Collective Phenomena in Neural Population Codes

Princeton University, Princeton NJ

Investigators

Abstract

In virtually every part of the brain, information about the sensory environment, internal body states, or intended movements is encoded by more than one neuron. This was apparent as early as the nineteenth century from the extensive interconnectivity of nearby neurons and continues to be apparent from numerous measurements of the tuning curves and correlation of nearby neurons. Despite its fundamental importance, population neural codes are poorly understood. In this project, the PI will combine large-scale neural recording methods with state-of-the-art theoretical analyses to study collective phenomena in population neural codes. The project will focus on the retina as a model system, where the quality and completeness of experimental data is the greatest. The project will give us a thorough and clear understanding of what are the "ingredients" needed at the population level to give rise to criticality, which will be important in generating hypotheses about what other regions of the brain might exhibit criticality in their population codes. It will also explore a novel hypothesis about how critical population states give rise to a discrete aspect of the neural code and one that is highly robust to neural noise. This could give us new insight into how we effortlessly divide the sensory world into objects. The PI has an extensive track record in multi-electrode recording from the vertebrate retina, along with applying maximum entropy models to analyze states of network activity. The PI hypothesizes that the retinal population code has an unusual and nontrivial structure that it analogous to the critical state in physical systems. This structure leads to the definition of a "collective mode" of neural activity, which is a set of neural activity states that groups visual stimuli into discrete classes. These collective modes constitute a novel hypothesis about population neural codes that is qualitatively different from the view of information encoding at the single-neuron level. The proposed projects aims are: understanding the origin of criticality, including studying the role of correlations in the stimulus, using adaptation to test how specifically retinal circuitry is tuned to give rise to criticality, studying the pattern and strength of correlation required for any network to give rise to criticality as well as formulating receptive field models to see what degrees of overlap and functional heterogeneity are required in defining collective modes of neural activity and studying what stimuli they encode and how reliably they are activated by a given visual stimulus. The broader impacts of the proposed work lie along three distinct directions:(i) the potential to develop new concepts about population neural codes that could prove applicable across many different brain regions; (ii) the development and dissemination of software to perform maximum entropy fits to neural data, which could help spur on the research programs of many labs that use these methods to analyze neural populations; (iii) the broadening of our ideas about critical systems in physics to include the kind of asymmetric, intermediate cases found in biology. This project is being jointly supported by the Physics of Living Systems program in the Division of Physics and the Cellular Dynamics and Function Program in the Division of Integrative Organismal Systems.