Intermediate Sensitivity of Neural Activities Induces the Optimal Learning Speed in a Multiple-Timescale Neural Activity Model

Abstract

Collective dynamics of the neural population are involved in a variety of cognitive functions. How such neural dynamics are shaped through learning and how the learning performance is related to the property of the individual neurons are fundamental questions in neuroscience. Previous model studies answered these questions by using developing machine-learning techniques for training a recurrent neural network. However, these techniques are not biologically plausible. Does another type of learning method, for instance, a more biologically plausible learning method, shape the similar neural dynamics and the similar relation between the learning performance and the property of the individual neurons to those observed in the previous studies? In this study, we have used the recently proposed learning model with multiple timescales in the neural activity, which is more biologically plausible, and analyzed the neural dynamics and the relation regarding the sensitivity of neurons. As result, we have found that our model shapes similar neural dynamics and the relation. Further, the intermediate sensitivity of neurons that is optimal for the learning speed generates a variety of neural activity patterns in line with the experimental observations in the neural system. This result suggests that the neural system might develop the sensitivity of neural activities to optimize the learning speed through evolution.