Engineered biological neuronal networks as basic logic operators

We present an <i>in vitro</i> neuronal network with controlled topology capable of performing basic Boolean computations, such as NAND and OR. Neurons cultured within polydimethylsiloxane (PDMS) microstructures on high-density microelectrode arrays (HD-MEAs) enable precise interaction through extracellular voltage stimulation and spiking activity recording. The architecture of our system allows for creating non-linear functions with two inputs and one output. Additionally, we analyze various encoding schemes, comparing the limitations of rate coding with the potential advantages of spike-timing-based coding strategies. This work contributes to the advancement of hybrid intelligence and biocomputing by offering insights into neural information encoding and decoding with the potential to create fully biological computational systems.

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Publication Details

Journal
Frontiers in Computational Neuroscience
Published
2025-04-28
DOI
https://doi.org/10.3389/fncom.2025.1559936
Citations
1
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.73

Funders

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article

Engineered biological neuronal networks as basic logic operators

Sean Weaver, János Vörös, Joël Küchler, Katarina Vulić et al.
1 citations
Frontiers in Computational Neuroscience
Advanced Memory and Neural Computing
0.73
article

Engineered biological neuronal networks as basic logic operators

Sean Weaver, János Vörös, Joël Küchler, Katarina Vulić, Stephan J. Ihle, Haotian Yao, Christian Valmaggia
article en
1 citations

Abstract

We present an <i>in vitro</i> neuronal network with controlled topology capable of performing basic Boolean computations, such as NAND and OR. Neurons cultured within polydimethylsiloxane (PDMS) microstructures on high-density microelectrode arrays (HD-MEAs) enable precise interaction through extracellular voltage stimulation and spiking activity recording. The architecture of our system allows for creating non-linear functions with two inputs and one output. Additionally, we analyze various encoding schemes, comparing the limitations of rate coding with the potential advantages of spike-timing-based coding strategies. This work contributes to the advancement of hybrid intelligence and biocomputing by offering insights into neural information encoding and decoding with the potential to create fully biological computational systems.

Frontiers in Computational NeuroscienceVol. 19
Institute for Biomedical Engineering (CH), BioElectronics (United States) (US)
National Science Foundation, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Eidgenössische Technische Hochschule Zürich
Openalex Percentile: Top 29%
Advanced Memory and Neural Computing
0.73
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