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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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Smith TJ, Smith TR, Faruk F, Bendea M, Tirumala Kumara S, Capadona JR, Hernandez-Reynoso AG, Pancrazio JJ. Real-Time Assessment of Rodent Engagement Using ArUco Markers: A Scalable and Accessible Approach for Scoring Behavior in a Nose-Poking Go/No-Go Task. eNeuro 2024; 11:ENEURO.0500-23.2024. [PMID: 38351132 PMCID: PMC11046262 DOI: 10.1523/eneuro.0500-23.2024] [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: 11/28/2023] [Revised: 01/30/2024] [Accepted: 02/05/2024] [Indexed: 03/07/2024] Open
Abstract
In the field of behavioral neuroscience, the classification and scoring of animal behavior play pivotal roles in the quantification and interpretation of complex behaviors displayed by animals. Traditional methods have relied on video examination by investigators, which is labor-intensive and susceptible to bias. To address these challenges, research efforts have focused on computational methods and image-processing algorithms for automated behavioral classification. Two primary approaches have emerged: marker- and markerless-based tracking systems. In this study, we showcase the utility of "Augmented Reality University of Cordoba" (ArUco) markers as a marker-based tracking approach for assessing rat engagement during a nose-poking go/no-go behavioral task. In addition, we introduce a two-state engagement model based on ArUco marker tracking data that can be analyzed with a rectangular kernel convolution to identify critical transition points between states of engagement and distraction. In this study, we hypothesized that ArUco markers could be utilized to accurately estimate animal engagement in a nose-poking go/no-go behavioral task, enabling the computation of optimal task durations for behavioral testing. Here, we present the performance of our ArUco tracking program, demonstrating a classification accuracy of 98% that was validated against the manual curation of video data. Furthermore, our convolution analysis revealed that, on average, our animals became disengaged with the behavioral task at ∼75 min, providing a quantitative basis for limiting experimental session durations. Overall, our approach offers a scalable, efficient, and accessible solution for automated scoring of rodent engagement during behavioral data collection.
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Affiliation(s)
- Thomas J Smith
- School of Behavioral and Brain Sciences, The University of Texas at Dallas, Richardson, Texas 75080
| | - Trevor R Smith
- Department of Mechanical and Aerospace Engineering, West Virginia University, Morgantown, West Virginia 26506
| | - Fareeha Faruk
- School of Behavioral and Brain Sciences, The University of Texas at Dallas, Richardson, Texas 75080
| | - Mihai Bendea
- School of Behavioral and Brain Sciences, The University of Texas at Dallas, Richardson, Texas 75080
| | - Shreya Tirumala Kumara
- Department of Bioengineering, The University of Texas at Dallas, Richardson, Texas 75080
| | - Jeffrey R Capadona
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio 44106
- Advanced Platform Technology Center, Louis Stokes Cleveland Veterans Affairs Medical Center, Cleveland, Ohio 44106
| | | | - Joseph J Pancrazio
- Department of Bioengineering, The University of Texas at Dallas, Richardson, Texas 75080
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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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