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Bittner SR, Palmigiano A, Piet AT, Duan CA, Brody CD, Miller KD, Cunningham J. Interrogating theoretical models of neural computation with emergent property inference. eLife 2021; 10:e56265. [PMID: 34323690 PMCID: PMC8321557 DOI: 10.7554/elife.56265] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Accepted: 06/30/2021] [Indexed: 11/13/2022] Open
Abstract
A cornerstone of theoretical neuroscience is the circuit model: a system of equations that captures a hypothesized neural mechanism. Such models are valuable when they give rise to an experimentally observed phenomenon -- whether behavioral or a pattern of neural activity -- and thus can offer insights into neural computation. The operation of these circuits, like all models, critically depends on the choice of model parameters. A key step is then to identify the model parameters consistent with observed phenomena: to solve the inverse problem. In this work, we present a novel technique, emergent property inference (EPI), that brings the modern probabilistic modeling toolkit to theoretical neuroscience. When theorizing circuit models, theoreticians predominantly focus on reproducing computational properties rather than a particular dataset. Our method uses deep neural networks to learn parameter distributions with these computational properties. This methodology is introduced through a motivational example of parameter inference in the stomatogastric ganglion. EPI is then shown to allow precise control over the behavior of inferred parameters and to scale in parameter dimension better than alternative techniques. In the remainder of this work, we present novel theoretical findings in models of primary visual cortex and superior colliculus, which were gained through the examination of complex parametric structure captured by EPI. Beyond its scientific contribution, this work illustrates the variety of analyses possible once deep learning is harnessed towards solving theoretical inverse problems.
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Affiliation(s)
- Sean R Bittner
- Department of Neuroscience, Columbia UniversityNew YorkUnited States
| | | | - Alex T Piet
- Princeton Neuroscience InstitutePrincetonUnited States
- Princeton UniversityPrincetonUnited States
- Allen Institute for Brain ScienceSeattleUnited States
| | - Chunyu A Duan
- Institute of Neuroscience, Chinese Academy of SciencesShanghaiChina
| | - Carlos D Brody
- Princeton Neuroscience InstitutePrincetonUnited States
- Princeton UniversityPrincetonUnited States
- Howard Hughes Medical InstituteChevy ChaseUnited States
| | - Kenneth D Miller
- Department of Neuroscience, Columbia UniversityNew YorkUnited States
| | - John Cunningham
- Department of Statistics, Columbia UniversityNew YorkUnited States
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Russo AA, Khajeh R, Bittner SR, Perkins SM, Cunningham JP, Abbott LF, Churchland MM. Neural Trajectories in the Supplementary Motor Area and Motor Cortex Exhibit Distinct Geometries, Compatible with Different Classes of Computation. Neuron 2020; 107:745-758.e6. [PMID: 32516573 DOI: 10.1016/j.neuron.2020.05.020] [Citation(s) in RCA: 64] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2019] [Revised: 12/25/2019] [Accepted: 05/11/2020] [Indexed: 12/21/2022]
Abstract
The supplementary motor area (SMA) is believed to contribute to higher order aspects of motor control. We considered a key higher order role: tracking progress throughout an action. We propose that doing so requires population activity to display low "trajectory divergence": situations with different future motor outputs should be distinct, even when present motor output is identical. We examined neural activity in SMA and primary motor cortex (M1) as monkeys cycled various distances through a virtual environment. SMA exhibited multiple response features that were absent in M1. At the single-neuron level, these included ramping firing rates and cycle-specific responses. At the population level, they included a helical population-trajectory geometry with shifts in the occupied subspace as movement unfolded. These diverse features all served to reduce trajectory divergence, which was much lower in SMA versus M1. Analogous population-trajectory geometry, also with low divergence, naturally arose in networks trained to internally guide multi-cycle movement.
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Affiliation(s)
- Abigail A Russo
- Department of Neuroscience, Columbia University, New York, NY 10027, USA; Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
| | - Ramin Khajeh
- Department of Neuroscience, Columbia University, New York, NY 10027, USA; Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA
| | - Sean R Bittner
- Department of Neuroscience, Columbia University, New York, NY 10027, USA; Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA
| | - Sean M Perkins
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; Department of Biomedical Engineering, Columbia University, New York, NY 10027, USA
| | - John P Cunningham
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; Grossman Center for the Statistics of Mind, Columbia University, New York, NY 10027, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA; Department of Statistics, Columbia University, New York, NY 10027, USA
| | - L F Abbott
- Department of Neuroscience, Columbia University, New York, NY 10027, USA; Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; Grossman Center for the Statistics of Mind, Columbia University, New York, NY 10027, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA; Department of Physiology and Cellular Biophysics, Columbia University Medical Center, New York, NY 10032, USA; Kavli Institute for Brain Science, Columbia University, New York, NY 10027, USA
| | - Mark M Churchland
- Department of Neuroscience, Columbia University, New York, NY 10027, USA; Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; Grossman Center for the Statistics of Mind, Columbia University, New York, NY 10027, USA; Kavli Institute for Brain Science, Columbia University, New York, NY 10027, USA.
