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Zavatone-Veth JA, Masset P, Tong WL, Zak JD, Murthy VN, Pehlevan C. Neural Circuits for Fast Poisson Compressed Sensing in the Olfactory Bulb. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.21.545947. [PMID: 37961548 PMCID: PMC10634677 DOI: 10.1101/2023.06.21.545947] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Within a single sniff, the mammalian olfactory system can decode the identity and concentration of odorants wafted on turbulent plumes of air. Yet, it must do so given access only to the noisy, dimensionally-reduced representation of the odor world provided by olfactory receptor neurons. As a result, the olfactory system must solve a compressed sensing problem, relying on the fact that only a handful of the millions of possible odorants are present in a given scene. Inspired by this principle, past works have proposed normative compressed sensing models for olfactory decoding. However, these models have not captured the unique anatomy and physiology of the olfactory bulb, nor have they shown that sensing can be achieved within the 100-millisecond timescale of a single sniff. Here, we propose a rate-based Poisson compressed sensing circuit model for the olfactory bulb. This model maps onto the neuron classes of the olfactory bulb, and recapitulates salient features of their connectivity and physiology. For circuit sizes comparable to the human olfactory bulb, we show that this model can accurately detect tens of odors within the timescale of a single sniff. We also show that this model can perform Bayesian posterior sampling for accurate uncertainty estimation. Fast inference is possible only if the geometry of the neural code is chosen to match receptor properties, yielding a distributed neural code that is not axis-aligned to individual odor identities. Our results illustrate how normative modeling can help us map function onto specific neural circuits to generate new hypotheses.
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Affiliation(s)
- Jacob A Zavatone-Veth
- Center for Brain Science, Harvard University Cambridge, MA 02138
- Department of Physics, Harvard University Cambridge, MA 02138
| | - Paul Masset
- Center for Brain Science, Harvard University Cambridge, MA 02138
- Department of Molecular and Cellular Biology, Harvard University Cambridge, MA 02138
| | - William L Tong
- Center for Brain Science, Harvard University Cambridge, MA 02138
- John A. Paulson School of Engineering and Applied Sciences, Harvard University Cambridge, MA 02138
- Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University Cambridge, MA 02138
| | - Joseph D Zak
- Department of Biological Sciences, University of Illinois at Chicago Chicago, IL 60607
| | - Venkatesh N Murthy
- Center for Brain Science, Harvard University Cambridge, MA 02138
- Department of Molecular and Cellular Biology, Harvard University Cambridge, MA 02138
| | - Cengiz Pehlevan
- Center for Brain Science, Harvard University Cambridge, MA 02138
- John A. Paulson School of Engineering and Applied Sciences, Harvard University Cambridge, MA 02138
- Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University Cambridge, MA 02138
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Cohen Y, Engel TA, Langdon C, Lindsay GW, Ott T, Peters MAK, Shine JM, Breton-Provencher V, Ramaswamy S. Recent Advances at the Interface of Neuroscience and Artificial Neural Networks. J Neurosci 2022; 42:8514-8523. [PMID: 36351830 PMCID: PMC9665920 DOI: 10.1523/jneurosci.1503-22.2022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Revised: 09/30/2022] [Accepted: 10/03/2022] [Indexed: 11/17/2022] Open
Abstract
Biological neural networks adapt and learn in diverse behavioral contexts. Artificial neural networks (ANNs) have exploited biological properties to solve complex problems. However, despite their effectiveness for specific tasks, ANNs are yet to realize the flexibility and adaptability of biological cognition. This review highlights recent advances in computational and experimental research to advance our understanding of biological and artificial intelligence. In particular, we discuss critical mechanisms from the cellular, systems, and cognitive neuroscience fields that have contributed to refining the architecture and training algorithms of ANNs. Additionally, we discuss how recent work used ANNs to understand complex neuronal correlates of cognition and to process high throughput behavioral data.
