Categorical signals in a single-trial neuron activity of the inferotemporal cortex.

We developed an algorithm that decodes categorical signals from the single-trial activity of a neuronal population in the monkey inferotemporal cortex. We defined a global category (i.e. human faces vs. monkey faces vs. shape) and fine categories (i.e. human identity, monkey expression, and shape form) from the single-trial activity. The accuracy of estimation for the trials was roughly 100% for the global category and 88.1% for the fine categories. The accuracy of stimulus identification for the trials was 70.4%. These results suggest that signals concerning global and fine categories as well as object identification can be decoded using the single-trial activity of a neuronal population in the inferotemporal cortex.