From communication to computation in neurons-on-a-chip: an in silico study of neurotopomorphic computing
Abstract
Living neuronal networks transform inputs through recurrent cellular and population dynamics, yet it is unknown which network architecture supports which computation. Neurons-on-a-chip turn this question into a design problem because microchannels guide axonal growth and set the network architecture. We introduce IC3, an Integrated Characterisation of Communication-Driven Computation, which characterizes network state through neuronal dynamics, functional communication, and structural support. We implemented nine architectures \textit{in silico} as conductance-based spiking networks and tested each on frequency decoding, temporal-order discrimination, and fading memory. Predominantly feedforward circuits decoded best. Sequential Chain and Microchannel Diode had the lowest IC3 and recruited a third of reachable neurons, yet achieved the two highest scores on both classification tasks. Across architectures, higher IC3 went with lower classification scores. We term this new direction \emph{neurotopomorphic computing}, in which the physical organisation of neuronal connectivity is engineered as part of the computing substrate.
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BibTeX
@misc{barros2026from,
title = {{From communication to computation in neurons-on-a-chip: an in silico study of neurotopomorphic computing}},
author = {Michael Taynnan Barros},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.06065},
eprint = {2610.06065},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.06065},
}
RIS
TY - UNPB
TI - From communication to computation in neurons-on-a-chip: an in silico study of neurotopomorphic computing
AU - Michael Taynnan Barros
PY - 2026
DA - 2026/10/05
PB - arXiv
DO - 10.48550/arxiv.2610.06065
UR - https://arxiv.org/abs/2610.06065
AB - Living neuronal networks transform inputs through recurrent cellular and population dynamics, yet it is unknown which network architecture supports which computation. Neurons-on-a-chip turn this question into a design problem because microchannels guide axonal growth and set the network architecture. We introduce IC^3, an Integrated Characterisation of Communication-Driven Computation, which characterizes network state through neuronal dynamics, functional communication, and structural support. We implemented nine architectures \textit{in silico} as conductance-based spiking networks and tested each on frequency decoding, temporal-order discrimination, and fading memory. Predominantly feedforward circuits decoded best. Sequential Chain and Microchannel Diode had the lowest IC^3 and recruited a third of reachable neurons, yet achieved the two highest scores on both classification tasks. Across architectures, higher IC^3 went with lower classification scores. We term this new direction \emph{neurotopomorphic computing}, in which the physical organisation of neuronal connectivity is engineered as part of the computing substrate.
ER -