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Affiliation(s)
- Yarden Cohen
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot, 76100, Israel
| | - Tatiana A Engel
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, NY 11724
| | | | - Grace W Lindsay
- Department of Psychology, Center for Data Science, New York University, New York, NY 10003
| | - Torben Ott
- Bernstein Center for Computational Neuroscience Berlin, Institute of Biology, Humboldt University of Berlin, 10117, Berlin, Germany
| | - Megan A K Peters
- Department of Cognitive Sciences, University of California-Irvine, Irvine, CA 92697
| | - James M Shine
- Brain and Mind Centre, University of Sydney, Sydney, NSW 2006, Australia
| | | | - Srikanth Ramaswamy
- Biosciences Institute, Newcastle University, Newcastle upon Tyne, NE2 4HH, United Kingdom
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Peters MA. Towards characterizing the canonical computations generating phenomenal experience. Neurosci Biobehav Rev 2022; 142:104903. [DOI: 10.1016/j.neubiorev.2022.104903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 09/27/2022] [Accepted: 10/01/2022] [Indexed: 10/31/2022]
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Redish AD, Kepecs A, Anderson LM, Calvin OL, Grissom NM, Haynos AF, Heilbronner SR, Herman AB, Jacob S, Ma S, Vilares I, Vinogradov S, Walters CJ, Widge AS, Zick JL, Zilverstand A. Computational validity: using computation to translate behaviours across species. Philos Trans R Soc Lond B Biol Sci 2022; 377:20200525. [PMID: 34957854 PMCID: PMC8710889 DOI: 10.1098/rstb.2020.0525] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 07/28/2021] [Indexed: 11/12/2022] Open
Abstract
We propose a new conceptual framework (computational validity) for translation across species and populations based on the computational similarity between the information processing underlying parallel tasks. Translating between species depends not on the superficial similarity of the tasks presented, but rather on the computational similarity of the strategies and mechanisms that underlie those behaviours. Computational validity goes beyond construct validity by directly addressing questions of information processing. Computational validity interacts with circuit validity as computation depends on circuits, but similar computations could be accomplished by different circuits. Because different individuals may use different computations to accomplish a given task, computational validity suggests that behaviour should be understood through the subject's point of view; thus, behaviour should be characterized on an individual level rather than a task level. Tasks can constrain the computational algorithms available to a subject and the observed subtleties of that behaviour can provide information about the computations used by each individual. Computational validity has especially high relevance for the study of psychiatric disorders, given the new views of psychiatry as identifying and mediating information processing dysfunctions that may show high inter-individual variability, as well as for animal models investigating aspects of human psychiatric disorders. This article is part of the theme issue 'Systems neuroscience through the lens of evolutionary theory'.
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Affiliation(s)
- A. David Redish
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA
| | - Adam Kepecs
- Department of Neuroscience, Washington University in St. Louis, St Louis, MO 63110, USA
- Department of Psychiatry, Washington University in St. Louis, St Louis, MO 63110, USA
| | - Lisa M. Anderson
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN 55455, USA
| | - Olivia L. Calvin
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN 55455, USA
| | - Nicola M. Grissom
- Department of Psychology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Ann F. Haynos
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN 55455, USA
| | | | - Alexander B. Herman
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN 55455, USA
| | - Suma Jacob
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN 55455, USA
| | - Sisi Ma
- Department of Medicine - Institute for Health Informatics, University of Minnesota, Minneapolis, MN 55455, USA
| | - Iris Vilares
- Department of Psychology, University of Minnesota, Minneapolis, MN 55455, USA
| | - Sophia Vinogradov
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN 55455, USA
| | - Cody J. Walters
- Graduate Program in Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA
| | - Alik S. Widge
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN 55455, USA
| | - Jennifer L. Zick
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN 55455, USA
| | - Anna Zilverstand
- Department of Psychiatry and Behavioral Sciences, University of Minnesota, Minneapolis, MN 55455, USA
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Ott T, Masset P, Gouvêa TS, Kepecs A. Apparent sunk cost effect in rational agents. SCIENCE ADVANCES 2022; 8:eabi7004. [PMID: 35148186 PMCID: PMC8836799 DOI: 10.1126/sciadv.abi7004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Accepted: 12/20/2021] [Indexed: 06/14/2023]
Abstract
Rational decision makers aim to maximize their gains, but humans and other animals often fail to do so, exhibiting biases and distortions in their choice behavior. In a recent study of economic decisions, humans, mice, and rats were reported to succumb to the sunk cost fallacy, making decisions based on irrecoverable past investments to the detriment of expected future returns. We challenge this interpretation because it is subject to a statistical fallacy, a form of attrition bias, and the observed behavior can be explained without invoking a sunk cost-dependent mechanism. Using a computational model, we illustrate how a rational decision maker with a reward-maximizing decision strategy reproduces the reported behavioral pattern and propose an improved task design to dissociate sunk costs from fluctuations in decision valuation. Similar statistical confounds may be common in analyses of cognitive behaviors, highlighting the need to use causal statistical inference and generative models for interpretation.
