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Salsabilian S, Lee C, Margolis D, Najafizadeh L. An LSTM-based adversarial variational autoencoder framework for self-supervised neural decoding of behavioral choices. J Neural Eng 2024; 21:036052. [PMID: 38621379 DOI: 10.1088/1741-2552/ad3eb3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 04/15/2024] [Indexed: 04/17/2024]
Abstract
Objective.This paper presents data-driven solutions to address two challenges in the problem of linking neural data and behavior: (1) unsupervised analysis of behavioral data and automatic label generation from behavioral observations, and (2) extraction of subject-invariant features for the development of generalized neural decoding models.Approach. For behavioral analysis and label generation, an unsupervised method, which employs an autoencoder to transform behavioral data into a cluster-friendly feature space is presented. The model iteratively refines the assigned clusters with soft clustering assignment loss, and gradually improves the learned feature representations. To address subject variability in decoding neural activity, adversarial learning in combination with a long short-term memory-based adversarial variational autoencoder (LSTM-AVAE) model is employed. By using an adversary network to constrain the latent representations, the model captures shared information among subjects' neural activity, making it proper for cross-subject transfer learning.Main results. The proposed approach is evaluated using cortical recordings of Thy1-GCaMP6s transgenic mice obtained via widefield calcium imaging during a motivational licking behavioral experiment. The results show that the proposed model achieves an accuracy of 89.7% in cross-subject neural decoding, outperforming other well-known autoencoder-based feature learning models. These findings suggest that incorporating an adversary network eliminates subject dependency in representations, leading to improved cross-subject transfer learning performance, while also demonstrating the effectiveness of LSTM-based models in capturing the temporal dependencies within neural data.Significance. Results demonstrate the feasibility of the proposed framework in unsupervised clustering and label generation of behavioral data, as well as achieving high accuracy in cross-subject neural decoding, indicating its potentials for relating neural activity to behavior.
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Affiliation(s)
- Shiva Salsabilian
- Integrated Systems and NeuroImaging Laboratory, Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ 08854, United States of America
| | - Christian Lee
- Department of Cell Biology and Neuroscience, Rutgers University, Piscataway, NJ, United States of America
| | - David Margolis
- Department of Cell Biology and Neuroscience, Rutgers University, Piscataway, NJ, United States of America
| | - Laleh Najafizadeh
- Integrated Systems and NeuroImaging Laboratory, Department of Electrical and Computer Engineering, Rutgers University, Piscataway, NJ 08854, United States of America
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2
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Dan C, Hulse BK, Kappagantula R, Jayaraman V, Hermundstad AM. A neural circuit architecture for rapid learning in goal-directed navigation. Neuron 2024:S0896-6273(24)00326-X. [PMID: 38795708 DOI: 10.1016/j.neuron.2024.04.036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 01/16/2024] [Accepted: 04/30/2024] [Indexed: 05/28/2024]
Abstract
Anchoring goals to spatial representations enables flexible navigation but is challenging in novel environments when both representations must be acquired simultaneously. We propose a framework for how Drosophila uses internal representations of head direction (HD) to build goal representations upon selective thermal reinforcement. We show that flies use stochastically generated fixations and directed saccades to express heading preferences in an operant visual learning paradigm and that HD neurons are required to modify these preferences based on reinforcement. We used a symmetric visual setting to expose how flies' HD and goal representations co-evolve and how the reliability of these interacting representations impacts behavior. Finally, we describe how rapid learning of new goal headings may rest on a behavioral policy whose parameters are flexible but whose form is genetically encoded in circuit architecture. Such evolutionarily structured architectures, which enable rapidly adaptive behavior driven by internal representations, may be relevant across species.
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Affiliation(s)
- Chuntao Dan
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Brad K Hulse
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Ramya Kappagantula
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA
| | - Vivek Jayaraman
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA.
| | - Ann M Hermundstad
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA.
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3
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Desai N, Bala P, Richardson R, Raper J, Zimmermann J, Hayden B. OpenApePose, a database of annotated ape photographs for pose estimation. eLife 2023; 12:RP86873. [PMID: 38078902 PMCID: PMC10712952 DOI: 10.7554/elife.86873] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2023] Open
Abstract
Because of their close relationship with humans, non-human apes (chimpanzees, bonobos, gorillas, orangutans, and gibbons, including siamangs) are of great scientific interest. The goal of understanding their complex behavior would be greatly advanced by the ability to perform video-based pose tracking. Tracking, however, requires high-quality annotated datasets of ape photographs. Here we present OpenApePose, a new public dataset of 71,868 photographs, annotated with 16 body landmarks of six ape species in naturalistic contexts. We show that a standard deep net (HRNet-W48) trained on ape photos can reliably track out-of-sample ape photos better than networks trained on monkeys (specifically, the OpenMonkeyPose dataset) and on humans (COCO) can. This trained network can track apes almost as well as the other networks can track their respective taxa, and models trained without one of the six ape species can track the held-out species better than the monkey and human models can. Ultimately, the results of our analyses highlight the importance of large, specialized databases for animal tracking systems and confirm the utility of our new ape database.
