The answer to that question now appears to be a cautious yes, at least in animal models. When the project started, the fundamental goal was to create a material that could act as a wireless interface between light and living tissue, explains Menglin Chen, Associate Professor at the Department of Biological and Chemical Engineering at Aarhus University, who leads the research. The team can now show that the particles are able to activate nerve cells in blind retinas, which brings them closer to their long-term ambition of developing a new type of retinal prosthesis. Unlike electronic implants, which require surgical placement of metal and silicon devices, or gene therapies and optogenetic approaches, which require genetic modification of surviving retinal cells, the nanoparticles are simply injected and do their work without altering the genome of any cell. That difference could matter enormously for patients, because retinal degeneration has many different genetic causes and a treatment that works independently of the underlying mutation could, in principle, help far more people.

To understand why this matters, it helps to look at the biology of the back of the eye. The retina is a thin layer of tissue lining the interior of the eyeball, and its light-sensitive photoreceptors, the rods and cones, normally capture incoming photons and initiate the cascades of signaling that the brain interprets as vision. In retinitis pigmentosa, a hereditary disease that affects roughly one in a few thousand people worldwide, these photoreceptors gradually degenerate and die, leading to progressive tunnel vision and eventually blindness. Crucially, however, the photoreceptors are not the only cells in the retina. Other nerve cells, including the retinal ganglion cells that collect information from the entire retina and transmit it to the brain through the optic nerve, can remain intact and functional for years after the photoreceptors have been lost. The Danish-led team set out to exploit precisely this surviving neuronal infrastructure, creating a new connection between incoming light and the remaining nerve cells without genetically modifying them.

The nanoparticles themselves are made from graphitic carbon nitride, a light-sensitive semiconductor, and measure around 300 nanometres in diameter, small enough that they can be delivered by injection. They are hollow, and their carefully engineered structure makes them particularly effective at capturing visible light. The design draws inspiration in part from chloroplasts, the organelles that plants use to harvest energy from sunlight through photosynthesis. When the particles are exposed to light, they trigger a series of physical and chemical processes in their immediate surroundings, and those processes can influence signaling in living cells. The researchers have investigated this photomodulation effect across several biological scales, from single molecules and single cells up to whole animals, building an unusually comprehensive picture of how the material interacts with biology.

At the finest scale, using a precisely focused laser, the team was able to activate individual nanoparticles inside individual cells and trigger a signal that traveled through the stimulated cell and onward to its neighbors. At the tissue scale, in cardiac muscle cells, the researchers used ordinary LED light to influence the cells’ rhythm, making them beat more synchronously. That cardiac result demonstrates that the technology is not limited to the eye; the same light-to-cell interface could in principle be used to stimulate heart tissue or other electrically active cells. But it is the results in the eye that bring the technology closest to a specific medical application. The researchers injected the nanoparticles into the eyes of mice with advanced retinitis pigmentosa, where the particles accumulated on the surface of the retina, close to the retinal ganglion cells that relay visual information toward the brain. When the researchers illuminated the treated eyes, they detected activity in the visual cortex, and the mice changed their behavior in response to light. In isolated pig retinal tissue, LED light could likewise activate ganglion cells when the nanoparticles were present.

The researchers are careful about what these results do and do not mean. The experiments do not show that the team has restored normal vision in the mice. What they show is that the technology can generate a measurable biological response to light even when the photoreceptors that normally detect light have largely degenerated. Long-term safety and function remain to be studied in much greater detail before the technology could potentially be tested as a treatment in humans. Henri Leinonen, a retina specialist and co-author of the study, emphasizes the context: once the photoreceptors are lost, the options for restoring light sensitivity are still very limited, and each approach currently in development carries its own constraints. Gene therapies are mutation-specific, meaning a separate therapy is needed for each genetic defect. Optogenetics requires genetically modifying the surviving retinal cells so that they become light-sensitive. Electronic implants require invasive surgery to place electrodes inside or on the eye. That is why, he argues, it is worth testing strategies that work independently of the cause of the disease. What the study demonstrates, he says, is a light-evoked response in a degenerated retina, an early step rather than a finished prosthesis.

