Information theoretic approaches to understanding circuit function

The analysis of stimulus/response patterns using information theoretic approaches requires the full probability distribution of stimuli and response. Recent progress in using information-based tools to understand circuit function has advanced understanding of neural coding at the single cell and population level. In advances over traditional reverse correlation approaches, the determination of receptive fields using information as a metric has allowed novel insights into stimulus representation and transformation. The application of maximum entropy methods to population codes has opened a rich exploration of the internal structure of these codes, revealing stimulus-driven functional connectivity. We speculate about the prospects and limitations of information as a general tool for dissecting neural circuits and relating their structure and function.

► Information can be used to determine receptive fields and response models with natural stimuli.
► New statistical methods are revealing the structure of population-level stimulus/response distributions.
► Population responses are often well approximated by including second-order correlations.
► Improved models can be constructed through selective sampling of higher order correlations.
► Efforts to link population correlation with anatomical circuit motifs are underway.