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Affiliation(s)
- Nisarg Desai
- Department of Neuroscience and Center for Magnetic Resonance Research, University of MinnesotaMinneapolisUnited States
| | - Praneet Bala
- Department of Computer Science, University of MinnesotaMinneapolisUnited States
| | - Rebecca Richardson
- Emory National Primate Research Center, Emory UniversityAtlantaUnited States
| | - Jessica Raper
- Emory National Primate Research Center, Emory UniversityAtlantaUnited States
| | - Jan Zimmermann
- Department of Neuroscience and Center for Magnetic Resonance Research, University of MinnesotaMinneapolisUnited States
| | - Benjamin Hayden
- Department of Neuroscience and Center for Magnetic Resonance Research, University of MinnesotaMinneapolisUnited States
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4
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Voloh B, Maisson DJN, Cervera RL, Conover I, Zambre M, Hayden B, Zimmermann J. Hierarchical action encoding in prefrontal cortex of freely moving macaques. Cell Rep 2023; 42:113091. [PMID: 37656619 PMCID: PMC10591875 DOI: 10.1016/j.celrep.2023.113091] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 06/23/2023] [Accepted: 08/18/2023] [Indexed: 09/03/2023] Open
Abstract
Our natural behavioral repertoires include coordinated actions of characteristic types. To better understand how neural activity relates to the expression of actions and action switches, we studied macaques performing a freely moving foraging task in an open environment. We developed a novel analysis pipeline that can identify meaningful units of behavior, corresponding to recognizable actions such as sitting, walking, jumping, and climbing. On the basis of transition probabilities between these actions, we found that behavior is organized in a modular and hierarchical fashion. We found that, after regressing out many potential confounders, actions are associated with specific patterns of firing in each of six prefrontal brain regions and that, overall, encoding of action category is progressively stronger in more dorsal and more caudal prefrontal regions. Together, these results establish a link between selection of units of primate behavior on one hand and neuronal activity in prefrontal regions on the other.
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Affiliation(s)
- Benjamin Voloh
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA
| | - David J-N Maisson
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA
| | | | - Indirah Conover
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA
| | - Mrunal Zambre
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA
| | - Benjamin Hayden
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA; Department of Neurosurgery, Baylor College of Medicine, Houston, TX 77030, USA
| | - Jan Zimmermann
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA; Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN 55455, USA.
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5
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Kumar S, Sharma AK, Tran A, Liu M, Leifer AM. Inhibitory feedback from the motor circuit gates mechanosensory processing in Caenorhabditis elegans. PLoS Biol 2023; 21:e3002280. [PMID: 37733772 PMCID: PMC10617738 DOI: 10.1371/journal.pbio.3002280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 10/31/2023] [Accepted: 07/27/2023] [Indexed: 09/23/2023] Open
Abstract
Animals must integrate sensory cues with their current behavioral context to generate a suitable response. How this integration occurs is poorly understood. Previously, we developed high-throughput methods to probe neural activity in populations of Caenorhabditis elegans and discovered that the animal's mechanosensory processing is rapidly modulated by the animal's locomotion. Specifically, we found that when the worm turns it suppresses its mechanosensory-evoked reversal response. Here, we report that C. elegans use inhibitory feedback from turning-associated neurons to provide this rapid modulation of mechanosensory processing. By performing high-throughput optogenetic perturbations triggered on behavior, we show that turning-associated neurons SAA, RIV, and/or SMB suppress mechanosensory-evoked reversals during turns. We find that activation of the gentle-touch mechanosensory neurons or of any of the interneurons AIZ, RIM, AIB, and AVE during a turn is less likely to evoke a reversal than activation during forward movement. Inhibiting neurons SAA, RIV, and SMB during a turn restores the likelihood with which mechanosensory activation evokes reversals. Separately, activation of premotor interneuron AVA evokes reversals regardless of whether the animal is turning or moving forward. We therefore propose that inhibitory signals from SAA, RIV, and/or SMB gate mechanosensory signals upstream of neuron AVA. We conclude that C. elegans rely on inhibitory feedback from the motor circuit to modulate its response to sensory stimuli on fast timescales. This need for motor signals in sensory processing may explain the ubiquity in many organisms of motor-related neural activity patterns seen across the brain, including in sensory processing areas.
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Affiliation(s)
- Sandeep Kumar
- Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, United States of America
| | - Anuj K. Sharma
- Department of Physics, Princeton University, Princeton, New Jersey, United States of America
| | - Andrew Tran
- Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, United States of America
| | - Mochi Liu
- Department of Physics, Princeton University, Princeton, New Jersey, United States of America
| | - Andrew M. Leifer
- Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, United States of America
- Department of Physics, Princeton University, Princeton, New Jersey, United States of America
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6
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Chen KS, Wu R, Gershow MH, Leifer AM. Continuous odor profile monitoring to study olfactory navigation in small animals. eLife 2023; 12:e85910. [PMID: 37489570 PMCID: PMC10425172 DOI: 10.7554/elife.85910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Accepted: 07/21/2023] [Indexed: 07/26/2023] Open
Abstract
Olfactory navigation is observed across species and plays a crucial role in locating resources for survival. In the laboratory, understanding the behavioral strategies and neural circuits underlying odor-taxis requires a detailed understanding of the animal's sensory environment. For small model organisms like Caenorhabditis elegans and larval Drosophila melanogaster, controlling and measuring the odor environment experienced by the animal can be challenging, especially for airborne odors, which are subject to subtle effects from airflow, temperature variation, and from the odor's adhesion, adsorption, or reemission. Here, we present a method to control and measure airborne odor concentration in an arena compatible with an agar substrate. Our method allows continuous controlling and monitoring of the odor profile while imaging animal behavior. We construct stationary chemical landscapes in an odor flow chamber through spatially patterned odorized air. The odor concentration is measured with a spatially distributed array of digital gas sensors. Careful placement of the sensors allows the odor concentration across the arena to be continuously inferred in space and monitored through time. We use this approach to measure the odor concentration that each animal experiences as it undergoes chemotaxis behavior and report chemotaxis strategies for C. elegans and D. melanogaster larvae populations as they navigate spatial odor landscapes.
