Chronically implantable μLED arrays for optogenetic cortical surface stimulation in mice

Domain: physics
Published: 2026-01-14
Editorial outcome: major_revision

Abstract
Cortical implants are a proven clinical neurotechnology with the potential to transform our understanding of cognitive processes. These processes rely on complex neuronal networks that are difficult to selectively probe or stimulate. Optogenetics offers cell-type specificity, but achieving the density and coverage required for chronic, high-resolution modulation remains a challenge. We fabricated 100-element μLED arrays (200 μm pixel pitch, 2 × 2 mm2 footprint) coupled into chronically implantable systems for optogenetic stimulation of the mouse cortex. The μLEDs remain stable for over 300 hours continuous operation time in vivo, allowing for months-long chronic experiments. Simultaneous electrophysiology recordings confirmed robust neuronal responses at low μLED drive currents (<5 mA), minimising thermal effects. Here we show that our device can be chronically implanted in freely-behaving mice and drive behavioural outcomes from spatiotemporal, patterned, optogenetic stimulation of auditory cortical circuits.

Introduction
Cortical implant technologies have received significant attention in recent years, demonstrating their potential in clinical neuroscience1,2. Among their many applications, the transfer of information to brain networks hold particular promise for investigating and restoring lost function or sensation. Optogenetics enables precise modulation of neuronal activity using light, offering cell-type specificity by employing light-sensitive proteins (opsins), which are genetically introduced into specific neuronal populations. This motivates the development of optical cortical implants, tailored for optogenetic neuroscience studies in behaving animals. These devices would ideally offer broad spatial coverage at high resolution, combined with millisecond-scale temporal precision, high light intensities and chronic implantability3.

Optical stimulation of the cortex is commonly achieved using external light sources, such as laser scanning single-photon4/two-photonsystems5, or digital light projectors (DLPs)6. These systems are capable of achieving very high spatial resolution. However, the associated optical components and electronic control systems preclude chronic implantation, hindering translation to experiments on freely-behaving animals. Fibre bundles can deliver high-density spatiotemporal optical stimulation within a cortical region7 with the possibility of chronic implantation and use in freely-behaving animals8, but still require complex bench-top optical systems that preclude further development towards wireless implementation. For that, implantable optoelectronic solutions are needed. Micro-LED (light-emitting diode) (μLED) probes offer implantable, spatiotemporal optogenetic control over neuronal populations, with the possibility of very high stimulation site density9–12 and fully-wireless options13,14. Chronic implantation of the optical source necessitates robust encapsulation strategies, addressing challenges such as biocompatibility, long-term stability15 and thermal effects16. Two-dimensional arrays of μLEDs offer fully implantable optogenetic stimulation coverage across the surface of the cortex. Previously, our group demonstrated the Utah optrode array (UOA), a μLED array with 181 sites, delivering light through penetrating optrodes17 to deeper-lying structures of the non-human primate (NHP) cortex. Other groups have also demonstrated that this optoelectronic approach is viable. For example, Pollmann et al.18 developed a μLED array coupled to a single-photon avalanche diode (SPAD) array capable of spatiotemporal optogenetic stimulation and fluorescence monitoring, with relatively large μLED dimensions and spacing, optimised for imaging performance requiring uniform illumination. Chronically implantable LED arrays were also demonstrated on the surface of the NHP cortex19–21, where stimulation site spacing and device size limited the application to NHPs. The technology has been adapted for rodent studies, demonstrating site-specific activation in the cortex22–24 with higher density devices being produced using organic LED (OLED) technology for in vitro studies25. The transfer of information to the mouse cortex requires high-density μLED arrays with spacing of a few hundred microns whilst covering significant tissue areas (up to 4 mm2)6. Previous μLED arrays match the stimulation site density and spacing of our device26 and have been coupled to arrays of electrodes (ECoG) integrating electrophysiology recording27. While both the density and scale sufficient for high-resolution cortical surface stimulation has been achieved, chronic implantation in mice and simultaneous demonstration of robust neuronal and behavioural responses remains an open challenge for the field (Supplementary Table 1).

