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Poster A160 in Poster Session A - Tuesday, August 6, 2024, 4:15 – 6:15 pm, Johnson Ice Rink
[GAC update] How does the brain compute with probability?
Ralf M Haefner1, Jeff Beck2, Cristina Savin3, Mehrdad Salmasi4, Xaq Pitkow5,6,7; 1Department of Brain and Cognitive Sciences, University of Rochester, 2Department of Neurobiology, Duke University, 3Departments of Neural Science and Data Science, New York University, 4Gatsby Computational Neuroscience Unit, Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, 5Neuroscience Institute, Department of Machine Learning, Carnegie Mellon University, 6Department of Neuroscience, Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, 7Department of Electrical and Computer Engineering, Department of Computer Science, Rice University
This perspective piece is the result of a Generative Adversarial Collaboration (GAC) tackling the question `How does neural activity represent probability distributions?’. We have addressed three major obstacles to progress on answering this question: first, we provide a unified language for defining competing hypotheses. Second, we explain the fundamentals of three prominent proposals for probabilistic computations -- Probabilistic Population Codes (PPCs), Distributed Distributional Codes (DDCs), and Neural Sampling Codes (NSCs) -- and describe similarities and differences in that common language. Third, we review key empirical data previously taken as evidence for at least one of these proposal, and describe how it may or may not be explainable by alternative proposals. Finally, we describe some key challenges in resolving the debate, and propose potential directions to address them through a combination of theory and experiments.
Keywords: GAC