A01Predictive coding on auditory processing: spatio-temporal structure of signal flow in whole-cortical electrocorticograms

 We are aiming to investigate the spatiotemporal structure of signal flow in the whole cortical electrocorticograms (ECoGs) related to the predictive coding on auditory processing. In this study, we are going to employ the recent knowledge of artificial intelligence to the analysis of the ECoGs, which were recorded form the entire cortex of monkeys exposed to sequential auditory stimuli.
 Our brain processes the incoming sensory inputs incessantly, and make predictions and generalizations of the environments to adapt our behavior. This process is often referred to as “predictive coding”. Variety of mathematical models of the predictive coding of the brain have been proposed already, and some of them are even thought that they were to reflect the layered structure of cortices and the cell types of neurons. Nowadays, these models have become basis of the deep learning models in terms of the machine learning. This deep learning theory is now considered to be one of the most powerful tool with the analysis of big date. However, even though we now have good mathematical models, there are lot to be found out about what really happening in our brain. Especially, with whole-brain level, we hardly know anything about the dynamical process of the predictive cording. We are recording the whole-cortical ECoG, which has superior temporal and spatial resolution compared with other recording techniques. Then the deep learning is applied to the analysis of the data to extract the spatiotemporal structure. By doing this research, we believe that we can examine the dynamical process of the predictive cording with the whole-brain level and provide useful information to approve the algorithms of the deep learning.