Constructing artificial neurons with functional parameters comprehensively matching biological values

Shuai Fu, Hongyan Gao, Siqi Wang, Xiaoyu Wang, Trevor Woodard, Zhien Wang, Jing Kong, Derek R. Lovley and Jun Yao ()
Additional contact information
Shuai Fu: University of Massachusetts
Hongyan Gao: University of Massachusetts
Siqi Wang: University of Massachusetts
Xiaoyu Wang: University of Massachusetts
Trevor Woodard: University of Massachusetts
Zhien Wang: Massachusetts Institute of Technology
Jing Kong: Massachusetts Institute of Technology
Derek R. Lovley: University of Massachusetts
Jun Yao: University of Massachusetts

Nature Communications, 2025, vol. 16, issue 1, 1-11

Abstract: Abstract The efficient signal processing in biosystems is largely attributed to the powerful constituent unit of a neuron, which encodes and decodes spatiotemporal information using spiking action potentials of ultralow amplitude and energy. Constructing devices that can emulate neuronal functions is thus considered a promising step toward advancing neuromorphic electronics and enhancing signal flow in bioelectronic interfaces. However, existent artificial neurons often have functional parameters that are distinctly mismatched with their biological counterparts, including signal amplitude and energy levels that are typically an order of magnitude larger. Here, we demonstrate artificial neurons that not only closely emulate biological neurons in functions but also match their parameters in key aspects such as signal amplitude, spiking energy, temporal features, and frequency response. Moreover, these artificial neurons can be modulated by extracellular chemical species in a manner consistent with neuromodulation in biological neurons. We further show that an artificial neuron can connect to a biological cell to process cellular signals in real-time and interpret cell states. These results advance the potential for constructing bio-emulated electronics to improve bioelectronic interface and neuromorphic integration.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-025-63640-7 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-63640-7

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-025-63640-7

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().