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Affiliation(s)
- Kevin S Chen
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Rui Wu
- Department of Physics, New York UniversityNew YorkUnited States
| | - Marc H Gershow
- Department of Physics, New York UniversityNew YorkUnited States
- Center for Neural Science, New York UniversityNew YorkUnited States
| | - Andrew M Leifer
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
- Department of Physics, Princeton UniversityPrincetonUnited States
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7
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Zhu SI, Goodhill GJ. From perception to behavior: The neural circuits underlying prey hunting in larval zebrafish. Front Neural Circuits 2023; 17:1087993. [PMID: 36817645 PMCID: PMC9928868 DOI: 10.3389/fncir.2023.1087993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 01/10/2023] [Indexed: 02/04/2023] Open
Abstract
A key challenge for neural systems is to extract relevant information from the environment and make appropriate behavioral responses. The larval zebrafish offers an exciting opportunity for studying these sensing processes and sensory-motor transformations. Prey hunting is an instinctual behavior of zebrafish that requires the brain to extract and combine different attributes of the sensory input and form appropriate motor outputs. Due to its small size and transparency the larval zebrafish brain allows optical recording of whole-brain activity to reveal the neural mechanisms involved in prey hunting and capture. In this review we discuss how the larval zebrafish brain processes visual information to identify and locate prey, the neural circuits governing the generation of motor commands in response to prey, how hunting behavior can be modulated by internal states and experience, and some outstanding questions for the field.
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Affiliation(s)
- Shuyu I. Zhu
- Departments of Developmental Biology and Neuroscience, Washington University in St. Louis, St. Louis, MO, United States
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8
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Dombrovski M, Peek MY, Park JY, Vaccari A, Sumathipala M, Morrow C, Breads P, Zhao A, Kurmangaliyev YZ, Sanfilippo P, Rehan A, Polsky J, Alghailani S, Tenshaw E, Namiki S, Zipursky SL, Card GM. Synaptic gradients transform object location to action. Nature 2023; 613:534-542. [PMID: 36599984 PMCID: PMC9849133 DOI: 10.1038/s41586-022-05562-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 11/11/2022] [Indexed: 01/06/2023]
Abstract
To survive, animals must convert sensory information into appropriate behaviours1,2. Vision is a common sense for locating ethologically relevant stimuli and guiding motor responses3-5. How circuitry converts object location in retinal coordinates to movement direction in body coordinates remains largely unknown. Here we show through behaviour, physiology, anatomy and connectomics in Drosophila that visuomotor transformation occurs by conversion of topographic maps formed by the dendrites of feature-detecting visual projection neurons (VPNs)6,7 into synaptic weight gradients of VPN outputs onto central brain neurons. We demonstrate how this gradient motif transforms the anteroposterior location of a visual looming stimulus into the fly's directional escape. Specifically, we discover that two neurons postsynaptic to a looming-responsive VPN type promote opposite takeoff directions. Opposite synaptic weight gradients onto these neurons from looming VPNs in different visual field regions convert localized looming threats into correctly oriented escapes. For a second looming-responsive VPN type, we demonstrate graded responses along the dorsoventral axis. We show that this synaptic gradient motif generalizes across all 20 primary VPN cell types and most often arises without VPN axon topography. Synaptic gradients may thus be a general mechanism for conveying spatial features of sensory information into directed motor outputs.
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Affiliation(s)
- Mark Dombrovski
- Department of Biological Chemistry, Howard Hughes Medical Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Martin Y Peek
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Jin-Yong Park
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Andrea Vaccari
- Department of Computer Science, Middlebury College, Middlebury, VT, USA
| | | | - Carmen Morrow
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Patrick Breads
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Arthur Zhao
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Yerbol Z Kurmangaliyev
- Department of Biological Chemistry, Howard Hughes Medical Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Piero Sanfilippo
- Department of Biological Chemistry, Howard Hughes Medical Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Aadil Rehan
- Department of Biological Chemistry, Howard Hughes Medical Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Jason Polsky
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Shada Alghailani
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Emily Tenshaw
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Shigehiro Namiki
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.,Research Center for Advanced Science and Technology, University of Tokyo, Tokyo, Japan
| | - S Lawrence Zipursky
- Department of Biological Chemistry, Howard Hughes Medical Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
| | - Gwyneth M Card
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA. .,Department of Neuroscience, Howard Hughes Medical Institute, The Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA.
