The technical premise of ICMS is deceptively simple. By passing carefully shaped pulses of electrical current through microelectrodes positioned in cortical tissue, researchers can evoke neural activity in targeted populations of neurons without requiring any natural sensory input. The timing, amplitude, frequency and spatial pattern of those pulses determine what the brain perceives. In a functioning brain-computer interface, this creates the possibility of a true two-way channel: the device decodes neural signals to infer intent, and it writes information back into the cortex through stimulation. The Tianjin University review, authored by Pengfei Hu, Chong Chen, Yunliang Zang, Xiaohong Li and Dong Ming, organizes the field around this delivery-and-parameter framework, tracing how pulse design and electrode placement jointly shape both perception and long-term circuit change.

The most direct and best-documented application is artificial touch. When electrodes stimulate the primary somatosensory cortex, human participants report localized sensations described as touch, pressure or tingling at specific points on the body, even though no peripheral nerve is involved. Early experiments established that individual electrodes reliably produce perceptible, place-specific sensations. More recent work has pushed further, using multiple electrodes and engineered spatiotemporal patterns to convey richer tactile information, including edges, curvature and even apparent motion across the skin. Human studies have shown that such patterned stimulation improves the controllability and internal structure of artificial touch, allowing users to discriminate features that single-pulse stimulation cannot convey. Yet the review is candid about the gap that remains: synthetic tactile signals still do not reproduce the complexity of natural touch, which combines thousands of mechanoreceptors firing with precisely coordinated timing.

Vision follows a parallel logic. Electrical stimulation of the primary visual cortex produces phosphenes, the perceived spots or lines of light that appear even in people who have been blind for years. Because phosphenes can be evoked across a grid of electrodes, coordinated stimulation of multiple sites can be composed into recognizable shapes and letters, much as individual pixels combine into an image. Experiments with blind participants have demonstrated simple two-dimensional visual patterns and basic object-localization tasks, offering proof of principle that cortical prostheses can deliver usable visual information. Still, the researchers note that current visual prosthesis studies remain confined to relatively simple shapes, letters and localization. Predicting how a given stimulation will be perceived, identifying which electrode combinations produce the most useful phosphenes, and maintaining stable stimulation over long implantation periods remain the field’s most stubborn engineering challenges.

One of the most consequential findings the review synthesizes is that the brain can learn. Animals trained with intracortical stimulation learn to interpret artificial patterns and use them to guide behavior, even when those patterns bear no direct resemblance to natural sensory codes. This observation suggests that the nervous system does not demand an exact mimicry of biology; it can assign meaning to an entirely synthetic neural signal through experience. The implications for future bidirectional brain-computer interfaces are substantial. Rather than painstakingly reverse-engineering natural sensory encoding, engineers may be able to design stimulation schemes that are simpler, more robust and more flexible, and trust cortical plasticity to do the interpretive work. The brain, in effect, becomes a co-designer of the interface.

The review then examines a more ambitious possibility: using ICMS not merely to create momentary sensations but to change how neural circuits function over longer periods. Repeated or precisely timed stimulation can induce plasticity-like changes in cortical networks, the same class of modifications through which the brain normally stores skills and recovers from injury. The authors discuss studies in which paired or activity-dependent stimulation altered functional connectivity between cortical regions. In one particularly striking closed-loop paradigm, spontaneous neural activity recorded from the motor cortex was used to trigger stimulation of the somatosensory cortex with a controlled delay. Because the stimulation arrived at a biologically meaningful time relative to the spontaneous activity, the temporally matched pairing strengthened intercortical coupling and was associated with improved motor recovery in a rat model of brain injury. The result points toward stimulation therapies that reshape circuits rather than simply activating them.

Despite these advances, the authors are explicit that such applications remain largely experimental. Reliable biomarkers that confirm a circuit has actually changed, reproducible stimulation parameters that work across individuals, implantation procedures that are safe over years, and therapeutic benefits that endure all require further validation before any clinical translation. The history of neuromodulation is littered with promising animal results that failed to survive the transition to human trials, and ICMS researchers are aware that plasticity induction, in particular, is exquisitely sensitive to timing, dosage and the state of the tissue being stimulated. What works in a healthy rat motor-sensory loop may behave very differently in an injured or aged human cortex.

