Using neural population decoding to understand high level visual processing

Abstract

The field of neuroscience has the potential to address profound questions including explaining how neural activity enables complex behaviors and conscious experience. However, currently the field is a long way from understanding these issues, and progress has been slow. One of the main problems holding back the pace of discovery is that it is still unclear how to interpret neural activity once it has been recorded. This lack of understanding has led to many different data analysis methods, which makes it difficult to evaluate the validity and importance of many reported results. If a clearer understanding of how to interpret neural data existed, it should be much easier to answer other questions about how the brain functions. In this thesis I describe how to use a data analysis method called 'neural population decoding' to analyze data in a way that is potentially more relevant for understanding neural information processing. By applying this method in novel ways to data from several vision experiments, I am able to make several new discoveries, including the fact that abstract category information is coded in the inferior temporal cortex (ITC) and prefrontal cortex (PFC) by dynamic patterns of neural activity, and that when a monkey attends to an object in a cluttered display, the pattern of ITC activity returns to a state that is similar to when the attended object is presented alone. These findings are not only interesting for insights that they give into the content and coding of information in high level visual areas, but they also demonstrate the benefits of using neural population decoding to analyze data. Thus, the methods developed in this thesis should enable more rapid progress toward an algorithmic level understanding of vision and information processing in other neural systems.