Here we present a chronically implantable array of 100 μLEDs that can deliver high-intensity blue light over a 2x2 mm2 footprint, enabling spatiotemporal optogenetic stimulation across the mouse auditory cortex. The μLED array is integrated into miniaturised, optoelectronic systems for in vivo experimentation in mice. Chronic implantation of the μLED array is enabled through integration to a flexible package (though the array itself is rigid), and can remain stable for up to 300 h experimental time. Electrophysiology recordings from the mouse auditory cortex demonstrate robust activation thresholds of 4-5 mW mm−2 on the cortical surface. This corresponds to a drive current of ~5 mA, resulting in safe temperature changes predicted by our modelling for single-pixel illumination. Spatial resolution within cortical layer 2/3 was estimated at ~600 μm, which agreed with optical modelling. Lastly, chronically implanted μLED arrays providing spatially confined optogenetic stimulation drove robust learning outcomes in behavioural discrimination experiments, in both head-fixed and freely-behaving mice.

Results
μLED array design, fabrication and packaging
We developed a 100-site matrix-addressable array of GaN-on-sapphire μLEDs through microfabrication (Fig. 1a, b, Supplementary Fig. 1 and Supplementary Movie 1). The device features a dense array of μLEDs, arranged in a 10x10 grid with 200 μm spacing, enabling precise individual site or patterned illumination within a cortical region. Detailed fabrication steps are provided in the Methods; briefly, the μLEDs are 40 μm squares emitting blue light at a wavelength of approximately 450 nm, close to the peak absorption of ChR2 and a number of other common opsins. Emission is through the sapphire substrate, which was thinned to 150 μm. Substrate thinning is important because the μLEDs emit with a cosine dependence, making the distance from the source to the brain surface the dominant factor affecting spatial resolution.

The μLED arrays were packaged to a lightweight, flexible device (Fig. 1c), enabling chronic implantation in the mouse model (Fig. 1d). However, the stimulation area of the μLED array remains rigid, constrained by the GaN-on-sapphire fabrication process. Therefore, the device cannot achieve conformal contact with the brain surface as with flexible neural interfaces, however, the brain locally adapts to this constraint in the same way as it does as for cranial window implantations28. Histological analysis after three months chronic implantation, demonstrates plastic deformation around the implant site, with the size of the affected region comparable to the geometry of the μLED array (Supplementary Fig. 2). Immune response was then quantified by examining microglial build-up from the surface through the auditory cortex (Supplementary Fig. 2g). This was statistically significant when comparing the Iba-1+ cell response from the implanted side (228 ± 24 per mm2) to the non-implanted (contralateral) side (181 ± 18 per mm2). Excluding layer 1 from the analysis shows no significant difference between the implanted side (200 ± 34 per mm2) and contralateral side (133 ± 11 per mm2), highlighting a small localised immune response, approximately limited to layer 1 of the mouse cortex.

To determine whether light from the device passes beyond this depth, understand the resultant spatial resolution and estimate the required light intensity, we modelled the device using a ray tracing (Monte Carlo) simulation (Zemax OpticStudio 2021). Brain tissue was modelled using the scattering and absorption parameters for grey/white matter29,30 and setting the refractive index to that of water (n=1.33). This omits contributions from structures such as the vasculature. The device model was experimentally validated with in-air optical measurements (Supplementary Fig. 3), giving us confidence in the optical model to provide a good estimate of experimental conditions. Optical modelling indicates that at a 10 mW mm−2 cortical surface irradiance, the 1 mW mm−2 contour (approximate activation threshold for ChR2) penetrates through to the mouse cortical layer 2/3, assuming the device is implanted directly on the cortical surface (Fig. 1e). Optical outputs from the μLEDs were experimentally characterised at both the μLED surface and cortical surface, providing inputs to the modelling. For acute electrophysiology experiments, a 150 μm thin cortical window was used, which increases the emitter-cortex distance, therefore increasing the illuminated area on the cortical surface, but reducing the irradiance. A cortical surface irradiance of 10 mW mm−2 can be achieved with a drive current of less than 3 mA when the device is directly implanted (Fig. 1f).

