{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T19:19:17Z","timestamp":1772997557529,"version":"3.50.1"},"reference-count":105,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2016,9,5]],"date-time":"2016-09-05T00:00:00Z","timestamp":1473033600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100004897","name":"Pennsylvania Department of Health","doi-asserted-by":"publisher","award":["Health Research Formula Fund"],"award-info":[{"award-number":["Health Research Formula Fund"]}],"id":[{"id":"10.13039\/100004897","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Micromachines"],"abstract":"<jats:p>Neural circuits are responsible for the brain\u2019s ability to process and store information. Reductionist approaches to understanding the brain include isolation of individual neurons for detailed characterization. When maintained in vitro for several days or weeks, dissociated neurons self-assemble into randomly connected networks that produce synchronized activity and are capable of learning. This review focuses on efforts to control neuronal connectivity in vitro and construct living neural circuits of increasing complexity and precision. Microfabrication-based methods have been developed to guide network self-assembly, accomplishing control over in vitro circuit size and connectivity. The ability to control neural connectivity and synchronized activity led to the implementation of logic functions using living neurons. Techniques to construct and control three-dimensional circuits have also been established. Advances in multiple electrode arrays as well as genetically encoded, optical activity sensors and transducers enabled highly specific interfaces to circuits composed of thousands of neurons. Further advances in on-chip neural circuits may lead to better understanding of the brain.<\/jats:p>","DOI":"10.3390\/mi7090157","type":"journal-article","created":{"date-parts":[[2016,9,5]],"date-time":"2016-09-05T10:02:01Z","timestamp":1473069721000},"page":"157","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Neural Circuits on a Chip"],"prefix":"10.3390","volume":"7","author":[{"given":"Md.","family":"Hasan","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA 18015, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7539-601X","authenticated-orcid":false,"given":"Yevgeny","family":"Berdichevsky","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Lehigh University, Bethlehem, PA 18015, USA"},{"name":"Bioengineering Program, Lehigh University, Bethlehem, PA 18015, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2016,9,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Banker, G., and Goslin, K. 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