The time is ripe to reverse engineer an entire nervous system: simulating behavior from neural interactions

Gal Haspel (NJIT), Ben Baker (Colby College), Isabel Beets (KU Leuven), Edward S Boyden (MIT), Jeffrey Brown (MIT), George Church (Harvard University), Netta Cohen (University of Leeds), Daniel Colon-Ramos (Yale University), Eva Dyer (Georgia Institute of Technology), Christopher Fang-Yen (Ohio State University), Steven Flavell (MIT), Miriam B Goodman (Stanford University), Anne C Hart (Brown University), Eduardo J Izquierdo (Rose-Hulman Institute of Technology), Konstantinos Kagias (MIT), Shawn Lockery (University of Oregon), Yangning Lu (MIT), Adam Marblestone (Convergent Research), Jordan Matelsky (University of Pennsylvania), Brett Mensh (Optimize Science), Talmo D Pereira (Salk Institute), Hanspeter Pfister (Harvard University), Kanaka Rajan (Harvard Medical School), Horacio G Rotstein (NJIT), Monika Scholz (Max Planck Institute for Neurobiology of Behavior), Joshua W. Shaevitz (Princeton University), Eli Shlizerman (University of Washington), Quilee Simeon (MIT), Michael A Skuhersky (MIT), Vineet Tiruvadi (Hume AI), Vivek Venkatachalam (Northeastern University), Donglai Wei (Boston College), Brock Wester (Johns Hopkins APL), Guangyu Robert Yang (MIT), Eviatar Yemini (UMass), Manuel Zimmer (University of Vienna), Konrad P Kording (University of Pennsylvania) (2023) The time is ripe to reverse engineer an entire nervous system: simulating behavior from neural interactions, arXiv:2308.06578 [q-bio.NC].

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Just like electrical engineers understand how microprocessors execute programs in terms of how transistor currents are affected by their inputs, neuroscientists want to understand behavior production in terms of how neuronal outputs are affected by their inputs and internal states. This dependency of neuronal outputs on inputs can be described by a state-dependent input-output (IO)-function. However, to reliably identify these IO-functions, we need to perturb each input and combinations of inputs while observing all the outputs. Here, we argue that such completeness is possible in C. elegans; a complete description that goes all the way from the activity of every neuron to predict behavior. The established and growing toolkit of optophysiology can non-invasively capture and control every neuron’s activity and scale to countless experiments. The information from many such experiments can be pooled while capturing the inter-individual variability because neuronal identity and function are largely conserved across individuals. Just like electrical engineers use transistor IO-functions to simulate program execution, we argue that neuronal IO-functions could be used to simulate the impressive breadth of brain states and behaviors of C. elegans.

Project

Understanding, and simulating, the brain

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