The new study also documents how dramatically the research project has evolved since it began. In 2019, Menglin Chen received a DKK 4.2 million Semper Ardens Accelerate grant from the Carlsberg Foundation for an idea then known as OptoMed. The aim was to develop light-sensitive nanomaterials that could be placed inside the body and stimulate cells wirelessly, without genetic modification. At that early stage, the researchers were investigating nanofibers and their potential to stimulate brain and heart cells. Since then, the team has continued to refine the technology and shifted its focus toward the hollow nanoparticles at the heart of the current work. Along the way, the researchers systematically studied how the particles absorb light, how they affect cellular signaling, how well cells tolerate them, and whether the effect also operates in intact tissue and in living animals. That progression is reflected in the scope of the new study: from activating a single nanoparticle inside a single cell, to synchronizing the beating of cardiac muscle cells, and ultimately to producing measurable light responses in blind mice.

The technology has also moved from fundamental research toward innovation. In 2024, the researchers filed an international patent application covering the technology, including the use of the nanoparticles as an injectable, light-activated retinal prosthesis and for stimulating cardiac cells. In 2025, Chen received a Pioneer Innovator Grant from the Novo Nordisk Foundation for a project called RetiNano: Biocompatible and injectable photovoltaic retinal prosthesis, which focuses specifically on developing the technology for use in the eye. Seven years ago, Chen notes, the team was working with a fundamental research idea; today it has preclinical results, an ongoing patent process and a concrete goal of developing the technology into a retinal prosthesis. There is still a long way to go before this could become a treatment for patients, but the researchers have reached a point where they can begin to ask very specific questions about what it will take to move the technology forward.

The coming years will therefore not simply be about demonstrating that the nanoparticles work. The team will need to improve the delivery route into the eye, document how the material behaves in the eye over longer periods, investigate its safety in greater depth, and continue developing the technology with a view to potential future clinical use. The research is led by Chen at Aarhus University in collaboration with Professor Bozhi Tian at the University of Chicago, Associate Professor Henri Leinonen at the University of Eastern Finland, Professor Thomas Corydon and Professor Yonglun Luo at the Department of Biomedicine at Aarhus University, Associate Professor Rasmus Schmidt Davidsen at the Department of Electrical and Computer Engineering at Aarhus University, Professor Mingdong Dong at the Department of Chemistry at Aarhus University, Professor Toke Bek at Aarhus University Hospital, and Professor Nikos Hatzakis at the University of Copenhagen. The work is now entering another new phase: moving from the question of whether microscopic solar cells can communicate with living cells at all, to whether the technology could one day help people who have lost their sight.

Subject of Research: Injectable light-sensitive nanoparticles for activating surviving retinal nerve cells in degenerated retinas as a new form of retinal prosthesis.

Article Title: Injectable nanoparticles make blind retinas respond to light

Article References: Injectable nanoparticles make blind retinas respond to light. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: retinal prosthesis, nanoparticles, graphitic carbon nitride, retinitis pigmentosa, Aarhus University, Nature Biomedical Engineering, photomodulation, blindness, retinal ganglion cells, optogenetics alternative, visual cortex, light-sensitive semiconductor

The team designed a behavioral paradigm that allowed them to measure and even influence how participants made perceptual decisions across two distinct tasks. In the setup, animals and human participants had to judge stimuli along two different feature dimensions, and critically, they had to figure out on their own which of the two tasks was currently in effect. Because the task rule changed unpredictably, participants had to maintain and update an internal belief about which task was relevant, informed by whether previous trials had been rewarded. This design meant that the experimenters could track, trial by trial, how confident each participant was about the current task and how that confidence shaped the quality of their perceptual judgments.

The behavioral results were striking and consistent across species. Both humans and monkeys made less accurate perceptual decisions when the task was uncertain, and both were slower to respond under those conditions. An ideal observer analysis showed that this drop in perceptual accuracy was not a necessary consequence of switching between tasks; a perfectly rational agent could, in principle, change its task belief without sacrificing perceptual precision. Yet the biological participants consistently did. This mismatch between what is achievable and what brains actually do set the stage for the central question of the study: what, mechanistically, causes perception itself to degrade when the task is ambiguous?

To generate mechanistic hypotheses, the researchers turned to recurrent neural networks. They trained two types of networks on the same tasks the monkeys performed. One network, called the correct choice network, was trained simply to produce the best possible answers. The other, called the monkey choice network, was trained to replicate the actual, suboptimal choices of the monkeys, including their delays in switching tasks and their errors. Remarkably, only the network trained to mimic the monkeys reproduced the uncertainty-related drop in perceptual accuracy. The correct choice network switched tasks efficiently and maintained high perceptual performance throughout. This dissociation suggested that the behavioral cost was not an unavoidable property of the task architecture but instead reflected something specific about how biological brains manage uncertainty.

