Intellectual Merit: Perceptual decision-making is an essential cognitive capability. It requires neural circuits that can accumulate sensory evidence, combine it with prior information, and select an appropriate action at an appropriate time. Theories of the brain's ability to perform these computations have primarily involved either """"""""mechanistic"""""""" models based on dynamical systems, or """"""""normative"""""""" models of optimal decision-making imported from psychology, statistics, or economics. However, existing theories do not account-or even attempt to account-for the detailed response properties of neurons believed to carry out these computations. Multi-neuron recordings necessary to evaluate such theories have not yet been collected. There is as yet no general theoretical framework for relating the various sensory, motor, memory, and reward variables involved in decision-making to the time-varying spike responses of multiple neurons that collectively compute decision. This proposal aims to fill that gap. The goal of the proposed research is a detailed and comprehensive understanding of the encoding and decoding of decision-related information by groups of neurons in lateral intraparietal cortex (area LIP), a brain region strongly implicated in decision-making. Multi-electrode recordings will be obtained from primates engaged in decision-making tasks;this will provide the first window into the simultaneous representation of decisions by groups of spiking neurons. The investigators will develop a highly flexible probabilistic spike train model to capture the spike responses of neural populations in LIP, incorporating correlations between neurons, spike-history and adaptation, and a complete set of dependencies on various sensory, motor, decision and reward variables. A novel feature of this research is that it does not presuppose a particular mechanistic or normative theory of LIP function;rather, it begins by seeking a descriptive model of LIP responses as they actually exist in the brain. This will allow for a full accounting of the time-varying information carried by LIP spikes and the optimality of various strategies for decoding them, and will provide a platform for deriving and evaluating simplified models of LIP function. The research will tightly integrate theory and experiment with several new experiments designed to examine the joint coding of decisions across multiple neurons. Collaboration: The proposed research represents a new collaboration between two young investigators with expertise in computational neuroscience and systems neurophysiology. It will combine state-of-the-art statistical methods for spike train modeling and experimental methods recording the simultaneous activity of multiple neurons. The goals of the proposal will be met by closely integrating theory and model development with electrophysiological experiments, which will be facilitated by the proximity of the two investigators. Broader Impacts: The parietal cortex plays a central role in decision-making, and is implicated in a variety of major brain disorders, including depression, anxiety, schizophrenia, and Parkinson's disease. By revealing the computational underpinnings of neural decision making in healthy brains, the proposed research holds great promise for advancing the understanding and treatment of these disorders. Moreover, the models and methodologies to be developed are very general, with applicability to a wide variety of brain areas involved in sensory and motor processing. These methods will aid in the design of advanced sensory and motor neural prosthetic devices, human-engineered systems that replace damaged portions of the sensory or motor system. All software will be made publicly available online, which will enhance the infrastructure for research and education in computational neuroscience. The research proposal will promote teaching and training in several key respects. The project is fundamentally interdisciplinary, combining cutting-edge physiological and computational techniques. Trainees will spend time in both investigator's labs, and will receive an invaluable hands-on, collaborative education. The project will also directly inform classes developed by both investigators. The investigators will promote public scientific understanding by making audio recordings of basic math and science textbooks for the visually impaired at the Learning Ally (Austin's recording studio for the visually impaired). The investigators will aim to recruit interns and graduates from traditionally under-represented groups, especially women. Finally, they will conduct outreach at local middle and high schools in order to spark enthusiasm for mathematics and computer science, disciplines which are fundamental to the exciting challenge of discovering how the brain works.

Public Health Relevance

Mental function of all forms derives from the coordinated patterns of electrical activity that flow through populations of neurons, and mental illness results from dysfunction of such circuitry. Although much is known about the behavioral and cognitive makers and impacts of mental illness and brain disease, relatively little is known about the actual information-processing deficits at the level, of neural circuits and neural computations. This work aims to lay the groundwork for understanding these impacts by unpacking the complex neural signals carried by neurons involved in working memory and decision making.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Research Project (R01)
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Special Emphasis Panel (ZRG1-IFCN-B (55))
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Glanzman, Dennis L
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University of Texas Austin
Schools of Arts and Sciences
United States
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Latimer, Kenneth W; Yates, Jacob L; Meister, Miriam L R et al. (2016) Response to Comment on ""Single-trial spike trains in parietal cortex reveal discrete steps during decision-making"". Science 351:1406
Latimer, Kenneth W; Yates, Jacob L; Meister, Miriam L R et al. (2015) NEURONAL MODELING. Single-trial spike trains in parietal cortex reveal discrete steps during decision-making. Science 349:184-7
Bonnen, Kathryn; Burge, Johannes; Yates, Jacob et al. (2015) Continuous psychophysics: Target-tracking to measure visual sensitivity. J Vis 15:
Knudson, Karin C; Yates, Jacob L; Huk, Alexander C et al. (2014) Inferring sparse representations of continuous signals with continuous orthogonal matching pursuit. Adv Neural Inf Process Syst 27:
Park, Il Memming; Meister, Miriam L R; Huk, Alexander C et al. (2014) Encoding and decoding in parietal cortex during sensorimotor decision-making. Nat Neurosci 17:1395-403