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Affiliation(s)
- Torben Ott
- Bernstein Center for Computational Neuroscience Berlin, Humboldt University of Berlin, Berlin, Germany
- Department of Neuroscience and Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Paul Masset
- Department of Molecular and Cellular Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Thiago S. Gouvêa
- German Research Center for Artificial Intelligence (DFKI), Oldenburg, Germany
| | - Adam Kepecs
- Department of Neuroscience and Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
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Berlemont K, Nadal JP. Confidence-Controlled Hebbian Learning Efficiently Extracts Category Membership From Stimuli Encoded in View of a Categorization Task. Neural Comput 2021; 34:45-77. [PMID: 34758479 DOI: 10.1162/neco_a_01452] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Accepted: 07/20/2021] [Indexed: 11/04/2022]
Abstract
In experiments on perceptual decision making, individuals learn a categorization task through trial-and-error protocols. We explore the capacity of a decision-making attractor network to learn a categorization task through reward-based, Hebbian-type modifications of the weights incoming from the stimulus encoding layer. For the latter, we assume a standard layer of a large number of stimulus-specific neurons. Within the general framework of Hebbian learning, we have hypothesized that the learning rate is modulated by the reward at each trial. Surprisingly, we find that when the coding layer has been optimized in view of the categorization task, such reward-modulated Hebbian learning (RMHL) fails to extract efficiently the category membership. In previous work, we showed that the attractor neural networks' nonlinear dynamics accounts for behavioral confidence in sequences of decision trials. Taking advantage of these findings, we propose that learning is controlled by confidence, as computed from the neural activity of the decision-making attractor network. Here we show that this confidence-controlled, reward-based Hebbian learning efficiently extracts categorical information from the optimized coding layer. The proposed learning rule is local and, in contrast to RMHL, does not require storing the average rewards obtained on previous trials. In addition, we find that the confidence-controlled learning rule achieves near-optimal performance. In accordance with this result, we show that the learning rule approximates a gradient descent method on a maximizing reward cost function.
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Affiliation(s)
- Kevin Berlemont
- Laboratoire de Physique de l'Ecole Normale Supérieure, CNRS, ENS, PSL University, Sorbonne Université, Université de Paris, 75005 Paris, France, and Center for Neural Science, New York University, NY 10002, U.S.A.