Peering inside the trained networks revealed the mechanism. Using distance covariance analysis, the team quantified how strongly the two stimulus features, the relevant and the irrelevant one, were represented in the network’s hidden layer activity during the interval between stimuli. In the monkey choice network, after unrewarded trials that lowered task certainty, the representation of the irrelevant feature became substantially stronger and the two feature representations lost their orthogonality, becoming entangled with one another. In the correct choice network, by contrast, the irrelevant feature was suppressed and the two feature axes remained nearly orthogonal even under high uncertainty. In other words, the network that replicated animal behavior literally mixed together the neural codes for features that should have been kept separate.

Armed with this hypothesis, the researchers turned to the brain itself. Recording from populations of neurons in the primary visual cortex, V1, of monkeys performing the task, they found the same signature of feature interference. Under high task certainty, V1 population activity primarily encoded the believed relevant feature, and information about the irrelevant feature decayed rapidly after stimulus offset. Under low task certainty, however, the irrelevant feature lingered in the population activity, and the neural representations of the two features became less separable. Decoders trained to read out feature values from V1 performed far better on the irrelevant feature during uncertain trials, mirroring the pattern seen in the monkey choice network and providing a direct neuronal correlate of the behavioral cost.

The team then pushed the analysis further, asking whether the animal’s own perceptual confidence could be decoded from V1 and used to predict behavior. The strength of the V1 representation of feature changes predicted how likely the monkey was to switch tasks after an unrewarded trial: when population activity indicated a strong, obvious change in the believed relevant feature, task switches were more frequent. Even among trials that were behaviorally identical, the decoded confidence from visual cortex carried information about the animal’s subsequent decision. This finding links a precise, measurable property of early sensory cortex to the higher-order process of deciding which task to perform, bridging perception and metacognition within a single neural circuit.

Crucially, the researchers went beyond correlation. Through behavioral experiments in humans, physiological recordings, and causal experiments using microstimulation in monkeys, they demonstrated that feature interference actively causes errors under uncertain conditions rather than merely accompanying them. Strengthening the representation of irrelevant features, whether through the natural dynamics of task uncertainty or through direct manipulation of the circuit, biased perceptual choices in predictable directions. Human participants showed idiosyncratic but systematic couplings between irrelevant and relevant feature judgments, and the monkeys exhibited similar directional interference across sessions, reinforcing the idea that the effect reflects a genuine representational mechanism rather than a generic lapse in attention or motivation.

The broader implications of the study reach into one of the oldest questions in psychology: why are cognitive capacities limited? The authors propose that capacity limitations, whether in multitasking, working memory, or attention, may stem fundamentally from interference between neural representations of different stimuli, tasks, or memories. When the brain cannot fully suppress one representation, it bleeds into another, and the resulting entanglement degrades both. This framework reframes the classic switch costs documented in decades of task-switching research as a consequence of representational geometry: under certainty, the brain maintains orthogonal codes for different features and tasks, but uncertainty erodes that orthogonality, allowing signals to contaminate one another.

The study also showcases a methodological blueprint that is likely to influence the field. By training one network to be optimal and another to match animal behavior, and then comparing their internal representations, the researchers could generate a falsifiable mechanistic hypothesis and test it with both observational and causal tools in biological brains. The monkey behavioral, electrophysiology, and microstimulation data, together with the human psychophysics data, have been made openly available, and the code for network training and analyses has been released to the research community. As artificial neural networks increasingly serve as models of cognition, this work demonstrates both the power and the subtlety of that approach: networks can reveal not only what brains do, but, when trained on behavior rather than on correctness, why brains fall short of their own impressive potential.

Subject of Research: Neuronal mechanisms of task uncertainty and feature interference in perceptual decision-making

Article Title: Feature interference underlies a neuronal basis for the behavioral cost of task uncertainty

Article References: Xue, C., Markman, S. K., Chen, R., Kramer, L. E., & Cohen, M. R. (2026). Feature interference underlies a neuronal basis for the behavioral cost of task uncertainty. Nature Neuroscience. https://doi.org/10.1038/s41593-026-02430-w

Image Credits: AI Generated

DOI: 10.1038/s41593-026-02430-w

Keywords: task uncertainty, feature interference, decision-making, visual cortex, neural networks, perception, task switching, neuroscience, Nature Neuroscience, cognitive capacity, microstimulation, psychophysics