A light-controlled nanofluidic device simulates dendritic signal integration and reflex-like responses, advancing neuromorphic hardware for sensorimotor systems.
(Nanowerk Spotlight) 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.
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, 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.
Characterization of nanofluidic memristor fabricated with layered GO embedded PDMS elastomer. a) Schematic diagram of the biological dendrite, which integrates signals from different pre-neurons to a post-neuron, and schematic diagram of the nanofluidic memristor. (Image: Reprinted with permission by Wiley-VCH Verlag)
The device is fabricated by embedding GO sheets in a PDMS elastomer. These GO layers form narrow channels with negatively charged surfaces that guide Na⁺ ion movement. When the device is illuminated by visible light, particularly at 532 nm, photo-excited electrons and holes are generated. Because electrons and holes diffuse at different rates, electrons accumulate in the illuminated region. This creates a local electric potential that draws Na⁺ ions from the darker regions toward the light. No external voltage is needed to drive this current.
The researchers demonstrated that light applied to different parts of the device generates directionally distinct ionic currents. Light on the left side produces a positive current. Light on the right produces a negative current. Center illumination causes no significant current to flow. These observations support the device’s ability to mimic excitatory and inhibitory synaptic inputs, depending on where stimulation occurs.
Beyond simple current generation, the system also performs spatial integration. When two excitatory light pulses are applied at neighboring positions, the combined current is smaller than the sum of the individual responses. This sublinear behavior mirrors the way biological dendrites dampen overlapping excitatory inputs. Conversely, when one excitatory and one inhibitory input are paired, the resulting current exceeds the expected total. This superlinear response reflects the complex modulation observed in real neurons, particularly in cortical pyramidal cells.
Temporal integration was also achieved. Using sequences of light pulses, the team reproduced behaviors known from neuroscience. One example is paired-pulse facilitation (PPF), where a second input pulse, delivered shortly after a first, evokes a stronger response. This happens because the ionic changes from the first pulse have not fully decayed, enhancing the second. The strength of this facilitation depended on the time between pulses and closely matched biological timescales.
They further tested spike-duration-dependent plasticity (SDDP) by varying the length of individual light pulses. Longer pulses produced stronger currents, reflecting the greater ion accumulation during prolonged stimulation. Similarly, in spike-rate-dependent plasticity (SRDP), higher-frequency input trains led to enhanced current responses. This behavior acts like a high-pass filter, selectively amplifying signals that arrive in rapid succession. These effects are central to how real neurons encode timing-based information and adjust their sensitivity to different stimuli.
To show that these dendritic properties could drive meaningful outputs, the team built a neuromorphic system that controls a robotic arm. They modeled a hand withdrawal reflex, a response regulated by both low-level spinal reflexes and high-level brain input. In the device, pain and tolerance signals were simulated by light pulses applied at different locations. If the pain signal dominated, the ionic current exceeded a threshold, and the robotic arm withdrew. If the tolerance signal was stronger, no action was triggered. The balance between these inputs, along with their timing and position, determined the outcome—just as in biological systems.
The robotic setup demonstrated that the device could integrate complex sensory inputs and produce conditional responses. Hand withdrawal occurred only when certain thresholds were met, and the extent of withdrawal varied with stimulus strength. The system accounted for delays, spatial positions, and the relative weight of inputs. This parallels how biological reflexes can be modulated by higher-level decision-making.
By simulating both spatial and temporal dendritic integration using a single ion-based device, this study offers a significant advance in neuromorphic engineering. The use of light for control simplifies circuit design and avoids direct electrical interfacing. The device structure—layered GO within a PDMS elastomer—is robust and compatible with wet environments, making it suitable for bio-interfacing applications.
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.
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