Michael Berger’s May 20, 2026 Nanowerk Spotlight article features a new (to me) aspect (or, if you prefer, challenge) to neuromorphic computing, Note: A link has been removed,

Efforts to design computing systems that operate more like the brain have pushed engineers to rethink how information is processed, transmitted, and stored. Biological neurons are not simple relays. Their ability to process input relies not just on synapses—the connections between neurons—but also on dendrites. These branching structures collect and integrate signals across both time and space, shaping how a neuron responds.

Most neuromorphic devices developed so far have focused on mimicking synaptic functions. Dendritic behavior, which governs how multiple inputs are combined and modulated, remains less explored. This gap limits the capacity of neuromorphic hardware to emulate the full computational complexity of biological neurons.

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For anyone unfamiliar with dendrites, here’s a description from the Dendrite Wikipedia entry, which follows the image, Note: Links not included in the caption for the image have been removed,

A dendrite (from Greek δένδρον déndron, “tree”) or dendron is a branched cytoplasmic process that extends from a nerve cell that propagates the electrochemical stimulation received from other neural cells to the cell body, or soma, of the neuron from which the dendrites project. Electrical stimulation is transmitted onto dendrites by upstream neurons (usually via their axons) via synapses which are located at various points throughout the dendritic tree.

Dendrites play a critical role in integrating these synaptic inputs and in determining the extent to which action potentials are produced by the neuron.^[1]

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Berger’s May 20, 2026 article explains how scientists are attempting to create artificial dendrites, Note: Links have been removed,

Artificial dendrites are difficult to construct. Unlike synapses, which can often be replicated with resistive memory elements (memristors), dendrites require spatially distributed signal processing and sensitivity to the timing of input spikes. Biological dendrites perform this by managing ion flow across complex membrane structures, often with localized chemical and electrical variations. Traditional electronic systems, which rely on electrons in solid-state circuits, struggle to reproduce these dynamics.

Ionic devices offer a more faithful analogue. In particular, nanofluidic memristors—devices that transport ions through confined channels—can mimic how neurons regulate ionic currents. Prior work has shown that such systems can simulate synaptic plasticity and memory. Yet most rely on electrical stimulation, which adds complexity to control circuitry.

In contrast, light offers a clean, contactless way to manipulate ion behavior. Optogenetics, a biological technique that uses light to activate ion channels in neurons, has shown how effective this can be. Researchers have started applying similar principles to synthetic systems, but artificial dendrites with full spatiotemporal integration remain rare.

A study published in Advanced Materials (“Optogenetics‐Inspired Nanofluidic Artificial Dendrite with Spatiotemporal Integration Functions”) introduces a nanofluidic device that addresses this challenge. Developed by a team at Northeast Normal University [NENU], the system integrates layered graphene oxide (GO) into a flexible polydimethylsiloxane (PDMS) matrix. It uses light to control sodium ion (Na⁺) transport through nanochannels. This approach simulates how dendrites integrate signals from different spatial locations and over time. It also lays the groundwork for more advanced neuromorphic machines that include artificial sensory-motor reflexes.

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This work shows how optical modulation of ionic pathways can be used to create functional artificial dendrites. It opens a path toward more realistic neural circuits in hardware, capable not just of memory and learning, but of the nuanced signal processing required for perception and motor control. As components like this are refined, they could play a central role in building autonomous systems that interact more naturally with their environment.

Here’s a link to and a citation for the paper,

Optogenetics-Inspired Nanofluidic Artificial Dendrite with Spatiotemporal Integration Functions by Zhuangzhuang Li, Ya Lin, Xuanyu Shan, Zhongqiang Wang, Xiaoning Zhao, Ye Tao, Haiyang Xu, Yichun Liu. Advanced Materials First published: 16 May 2025 Online Version of Record before inclusion in an issue 2502438 DOI: https://doi.org/10.1002/adma.202502438

This paper is behind a paywall.

If you have the time, Berger’s May 20, 2026 article provides more detail about the device.