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9
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Kennedy A. The what, how, and why of naturalistic behavior. Curr Opin Neurobiol 2022; 74:102549. [DOI: 10.1016/j.conb.2022.102549] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 03/23/2022] [Accepted: 03/28/2022] [Indexed: 01/03/2023]
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10
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Lin A, Witvliet D, Hernandez-Nunez L, Linderman SW, Samuel ADT, Venkatachalam V. Imaging whole-brain activity to understand behavior. NATURE REVIEWS. PHYSICS 2022; 4:292-305. [PMID: 37409001 PMCID: PMC10320740 DOI: 10.1038/s42254-022-00430-w] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 01/25/2022] [Indexed: 07/07/2023]
Abstract
The brain evolved to produce behaviors that help an animal inhabit the natural world. During natural behaviors, the brain is engaged in many levels of activity from the detection of sensory inputs to decision-making to motor planning and execution. To date, most brain studies have focused on small numbers of neurons that interact in limited circuits. This allows analyzing individual computations or steps of neural processing. During behavior, however, brain activity must integrate multiple circuits in different brain regions. The activities of different brain regions are not isolated, but may be contingent on one another. Coordinated and concurrent activity within and across brain areas is organized by (1) sensory information from the environment, (2) the animal's internal behavioral state, and (3) recurrent networks of synaptic and non-synaptic connectivity. Whole-brain recording with cellular resolution provides a new opportunity to dissect the neural basis of behavior, but whole-brain activity is also mutually contingent on behavior itself. This is especially true for natural behaviors like navigation, mating, or hunting, which require dynamic interaction between the animal, its environment, and other animals. In such behaviors, the sensory experience of an unrestrained animal is actively shaped by its movements and decisions. Many of the signaling and feedback pathways that an animal uses to guide behavior only occur in freely moving animals. Recent technological advances have enabled whole-brain recording in small behaving animals including nematodes, flies, and zebrafish. These whole-brain experiments capture neural activity with cellular resolution spanning sensory, decision-making, and motor circuits, and thereby demand new theoretical approaches that integrate brain dynamics with behavioral dynamics. Here, we review the experimental and theoretical methods that are being employed to understand animal behavior and whole-brain activity, and the opportunities for physics to contribute to this emerging field of systems neuroscience.
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Affiliation(s)
- Albert Lin
- Department of Physics, Harvard University, Cambridge, MA, USA
- Center for Brain Science, Harvard University, Cambridge, MA, USA
- Center for the Physics of Biological Function, Princeton University, Princeton, NJ, USA
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Daniel Witvliet
- Department of Physics, Harvard University, Cambridge, MA, USA
- Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Luis Hernandez-Nunez
- Center for Brain Science, Harvard University, Cambridge, MA, USA
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA
| | - Scott W Linderman
- Department of Statistics, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Aravinthan D T Samuel
- Department of Physics, Harvard University, Cambridge, MA, USA
- Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Vivek Venkatachalam
- Center for Brain Science, Harvard University, Cambridge, MA, USA
- Department of Physics, Northeastern University, Boston, MA, USA
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11
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Liu M, Kumar S, Sharma AK, Leifer AM. A high-throughput method to deliver targeted optogenetic stimulation to moving C. elegans populations. PLoS Biol 2022; 20:e3001524. [PMID: 35089912 PMCID: PMC8827482 DOI: 10.1371/journal.pbio.3001524] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2021] [Revised: 02/09/2022] [Accepted: 12/22/2021] [Indexed: 11/30/2022] Open
Abstract
We present a high-throughput optogenetic illumination system capable of simultaneous closed-loop light delivery to specified targets in populations of moving Caenorhabditis elegans. The instrument addresses three technical challenges: It delivers targeted illumination to specified regions of the animal's body such as its head or tail; it automatically delivers stimuli triggered upon the animal's behavior; and it achieves high throughput by targeting many animals simultaneously. The instrument was used to optogenetically probe the animal's behavioral response to competing mechanosensory stimuli in the the anterior and posterior gentle touch receptor neurons. Responses to more than 43,418 stimulus events from a range of anterior-posterior intensity combinations were measured. The animal's probability of sprinting forward in response to a mechanosensory stimulus depended on both the anterior and posterior stimulation intensity, while the probability of reversing depended primarily on the anterior stimulation intensity. We also probed the animal's response to mechanosensory stimulation during the onset of turning, a relatively rare behavioral event, by delivering stimuli automatically when the animal began to turn. Using this closed-loop approach, over 9,700 stimulus events were delivered during turning onset at a rate of 9.2 events per worm hour, a greater than 25-fold increase in throughput compared to previous investigations. These measurements validate with greater statistical power previous findings that turning acts to gate mechanosensory evoked reversals. Compared to previous approaches, the current system offers targeted optogenetic stimulation to specific body regions or behaviors with many fold increases in throughput to better constrain quantitative models of sensorimotor processing.
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Affiliation(s)
- Mochi Liu
- Department of Physics, Princeton University, Princeton, New Jersey, United States of America
| | - Sandeep Kumar
- Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, United States of America
| | - Anuj K. Sharma
- Department of Physics, Princeton University, Princeton, New Jersey, United States of America
| | - Andrew M. Leifer
- Department of Physics, Princeton University, Princeton, New Jersey, United States of America
- Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, United States of America
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12
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McCullough MH, Goodhill GJ. Unsupervised quantification of naturalistic animal behaviors for gaining insight into the brain. Curr Opin Neurobiol 2021; 70:89-100. [PMID: 34482006 DOI: 10.1016/j.conb.2021.07.014] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 07/20/2021] [Accepted: 07/21/2021] [Indexed: 01/02/2023]
Abstract
Neural computation has evolved to optimize the behaviors that enable our survival. Although much previous work in neuroscience has focused on constrained task behaviors, recent advances in computer vision are fueling a trend toward the study of naturalistic behaviors. Automated tracking of fine-scale behaviors is generating rich datasets for animal models including rodents, fruit flies, zebrafish, and worms. However, extracting meaning from these large and complex data often requires sophisticated computational techniques. Here we review the latest methods and modeling approaches providing new insights into the brain from behavior. We focus on unsupervised methods for identifying stereotyped behaviors and for resolving details of the structure and dynamics of behavioral sequences.
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Affiliation(s)
- Michael H McCullough
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, 4072, Australia
| | - Geoffrey J Goodhill
- Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, 4072, Australia; School of Mathematics and Physics, The University of Queensland, Brisbane, Queensland, 4072, Australia.