In vivo electrophysiology experiments were carried out with the μLED array placed within a compact craniotomy in an acute preparation. This necessitated a miniaturised rigid package to lower the μLED array onto a cortical window and allow space for a silicon probe to be inserted (Fig. 1g and Supplementary Fig. 4a). Chronic implantation was then achieved by optimising this packaging (Fig. 1h and Supplementary Fig. 4b). Highly compact interconnection was achieved using thermosonic flip-chip bonding, reducing the thickness of the device tip to 250 μm and enabling chronic implantation, and the device to be flush with the mouse skull and surrounding tissue (Supplementary Fig. 12a). The μLED array design methodology enables the rapid development of custom packaging to facilitate a range of in vivo experimental paradigms, brain regions or different animal models. Furthermore, monolithic packaging of multiple μLED arrays could enable high-density information transfer within and between cortical regions in a single chronic implantation (Supplementary Fig. 5).

Thermalevaluation and lifetime testing
Tissue heating is known to perturb neuronal activity, elevating or suppressing the firing activity of neurons16,31. This is particularly important when using implantable μLEDs for optogenetic stimulation because they are actively powered, dissipating most of this power as heat. Ideally, the temperature increase would remain within the commonly quoted limit of 1-2 °C32. Our specific device geometry and a custom material stack results in complex heat flows, necessitating an in-depth thermal evaluation33. Infrared (IR) imaging was used in conjunction with an equivalent 2D rotationally-symmetric mathematical model replicating the device with flexible packaging, chronically implanted with the sapphire surface in direct contact with cortical tissue. Appropriate material thermal properties were applied, with the brain approximated as water. To verify the accuracy of the modelled device, the sapphire surface of the μLED array was imaged using an IR camera in air (Supplementary Fig. 6), which agreed closely with the modelled output (Fig. 2a). These experiments, along with previous work demonstrating the accuracy of this model architecture with in vivo IR imaging34, gave us confidence in our thermal models to provide a good approximation of experimental conditions.

The thermal model was then used to assess the temperature increase in vivo by simulating heat pulses equivalent to 3 mA and 5 mA per μLED (Fig. 2b and Supplementary Fig. 7). The peak temperature as a function of depth is shown (Fig. 2b right). For a single μLED driven at 5 mA, the modelled peak temperature increase remained well below 1 °C. For 2×2 and 3×3 patterns, the peak temperature increase can exceed 1 °C, thus the drive current must be selected carefully. As an example, driving a 3×3 pattern at 3 mA per μLED results in a peak temperature increase of ~2 °C on the brain surface and ~1 °C in layer 2/3. Despite being minimal, temperature increases stress the need for control experiments to rule out thermal effects influencing neuronal or behavioural responses. Thus, we carried out control experiments, both during electrophysiology and behavioural studies.

Accelerated ageing tests were conducted to ensure the devices would survive under the conditions of chronic implantation (Fig. 2c and Supplementary Fig. 8). Our encapsulation stack includes a thin-film oxide and SU-8 layer atop the μLEDs, as well as the sapphire substrate. Subsequent packaging steps include a polyimide PCB, medical-grade epoxy underfill and 15 μm parylene-C (PaC) conformal coating. For accelerated testing, an enhanced encapsulation strategy consisting of an additional 15 μm PaC coating and medical-grade silicone dip coating was evaluated. The devices were submerged in phosphate-buffered saline (PBS) solution at approximately 60 °C for two weeks. Heating of the solution allowed for an accelerated timescale to be evaluated35; in this case, the acceleration factor was ~4.10. μLEDs across the array were actively current-driven in a repeated sequence, and the voltage recorded. The μLEDs were also monitored optically to ensure the current was driving light output and not flowing through an alternative path.

The experiment duration was over 1000 h (42.5 days) of in vivo equivalent time. Under continuous operation, measured voltage remained stable across all μLEDs for up to 324 h (13.5 days) before the μLEDs subsequently failed (Fig. 2d). To quantify the failure mechanism, μLEDs from untested rows and columns of the array were evaluated, with all untested μLEDs remaining functional for the entire experiment duration (Fig. 2e). The encapsulation strategy is therefore stable when the device is passive, but actively driving the μLEDs shortens the lifetime of the devices, suggesting either higher electric fields or thermal effects degrade the encapsulation. Nevertheless, devices remain stable for over 300 h of continuous operation time. Since most in vivo experiments typically run for ~1–3 hours per day, we conservatively estimate that the devices should remain stable over months long chronic experimentation. Other encapsulation strategies were evaluated (Supplementary Fig. 8b), with 30 μm of PaC and silicone providing the best performance. Lastly, we evaluated devices which were chronically implanted for 29 days, actively used for behavioural experiments, and then explanted (Supplementary Fig. 8c). These devices showed a minimal degradation in performance, demonstrating the robustness and re-usability of the chronically implantable devices. Furthermore, implanted devices did not have the additional medical-grade silicone layer, which has been shown to be more effective than parylene-C alone in chronically implanted devices15.

