Feb 20, 2019

A Recurrent Neural Network Model for Flexible and Adaptive Decision Making based on Sequence Learning

BioRxiv : the Preprint Server for Biology
Zhewei ZhangTianming Yang

Abstract

The brain makes flexible and adaptive responses in the complicated and ever-changing environment for the organism's survival. To achieve this, the brain needs to choose appropriate actions flexibly in response to sensory inputs. Moreover, the brain also has to understand how its actions affect future sensory inputs and what reward outcomes should be expected, and adapts its behavior based on the actual outcomes. A modeling approach that takes into account of the combined contingencies between sensory inputs, actions, and reward outcomes may be the key to understanding the underlying neural computation. Here, we train a recurrent neural network model based on sequence learning to predict future events based on the past event sequences that combine sensory, action, and reward events. We use four exemplary tasks that have been used in previous animal and human experiments to study different aspects of decision making and learning. We first show that the model reproduces the animals' choice and reaction time pattern in a probabilistic reasoning task, and its units' activities mimics the classical findings of the ramping pattern of the parietal neurons that reflects the evidence accumulation process during decision making. We furthe...Continue Reading

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Mentioned in this Paper

Biological Neural Networks
Decision Making
Brain
Psychological Reinforcement
Binding (Molecular Function)
Monkeys
Structure
Learning
Analysis
Hospital Units

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