Training a two-choice decision-making model with environment feedback

摘要

Animals' decision-making behaviors are widely studied in two-alternative forced-choice tasks. Many models have been proposed to model the decision-making process and explain results of behavioral experiments. These models can fit the data of behavioral experiments well. However, the process of learning the correct decision with reward or punishment feedback is ignored in these models. Learning with reward or punishment feedback is very common in behavior experiments that are conducted to animals. It is closely linked to the decision-making process. In this paper, we investigated how to integrate learning process into two-choice decision-making models. We show that we can combine a two-choice decision-making model implemented with spiking neurons with synaptic plasticity to include the decision-learning process. The decision-making model can learn its output according to either reward or punishment feedback from the environment. And output of the decision-making model can be explained at the synapse level.