Acute electrophysiology recordings reveal stimulation threshold
Electrophysiology recordings were conducted in the auditory cortex (AC) of awake, head-fixed mice under simultaneous stimulation from the μLED array, driven with our custom control system (Supplementary Fig. 9). Experiments were conducted in 4 transgenic mice expressing ChR2 in all excitatory neurons (Emx1-IRES-CrexAi27)36,37 (denoted ChR2+). An additional control experiment was conducted in an Emx1-IRES-Cre transgenic mouse that did not express ChR2 (denoted ChR2-). We delivered high-intensity blue light to the AC to optogenetically stimulate neuronal populations within layer 2/3, allowing us to establish both a stimulation threshold and spatial resolution. Each mouse was prepared with a 5 mm diameter craniotomy with 150 μm transparent cortical window, optically exposing the AC. A μLED array, in the acute device configuration (Fig. 1g), was lowered onto the cortical window. A 64-channel linear electrode array was then inserted through a small hole in the cortical window (0.5 mm diameter) at an angle of 28° to the cortical surface, with recording sites extending from the cortical surface to a depth of approximately 680 μm, consistent with layer 5 (Fig. 3a, b).

Six recording sessions were conducted in total, five ChR2+ mice (one mouse was repeated) and a control mouse (ChR2-) to ensure detected neuronal responses were not thermally induced or stimulation artefacts. The linear electrode array was positioned at approximately the same location relative to the μLED array for all experiments (Supplementary Fig. 10a). Each experiment comprised a set of trials, each trial illuminating an individual μLED at drive current for 10 pulses (20 Hz, 50% duty cycle) with 500 ms wait between trials. This optogenetic protocol was chosen based on previous use of these parameters to drive strong neuronal activation6,38. The μLED location was varied in a pseudo-random sequence for each trial. Trials were repeated up to 50 times for each combination of μLED and cortical surface irradiance. For each μLED, the drive current was individually calibrated in software to account for variations in optical output due to device fabrication (shown in Supplementary Fig. 3c). In this section, the quoted standard deviations represent the variation in cortical irradiance delivered by each μLED at the drive currents used during the experiments. The optical power of each μLED on the tested device was measured before and after the experiment, with this minor degradation in μLED performance assumed to be due to conductive solution ingress in the acute device.

The following analysis identifies a threshold cortical surface irradiance and μLED drive current needed to reliably elicit neuronal responses, allowing us to determine electronic driver specifications and examine thermal effects. An example of a recorded waveform is shown (Fig. 3c) for 5 trials of stimulation by a single μLED, stepping through cortical surface irradiances; this μLED was chosen as it provided reasonably consistent irradiance and was located near the insertion point of the recording electrode array. Spike sorting was used to attribute the recorded electrophysiology signal to the neuronal firing activity of individual neurons (Fig. 3c right), allowing us to determine if neuronal responses were time-locked to the optogenetic stimulus and under what conditions. For the following analyses, only single-unit activity is considered. Spike times are shown as a raster plot for all neurons (Fig. 3d top). These were summed in 25 ms bins and averaged over all neurons to yield a peri-stimulus time histogram (PSTH) (Fig. 3d bottom). The background was taken as the mean spike counts per bin in both 200 ms periods before the 10 pulses start and after the 10 pulses end. The PSTH shows the neuronal response is time-locked to the stimulus, and is particularly strong at cortical surface irradiance values exceeding 4 mW mm−2. A dose response was plotted, showing average spike counts per trial with background subtracted (ChR2+: time-locked neurons, ChR2-: all neurons), against estimated cortical surface irradiance (Fig. 3e); we quote the cortical surface irradiance as an estimate due to the aforementioned reduction in optical performance, as shown by the horizontal error bars. Neurons were determined as time-locked if the average spike counts per trial during the on period were greater than 2σ from the average background spike counts. Here, threshold activation is defined as a 50% success rate - 1 spike per 2 pulses on average per neuron. This is equivalent to 1 spike per 50 ms time window, which remains consistent with the temporal kinetics of ChR2/H134R39. This spike rate was achieved at moderate cortical surface irradiances between 4.0 ± 0.1 mW mm−2 and 5.3 ± 0.3 mW mm−2, corresponding to a drive current of between 8.0 mA and 10.4 mA. These drive currents are slightly higher than expected due to the reduction in optical performance observed with this device. In stable devices, we demonstrate a consistent optical output of 5 mW mm−2 at the cortical window/tissue interface for ~5 mA drive current (Supplementary Fig. 1d). The average spike counts per trial were plotted for different μLEDs illuminated at approximately the same threshold irradiance, across the width of the device (Fig. 3f). For ChR2+ mice, mean spike counts were higher for μLEDs located near the electrode array. The control mouse (ChR2-) showed no time-locked responses, indicating no confounding effects of temperature or experimental artefacts. Variation in the neuronal response between experiments could be due to variation in the relative positioning of the recording electrode array to the μLED stimulation site (Supplementary Fig. 10a), the flatness of the μLED array placement on the cortical window, or the overall electrode array insertion quality and surrounding tissue health. Improved consistency between experiments is possible with the removal of the cortical window and with our chronically implantable devices, where the supporting polyimide PCB is flexible, making it easier to fix into position.

