RNN, and from the
RNN to the robot's motors, the Webots agent successfully navigated the space to
reach all four reward arms with minimal repeats before time-out. Our current
findings suggest that it is the RNN dynamics that are key to performance, and
that performance is not dependent on any one sensory type, which suggests that
neurons in the RNN are performing mixed selectivity and conjunctive coding.
Moreover, the RNN activity resembles spatial information and
trajectory-dependent coding observed in the hippocampus. Collectively, the
evolved RNN exhibits navigation skills, spatial memory, and working memory. Our
method demonstrates how the dynamic activity in evolved RNNs can capture
interesting and complex cognitive behavior and may be used to create RNN
controllers for robotic applications.
