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Madhav MS, Jayakumar RP, Li BY, Lashkari SG, Wright K, Savelli F, Knierim JJ, Cowan NJ. Control and recalibration of path integration in place cells using optic flow. Nat Neurosci 2024; 27:1599-1608. [PMID: 38937582 DOI: 10.1038/s41593-024-01681-9] [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: 06/28/2022] [Accepted: 05/13/2024] [Indexed: 06/29/2024]
Abstract
Hippocampal place cells are influenced by both self-motion (idiothetic) signals and external sensory landmarks as an animal navigates its environment. To continuously update a position signal on an internal 'cognitive map', the hippocampal system integrates self-motion signals over time, a process that relies on a finely calibrated path integration gain that relates movement in physical space to movement on the cognitive map. It is unclear whether idiothetic cues alone, such as optic flow, exert sufficient influence on the cognitive map to enable recalibration of path integration, or if polarizing position information provided by landmarks is essential for this recalibration. Here, we demonstrate both recalibration of path integration gain and systematic control of place fields by pure optic flow information in freely moving rats. These findings demonstrate that the brain continuously rebalances the influence of conflicting idiothetic cues to fine-tune the neural dynamics of path integration, and that this recalibration process does not require a top-down, unambiguous position signal from landmarks.
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Affiliation(s)
- Manu S Madhav
- Mind/Brain Institute, Johns Hopkins University, Baltimore, MD, USA.
- Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, USA.
- Laboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD, USA.
- School of Biomedical Engineering, University of British Columbia, Vancouver, British Columbia, Canada.
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, British Columbia, Canada.
| | - Ravikrishnan P Jayakumar
- Mind/Brain Institute, Johns Hopkins University, Baltimore, MD, USA
- Laboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD, USA
- Mechanical Engineering Department, Johns Hopkins University, Baltimore, MD, USA
| | - Brian Y Li
- Mind/Brain Institute, Johns Hopkins University, Baltimore, MD, USA
| | - Shahin G Lashkari
- Laboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD, USA
- Mechanical Engineering Department, Johns Hopkins University, Baltimore, MD, USA
| | - Kelly Wright
- Mind/Brain Institute, Johns Hopkins University, Baltimore, MD, USA
| | - Francesco Savelli
- Mind/Brain Institute, Johns Hopkins University, Baltimore, MD, USA
- Department of Neuroscience, Developmental and Regenerative Biology, The University of Texas at San Antonio, San Antonio, TX, USA
| | - James J Knierim
- Mind/Brain Institute, Johns Hopkins University, Baltimore, MD, USA.
- Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, USA.
- Solomon H. Snyder Department of Neuroscience, Johns Hopkins University, Baltimore, MD, USA.
| | - Noah J Cowan
- Laboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD, USA.
- Mechanical Engineering Department, Johns Hopkins University, Baltimore, MD, USA.
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Hoffmann S, Beetz MJ, Stöckl A, Mesce KA. Editorial: Naturalistic neuroscience - Towards a full cycle from lab to field. Front Neural Circuits 2023; 17:1251771. [PMID: 37614244 PMCID: PMC10442932 DOI: 10.3389/fncir.2023.1251771] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Accepted: 07/26/2023] [Indexed: 08/25/2023] Open
Affiliation(s)
- Susanne Hoffmann
- Department of Behavioural Neurobiology, Max Planck Institute for Ornithology, Seewiesen, Germany
- Department of Behavioural Neurobiology, Max Planck Institute for Biological Intelligence, Seewiesen, Germany
| | - M. Jerome Beetz
- Department Zoology II, Julius Maximilian University of Würzburg, Würzburg, Germany
| | - Anna Stöckl
- Department Zoology II, Julius Maximilian University of Würzburg, Würzburg, Germany
- Department of Neurobiology, University of Konstanz, Konstanz, Germany
| | - Karen A. Mesce
- Department of Entomology, University of Minnesota, St. Paul, MN, United States
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Saleem AB, Busse L. Interactions between rodent visual and spatial systems during navigation. Nat Rev Neurosci 2023:10.1038/s41583-023-00716-7. [PMID: 37380885 DOI: 10.1038/s41583-023-00716-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/31/2023] [Indexed: 06/30/2023]
Abstract
Many behaviours that are critical for animals to survive and thrive rely on spatial navigation. Spatial navigation, in turn, relies on internal representations about one's spatial location, one's orientation or heading direction and the distance to objects in the environment. Although the importance of vision in guiding such internal representations has long been recognized, emerging evidence suggests that spatial signals can also modulate neural responses in the central visual pathway. Here, we review the bidirectional influences between visual and navigational signals in the rodent brain. Specifically, we discuss reciprocal interactions between vision and the internal representations of spatial position, explore the effects of vision on representations of an animal's heading direction and vice versa, and examine how the visual and navigational systems work together to assess the relative distances of objects and other features. Throughout, we consider how technological advances and novel ethological paradigms that probe rodent visuo-spatial behaviours allow us to advance our understanding of how brain areas of the central visual pathway and the spatial systems interact and enable complex behaviours.