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Russo AA, Bittner SR, Perkins SM, Seely JS, London BM, Lara AH, Miri A, Marshall NJ, Kohn A, Jessell TM, Abbott LF, Cunningham JP, Churchland MM. Motor Cortex Embeds Muscle-like Commands in an Untangled Population Response. Neuron 2018; 97:953-966.e8. [PMID: 29398358 DOI: 10.1016/j.neuron.2018.01.004] [Citation(s) in RCA: 134] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2017] [Revised: 10/24/2017] [Accepted: 12/31/2017] [Indexed: 01/02/2023]
Abstract
Primate motor cortex projects to spinal interneurons and motoneurons, suggesting that motor cortex activity may be dominated by muscle-like commands. Observations during reaching lend support to this view, but evidence remains ambiguous and much debated. To provide a different perspective, we employed a novel behavioral paradigm that facilitates comparison between time-evolving neural and muscle activity. We found that single motor cortex neurons displayed many muscle-like properties, but the structure of population activity was not muscle-like. Unlike muscle activity, neural activity was structured to avoid "tangling": moments where similar activity patterns led to dissimilar future patterns. Avoidance of tangling was present across tasks and species. Network models revealed a potential reason for this consistent feature: low tangling confers noise robustness. Finally, we were able to predict motor cortex activity from muscle activity by leveraging the hypothesis that muscle-like commands are embedded in additional structure that yields low tangling.
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Affiliation(s)
- Abigail A Russo
- Department of Neuroscience, Columbia University Medical Center, New York, NY 10032, USA; Zuckerman Institute, Columbia University, New York, NY 10027, USA
| | - Sean R Bittner
- Department of Neuroscience, Columbia University Medical Center, New York, NY 10032, USA; Zuckerman Institute, Columbia University, New York, NY 10027, USA
| | - Sean M Perkins
- Zuckerman Institute, Columbia University, New York, NY 10027, USA; Department of Biomedical Engineering, Columbia University, New York, NY 10027, USA
| | - Jeffrey S Seely
- Department of Neuroscience, Columbia University Medical Center, New York, NY 10032, USA; Zuckerman Institute, Columbia University, New York, NY 10027, USA
| | | | - Antonio H Lara
- Department of Neuroscience, Columbia University Medical Center, New York, NY 10032, USA; Zuckerman Institute, Columbia University, New York, NY 10027, USA
| | - Andrew Miri
- Department of Neuroscience, Columbia University Medical Center, New York, NY 10032, USA; Zuckerman Institute, Columbia University, New York, NY 10027, USA; Departments of Biochemistry and Molecular Biophysics, Columbia University Medical Center, New York, NY 10032, USA
| | - Najja J Marshall
- Department of Neuroscience, Columbia University Medical Center, New York, NY 10032, USA; Zuckerman Institute, Columbia University, New York, NY 10027, USA
| | - Adam Kohn
- Department of Ophthalmology and Visual Sciences, Dominick Purpura Department of Neuroscience, Albert Einstein College of Medicine, Yeshiva University, Bronx, NY 10461, USA
| | - Thomas M Jessell
- Department of Neuroscience, Columbia University Medical Center, New York, NY 10032, USA; Zuckerman Institute, Columbia University, New York, NY 10027, USA; Kavli Institute for Brain Science, Columbia University Medical Center, New York, NY 10032, USA; Howard Hughes Medical Institute, Columbia University, New York, NY 10032, USA; Departments of Biochemistry and Molecular Biophysics, Columbia University Medical Center, New York, NY 10032, USA
| | - Laurence F Abbott
- Department of Neuroscience, Columbia University Medical Center, New York, NY 10032, USA; Zuckerman Institute, Columbia University, New York, NY 10027, USA; Kavli Institute for Brain Science, Columbia University Medical Center, New York, NY 10032, USA; Department of Physiology and Cellular Biophysics, Columbia University Medical Center, New York, NY 10032, USA; Center for Theoretical Neuroscience, Columbia University Medical Center, New York, NY 10032, USA
| | - John P Cunningham
- Zuckerman Institute, Columbia University, New York, NY 10027, USA; Grossman Center for the Statistics of Mind, Columbia University, New York, NY 10027, USA; Center for Theoretical Neuroscience, Columbia University Medical Center, New York, NY 10032, USA; Department of Statistics, Columbia University, New York, NY 10027, USA
| | - Mark M Churchland
- Department of Neuroscience, Columbia University Medical Center, New York, NY 10032, USA; Zuckerman Institute, Columbia University, New York, NY 10027, USA; Kavli Institute for Brain Science, Columbia University Medical Center, New York, NY 10032, USA; Grossman Center for the Statistics of Mind, Columbia University, New York, NY 10027, USA.
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