A three-dimensional micro-instrumented neural network device

TY - JOUR

T1 - A three-dimensional micro-instrumented neural network device

AU - Mritunjay, Kumar

AU - Sturm, James C.

AU - Fu, Tian Ming

N1 - Publisher Copyright: © The Author(s), under exclusive licence to Springer Nature Limited 2026.

PY - 2026/5

Y1 - 2026/5

N2 - Three-dimensional (3D) cultured neural networks that emulate the structures and computational principles of the brain could be of use in the development of brain-inspired computing and artificial intelligence, as well as in the understanding of neural development and disease progression. However, creating such stable device–neural network interfaces remains challenging, limiting the potential of such 3D neural networks. Here we report a 3D micro-instrumented neural network device in which a 3D flexible electronic sensor and stimulator array is integrated with a 3D cultured neural network. Our device can be used to record action potentials from multiple planes over a period of 6 months, allowing the quantitative monitoring of the evolving connectivity maps and the pharmacological stimulation responses of the neural networks. This approach also supports chronic electrical stimulation, which we use to train neural networks by tuning the connectivity strengths between neurons, creating a reservoir neural network for biocomputing.

AB - Three-dimensional (3D) cultured neural networks that emulate the structures and computational principles of the brain could be of use in the development of brain-inspired computing and artificial intelligence, as well as in the understanding of neural development and disease progression. However, creating such stable device–neural network interfaces remains challenging, limiting the potential of such 3D neural networks. Here we report a 3D micro-instrumented neural network device in which a 3D flexible electronic sensor and stimulator array is integrated with a 3D cultured neural network. Our device can be used to record action potentials from multiple planes over a period of 6 months, allowing the quantitative monitoring of the evolving connectivity maps and the pharmacological stimulation responses of the neural networks. This approach also supports chronic electrical stimulation, which we use to train neural networks by tuning the connectivity strengths between neurons, creating a reservoir neural network for biocomputing.

UR - https://www.scopus.com/pages/publications/105037203645

UR - https://www.scopus.com/pages/publications/105037203645#tab=citedBy

U2 - 10.1038/s41928-026-01608-1

DO - 10.1038/s41928-026-01608-1

M3 - Article

SN - 2520-1131

VL - 9

SP - 532

EP - 543

JO - Nature Electronics

JF - Nature Electronics

IS - 5

ER -