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Wilson NR, Wang FL, Chen N, Yan SX, Daitch AL, Shi B, Sharma S, Sur M. A Platform for Spatiotemporal "Matrix" Stimulation in Brain Networks Reveals Novel Forms of Circuit Plasticity. Front Neural Circuits 2022; 15:792228. [PMID: 35069127 PMCID: PMC8766665 DOI: 10.3389/fncir.2021.792228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Accepted: 12/13/2021] [Indexed: 12/16/2022] Open
Abstract
Here we demonstrate a facile method by which to deliver complex spatiotemporal stimulation to neural networks in fast patterns, to trigger interesting forms of circuit-level plasticity in cortical areas. We present a complete platform by which patterns of electricity can be arbitrarily defined and distributed across a brain circuit, either simultaneously, asynchronously, or in complex patterns that can be easily designed and orchestrated with precise timing. Interfacing with acute slices of mouse cortex, we show that our system can be used to activate neurons at many locations and drive synaptic transmission in distributed patterns, and that this elicits new forms of plasticity that may not be observable via traditional methods, including interesting measurements of associational and sequence plasticity. Finally, we introduce an automated "network assay" for imaging activation and plasticity across a circuit. Spatiotemporal stimulation opens the door for high-throughput explorations of plasticity at the circuit level, and may provide a basis for new types of adaptive neural prosthetics.
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Affiliation(s)
- Nathan R. Wilson
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States,Nara Logics, Inc., Boston, MA, United States,*Correspondence: Nathan R. Wilson
| | - Forea L. Wang
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Naiyan Chen
- Program in Computational and Systems Biology, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Sherry X. Yan
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Amy L. Daitch
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Bo Shi
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Samvaran Sharma
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Mriganka Sur
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, United States,Mriganka Sur
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Li J, Liu X, Mao W, Chen T, Yu H. Advances in Neural Recording and Stimulation Integrated Circuits. Front Neurosci 2021; 15:663204. [PMID: 34421507 PMCID: PMC8377741 DOI: 10.3389/fnins.2021.663204] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Accepted: 07/08/2021] [Indexed: 11/13/2022] Open
Abstract
In the past few decades, driven by the increasing demands in the biomedical field aiming to cure neurological diseases and improve the quality of daily lives of the patients, researchers began to take advantage of the semiconductor technology to develop miniaturized and power-efficient chips for implantable applications. The emergence of the integrated circuits for neural prosthesis improves the treatment process of epilepsy, hearing loss, retinal damage, and other neurological diseases, which brings benefits to many patients. However, considering the safety and accuracy in the neural prosthesis process, there are many research directions. In the process of chip design, designers need to carefully analyze various parameters, and investigate different design techniques. This article presents the advances in neural recording and stimulation integrated circuits, including (1) a brief introduction of the basics of neural prosthesis circuits and the repair process in the bionic neural link, (2) a systematic introduction of the basic architecture and the latest technology of neural recording and stimulation integrated circuits, (3) a summary of the key issues of neural recording and stimulation integrated circuits, and (4) a discussion about the considerations of neural recording and stimulation circuit architecture selection and a discussion of future trends. The overview would help the designers to understand the latest performances in many aspects and to meet the design requirements better.