A parallel challenge is the hardware itself. Conventional microwire and silicon electrodes are far stiffer than the soft, delicate tissue of the brain, and this mechanical mismatch has consequences. Micromotion between implant and tissue causes damage, inflammation and glial scar formation, which progressively isolate the electrode from the neurons it needs to reach and degrade signal quality over time. Flexible electrodes soften the mechanical mismatch, and the review describes the field’s migration from rigid probes toward flexible, biomimetic designs. But flexibility alone cannot eliminate the biological barrier between an artificial material and living neural tissue, which is why the researchers highlight a further step: biohybrid neural interfaces. These incorporate living biological components, including neural stem cells, neural progenitor cells and other neural cells, directly into the implanted device. The goal is twofold: improve integration with host tissue so the interface survives longer, and allow living tissue to participate in signal transmission itself. More advanced designs can even guide axon growth, creating new biological connections between neural tissue and the electronic device. In the most optimistic framing, biohybrid interfaces could extend beyond better electrodes to repairing damaged neural circuits, combining electronics, living cells and host tissue into a single functioning system.

None of this, the review stresses, makes ICMS a plug-and-play technology. Long-term performance depends on the stability of implanted electrodes, and stimulation effects vary between individuals and can drift over time within the same person. The authors therefore call for coordinated progress across electrode design, stimulation encoding, closed-loop calibration and safety evaluation, together with longer follow-up periods, cross-species validation, standardized safety assessments and reproducible behavioral and neural-network outcomes. Rather than replacing existing neuromodulation technologies, they argue, ICMS may ultimately serve as a complementary tool offering far finer control over local neural populations than surface stimulation or pharmacological approaches can achieve. Its long-term promise lies in combining several capabilities at once: delivering artificial sensory information, letting the brain learn entirely new information channels, reshaping dysfunctional circuits and integrating electronic devices more naturally with living neural tissue. As the review concludes, translating intracortical microstimulation from an experimental technique into durable brain-computer interface systems will require progress not in any single technology, but simultaneously across interface reliability, stimulation encoding, closed-loop control, safety and biohybrid integration. The work was supported by the National Key Research and Development Program of China, the Major Program of the National Natural Science Foundation of China and the National Natural Science Foundation of China.

Subject of Research: Intracortical microstimulation as a technique for evoking artificial perception and inducing plasticity in brain-computer interfaces

Article Title: Intracortical microstimulation in brain–computer interfaces: Evoking perception and plasticity

Article References: Intracortical microstimulation in brain–computer interfaces: Evoking perception and plasticity. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: intracortical microstimulation, brain-computer interfaces, sensory feedback, phosphenes, neuroplasticity, closed-loop neuromodulation, biohybrid neural interfaces, somatosensory cortex, visual prosthesis, neural electrodes, Intracortical, microstimulation

The central challenge the team set out to address concerns scalability and flexibility in ionic neuromorphic devices. Neuromorphic nanofluidics, which harness ionic rather than electronic transport to emulate neural dynamics, have generally depended on nanoconfinement—squeezing ionic motion into channels with dimensions comparable to the electrical double layers that form at solid-liquid interfaces. This confinement gives researchers exquisite control over how quickly ions accumulate, dissipate, and relax, which in turn determines whether a device exhibits the short-term and long-term memory effects characteristic of biological synapses. But the inherent scales of the spatial interactions at nanoconfined interfaces impose hard limits: fabricating angstrom-scale or nanometer-scale channels across large areas is difficult, integrating them into soft, stretchable, or biologically coupled systems is harder still, and the spatial scale of the governing physics cannot easily be tuned after fabrication.