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13
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Ebbesen CL, Froemke RC. Body language signals for rodent social communication. Curr Opin Neurobiol 2021; 68:91-106. [PMID: 33582455 PMCID: PMC8243782 DOI: 10.1016/j.conb.2021.01.008] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2020] [Revised: 01/09/2021] [Accepted: 01/25/2021] [Indexed: 12/15/2022]
Abstract
Integration of social cues to initiate adaptive emotional and behavioral responses is a fundamental aspect of animal and human behavior. In humans, social communication includes prominent nonverbal components, such as social touch, gestures and facial expressions. Comparative studies investigating the neural basis of social communication in rodents has historically been centered on olfactory signals and vocalizations, with relatively less focus on non-verbal social cues. Here, we outline two exciting research directions: First, we will review recent observations pointing to a role of social facial expressions in rodents. Second, we will review observations that point to a role of 'non-canonical' rodent body language: body posture signals beyond stereotyped displays in aggressive and sexual behavior. In both sections, we will outline how social neuroscience can build on recent advances in machine learning, robotics and micro-engineering to push these research directions forward towards a holistic systems neurobiology of rodent body language.
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Affiliation(s)
- Christian L Ebbesen
- Skirball Institute of Biomolecular Medicine, Neuroscience Institute, Departments of Otolaryngology, Neuroscience and Physiology, New York University School of Medicine, New York, NY, 10016, USA; Center for Neural Science, New York University, New York, NY, 10003, USA.
| | - Robert C Froemke
- Skirball Institute of Biomolecular Medicine, Neuroscience Institute, Departments of Otolaryngology, Neuroscience and Physiology, New York University School of Medicine, New York, NY, 10016, USA; Center for Neural Science, New York University, New York, NY, 10003, USA; Howard Hughes Medical Institute Faculty Scholar, USA.
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14
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Nastase SA, Goldstein A, Hasson U. Keep it real: rethinking the primacy of experimental control in cognitive neuroscience. Neuroimage 2020; 222:117254. [PMID: 32800992 PMCID: PMC7789034 DOI: 10.1016/j.neuroimage.2020.117254] [Citation(s) in RCA: 99] [Impact Index Per Article: 24.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2019] [Revised: 07/08/2020] [Accepted: 08/04/2020] [Indexed: 01/17/2023] Open
Abstract
Naturalistic experimental paradigms in neuroimaging arose from a pressure to test the validity of models we derive from highly-controlled experiments in real-world contexts. In many cases, however, such efforts led to the realization that models developed under particular experimental manipulations failed to capture much variance outside the context of that manipulation. The critique of non-naturalistic experiments is not a recent development; it echoes a persistent and subversive thread in the history of modern psychology. The brain has evolved to guide behavior in a multidimensional world with many interacting variables. The assumption that artificially decoupling and manipulating these variables will lead to a satisfactory understanding of the brain may be untenable. We develop an argument for the primacy of naturalistic paradigms, and point to recent developments in machine learning as an example of the transformative power of relinquishing control. Naturalistic paradigms should not be deployed as an afterthought if we hope to build models of brain and behavior that extend beyond the laboratory into the real world.
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Affiliation(s)
- Samuel A Nastase
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.
| | - Ariel Goldstein
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Uri Hasson
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA; Department of Psychology, Princeton University, Princeton, NJ, USA
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15
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Mishra S, van Rees WM, Mahadevan L. Coordinated crawling via reinforcement learning. J R Soc Interface 2020; 17:20200198. [PMID: 32842883 PMCID: PMC7482564 DOI: 10.1098/rsif.2020.0198] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Accepted: 07/27/2020] [Indexed: 11/12/2022] Open
Abstract
Rectilinear crawling locomotion is a primitive and common mode of locomotion in slender soft-bodied animals. It requires coordinated contractions that propagate along a body that interacts frictionally with its environment. We propose a simple approach to understand how this coordination arises in a neuromechanical model of a segmented, soft-bodied crawler via an iterative process that might have both biological antecedents and technological relevance. Using a simple reinforcement learning algorithm, we show that an initial all-to-all neural coupling converges to a simple nearest-neighbour neural wiring that allows the crawler to move forward using a localized wave of contraction that is qualitatively similar to what is observed in Drosophila melanogaster larvae and used in many biomimetic solutions. The resulting solution is a function of how we weight gait regularization in the reward, with a trade-off between speed and robustness to proprioceptive noise. Overall, our results, which embed the brain-body-environment triad in a learning scheme, have relevance for soft robotics while shedding light on the evolution and development of locomotion.
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Affiliation(s)
- Shruti Mishra
- Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
| | - Wim M. van Rees
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - L. Mahadevan
- Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
- Department of Physics, Harvard University, Cambridge, MA 02138, USA
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA
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16
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Abstract
Neuroscience needs behavior. However, it is daunting to render the behavior of organisms intelligible without suppressing most, if not all, references to life. When animals are treated as passive stimulus-response, disembodied and identical machines, the life of behavior perishes. Here, we distill three biological principles (materiality, agency, and historicity), spell out their consequences for the study of animal behavior, and illustrate them with various examples from the literature. We propose to put behavior back into context, with the brain in a species-typical body and with the animal's body situated in the world; stamp Newtonian time with nested ontogenetic and phylogenetic processes that give rise to individuals with their own histories; and supplement linear cause-and-effect chains and information processing with circular loops of purpose and meaning. We believe that conceiving behavior in these ways is imperative for neuroscience.