Spatial resolution
Simultaneous electrophysiology recordings under stimulation from individual μLEDs allowed examination of the spatial resolution of the device. Spatial maps were plotted showing average spike counts per trial with background subtracted for all tested μLEDs (Fig. 4a), plotted for the two experiments with the strongest responses; Experiment 1 and Experiment 3. Weighted Gaussian curves were fitted to rows 3 and 4 of the map corresponding to the highest cortical surface irradiance (Fig. 4b). The spatial resolution was inferred from the full width at half maximum (FWHM) of these fits. Due to the use of a single shank recording electrode device with a fixed position, spatial resolution estimates could only be obtained from μLEDs located in rows near the edge where the electrode array was inserted.

Optical models were developed and compared with spatial resolution estimates obtained through electrophysiology. The 1 mW mm−2 contour (approximate threshold for ChR2) was plotted, given the cortical surface irradiances evaluated during the in vivo experiments (Fig. 4c); the shaded error is not indicative of the model, but is based on the variance in experimental cortical irradiance levels. To make a comparison of the experimental spatial resolution (Fig. 4b) and the optical model (Fig. 4c), we take a cut of the model at a depth of 150 μm (consistent with layer 2/3, which is the target neuronal layer) and use the width of the 1 mW mm−2 threshold contour as an estimate of the width of activation. Experimental and modelled estimations of the width of activation are in reasonable agreement (Fig. 4d). This comparison is an approximation, ignoring dendritic contributions and second-order network effects, and implies that the optical models could be used to inform device optimisation to further improve spatial resolution. These estimates assume the highest tested cortical irradiance, however, responses can be seen at lower irradiances and would result in a smaller illuminated volume and width. This comparison is also based on the cortical window between the sapphire surface and the brain used in electrophysiology experiments. This exacerbates the spread of light caused by the Lambertian emission profile from the μLEDs and can be removed for chronic implantation.

The 1 mW mm−2 contour was plotted with the μLED array modelled directly in contact with the brain tissue (Fig. 4e). In both simulations, shown in Fig. 4c, e, the same optical power was delivered to the brain (hence requiring the same drive current); although the cortical surface irradiance without the window is higher because the illuminated spot size is smaller (Supplementary Fig. 1d). Both the depth and width of the 1 mW mm−2 threshold contour were compared in each case (Fig. 4f). For equivalent optical power (1.2 mW), removal of the cortical window allows deeper penetration of light with the same lateral width (Fig. 4f middle column), while reducing the optical power to 0.7 mW results in a reduction in light spread with the same depth of penetration (Fig. 4f right column). Different patterns (2×2 and 3×3) were also modelled based on the same delivered optical power per μLED (Fig. 4g). The activated volume significantly increases in both cases, demonstrating the ability of the μLED array to activate an entire cortical region if necessary, at modest drive currents.

