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A Modular 512-Channel Neural Signal Acquisition ASIC for High-Density 4096 Channel Electrophysiology.

Aikaterini Papadopoulou1, John Hermiz1, Carl Grace1

  • 1Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA.

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Summary
This summary is machine-generated.

We developed a scalable, ultra-low noise 512-channel neural recording circuit for high-resolution brain activity monitoring. This flexible module enables dense electrode integration for advanced neuroscience research.

Keywords:
biomedical electronicsbiopotential recordingbrain–machine interfacefront-end circuitshigh-channel countin vivoneural readout

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Area of Science:

  • Neuroscience
  • Electrical Engineering
  • Biomedical Engineering

Background:

  • Brain information processing demands high spatial and temporal resolution.
  • Dense electrode arrays and high-channel count electronics are crucial for advanced neural recording.
  • Existing technologies face limitations in scalability and noise performance.

Purpose of the Study:

  • To develop an ultra-low noise, scalable neural recording circuit.
  • To create a flexible, high-channel count module for brain-computer interfaces.
  • To demonstrate the capability for in vivo neural signal acquisition.

Main Methods:

  • Designed a 512-channel neural readout application-specific integrated circuit (ASIC) with a 2D layout.
  • Fabricated the chip using TSMC 0.18 µm 1.8 V CMOS technology.
  • Integrated the ASIC into an ultra-light, flexible module with programmable analog front-ends and 14-bit ADCs.

Main Results:

  • Achieved ultra-low noise performance with 512 simultaneously recording channels, scalable to 4096.
  • Demonstrated a flexible, lightweight module (350 mg) suitable for small animal headstages.
  • Successfully recorded neural signals, including spikes and field potentials, in vivo.

Conclusions:

  • The developed neural recording circuit offers a scalable and high-performance solution for dense neural interfacing.
  • The flexible module facilitates advanced neuroscience research by enabling high-resolution brain activity monitoring.
  • The technology shows promise for applications requiring precise measurement of neural dynamics.