| | - Jean-Pierre Nadal
- Laboratoire de Physique de l'Ecole Normale Supérieure, CNRS, ENS, PSL University, Sorbonne Université, Université de Paris, 75005 Paris, France, and Centre d'Analyse et de Mathématique Sociales, École des Hautes Études en Sciences Sociales, CNRS, 75006 Paris, France
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7
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Joo HR, Liang H, Chung JE, Geaghan-Breiner C, Fan JL, Nachman BP, Kepecs A, Frank LM. Rats use memory confidence to guide decisions. Curr Biol 2021; 31:4571-4583.e4. [PMID: 34473948 DOI: 10.1016/j.cub.2021.08.013] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Revised: 05/29/2021] [Accepted: 08/03/2021] [Indexed: 12/20/2022]
Abstract
Memory enables access to past experiences to guide future behavior. Humans can determine which memories to trust (high confidence) and which to doubt (low confidence). How memory retrieval, memory confidence, and memory-guided decisions are related, however, is not understood. In particular, how confidence in memories is used in decision making is unknown. We developed a spatial memory task in which rats were incentivized to gamble their time: betting more following a correct choice yielded greater reward. Rat behavior reflected memory confidence, with higher temporal bets following correct choices. We applied machine learning to identify a memory decision variable and built a generative model of memories evolving over time that accurately predicted both choices and confidence reports. Our results reveal in rats an ability thought to exist exclusively in primates and introduce a unified model of memory dynamics, retrieval, choice, and confidence.
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Affiliation(s)
- Hannah R Joo
- Medical Scientist Training Program, University of California, San Francisco, 513 Parnassus Avenue, San Francisco, CA 94143, USA; Kavli Institute for Fundamental Neuroscience, Center for Integrative Neuroscience, University of California, San Francisco, 675 Nelson Rising Lane, San Francisco, CA 94158, USA; Department of Physiology, University of California, San Francisco, 401 Parnassus Avenue, San Francisco, CA 94158, USA; Department of Psychiatry, University of California, San Francisco, 401 Parnassus Avenue, San Francisco, CA 94158, USA.
| | - Hexin Liang
- Neuroscience Graduate Program, The Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, 725 N. Wolfe Street, Baltimore, MD 21205, USA
| | - Jason E Chung
- Kavli Institute for Fundamental Neuroscience, Center for Integrative Neuroscience, University of California, San Francisco, 675 Nelson Rising Lane, San Francisco, CA 94158, USA; Department of Physiology, University of California, San Francisco, 401 Parnassus Avenue, San Francisco, CA 94158, USA; Department of Psychiatry, University of California, San Francisco, 401 Parnassus Avenue, San Francisco, CA 94158, USA; Department of Neurological Surgery, University of California, San Francisco, 505 Parnassus Avenue, San Francisco, CA 94143, USA
| | - Charlotte Geaghan-Breiner
- Kavli Institute for Fundamental Neuroscience, Center for Integrative Neuroscience, University of California, San Francisco, 675 Nelson Rising Lane, San Francisco, CA 94158, USA; Department of Physiology, University of California, San Francisco, 401 Parnassus Avenue, San Francisco, CA 94158, USA; Department of Psychiatry, University of California, San Francisco, 401 Parnassus Avenue, San Francisco, CA 94158, USA
| | - Jiang Lan Fan
- Bioengineering Graduate Program, University of California, Berkeley/University of California, San Francisco, 1675 Owens Street, San Francisco, CA 94158, USA
| | - Benjamin P Nachman
- Physics Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA; Berkeley Institute of Data Science, University of California, Berkeley, 190 Doe Library, Berkeley, CA 94720, USA
| | - Adam Kepecs
- Department of Psychiatry, Washington University School of Medicine, 660 S. Euclid Avenue, St. Louis, MO 63110, USA
| | - Loren M Frank
- Kavli Institute for Fundamental Neuroscience, Center for Integrative Neuroscience, University of California, San Francisco, 675 Nelson Rising Lane, San Francisco, CA 94158, USA; Department of Physiology, University of California, San Francisco, 401 Parnassus Avenue, San Francisco, CA 94158, USA; Department of Psychiatry, University of California, San Francisco, 401 Parnassus Avenue, San Francisco, CA 94158, USA; Howard Hughes Medical Institute, 4000 Jones Bridge Road, Chevy Chase, MD 20815, USA.
