Play, but not observing play, engages rat medial prefrontal cortex

Concha-Miranda, Miguel; Hartmann, Konstantin; Reinhold, Annika; Brecht, Michael; Sanguinetti-Scheck, Juan Ignacio

doi:10.1111/ejn.14908

Cited by 7 publications

(8 citation statements)

References 21 publications

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“…To test whether neural states in the PFC mirror states of another rat, we applied the same method to a dataset that compares playing hide-and-seek to observing hide-and-seek. 24 Rats (N = 4, 12 sessions) were recorded while playing hide-and-seek with the experimenter and while observing another rat play the game from inside a glass box. Previous work 24 showed that cells during observing have lower firing rates and are not responsive to the play going on with the other rat.…”

Section: Resultsmentioning

confidence: 99%

“… 24 Rats (N = 4, 12 sessions) were recorded while playing hide-and-seek with the experimenter and while observing another rat play the game from inside a glass box. Previous work 24 showed that cells during observing have lower firing rates and are not responsive to the play going on with the other rat. We re-analyzed this data using our bottom-up framework.…”

Section: Resultsmentioning

confidence: 99%

“…Our previous study 23 showed that rats can play a simple version of hide-and-seek with a human, and we later extended this study to pairs of rats observing each other play hide-and-seek. 24 In both studies, we recorded the PFC of rats using tetrodes and a wireless system. In this paper, we use these datasets, attempting an inverse physiology approach to study the neural basis of this behavior and to probe methods of unsupervised population analysis during naturalistic behaviors.…”

Section: Introductionmentioning

confidence: 99%

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Unsupervised discovery of behaviorally relevant brain states in rats playing hide-and-seek

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Unsupervised discovery of behaviorally relevant brain states in rats playing hide-and-seek

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Section: Multi-animal Body Tracking and Mirror Neuronsmentioning

confidence: 99%

Automatic mapping of multiplexed social receptive fields by deep learning and GPU-accelerated 3D videography

Ebbesen

Froemke

2021

Preprint

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Social interactions powerfully impact the brain and the body, but high-resolution descriptions of these important physical interactions are lacking. Currently, most studies rely on labor-intensive methods such as manual annotation. Scalable and objective tracking methods are required to understand the neural circuits underlying social behavior. Here we describe a hardware/software system and analysis pipeline that combines 3D videography, deep learning, physical modeling, and GPU-accelerated robust optimization, with automatic analysis of neuronal receptive fields recorded in interacting mice. Our system is capable of fully automatic multi-animal tracking with minimal errors (including in complete darkness) during complex, spontaneous social encounters, together with simultaneous electrophysiological recordings. We capture posture dynamics of multiple unmarked mice with high spatiotemporal precision (~2 mm, 60 frames/s). A generative model revealed the multiplexed ‘social receptive field’ of neurons in barrel cortex. This approach could be broadly useful for neurobehavioral studies of multiple animals interacting in complex low-light environments.

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“…In social interactions, rodents respond to the behavior of conspecifics, but we are only beginning to discover how the rodent brain encodes complex features such as gaze direction or body postures of others 3,21,65,66 . Compared to our knowledge about sensorimotor mirror neurons in monkeys 67 and vicarious sensory responses in human subjects 60 (both foundational to theories about human social cognition 68 and empathy 69 ), we still know very little about a putative rodent mirror neuron system 69 .…”

Section: Multi-animal Body Tracking and Mirror Neuronsmentioning

confidence: 99%

Automatic mapping of multiplexed social receptive fields by deep learning and GPU-accelerated 3D videography

Ebbesen

Froemke

2020

Preprint

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13Social interactions powerfully impact both the brain and the body, but high-resolution descriptions of these 14 important physical interactions are lacking. Currently, most studies of social behavior rely on labor-intensive 15 methods such as manual annotation of individual video frames. These methods are susceptible to experimenter 16 bias and have limited throughput. To understand the neural circuits underlying social behavior, scalable and 17 objective tracking methods are needed. We present a hardware/software system that combines 3D videography, 18 deep learning, physical modeling and GPU-accelerated robust optimization. Our system is capable of fully 19 automatic multi-animal tracking during naturalistic social interactions and allows for simultaneous electro-20 physiological recordings. We capture the posture dynamics of multiple unmarked mice with high spatial (~2 21 mm) and temporal precision (60 frames/s). This method is based on inexpensive consumer cameras and is 22 implemented in python, making our method cheap and straightforward to adopt and customize for studies of 23 neurobiology and animal behavior. 24 RESULTS 67 Raw data acquisition 68We established an experimental setup that allowed us to capture synchronized color images and depth images 69 from multiple angles, while simultaneously recording synchronized neural data ( Fig. 1a). We used inexpen-70 sive, state-of-the-art 'depth cameras' for computer vision and robotics. These cameras contain several imaging 71 modules: one color sensor, two infrared sensors and an infrared laser projector ( Fig. 1b). Imaging data pipe-72 lines, as well as intrinsic and extrinsic sensor calibration parameters can be accessed over USB through a 73 C/C++ SDK with Python bindings. We placed four depth cameras, as well as four synchronization LEDs 74 around a transparent acrylic cylinder which served as our behavioral arena ( Fig. 1c). 75 76 Each depth camera projects a static dot pattern across the imaged scene, adding texture in the infrared spec-77 trum to reflective surfaces ( Fig. 1d). By imaging this highly-textured surface simultaneously with two infrared 78 sensors per depth camera, it is possible to estimate the distance of each pixel in the infrared image to the depth 79 camera by stereopsis (by locally estimating the binocular disparity between the textured images). Since the 80 dot pattern is static and only serves to add texture, multiple cameras do not interfere with each other and it is 81 possible to image the same scene from multiple angles. This is one key aspect of our method, not possible 82 with depth imaging systems that rely on actively modulated light (such as the Microsoft Kinect system and 83 earlier versions of the Intel Realsense cameras). 84 85Since mouse movement is fast 13 , it is vital to minimize motion blur in the infrared images and thus the final 86 3D data ('point-cloud'). To this end, our method relies on two key features. First, we use depth cameras where 87 the infrared sensors have a global shutter (e.g., Intel D435) rathe...

show abstract

Play, but not observing play, engages rat medial prefrontal cortex

Cited by 7 publications

References 21 publications

Unsupervised discovery of behaviorally relevant brain states in rats playing hide-and-seek

Unsupervised discovery of behaviorally relevant brain states in rats playing hide-and-seek

Automatic mapping of multiplexed social receptive fields by deep learning and GPU-accelerated 3D videography

Automatic mapping of multiplexed social receptive fields by deep learning and GPU-accelerated 3D videography

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