Behavioural experiments with chronically implanted devices
Chronic implantation of μLED arrays can facilitate repeated optogenetic stimulation of distinct ensembles of neurons from a cortical region, over days or weeks. To demonstrate this, we trained mice to respond to artificial perceptions generated by illuminating spatially confined patterns on the surface of the auditory cortex (AC). To motivate the mice to learn the task, we delivered bi-phasic electrical charge through intra-cranial stimulation of the medial forebrain bundle (MFB). This reliably activates reward centres in the brain, and the animals do not become sated as with water or food rewards40. Mice were first implanted with the MFB stimulator electrode, then a square-shaped craniotomy was performed to place the μLED array in direct contact with the cortical tissue after careful removal of the dura mater, with dental cement used to secure the device to the skull (Fig. 5a and Supplementary Fig. 12a). After a 5-day recovery period, implanted mice were head-fixed and placed in a contention tube, with a lick detector within reach (Fig. 5b and Supplementary Fig. 12). Between behavioural experimental sessions, mice were able to remain non-encumbered due to the lightweight device packaging, however, they were sometimes housed individually to avoid damage to the connector assembly.

Experiments were carried out over a number of sessions (1 session per day), each consisting of several hundred trials usually over a 1-2 hour learning period, dependent on mouse performance (Supplementary Tables 3 and 4). On each trial, the μLED array delivers a 10-pulse burst (20 Hz, 50% duty cycle) of illumination at between 5–10 mW mm−2 per μLED to the surface of the AC. Again, this optogenetic protocol was selected to drive consistent neuronal activation, aiming to achieve strong salience for the animal. Although the device facilitates individual pixel operation, a 3×3 square of μLEDs was used to illuminate one corner of the diagonal depending on the trial type (go or no-go). This achieved a broad area of illumination and was based on previous experiments using a digital light projector (DLP) system to illuminate 400 μm diameter regions of the AC, to drive behavioural outcomes in ChR2-expressing mice6,41. The response window opens for 2.3 seconds on the onset of the illumination, during which the mouse has the opportunity to receive a reward of MFB stimulation if it licks in response to the go stimulus. If the mouse licks in response to the no-go stimulus, a time penalty is incurred, which is added to the random inter-trial interval to delay the beginning of the next trial. All other responses result in no outcome, including those during the blank trial, where no illumination is presented. Prior to the illumination starting and response window opening, the mouse must not lick the detector for a set amount of time, to avoid continuous licking to receive the reward without learning the task; an experimental trial is illustrated in Fig. 5b. The mice are trained on two distinct tasks - the association experiment only presents go trials and the discrimination experiment presents both go and no-go trials; in both cases there are a small number of blank trials where no μLEDs are illuminated, to indicate background licking activity. Initially, the mouse is trained to lick in response to the go stimulus in the association experiment. After learning the association task, defined as maintaining consistent accuracy above 80% (typically after 4–6 sessions), the mouse moves onto the discrimination experiment, where the no-go stimulus is introduced. In the discrimination experiment, the mouse should continue licking in response to the go stimulus while ignoring the no-go stimulus. Figure 5c shows a lick raster plot for both association and discrimination experiments, with the mean and standard deviation given for the go and no-go licks within the response window; these are shown for the same animal (Mouse 2), which appears to learn the task well.

