There are many theories of how behavior may be controlled by neurons. Testing
and refining these theories would be greatly facilitated if we could correctly
simulate an entire nervous system so we could replicate the brain dynamics in
response to any stimuli or contexts. Besides, simulating a nervous system is in
itself one of the big dreams in systems neuroscience. However, doing so
requires us to identify how each neuron's output depends on its inputs, a
process we call reverse engineering. Current efforts at this focus on the
mammalian nervous system, but these brains are mind-bogglingly complex,
allowing only recordings of tiny subsystems. Here we argue that the time is
ripe for systems neuroscience to embark on a concerted effort to reverse
engineer a smaller system and that Caenorhabditis elegans is the ideal
candidate system as the established optophysiology techniques can capture and
control each neuron's activity and scale to hundreds of thousands of
experiments. Data across populations and behaviors can be combined because
across individuals the nervous system is largely conserved in form and
function. Modern machine-learning-based modeling should then enable a
simulation of C. elegans' impressive breadth of brain states and behaviors. The
ability to reverse engineer an entire nervous system will benefit the design of
artificial intelligence systems and all of systems neuroscience, enabling
fundamental insights as well as new approaches for investigations of
progressively larger nervous systems.
%0 Generic
%1 haspel2023reverse
%A Haspel, Gal
%A Boyden, Edward S
%A Brown, Jeffrey
%A Church, George
%A Cohen, Netta
%A Fang-Yen, Christopher
%A Flavell, Steven
%A Goodman, Miriam B
%A Hart, Anne C
%A Hobert, Oliver
%A Kagias, Konstantinos
%A Lockery, Shawn
%A Lu, Yangning
%A Marblestone, Adam
%A Matelsky, Jordan
%A Pfister, Hanspeter
%A Rotstein, Horacio G
%A Scholz, Monika
%A Shlizerman, Eli
%A Simeon, Quilee
%A Skuhersky, Michael A
%A Venkatachalam, Vivek
%A Yang, Guangyu Robert
%A Yemini, Eviatar
%A Zimmer, Manuel
%A Kording, Konrad P
%D 2023
%K brain_dynamics cognition neuroscience reverse_engineering
%T To reverse engineer an entire nervous system
%U http://arxiv.org/abs/2308.06578
%X There are many theories of how behavior may be controlled by neurons. Testing
and refining these theories would be greatly facilitated if we could correctly
simulate an entire nervous system so we could replicate the brain dynamics in
response to any stimuli or contexts. Besides, simulating a nervous system is in
itself one of the big dreams in systems neuroscience. However, doing so
requires us to identify how each neuron's output depends on its inputs, a
process we call reverse engineering. Current efforts at this focus on the
mammalian nervous system, but these brains are mind-bogglingly complex,
allowing only recordings of tiny subsystems. Here we argue that the time is
ripe for systems neuroscience to embark on a concerted effort to reverse
engineer a smaller system and that Caenorhabditis elegans is the ideal
candidate system as the established optophysiology techniques can capture and
control each neuron's activity and scale to hundreds of thousands of
experiments. Data across populations and behaviors can be combined because
across individuals the nervous system is largely conserved in form and
function. Modern machine-learning-based modeling should then enable a
simulation of C. elegans' impressive breadth of brain states and behaviors. The
ability to reverse engineer an entire nervous system will benefit the design of
artificial intelligence systems and all of systems neuroscience, enabling
fundamental insights as well as new approaches for investigations of
progressively larger nervous systems.
@misc{haspel2023reverse,
abstract = {There are many theories of how behavior may be controlled by neurons. Testing
and refining these theories would be greatly facilitated if we could correctly
simulate an entire nervous system so we could replicate the brain dynamics in
response to any stimuli or contexts. Besides, simulating a nervous system is in
itself one of the big dreams in systems neuroscience. However, doing so
requires us to identify how each neuron's output depends on its inputs, a
process we call reverse engineering. Current efforts at this focus on the
mammalian nervous system, but these brains are mind-bogglingly complex,
allowing only recordings of tiny subsystems. Here we argue that the time is
ripe for systems neuroscience to embark on a concerted effort to reverse
engineer a smaller system and that Caenorhabditis elegans is the ideal
candidate system as the established optophysiology techniques can capture and
control each neuron's activity and scale to hundreds of thousands of
experiments. Data across populations and behaviors can be combined because
across individuals the nervous system is largely conserved in form and
function. Modern machine-learning-based modeling should then enable a
simulation of C. elegans' impressive breadth of brain states and behaviors. The
ability to reverse engineer an entire nervous system will benefit the design of
artificial intelligence systems and all of systems neuroscience, enabling
fundamental insights as well as new approaches for investigations of
progressively larger nervous systems.},
added-at = {2023-09-09T19:53:13.000+0200},
author = {Haspel, Gal and Boyden, Edward S and Brown, Jeffrey and Church, George and Cohen, Netta and Fang-Yen, Christopher and Flavell, Steven and Goodman, Miriam B and Hart, Anne C and Hobert, Oliver and Kagias, Konstantinos and Lockery, Shawn and Lu, Yangning and Marblestone, Adam and Matelsky, Jordan and Pfister, Hanspeter and Rotstein, Horacio G and Scholz, Monika and Shlizerman, Eli and Simeon, Quilee and Skuhersky, Michael A and Venkatachalam, Vivek and Yang, Guangyu Robert and Yemini, Eviatar and Zimmer, Manuel and Kording, Konrad P},
biburl = {https://www.bibsonomy.org/bibtex/237138521a16b4115061266edcd75bd32/tabularii},
description = {To reverse engineer an entire nervous system},
interhash = {e8898841187097e6c7699e3e6c8b0bf2},
intrahash = {37138521a16b4115061266edcd75bd32},
keywords = {brain_dynamics cognition neuroscience reverse_engineering},
note = {cite arxiv:2308.06578Comment: 23 pages, 2 figures, opinion paper},
timestamp = {2023-09-09T19:53:13.000+0200},
title = {To reverse engineer an entire nervous system},
url = {http://arxiv.org/abs/2308.06578},
year = 2023
}