Computing by Robust Transience: How the Fronto-Parietal Network Performs Sequential, Category-Based Decisions.
Decision making involves dynamic interplay between internal judgements and external perception, which has been investigated in delayed match-to-category (DMC) experiments. Our analysis of neural recordings shows that, during DMC tasks, LIP and PFC neurons demonstrate mixed, time-varying, and heterogeneous selectivity, but previous theoretical work has not established the link between these neural characteristics and population-level computations. We trained a recurrent network model to perform DMC tasks and found that the model can remarkably reproduce key features of neuronal selectivity at the single-neuron and population levels. Analysis of the trained networks elucidates that robust transient trajectories of the neural population are the key driver of sequential categorical decisions. The directions of trajectories are governed by network self-organized connectivity, defining a "neural landscape" consisting of a task-tailored arrangement of slow states and dynamical tunnels. With this model, we can identify functionally relevant circuit motifs and generalize the framework to solve other categorization tasks.
Department of Neurobiology and Kavli Institute for Neuroscience, Yale University School of Medicine, New Haven, CT 06511, USA; Institute of Field Robotics, King Mongkut's University of Technology Thonburi, Bangkok 10140, Thailand.
Warasinee
W
Chaisangmongkon
Department of Neurobiology, The University of Chicago, Chicago, IL 60637, USA.
Sruthi K
SK
Swaminathan
Department of Neurobiology, The University of Chicago, Chicago, IL 60637, USA; Grossman Institute for Neuroscience, Quantitative Biology, and Human Behavior, Chicago, IL 60637, USA.
David J
DJ
Freedman
Department of Neurobiology and Kavli Institute for Neuroscience, Yale University School of Medicine, New Haven, CT 06511, USA; Center for Neural Science, New York University, New York, NY 10003, USA; NYU-ECNU Joint Institute of Brain and Cognitive Science, NYU-Shanghai, Shanghai 200122, China. Electronic address: xjwang@nyu.edu.
Xiao-Jing
XJ
Wang
D000818:Animals; D003657:Decision Making / Q000502:physiology*; D008253:Macaca mulatta; D008297:Male; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology; D010296:Parietal Lobe / Q000502:physiology*; D017397:Prefrontal Cortex / Q000502:physiology*
LIP; PFC; category learning; decision making; delayed match-to-category task; hessian-free algorithm; lateral intraparietal cortex; prefrontal cortex; recurrent neural network; working memory