UC Irvine

My dissertation focuses on three research problems to investigate how the robot's behavior leads to a qualitative and quantitative explanation of neural activities, and vice versa, that is, how neural activities lead to behavior. In the first problem, we simulated a rat in a robot simulator to replicate the behavior and neural activity observed in rats during a spatial and working memory task. A recurrent neural network (RNN) with sensory and vision inputs was evolved to control the robot motor wheels and navigate a virtual T-maze. Our current findings suggest 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. In the second problem, we developed a goal-driven perception algorithm inspired by effects of the cholinergic (ACh) and noradrenergic (NE) neuromodulatory systems on attention and tracking uncertainties. We tested the network architecture, which extended the contrastive excitation backprop (c-EB), in a noisy MNIST-pair task and an action-based human support robot task. The network architecture could quickly learn the context without supervision, flexibly apply attention to the appropriate goal, and rapidly detect and re-adapt to context changes. In the third problem, we developed a reservoir-based spiking neural network (r-SNN) to classify three terrain types in a botanical garden. The input spike trains were generated from the linear accelerometer, gyroscope, and image data collected by a six-wheel Android-based robot (ABR). Our r-SNN terrain prediction can be used to evaluate the cost of traversal for path planning. It is a promising approach to develop a complete neuromorphic robot navigation system capable of operating over long durations with minimal power consumption. We suggest that neurorobotic investigation of biologically plausible neural networks can be a powerful methodology for understanding neuroscience, as well as for artificial intelligence and machine learning.