Probabilistic Information Processing in Humans and Recurrent Neural NetworksDownload PDF

Anonymous

09 Oct 2020 (modified: 05 May 2023)Submitted to SVRHM@NeurIPSReaders: Everyone
Keywords: Information processing, probabilistic learning, decision making, recurrent neural network
TL;DR: Competitive inhibition and recurrent excitation form the basis for neural circuitry underlying probabilistic perceptual decision making.
Abstract: In nature, sensory inputs are often highly structured, and statistical regularities of these signals can be extracted to form expectation about future sensorimotor associations, thereby facilitating optimal behavior. To date, the circuit mechanisms that underlie these probabilistic computations are not well understood. Through a human electrophysiolgical experiment and a recurrent neural network (RNN) model, the present study investigates how the brain extracts, processes, and utilizes probabilistic structures of sensory signals to guide behavior. To achieve this goal, we first constructed and trained a biophysically constrained RNN model to perform a probabilistic decision making task similar to task paradigms designed for humans. Specifically, the training environment was probabilistic such that one stimulus was more probable than the others. We show that both humans and the RNN model successfully extract information about stimulus probability and integrate this knowledge into their decisions and task strategy in a new environment. Specifically, performance of both humans and the RNN model varied with the degree to which the stimulus probability of the new environment matched the formed expectation. In both humans and RNNs, this expectation effect was more prominent when the strength of sensory evidence was low. These findings suggest that both humans and our RNN model placed more emphasis on prior expectation (top-down signals) when the available sensory information (bottom-up signals) was limited. Finally, by dissecting the trained RNN model, we demonstrate how competitive inhibition and recurrent excitation form the basis for neural circuitry optimized to perform probabilistic information processing.
1 Reply

Loading