The new system sidesteps nanoconfinement altogether by introducing what the researchers call cascaded-heterointerfacial ion traps within a multiphasic gel ion transporter, or GIT. Rather than a single confining channel, the device consists of multiple gel phases joined at heterointerfaces—boundaries between chemically distinct soft phases with differing ionic environments. When ions attempt to cross these interfaces, they encounter what the authors describe as interionic hierarchical cross-interface retardation and dissipation. In practical terms, the coupled dynamics of ion accumulation, crowding, and relaxation on either side of each boundary slow and scatter ionic flux in a hierarchy of timescales. Each interface therefore behaves like a trap: it can hold ionic charge transiently, release it gradually, and modulate the transmission of subsequent ionic spikes. By cascading several such interfaces in series, the system multiplies these trapping effects, creating rich temporal dynamics from macroscopically scalable soft materials.

One of the clearest signatures of this design is a pronounced ionic bipolar rectification effect, with rectification ratios exceeding one thousand. Rectification means that ionic current flows far more readily in one polarity than the other, analogous to the behavior of a diode in an electronic circuit. In biological terms, it resembles the one-way gating of signals at synapses and ion channels. Achieving ratios above 10^3 in an entirely soft, gel-based architecture indicates that the cascaded traps do not merely attenuate signals but actively sculpt their directionality. The rectification arises because ion enrichment and depletion at successive heterointerfaces depend strongly on the polarity of the applied bias, so the same physical structure can either promote or suppress transmission depending on which way the ionic spike travels through the system.

Beyond directionality, the device exhibits what the researchers characterize as spike-strength-dependent and timing-dependent dual-order plasticity. This is a cornerstone of neural computation. In the brain, synapses strengthen or weaken depending both on how strongly they are activated and on the precise relative timing of pre- and postsynaptic spikes—a phenomenon known as spike-timing-dependent plasticity, which is widely believed to underpin learning and memory formation. By reproducing both amplitude-dependent and timing-dependent forms of plasticity within a single ionic platform, the cascaded-heterointerface system captures two distinct but intertwined orders of synaptic adaptability. The interionic retardation and dissipation at each trap accumulate across the cascade, so the device’s response to any given spike depends on its own recent history—exactly the property that distinguishes a memristive, memory-bearing element from a passive conductor.

Particularly significant is the integration of both short-term and long-term memory effects within the same material system. Short-term plasticity, in which synaptic efficacy transiently changes over milliseconds to seconds, enables computational functions such as filtering, adaptation to stimulus statistics, and temporal differentiation. Long-term plasticity, persisting over much longer durations, provides the substrate for durable memory and learned associations. Biological synapses blend the two seamlessly, and neuromorphic engineers have long struggled to replicate that blend with adequate dynamic range. The hierarchical trapping timescales of the multiphasic gel naturally generate both regimes, allowing the team to demonstrate complex ion-based synaptic adaptability and multiple biologically grounded learning rules, including forms of spike-timing-dependent behavior implemented through purely ionic dynamics.

The energy figures reported are remarkable for a soft material system: multistate ionic neuromorphic processing at as little as 0.61 picojoules per spike. For context, individual synaptic transmission events in the human brain are often estimated to consume on the order of tens of femtojoules to picojoules, and modern semiconductor-based artificial synapses frequently require far more energy per operation, particularly when overheads of converting signals between electronic and ionic or chemical domains are counted. Operating in the picojoule regime means the gel system is not merely a conceptual demonstration but approaches the energy budgets at which practical, body-attached, or even body-implanted neuromorphic hardware becomes feasible. Soft matter also offers mechanical compliance and chemical compatibility that silicon cannot match, which points directly toward the study’s most eye-catching experiment.

That experiment involved creating a biohybrid neural circuit by interfacing the in vivo neuro-iontronic processor with the transected sciatic nerve of a rat. The sciatic nerve, the major peripheral nerve running down the hind limb, was cut and the processor was connected across the transection, allowing nerve-generated spikes to drive ionic processing in the gel and, reciprocally, allowing the processor’s output to stimulate downstream nerve segments. The result was bioneuron-driven ionic neuromorphic processing: the device’s synaptic states were updated by genuine biological action potentials, and in turn the device reconfigured the interneural signals passing through the injured nerve. The experiment, approved by the Animal Protection Ethics Committee of Capital Medical University, suggests a route toward prosthetic or regenerative interfaces in which a soft computational material does not simply relay nerve signals but adaptively reshapes them according to neuromorphic learning rules.