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Affiliation(s)
- Aman B Saleem
- UCL Institute of Behavioural Neuroscience, Department of Experimental Psychology, University College London, London, UK.
| | - Laura Busse
- Division of Neuroscience, Faculty of Biology, LMU Munich, Munich, Germany.
- Bernstein Centre for Computational Neuroscience Munich, Munich, Germany.
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Thurley K. Naturalistic neuroscience and virtual reality. Front Syst Neurosci 2022; 16:896251. [PMID: 36467978 PMCID: PMC9712202 DOI: 10.3389/fnsys.2022.896251] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 10/31/2022] [Indexed: 04/04/2024] Open
Abstract
Virtual reality (VR) is one of the techniques that became particularly popular in neuroscience over the past few decades. VR experiments feature a closed-loop between sensory stimulation and behavior. Participants interact with the stimuli and not just passively perceive them. Several senses can be stimulated at once, large-scale environments can be simulated as well as social interactions. All of this makes VR experiences more natural than those in traditional lab paradigms. Compared to the situation in field research, a VR simulation is highly controllable and reproducible, as required of a laboratory technique used in the search for neural correlates of perception and behavior. VR is therefore considered a middle ground between ecological validity and experimental control. In this review, I explore the potential of VR in eliciting naturalistic perception and behavior in humans and non-human animals. In this context, I give an overview of recent virtual reality approaches used in neuroscientific research.
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Affiliation(s)
- Kay Thurley
- Faculty of Biology, Ludwig-Maximilians-Universität München, Munich, Germany
- Bernstein Center for Computational Neuroscience Munich, Munich, Germany
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Vagvolgyi BP, Jayakumar RP, Madhav MS, Knierim JJ, Cowan NJ. Wide-angle, monocular head tracking using passive markers. J Neurosci Methods 2022; 368:109453. [PMID: 34968626 PMCID: PMC8857048 DOI: 10.1016/j.jneumeth.2021.109453] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 11/22/2021] [Accepted: 12/17/2021] [Indexed: 10/19/2022]
Abstract
BACKGROUND Camera images can encode large amounts of visual information of an animal and its environment, enabling high fidelity 3D reconstruction of the animal and its environment using computer vision methods. Most systems, both markerless (e.g. deep learning based) and marker-based, require multiple cameras to track features across multiple points of view to enable such 3D reconstruction. However, such systems can be expensive and are challenging to set up in small animal research apparatuses. NEW METHODS We present an open-source, marker-based system for tracking the head of a rodent for behavioral research that requires only a single camera with a potentially wide field of view. The system features a lightweight visual target and computer vision algorithms that together enable high-accuracy tracking of the six-degree-of-freedom position and orientation of the animal's head. The system, which only requires a single camera positioned above the behavioral arena, robustly reconstructs the pose over a wide range of head angles (360° in yaw, and approximately ± 120° in roll and pitch). RESULTS Experiments with live animals demonstrate that the system can reliably identify rat head position and orientation. Evaluations using a commercial optical tracker device show that the system achieves accuracy that rivals commercial multi-camera systems. COMPARISON WITH EXISTING METHODS Our solution significantly improves upon existing monocular marker-based tracking methods, both in accuracy and in allowable range of motion. CONCLUSIONS The proposed system enables the study of complex behaviors by providing robust, fine-scale measurements of rodent head motions in a wide range of orientations.
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Affiliation(s)
- Balazs P. Vagvolgyi
- Laboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD, U.S.A.,Corresponding author: (Balazs P. Vagvolgyi)
| | - Ravikrishnan P. Jayakumar
- Mind/Brain Institute, Johns Hopkins University, Baltimore, MD, U.S.A.,Laboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD, U.S.A.,Mechanical Engineering Department, Johns Hopkins University, Baltimore, MD, U.S.A
| | - Manu S. Madhav
- Mind/Brain Institute, Johns Hopkins University, Baltimore, MD, U.S.A.,Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, U.S.A.,Laboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD, U.S.A.,School of Biomedical Engineering, Djawad Mowafaghian Centre for Brain Health, University of British Columbia, BC, Canada,Corresponding author: (Balazs P. Vagvolgyi)
| | - James J. Knierim
- Mind/Brain Institute, Johns Hopkins University, Baltimore, MD, U.S.A.,Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, U.S.A
| | - Noah J. Cowan
- Laboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD, U.S.A.,Mechanical Engineering Department, Johns Hopkins University, Baltimore, MD, U.S.A
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