A Wireless Sensorbrain Interface System For Tracking And Guiding Animal Behaviors Through Closedloop Neuromodulation In Water Mazes

• doi: 10.1109/JSSC.2024.3355809
• title: A Wireless Sensor-Brain Interface System for Tracking and Guiding Animal Behaviors Through Closed-Loop Neuromodulation in Water Mazes
• publisher: IEEE
• isbn:
• issn: 1558-173X
• rank: 997
• access_type: LOCKED
• content_type: Journals
• abstract: This article presents a wireless sensor-brain interface (SBI) system designed for closed-loop neuromodulation in freely behaving animals. The system enables a novel experiment in which swimming rats navigate a water maze guided exclusively by neural stimulation. The system consists of two wirelessly linked application-specific integrated circuits (ASICs): a bidirectional neural interface and an animal-tracking image sensor. The neural interface ASIC features a novel stimulator design with adaptive termination-based charge-balancing and an energy-efficient neural recording front end that rejects stimulation artifacts. The animal-tracking image sensor extracts the swimming features of rats and maps them into stimulation parameters with hardware acceleration. Low-power ultra-wideband transceivers with a custom protocol have been developed to ensure reliable wireless communication between devices with a short latency, which is critical to closed-loop neuromodulation. Wireless power transfer and near-field data communication have been developed to allow a part of the neural interface to be implanted beneath the skin, eliminating the need for through-skin connectors. Both ASICs were fabricated using 180-nm CMOS technology, fully characterized on the bench, and have been successfully used in animal experiments. Through rigorous testing, the system demonstrated consistent success in guiding multiple rats through the water maze using only stimulation. This work advances closed-loop neuromodulation technology and also serves as a promising platform for future neuroscientific investigations.
• article_number: 10415452
• pdf_url: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10415452
• html_url: https://ieeexplore.ieee.org/document/10415452/
• abstract_url: https://ieeexplore.ieee.org/document/10415452/
• publication_title: IEEE Journal of Solid-State Circuits
• conference_location:
• conference_dates:
• publication_number: 4
• is_number: 10480871
• publication_year: 2024
• publication_date: April 2024
• start_page: 1093
• end_page: 1109
• citing_paper_count: 1
• citing_patent_count: 0
• download_count: 1412
• insert_date: 20240129
• index_terms:
  • ieee_terms:
    • Rats
    • Neuromodulation
    • Wireless communication
    • Encoding
    • Wireless sensor networks
    • Transceivers
    • Real-time systems
  • author_terms:
    • Animal tracking
    • closed-loop neuromodulation
    • neural interface
    • sensor-brain interface (SBI)
    • water maze
  • dynamic_index_terms:
    • Morris Water Maze
    • Water Maze
    • Morris Water Maze Test
    • Interface System
    • Closed-loop Neuromodulation
    • Image Sensor
    • Camera Sensor
    • Power Transfer
    • Front End
    • Wireless Power Transfer
    • Beam Power
    • Wireless Energy Transfer
    • Stimulation Parameters
    • Wireless Power
    • Neural Recordings
    • Hardware Accelerators
    • Animal Tracking
    • Stimulation Artifact
    • Near Field Communication
    • Spinal Cord Injury
    • Spinal Injury
    • Visual Cues
    • Wearable Devices
    • Wearable Technology
    • Data Transfer
    • Sensory Encoding
    • Stimulation Phase
    • Commercial Camera
    • Operational Amplifier
    • Op-amp
    • Analog-to-digital Converter
    • Analog-to-digital
    • Digital-to-analog Converter
    • Minimum Latency
    • Sleep Mode
    • Low-noise Amplifier
    • Barnes Maze
• authors:
  • Author Name: Yuhan Hou
    Affiliation: The Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, Toronto, Canada
    Author URL: https://ieeexplore.ieee.org/author/37089840891
    ID: 37089840891
    Order: 1
    Author Affiliations:
    • The Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, Toronto, Canada
  • Author Name: Yi Zhu
    Affiliation: The Edward S. Rogers Sr. Department of Electrical and Computer Engineering, The University of Toronto, Toronto, Canada
    Author URL: https://ieeexplore.ieee.org/author/37089938854
    ID: 37089938854
    Order: 2
    Author Affiliations:
    • The Edward S. Rogers Sr. Department of Electrical and Computer Engineering, The University of Toronto, Toronto, Canada
    • Synaptive Medical, Inc., Toronto, Canada
  • Author Name: Xing Ji
    Affiliation: The Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, Toronto, Canada
    Author URL: https://ieeexplore.ieee.org/author/542669504878722
    ID: 542669504878722
    Order: 3
    Author Affiliations:
    • The Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, Toronto, Canada
    • Meta Platforms, Inc., Seattle, WA, USA
  • Author Name: Andrew G. Richardson
    Affiliation: Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, USA
    Author URL: https://ieeexplore.ieee.org/author/37085473811
    ID: 37085473811
    Order: 4
    Author Affiliations:
    • Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, USA
  • Author Name: Xilin Liu
    Affiliation: The Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, Toronto, Canada
    Author URL: https://ieeexplore.ieee.org/author/37072402700
    ID: 37072402700
    Order: 5
    Author Affiliations:
    • The Edward S. Rogers Sr. Department of Electrical and Computer Engineering, University of Toronto, Toronto, Canada
    • KITE Research Institute, University Health Network, Toronto, Canada

Image Sensor

• sensor_type: CMOS
• resolution: 160x160
• dynamic_range: Not specified
• pixel_size: Not specified
• dark_current: Not specified

Optical Data

• focal_length: Not specified
• aperture: Not specified
• field_of_view: Not specified
• distortion: Not specified

Performance Metrics

• frame_rate: 100 fps (extracted coordinates)
• signal_to_noise_ratio: 41.1 dB (1 ms threshold crossing), 37.4 dB (10 ms threshold crossing)

Applications & Benefits

• cell_imaging: Used for tracking swimming behavior of rats in closed-loop neuromodulation experiments.
• benefits: Real-time animal movement tracking and feature extraction for closed-loop neuromodulation.

Supporting Organizations

• supported_by: University of Pennsylvania Institutional Animal Care and Use Committee (IACUC)

Manuscript Details

• publication_date: April 2024

Relevancy Score

• score: 9
• missing_fields:
  • dynamic_range
  • pixel_size
  • dark_current
  • focal_length
  • aperture
  • field_of_view
  • distortion
  • sensitivity
  • shutter_speed
  • noise

Processed JSON Filename

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