The theoretical underpinnings of the system were developed in parallel with the experiments, with collaborators at Tsinghua University performing calculations that connect the observed macroscopic behavior to the microscopic interionic dynamics at each heterointerface. This modeling work clarifies how hierarchical cross-interface retardation and dissipation give rise to the device’s memristive characteristics and provides a design framework for tuning trap strength, cascade depth, and phase chemistry. The work builds on a rapidly maturing field of iontronics and nanofluidic computing, in which recent years have seen fluidic memristors, droplet-based modular iontronics, and mechano-ionic logic switches emerge from laboratories around the world. What distinguishes the present contribution is the deliberate move away from spatial confinement as the sole control lever and toward interfacial design in soft, multiphasic matter—a shift that promises devices that are cheaper to fabricate, easier to scale, and far more amenable to integration with living tissue.

The implications extend across several frontiers. For brain-inspired computing, the system offers a hardware substrate in which the informational carriers, the physical dynamics, and the material compliance all resemble biology far more closely than conventional transistors do, potentially enabling machine learning implementations that exploit the same temporal plasticity principles the brain uses. For medicine, a soft iontronic processor that can learn from and modulate peripheral nerve activity hints at adaptive neural prostheses, smart neuro-repair scaffolds, and closed-loop bioelectronic therapies that reconfigure damaged signaling pathways in real time. And for the broader materials community, the demonstration that cascaded heterointerfaces can substitute for nanoconfinement opens a design space in which chemistry, phase architecture, and interfacial engineering replace lithographic miniaturization. Much work remains before such devices leave the laboratory—long-term biostability, manufacturing reproducibility, and integration with clinical hardware all present substantial hurdles—but the convergence of picojoule energy consumption, dual-order synaptic plasticity, and verified in vivo biohybrid operation marks a genuinely notable step toward computing materials that think the way biology does.

The choice of gel-based ion transporters also reflects a broader trend in which soft ionic conductors are increasingly viewed as viable active materials rather than passive wiring. Because ions are the native charge carriers of living systems, devices that process signals ionically can, in principle, couple to tissue without the transduction penalties that arise when electronic implants must convert ionic bioelectricity into electron flow and back again. The multiphasic architecture takes advantage of this by letting phase chemistry, rather than channel geometry, define the functional behavior, which means trap dynamics can in principle be tuned through material formulation.

The dual-order plasticity reported here is also notable from a computational standpoint. Spike-strength dependence and timing dependence together enable learning rules that resemble Hebbian and predictive forms of adaptation studied in neuroscience, and memristive hardware implementing such rules has been proposed as a route to energy-efficient spatiotemporal learning. Demonstrating both orders of plasticity in a purely ionic, soft-matter platform suggests that such rules need not depend on solid-state electronics.

The in vivo sciatic nerve experiment, conducted under ethics approval AEEI-2025-036, additionally illustrates how a processor driven by genuine biological action potentials could serve as a testbed for studying signal reconfiguration in injured nerves, complementing conventional cuff electrodes and stimulation implants.

Subject of Research: Soft-matter iontronic devices using cascaded-heterointerface ion traps for multistate ionic neuromorphic processing and biohybrid neural interfacing.

Article Title: Soft-matter multistate ionic neuromorphic processing based on cascaded-heterointerface ion traps

Article References: Wu, Z., Zhang, S., Zhai, L., Zhang, A., Zhu, X., Xu, J., Liu, H., Xu, Z., Jiang, L., & Zhao, Z. (2026). Soft-matter multistate ionic neuromorphic processing based on cascaded-heterointerface ion traps. Nature Electronics. https://doi.org/10.1038/s41928-026-01688-z

Image Credits: AI Generated

DOI: 10.1038/s41928-026-01688-z

Keywords: neuromorphic computing, iontronics, soft matter, ionic memory, synaptic plasticity, nanofluidics, biohybrid circuits, gel ion transporter, ion traps, energy-efficient computing, neural interfaces, Nature Electronics