Towards manipulative neuroscience based on Brain Network Interface

Abstract

In ATR Computational Neuroscience Laboratories, we proposed several computational models such as cerebellar internal models, MOSAIC, and modular and hierarchical reinforcement-learning models. Some of these models can quantitatively reproduce subject behaviors given sensory inputs and reward and action sequences that subjects received and generated. These computational models possess putative information representation such as error signals for internal models and action stimulus dependent reward prediction, and they can be used as explanatory variables in neuroimaging and neurophysiology experiments. We named this approach computational-model-based neuroimaging, as well as computational-model-based neurophysiology. This new approach is very appealing since it is likely the only method with which we can explore neural representations remotely from either sensory or motor interfaces. However, sometimes the limitation of a mere temporal correlation between the theory and data became apparent, so we started to develop a new paradigm, “manipulative neuroscience”, where physical causality is guaranteed.