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17
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Calhoun AJ, Pillow JW, Murthy M. Unsupervised identification of the internal states that shape natural behavior. Nat Neurosci 2019; 22:2040-2049. [PMID: 31768056 DOI: 10.1038/s41593-019-0533-x] [Citation(s) in RCA: 90] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2019] [Accepted: 10/07/2019] [Indexed: 02/02/2023]
Abstract
Internal states shape stimulus responses and decision-making, but we lack methods to identify them. To address this gap, we developed an unsupervised method to identify internal states from behavioral data and applied it to a dynamic social interaction. During courtship, Drosophila melanogaster males pattern their songs using feedback cues from their partner. Our model uncovers three latent states underlying this behavior and is able to predict moment-to-moment variation in song-patterning decisions. These states correspond to different sensorimotor strategies, each of which is characterized by different mappings from feedback cues to song modes. We show that a pair of neurons previously thought to be command neurons for song production are sufficient to drive switching between states. Our results reveal how animals compose behavior from previously unidentified internal states, which is a necessary step for quantitative descriptions of animal behavior that link environmental cues, internal needs, neuronal activity and motor outputs.
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Affiliation(s)
- Adam J Calhoun
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Jonathan W Pillow
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Mala Murthy
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.
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18
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Gonzales DL, Zhou J, Fan B, Robinson JT. A microfluidic-induced C. elegans sleep state. Nat Commun 2019; 10:5035. [PMID: 31695031 PMCID: PMC6834590 DOI: 10.1038/s41467-019-13008-5] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Accepted: 10/02/2019] [Indexed: 01/01/2023] Open
Abstract
An important feature of animal behavior is the ability to switch rapidly between activity states, however, how the brain regulates these spontaneous transitions based on the animal's perceived environment is not well understood. Here we show a C. elegans sleep-like state on a scalable platform that enables simultaneous control of multiple environmental factors including temperature, mechanical stress, and food availability. This brief quiescent state, which we refer to as microfluidic-induced sleep, occurs spontaneously in microfluidic chambers, which allows us to track animal movement and perform whole-brain imaging. With these capabilities, we establish that microfluidic-induced sleep meets the behavioral requirements of C. elegans sleep and depends on multiple factors, such as satiety and temperature. Additionally, we show that C. elegans sleep can be induced through mechanosensory pathways. Together, these results establish a model system for studying how animals process multiple sensory pathways to regulate behavioral states.
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Affiliation(s)
- Daniel L Gonzales
- Applied Physics Program, Rice University, 6100 Main St., Houston, TX, 77005, USA
- Department of Electrical and Computer Engineering, Rice University, 6100 Main St., Houston, TX, 77005, USA
| | - Jasmine Zhou
- Department of Bioengineering, Rice University, 6100 Main St., Houston, TX, 77005, USA
| | - Bo Fan
- Department of Electrical and Computer Engineering, Rice University, 6100 Main St., Houston, TX, 77005, USA
| | - Jacob T Robinson
- Applied Physics Program, Rice University, 6100 Main St., Houston, TX, 77005, USA.
- Department of Electrical and Computer Engineering, Rice University, 6100 Main St., Houston, TX, 77005, USA.
- Department of Bioengineering, Rice University, 6100 Main St., Houston, TX, 77005, USA.
- Department of Neuroscience, Baylor College of Medicine, One Baylor Plaza, Houston, TX, 77030, USA.
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19
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Moreira JM, Itskov PM, Goldschmidt D, Baltazar C, Steck K, Tastekin I, Walker SJ, Ribeiro C. optoPAD, a closed-loop optogenetics system to study the circuit basis of feeding behaviors. eLife 2019; 8:43924. [PMID: 31226244 PMCID: PMC6589098 DOI: 10.7554/elife.43924] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2018] [Accepted: 06/02/2019] [Indexed: 12/19/2022] Open
Abstract
The regulation of feeding plays a key role in determining the fitness of animals through its impact on nutrition. Elucidating the circuit basis of feeding and related behaviors is an important goal in neuroscience. We recently used a system for closed-loop optogenetic manipulation of neurons contingent on the feeding behavior of Drosophila to dissect the impact of a specific subset of taste neurons on yeast feeding. Here, we describe the development and validation of this system, which we term the optoPAD. We use the optoPAD to induce appetitive and aversive effects on feeding by activating or inhibiting gustatory neurons in closed-loop – effectively creating virtual taste realities. The use of optogenetics allowed us to vary the dynamics and probability of stimulation in single flies and assess the impact on feeding behavior quantitatively and with high throughput. These data demonstrate that the optoPAD is a powerful tool to dissect the circuit basis of feeding behavior, allowing the efficient implementation of sophisticated behavioral paradigms to study the mechanistic basis of animals’ adaptation to dynamic environments.
