Evidence map›Paper›PMID 42095419›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026

Engineering Neuronal Network Connectivity Through Precise and Scalable Electrical Modulation.

Sreedhar S Kumar, Yannaël Bossard, Rachel Sava, Tobias Gänswein, Lorenca Sadiraj, Jean-Samuel Dupré, Manuel Schröter, J Gray Camp, Fernando Cardes, Julian Bartram and 1 more

Abstract read
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 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

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Sreedhar S KumarBio Engineering Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.ORCID https://orcid.org/0000-0002-5272-6474
Yannaël BossardBio Engineering Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.ORCID https://orcid.org/0009-0008-2285-3537
Rachel SavaBio Engineering Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.ORCID https://orcid.org/0009-0004-2996-0547
Tobias GänsweinBio Engineering Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.ORCID https://orcid.org/0000-0003-0323-6767
Lorenca SadirajBio Engineering Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.ORCID https://orcid.org/0009-0003-2673-4977
Jean-Samuel DupréInstitute of Human Biology (IHB), Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland.ORCID https://orcid.org/0000-0002-3708-0425
Manuel SchröterBio Engineering Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.ORCID https://orcid.org/0000-0002-9347-9203
J Gray CampInstitute of Human Biology (IHB), Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland.ORCID https://orcid.org/0000-0003-3295-1225
Fernando CardesBio Engineering Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.ORCID https://orcid.org/0000-0003-1880-6734
Julian BartramBio Engineering Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.ORCID https://orcid.org/0000-0001-7513-1716
Andreas HierlemannBio Engineering Laboratory, Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.ORCID https://orcid.org/0000-0002-3838-2468

Funding

Eidgenossische Technische Hochschule ZurichSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung 10002257Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung 205320_188910Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung 206021_198231Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung 216632Staatssekretariat für Bildung, Forschung und InnovationWellcome Trust 216632
6 · The paper itself

Abstract

Achieving precise and scalable control of neuronal connectivity is a key requirement for next-generation neurotherapeutic and neuroengineering applications. Yet, implementing Hebbian-like plasticity rules and verifying induced changes at a larger scale remain technically challenging. Here, high-density microelectrode arrays (HD-MEAs) are combined with programmable stimulation and analytics to induce and confirm targeted connectivity changes across multiple different in vitro and ex vivo preparations. Conditional Activity Metrics (CAM) are introduced to quantify stimulation-evoked changes in relative timing and spiking density between spike train pairs. CAM changes strongly correlated with synaptic weight changes in simulations. Experimentally, we observed robust synaptic strengthening/weakening in roughly 40% of 279 tested pairs following targeted stimulation. In a subset of pairs, tracked for extended durations, the induced effects persisted for at least 90 min. Synaptic modifications were also directly validated by using simultaneous HD-MEA and patch-clamp recordings. This work establishes a foundation for precise, high-throughput neuronal circuit reconfiguration, offering a versatile platform for advancing fundamental neuroscience and enabling novel neurotherapeutic and biohybrid computing strategies.

Indexed as

Nerve NetNeuronal PlasticityNeuronsAction PotentialsAnimalsElectric StimulationMicroelectrodesPatch-Clamp TechniquesSynapsescircuit shapingelectrical stimulationelectrophysiologyhd‐meaneuroengineeringspike timing‐dependent plasticity

Identifiers

PMID42095419
PMCPMC13335577

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

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 Socratic graph.