Identifying and Predicting Rat Behavior Using Neural Networks

The hippocampus is known to play a critical role in episodic memory function. Understanding the relation between electrophysiological activity in a rat hippocampus and rat behavior may be helpful in studying pathological diseases that corrupt electrical signaling in the hippocampus, such as Parkinson’s and Alzheimer’s. Additionally, having a method to interpret rat behaviors from neural activity may help in understanding the dynamics of rat neural activity that are associated with certain identified behaviors. In this thesis, neural networks are used as a black-box model to map electrophysiological data, representative of an ensemble of neurons in the hippocampus, to a T-maze, wheel running or open exploration behavior. The velocity and spatial coordinates of the identified behavior are then predicted using the same neurological input data that was used for behavior identification. Results show that a nonlinear autoregressive process with exogenous inputs (NARX) neural network can partially identify between different behaviors and can generally determine the velocity and spatial position attributes of the identified behavior inside and outside of the trained interva