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Affiliation(s)
| | | | | | | | - Kathrin Steck
- Champalimaud Centre for the Unknown, Lisbon, Portugal
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20
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Vaadia RD, Li W, Voleti V, Singhania A, Hillman EMC, Grueber WB. Characterization of Proprioceptive System Dynamics in Behaving Drosophila Larvae Using High-Speed Volumetric Microscopy. Curr Biol 2019; 29:935-944.e4. [PMID: 30853438 PMCID: PMC6624193 DOI: 10.1016/j.cub.2019.01.060] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2018] [Revised: 01/16/2019] [Accepted: 01/24/2019] [Indexed: 01/09/2023]
Abstract
Proprioceptors provide feedback about body position that is essential for coordinated movement. Proprioceptive sensing of the position of rigid joints has been described in detail in several systems; however, it is not known how animals with a flexible skeleton encode their body positions. Understanding how diverse larval body positions are dynamically encoded requires knowledge of proprioceptor activity patterns in vivo during natural movement. Here we used high-speed volumetric swept confocally aligned planar excitation (SCAPE) microscopy in crawling Drosophila larvae to simultaneously track the position, deformation, and intracellular calcium activity of their multidendritic proprioceptors. Most proprioceptive neurons were found to activate during segment contraction, although one subtype was activated by extension. During cycles of segment contraction and extension, different proprioceptor types exhibited sequential activity, providing a continuum of position encoding during all phases of crawling. This sequential activity was related to the dynamics of each neuron’s terminal processes, and could endow each proprioceptor with a specific role in monitoring different aspects of body-wall deformation. We demonstrate this deformation encoding both during progression of contraction waves during locomotion as well as during less stereotyped, asymmetric exploration behavior. Our results provide powerful new insights into the body-wide neuronal dynamics of the proprioceptive system in crawling Drosophila, and demonstrate the utility of our SCAPE microscopy approach for characterization of neural encoding throughout the nervous system of a freely behaving animal. Vaadia, Li et al. use high-speed 3D SCAPE microscopy to monitor proprioceptor morphology and calcium dynamics in behaving Drosophila larvae. Neural activity is coincident with dendritic deformations, and different subtypes exhibit sequential onset of activity during crawling. Analysis during non-stereotyped exploration shows proprioceptor versatility.
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Affiliation(s)
- Rebecca D Vaadia
- Grueber Laboratory, Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; Department of Neuroscience, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Wenze Li
- Laboratory for Functional Optical Imaging, Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; Department of Electrical Engineering, Columbia University, New York, NY 10027, USA
| | - Venkatakaushik Voleti
- Laboratory for Functional Optical Imaging, Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; Department of Biomedical Engineering, Columbia University, New York, NY 10027, USA
| | - Aditi Singhania
- Grueber Laboratory, Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; Department of Genetics and Development, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Elizabeth M C Hillman
- Laboratory for Functional Optical Imaging, Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; Department of Biomedical Engineering, Columbia University, New York, NY 10027, USA; Department of Radiology, Columbia University Irving Medical Center, New York, NY 10032, USA; Kavli Institute for Brain Science, Columbia University, New York, NY 10032, USA.
| | - Wesley B Grueber
- Grueber Laboratory, Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; Department of Neuroscience, Columbia University Irving Medical Center, New York, NY 10032, USA; Department of Physiology and Cellular Biophysics, Columbia University Irving Medical Center, New York, NY 10032, USA.
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21
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Abstract
The dynamics of complex systems generally include high-dimensional, nonstationary, and nonlinear behavior, all of which pose fundamental challenges to quantitative understanding. To address these difficulties, we detail an approach based on local linear models within windows determined adaptively from data. While the dynamics within each window are simple, consisting of exponential decay, growth, and oscillations, the collection of local parameters across all windows provides a principled characterization of the full time series. To explore the resulting model space, we develop a likelihood-based hierarchical clustering, and we examine the eigenvalues of the linear dynamics. We demonstrate our analysis with the Lorenz system undergoing stable spiral dynamics and in the standard chaotic regime. Applied to the posture dynamics of the nematode Caenorhabditis elegans, our approach identifies fine-grained behavioral states and model dynamics which fluctuate about an instability boundary, and we detail a bifurcation in a transition from forward to backward crawling. We analyze whole-brain imaging in C. elegans and show that global brain dynamics is damped away from the instability boundary by a decrease in oxygen concentration. We provide additional evidence for such near-critical dynamics from the analysis of electrocorticography in monkey and the imaging of a neural population from mouse visual cortex at single-cell resolution.
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Affiliation(s)
- Antonio C Costa
- Department of Physics and Astronomy, Vrije Universiteit Amsterdam, 1081HV Amsterdam, The Netherlands
| | - Tosif Ahamed
- Biological Physics Theory Unit, Okinawa Institute of Science and Technology Graduate University, Okinawa 904-0495, Japan
| | - Greg J Stephens
- Department of Physics and Astronomy, Vrije Universiteit Amsterdam, 1081HV Amsterdam, The Netherlands;
- Biological Physics Theory Unit, Okinawa Institute of Science and Technology Graduate University, Okinawa 904-0495, Japan
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22
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Ouellette MH, Desrochers MJ, Gheta I, Ramos R, Hendricks M. A Gate-and-Switch Model for Head Orientation Behaviors in Caenorhabditis elegans. eNeuro 2018; 5:ENEURO.0121-18.2018. [PMID: 30627635 PMCID: PMC6325537 DOI: 10.1523/eneuro.0121-18.2018] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2018] [Revised: 09/29/2018] [Accepted: 10/04/2018] [Indexed: 11/24/2022] Open
Abstract
The nervous system seamlessly integrates perception and action. This ability is essential for stable representation of and appropriate responses to the external environment. How the sensorimotor integration underlying this ability occurs at the level of individual neurons is of keen interest. In Caenorhabditis elegans, RIA interneurons receive input from sensory pathways and have reciprocal connections with head motor neurons. RIA simultaneously encodes both head orientation and sensory stimuli, which may allow it to integrate these two signals to detect the spatial distribution of stimuli across head sweeps and generate directional head responses. Here, we show that blocking synaptic release from RIA disrupts head orientation behaviors in response to unilaterally presented stimuli. We found that sensory encoding in RIA is gated according to head orientation. This dependence on head orientation is independent of motor encoding in RIA, suggesting a second, posture-dependent pathway upstream of RIA. This gating mechanism may allow RIA to selectively attend to stimuli that are asymmetric across head sweeps. Attractive odor removal during head bends triggers rapid head withdrawal in the opposite direction. Unlike sensory encoding, this directional response is dependent on motor inputs to and synaptic output from RIA. Together, these results suggest that RIA is part of a sensorimotor pathway that is dynamically regulated according to head orientation at two levels: the first is a gate that filters sensory representations in RIA, and the second is a switch that routes RIA synaptic output to dorsal or ventral head motor neurons.
