Evidence map›Paper›PMID 41889927›Full record

ArticlebioRxiv : the preprint server for biology2026

High-channel-count neural recording and stimulation platform with 5,376 simultaneous recording channels.

Yingying Fan, Yuhang Ma, Pavlo Zolotavin, Gerald Topalli, Weinan Wang, Mattias Karlsson, Magnus Karlsson, Lan Luan, Chong Xie, Taiyun Chi

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Yingying FanDepartment of Electrical and Computer Engineering, Houston, TX, USA.
Yuhang MaDepartment of Chemical Engineering, Rice University, Houston, TX, USA.
Pavlo ZolotavinDepartment of Electrical and Computer Engineering, Houston, TX, USA.
Gerald TopalliDepartment of Electrical and Computer Engineering, Houston, TX, USA.
Weinan WangApplied Physics Program, Rice University, Houston, TX, USA.
Mattias KarlssonSpikegadgets, San Francisco, CA, USA.
Magnus KarlssonSpikegadgets, San Francisco, CA, USA.
Lan LuanDepartment of Electrical and Computer Engineering, Houston, TX, USA.
Chong XieDepartment of Electrical and Computer Engineering, Houston, TX, USA.
Taiyun ChiDepartment of Electrical and Computer Engineering, Houston, TX, USA.

Funding

Optimizing ultraflexible electrodes and integrated electronics for high-resolution, large-scale intraspinal recording and modulationU01NS131086 · NINDS · RICE UNIVERSITY · PI Lan Luan, Chong Xie · 2023 to 2026
$5.8M
NINDS NIH HHS U01 NS131086
6 · The paper itself

Abstract

Advancing neural interfaces requires large-scale, high-density recording technologies capable of capturing full-spectrum neural activity across cortical and subcortical regions. Here, we present a scalable approach to integrate neural electrodes with advanced application-specific integrated circuits (ASICs). Specifically, we custom-designed an ASIC with 5,376 simultaneous channels, each sampling at 20 kS/s and enabling >1.3 Gb/s total data streaming throughput. The ASIC incorporates in-pixel amplification, time-division multiplexed ADCs, and on-chip stimulation capabilities, ensuring precise signal acquisition with minimal power consumption while maintaining a low noise level of 5.5 μVrms. We further developed an interconnect strategy using gold bump bonding, which allows for high-density integration of the flexible probe and rigid chip. We demonstrate the capacity of this platform through the integration with a flexible μECoG array. The resulting device allows for the high-resolution mapping of

Identifiers

PMID41889927
PMCPMC13015372

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.