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Affiliation(s)
- Juzhe Li
- College of Microelectronics, Beijing University of Technology, Beijing, China
| | - Xu Liu
- College of Microelectronics, Beijing University of Technology, Beijing, China
| | - Wei Mao
- School of Microelectronics, Southern University of Science and Technology, Shenzhen, China
| | - Tao Chen
- Advanced Photonics Institute, Beijing University of Technology, Beijing, China
| | - Hao Yu
- School of Microelectronics, Southern University of Science and Technology, Shenzhen, China
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Samiei A, Hashemi H. A Bidirectional Neural Interface SoC With Adaptive IIR Stimulation Artifact Cancelers. IEEE JOURNAL OF SOLID-STATE CIRCUITS 2021; 56:2142-2157. [PMID: 34483356 PMCID: PMC8409175 DOI: 10.1109/jssc.2021.3056040] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
We present a 180-nm CMOS bidirectional neural interface system-on-chip that enables simultaneous recording and stimulation with on-chip stimulus artifact cancelers. The front-end cancellation scheme incorporates a least-mean-square engine that adapts the coefficients of a 2-tap infinite-impulse-response filter to replicate the stimulation artifact waveform and subtract it at the front-end. Measurements demonstrate the efficacy of the canceler in mitigating artifacts up to 700 mVpp and reducing the front-end amplifier saturation recovery time in response to a 2.5 Vpp artifact. Each recording channel houses a pair of adaptive infinite-impulse-response filters, which enable cancellation of the artifacts generated by the simultaneous operation of the 2 on-chip stimulators. The analog front-end consumes 2.5 μW of power per channel, has a maximum gain of 50 dB and a bandwidth of 9.0 kHz with 6.2 μVrms integrated input-referred noise.
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Affiliation(s)
- Aria Samiei
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90089 USA
| | - Hossein Hashemi
- Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA 90089 USA
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Elyahoodayan S, Jiang W, Lee CD, Shao X, Weiland G, Whalen JJ, Petrossians A, Song D. Stimulation and Recording of the Hippocampus Using the Same Pt-Ir Coated Microelectrodes. Front Neurosci 2021; 15:616063. [PMID: 33716647 PMCID: PMC7943859 DOI: 10.3389/fnins.2021.616063] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2020] [Accepted: 01/28/2021] [Indexed: 01/11/2023] Open
Abstract
Same-electrode stimulation and recording with high spatial resolution, signal quality, and power efficiency is highly desirable in neuroscience and neural engineering. High spatial resolution and signal-to-noise ratio is necessary for obtaining unitary activities and delivering focal stimulations. Power efficiency is critical for battery-operated implantable neural interfaces. This study demonstrates the capability of recording single units as well as evoked potentials in response to a wide range of electrochemically safe stimulation pulses through high-resolution microelectrodes coated with co-deposition of Pt-Ir. It also compares signal-to-noise ratio, single unit activity, and power efficiencies between Pt-Ir coated and uncoated microelectrodes. To enable stimulation and recording with the same microelectrodes, microelectrode arrays were treated with electrodeposited platinum-iridium coating (EPIC) and tested in the CA1 cell body layer of rat hippocampi. The electrodes' ability to (1) inject a large range of electrochemically reversable stimulation pulses to the tissue, and (2) record evoked potentials and single unit activities were quantitively assessed over an acute time period. Compared to uncoated electrodes, EPIC electrodes recorded signals with higher signal-to-noise ratios (coated: 9.77 ± 1.95 dB; uncoated: 1.95 ± 0.40 dB) and generated lower voltages (coated: 100 mV; uncoated: 650 mV) for a given stimulus (5 μA). The improved performance corresponded to lower energy consumptions and electrochemically safe stimulation above 5 μA (>0.38 mC/cm2), which enabled elicitation of field excitatory post synaptic potentials and population spikes. Spontaneous single unit activities were also modulated by varying stimulation intensities and monitored through the same electrodes. This work represents an example of stimulation and recording single unit activities from the same microelectrode, which provides a powerful tool for monitoring and manipulating neural circuits at the single neuron level.
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Affiliation(s)
- Sahar Elyahoodayan
- Department of Biomedical Engineering, Center for Neural Engineering, University of Southern California, Los Angeles, CA, United States
| | - Wenxuan Jiang
- Department of Biomedical Engineering, Center for Neural Engineering, University of Southern California, Los Angeles, CA, United States
| | | | - Xiecheng Shao
- Department of Biomedical Engineering, Center for Neural Engineering, University of Southern California, Los Angeles, CA, United States
| | | | | | | | - Dong Song
- Department of Biomedical Engineering, Center for Neural Engineering, University of Southern California, Los Angeles, CA, United States
- Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, United States
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