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(a–d) Computer-aided design (CAD) renderings showing the modular enclosure, camera mount, and removable front panel. (e) Top-mounted USB3 machine-vision camera with visible and 850 nm infrared (IR) LED illumination for light and dark recordings. (f–g) Configurable arena fixtures for pellet wells and capacitive lickometers supporting fluid-access or operant tasks. (h) System wiring diagram linking the NVIDIA Jetson Orin Nano Super (6-core ARM CPU, 1024 CUDA cores, 32 tensor cores, 8 GB LPDDR5, 67 INT8 TOPS) to an RP2040 controller and <t>MPR121</t> capacitive sensors. (i) Example configuration showing a two-bottle choice and fixed-ratio 1 (FR1) feeding task. Each unit operates as an independent edge device performing on-board YOLO inference, sensor synchronization via TTL, and bidirectional task control—enabling scalable, low-cost, networked behavioral experiments without workstation-grade GPUs.
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(a–d) Computer-aided design (CAD) renderings showing the modular enclosure, camera mount, and removable front panel. (e) Top-mounted USB3 machine-vision camera with visible and 850 nm infrared (IR) LED illumination for light and dark recordings. (f–g) Configurable arena fixtures for pellet wells and capacitive lickometers supporting fluid-access or operant tasks. (h) System wiring diagram linking the NVIDIA Jetson Orin Nano Super (6-core ARM CPU, 1024 CUDA cores, 32 tensor cores, 8 GB LPDDR5, 67 INT8 TOPS) to an RP2040 controller and <t>MPR121</t> capacitive sensors. (i) Example configuration showing a two-bottle choice and fixed-ratio 1 (FR1) feeding task. Each unit operates as an independent edge device performing on-board YOLO inference, sensor synchronization via TTL, and bidirectional task control—enabling scalable, low-cost, networked behavioral experiments without workstation-grade GPUs.
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(a–d) Computer-aided design (CAD) renderings showing the modular enclosure, camera mount, and removable front panel. (e) Top-mounted USB3 machine-vision camera with visible and 850 nm infrared (IR) LED illumination for light and dark recordings. (f–g) Configurable arena fixtures for pellet wells and capacitive lickometers supporting fluid-access or operant tasks. (h) System wiring diagram linking the NVIDIA Jetson Orin Nano Super (6-core ARM CPU, 1024 CUDA cores, 32 tensor cores, 8 GB LPDDR5, 67 INT8 TOPS) to an RP2040 controller and <t>MPR121</t> capacitive sensors. (i) Example configuration showing a two-bottle choice and fixed-ratio 1 (FR1) feeding task. Each unit operates as an independent edge device performing on-board YOLO inference, sensor synchronization via TTL, and bidirectional task control—enabling scalable, low-cost, networked behavioral experiments without workstation-grade GPUs.
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(a–d) Computer-aided design (CAD) renderings showing the modular enclosure, camera mount, and removable front panel. (e) Top-mounted USB3 machine-vision camera with visible and 850 nm infrared (IR) LED illumination for light and dark recordings. (f–g) Configurable arena fixtures for pellet wells and capacitive lickometers supporting fluid-access or operant tasks. (h) System wiring diagram linking the NVIDIA Jetson Orin Nano Super (6-core ARM CPU, 1024 CUDA cores, 32 tensor cores, 8 GB LPDDR5, 67 INT8 TOPS) to an RP2040 controller and MPR121 capacitive sensors. (i) Example configuration showing a two-bottle choice and fixed-ratio 1 (FR1) feeding task. Each unit operates as an independent edge device performing on-board YOLO inference, sensor synchronization via TTL, and bidirectional task control—enabling scalable, low-cost, networked behavioral experiments without workstation-grade GPUs.

Journal: bioRxiv

Article Title: SqueakPose Studio: An end-to-end platform for pose estimation and real-time edge-AI deployment

doi: 10.64898/2026.01.24.700912

Figure Lengend Snippet: (a–d) Computer-aided design (CAD) renderings showing the modular enclosure, camera mount, and removable front panel. (e) Top-mounted USB3 machine-vision camera with visible and 850 nm infrared (IR) LED illumination for light and dark recordings. (f–g) Configurable arena fixtures for pellet wells and capacitive lickometers supporting fluid-access or operant tasks. (h) System wiring diagram linking the NVIDIA Jetson Orin Nano Super (6-core ARM CPU, 1024 CUDA cores, 32 tensor cores, 8 GB LPDDR5, 67 INT8 TOPS) to an RP2040 controller and MPR121 capacitive sensors. (i) Example configuration showing a two-bottle choice and fixed-ratio 1 (FR1) feeding task. Each unit operates as an independent edge device performing on-board YOLO inference, sensor synchronization via TTL, and bidirectional task control—enabling scalable, low-cost, networked behavioral experiments without workstation-grade GPUs.

Article Snippet: Capacitive sensing for fluid intake was handled via Adafruit MPR121 12-key capacitive touch sensors, communicating with the RP2040 over I2C.

Techniques: Control