The time is ripe to reverse engineer an entire nervous system: simulating behavior from neural interactions
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2023
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| author | Haspel, Gal Baker, Ben Beets, Isabel Boyden, Edward S Brown, Jeffrey Church, George Cohen, Netta Colon-Ramos, Daniel Dyer, Eva Fang-Yen, Christopher Flavell, Steven Goodman, Miriam B Hart, Anne C Izquierdo, Eduardo J Kagias, Konstantinos Lockery, Shawn Lu, Yangning Marblestone, Adam Matelsky, Jordan Mensh, Brett Pereira, Talmo D Pfister, Hanspeter Rajan, Kanaka Rotstein, Horacio G Scholz, Monika Shaevitz, Joshua W. Shlizerman, Eli Simeon, Quilee Skuhersky, Michael A Tiruvadi, Vineet Venkatachalam, Vivek Wei, Donglai Wester, Brock Yang, Guangyu Robert Yemini, Eviatar Zimmer, Manuel Kording, Konrad P |
| author_facet | Haspel, Gal Baker, Ben Beets, Isabel Boyden, Edward S Brown, Jeffrey Church, George Cohen, Netta Colon-Ramos, Daniel Dyer, Eva Fang-Yen, Christopher Flavell, Steven Goodman, Miriam B Hart, Anne C Izquierdo, Eduardo J Kagias, Konstantinos Lockery, Shawn Lu, Yangning Marblestone, Adam Matelsky, Jordan Mensh, Brett Pereira, Talmo D Pfister, Hanspeter Rajan, Kanaka Rotstein, Horacio G Scholz, Monika Shaevitz, Joshua W. Shlizerman, Eli Simeon, Quilee Skuhersky, Michael A Tiruvadi, Vineet Venkatachalam, Vivek Wei, Donglai Wester, Brock Yang, Guangyu Robert Yemini, Eviatar Zimmer, Manuel Kording, Konrad P |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_06578 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | The time is ripe to reverse engineer an entire nervous system: simulating behavior from neural interactions Haspel, Gal Baker, Ben Beets, Isabel Boyden, Edward S Brown, Jeffrey Church, George Cohen, Netta Colon-Ramos, Daniel Dyer, Eva Fang-Yen, Christopher Flavell, Steven Goodman, Miriam B Hart, Anne C Izquierdo, Eduardo J Kagias, Konstantinos Lockery, Shawn Lu, Yangning Marblestone, Adam Matelsky, Jordan Mensh, Brett Pereira, Talmo D Pfister, Hanspeter Rajan, Kanaka Rotstein, Horacio G Scholz, Monika Shaevitz, Joshua W. Shlizerman, Eli Simeon, Quilee Skuhersky, Michael A Tiruvadi, Vineet Venkatachalam, Vivek Wei, Donglai Wester, Brock Yang, Guangyu Robert Yemini, Eviatar Zimmer, Manuel Kording, Konrad P Neurons and Cognition 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. |
| title | The time is ripe to reverse engineer an entire nervous system: simulating behavior from neural interactions |
| topic | Neurons and Cognition |
| url | https://arxiv.org/abs/2308.06578 |