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Asher JM, Hibbard PB. No effect of feedback, level of processing or stimulus presentation protocol on perceptual learning when easy and difficult trials are interleaved. Vision Res 2020; 176:100-117. [DOI: 10.1016/j.visres.2020.07.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Revised: 07/26/2020] [Accepted: 07/29/2020] [Indexed: 11/24/2022]
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9
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Masset P, Ott T, Lak A, Hirokawa J, Kepecs A. Behavior- and Modality-General Representation of Confidence in Orbitofrontal Cortex. Cell 2020; 182:112-126.e18. [PMID: 32504542 DOI: 10.1016/j.cell.2020.05.022] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2019] [Revised: 02/27/2020] [Accepted: 05/11/2020] [Indexed: 02/06/2023]
Abstract
Every decision we make is accompanied by a sense of confidence about its likely outcome. This sense informs subsequent behavior, such as investing more-whether time, effort, or money-when reward is more certain. A neural representation of confidence should originate from a statistical computation and predict confidence-guided behavior. An additional requirement for confidence representations to support metacognition is abstraction: they should emerge irrespective of the source of information and inform multiple confidence-guided behaviors. It is unknown whether neural confidence signals meet these criteria. Here, we show that single orbitofrontal cortex neurons in rats encode statistical decision confidence irrespective of the sensory modality, olfactory or auditory, used to make a choice. The activity of these neurons also predicts two confidence-guided behaviors: trial-by-trial time investment and cross-trial choice strategy updating. Orbitofrontal cortex thus represents decision confidence consistent with a metacognitive process that is useful for mediating confidence-guided economic decisions.
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Affiliation(s)
- Paul Masset
- Cold Spring Harbor Laboratory, 1 Bungtown Road, Cold Spring Harbor, NY 11724, USA; Watson School of Biological Sciences, 1 Bungtown Road, Cold Spring Harbor, NY 11724, USA
| | - Torben Ott
- Cold Spring Harbor Laboratory, 1 Bungtown Road, Cold Spring Harbor, NY 11724, USA; Department of Neuroscience and Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110, USA
| | - Armin Lak
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford OX1 3PT, UK
| | - Junya Hirokawa
- Cold Spring Harbor Laboratory, 1 Bungtown Road, Cold Spring Harbor, NY 11724, USA
| | - Adam Kepecs
- Cold Spring Harbor Laboratory, 1 Bungtown Road, Cold Spring Harbor, NY 11724, USA; Watson School of Biological Sciences, 1 Bungtown Road, Cold Spring Harbor, NY 11724, USA; Department of Neuroscience and Department of Psychiatry, Washington University in St. Louis, St. Louis, MO 63110, USA.
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Kepecs A. Summary: Order and Disorder in Brains and Behavior. COLD SPRING HARBOR SYMPOSIA ON QUANTITATIVE BIOLOGY 2019; 83:219-225. [PMID: 31358660 DOI: 10.1101/sqb.2018.83.038885] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
The 83rd Cold Spring Harbor Symposium on Quantitative Biology on Brains and Behavior: Order and Disorder in the Nervous System explored the tremendous recent progress in neuroscience and how these advances may be used to improve brain health and address psychiatric and neurological disorders. The Symposium explored a vast array of topics from cell types to cognition. My summary focuses on a few emerging themes. Innovative techniques were ever-present, opening up new experimental possibilities. The commoditization of many state-of-the-art technologies is pushing neuroscience beyond its artisanal ways. Another important theme was "circuits in the middle": Numerous presentations dissected cell type-specific circuits that connect different levels of analysis from molecules to behavior. These new technologies have enabled curiosity-driven investigations in animals to connect more directly with preclinical and clinical studies of human brain disorders. Numerous emerging approaches were presented in human neuroscience, bolstering the hope that circuit-specific manipulations will soon provide improved treatments for brain disorders.
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Affiliation(s)
- Adam Kepecs
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York 11724, USA
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