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Affiliation(s)
| | | | - Ioana Gheta
- Department of Biology, McGill University, Montreal, Quebec H3A 1B1, Canada
| | - Ryan Ramos
- Department of Biology, McGill University, Montreal, Quebec H3A 1B1, Canada
| | - Michael Hendricks
- Department of Biology, McGill University, Montreal, Quebec H3A 1B1, Canada
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23
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Clemens J, Coen P, Roemschied FA, Pereira TD, Mazumder D, Aldarondo DE, Pacheco DA, Murthy M. Discovery of a New Song Mode in Drosophila Reveals Hidden Structure in the Sensory and Neural Drivers of Behavior. Curr Biol 2018; 28:2400-2412.e6. [PMID: 30057309 PMCID: PMC6830513 DOI: 10.1016/j.cub.2018.06.011] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2017] [Revised: 03/10/2018] [Accepted: 06/07/2018] [Indexed: 11/30/2022]
Abstract
Deciphering how brains generate behavior depends critically on an accurate description of behavior. If distinct behaviors are lumped together, separate modes of brain activity can be wrongly attributed to the same behavior. Alternatively, if a single behavior is split into two, the same neural activity can appear to produce different behaviors. Here, we address this issue in the context of acoustic communication in Drosophila. During courtship, males vibrate their wings to generate time-varying songs, and females evaluate songs to inform mating decisions. For 50 years, Drosophila melanogaster song was thought to consist of only two modes, sine and pulse, but using unsupervised classification methods on large datasets of song recordings, we now establish the existence of at least three song modes: two distinct pulse types, along with a single sine mode. We show how this seemingly subtle distinction affects our interpretation of the mechanisms underlying song production and perception. Specifically, we show that visual feedback influences the probability of producing each song mode and that male song mode choice affects female responses and contributes to modulating his song amplitude with distance. At the neural level, we demonstrate how the activity of four separate neuron types within the fly’s song pathway differentially affects the probability of producing each song mode. Our results highlight the importance of carefully segmenting behavior to map the underlying sensory, neural, and genetic mechanisms. For 50 years, Drosophila melanogaster song was thought to consist of only two modes: sine and pulse. Using unsupervised classification methods, Clemens et al. establish the existence of three distinct song modes. They show how this distinction affects the interpretation of the mechanisms underlying song production and perception.
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Affiliation(s)
- Jan Clemens
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Philip Coen
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | | | - Talmo D Pereira
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - David Mazumder
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA; Department of Molecular Biology, Princeton University, Princeton, NJ, USA
| | - Diego E Aldarondo
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Diego A Pacheco
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Mala Murthy
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA; Department of Molecular Biology, Princeton University, Princeton, NJ, USA.
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24
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Liu M, Sharma AK, Shaevitz JW, Leifer AM. Temporal processing and context dependency in Caenorhabditis elegans response to mechanosensation. eLife 2018; 7:e36419. [PMID: 29943731 PMCID: PMC6054533 DOI: 10.7554/elife.36419] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2018] [Accepted: 06/10/2018] [Indexed: 11/13/2022] Open
Abstract
A quantitative understanding of how sensory signals are transformed into motor outputs places useful constraints on brain function and helps to reveal the brain's underlying computations. We investigate how the nematode Caenorhabditis elegans responds to time-varying mechanosensory signals using a high-throughput optogenetic assay and automated behavior quantification. We find that the behavioral response is tuned to temporal properties of mechanosensory signals, such as their integral and derivative, that extend over many seconds. Mechanosensory signals, even in the same neurons, can be tailored to elicit different behavioral responses. Moreover, we find that the animal's response also depends on its behavioral context. Most dramatically, the animal ignores all tested mechanosensory stimuli during turns. Finally, we present a linear-nonlinear model that predicts the animal's behavioral response to stimulus.
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Affiliation(s)
- Mochi Liu
- Lewis-Sigler Institute for Integrative GenomicsPrinceton UniversityNew JerseyUnited States
| | - Anuj K Sharma
- Department of PhysicsPrinceton UniversityNew JerseyUnited States
| | - Joshua W Shaevitz
- Lewis-Sigler Institute for Integrative GenomicsPrinceton UniversityNew JerseyUnited States
- Department of PhysicsPrinceton UniversityNew JerseyUnited States
| | - Andrew M Leifer
- Department of PhysicsPrinceton UniversityNew JerseyUnited States
- Princeton Neuroscience InstitutePrinceton UniversityNew JerseyUnited States
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25
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Abstract
The need for high-throughput, precise, and meaningful methods for measuring behavior has been amplified by our recent successes in measuring and manipulating neural circuitry. The largest challenges associated with moving in this direction, however, are not technical but are instead conceptual: what numbers should one put on the movements an animal is performing (or not performing)? In this review, I will describe how theoretical and data analytical ideas are interfacing with recently-developed computational and experimental methodologies to answer these questions across a variety of contexts, length scales, and time scales. I will attempt to highlight commonalities between approaches and areas where further advances are necessary to place behavior on the same quantitative footing as other scientific fields.
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Affiliation(s)
- Gordon J Berman
- Department of Biology, Emory University, 1510 Clifton Road NE, Atlanta, 30322, GA, USA.
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