Learning curves were plotted to show how the accuracy evolves over the duration of the experiments (Fig. 5d). The accuracy was computed for each trial from the percentage of go trials where the mouse licked (for the association experiment) or the combined percentage of go trials where the mouse licked and no-go trials where the mouse did not lick (for the discrimination experiment). A trial is only counted towards the accuracy if the number of licks within the response window is greater than 2σ from the mean of the background lick rate, determined from the average lick counts per session from the inter-trial interval, as shown by the grey bars. Since the mice appear to stop licking once they receive a reward, this background lick rate is not necessarily present over the course of the response window, and may provide an underestimate of the accuracy. Mice consistently learned the association task, with the exception of Mouse 3 demonstrating more variability in performance between sessions. Good accuracy is maintained during the discrimination experiment, demonstrating adaptive behaviour based on the same intensity but different spatial location of the illumination pattern. Lick raster plots have been generated for each trial type and all experiments, which demonstrate elevated licking timed with the onset of the go optogenetic stimulus (Supplementary Fig. 13). Two control mice (denoted ChR2-) received the same implantation of MFB stimulator and μLED array and were trained only on the association task (Fig. 5e). In both cases, onset of licking to receive a reward was delayed until the MFB voltage was increased to encourage some licking (at ~1000 trials in each case). Mice began to lick continually, but this was not elevated above background licking during the response window, as with the ChR2-expressing mice. Mouse 5 did appear to lick timed to the illumination during the final session, which can be seen in the lick raster plot in Supplementary Fig. 13b, although this was not statistically significant when compared with the background lick rate. This delayed learning could be the result of the mouse visually perceiving the blue light. In future experiments, the transparent area above the μLED array could be covered, or a masking light used to saturate the mouses’ vision. The overall accuracy for each trial type is summarised for the 500 consecutive trials with the highest accuracy for each experiment (Fig. 5f). On average, mice were able to learn both tasks, achieving an accuracy of 80% at cortical surface irradiance levels similar to the thresholds determined from electrophysiology experimentation (5–10 mW mm−2) but over an increased illuminated area and volume as was shown by the optical modelling in Fig. 4g. Consistent behavioural responses were observed as low as 5 mW mm−2, which can be driven at a per-μLED current of less than 3 mA (Supplementary Fig. 1d). Thermal analyses suggest this current level is safe, even for up to nine μLEDs illuminated simultaneously (Fig. 2b and Supplementary Fig. 7j).

Behavioural discrimination experiments in freely-moving mice
Freely-behaving mice implanted chronically with μLED arrays over the auditory cortex conducted a similar behavioural discrimination task; this time using water deprivation to motivate learning. A flexible cable was mounted above a behavioural chamber to allow the mice to conduct the experiment while moving freely. The box was equipped with an infrared (IR) beam sensor to detect mouse presence, and a lick port to deliver the reward (Fig. 6a, b, Supplementary Fig. 14 and Supplementary Movie 2). For these experiments, we expressed the opsin ChRmine, which has a much larger photocurrent than ChR2 and a broad excitation spectrum, resulting in ~10x sensitivity at 450 nm42. Control (opsin-negative) mice were not included as the stimulation protocol used here (Fig. 6c) was identical to the head-fixed behavioural experiments - these already confirmed successful optogenetic activation and no confounding effects influencing behaviour. Per-μLED cortical irradiance was set at 10 mW mm−2 and progressively reduced to 1 mW mm−2 in the cases where the behavioural outcome accuracy was maintained at a strong level (typically >80%) across sessions. Mice performed daily experimental sessions, each containing several hundred trials (Supplementary Data 1). Each trial begins when the mouse voluntarily breaks the IR beam, triggering optogenetic stimulus presentation and beginning a 1 s response window. During go trials, mice needed to remain in the IR beam for a cumulative time of at least 75% of the response window to receive a reward. For no-go trials, correct performance required remaining in the IR beam for less than 75% of the response window to avoid a time penalty. Between trials, mice had to remain outside the IR beam for a set refractory period, which encouraged movement away from the reward port.

During initial sessions, mice showed no difference in the average time spent in the IR beam between the two stimuli types (Fig. 6d left), with performance remaining at chance level (~50%) (Fig. 6e). However, in subsequent sessions the accuracy (computed as the proportion of trials correct) improved, with consistent accuracy at or above 80% indicating the task was learned (Fig. 6d right). The number of sessions required for learning varied between subjects, with Mouse 1 requiring 5 sessions to reach and maintain 80% accuracy compared to 7 sessions for Mouse 2, although performance was less consistent for the latter (Fig. 6e). Once discrimination learning was established, we observed greater consistency in the time spent in the beam during go trials and a marked decrease during the no-go trials (Fig. 6d right). After achieving stable task performance, we explored progressively reducing cortical irradiance per μLED and noted discrimination performance at irradiance levels that tended to be lower than those tested in the head-fixed experiment (5 mW mm−2), presumably due to the use of the more sensitive ChRmine opsin. All mice were able to perform the discrimination task above chance level, for irradiances as low as 1 mW mm−2, except for Mouse 2, which failed to discriminate when the cortical irradiance was reduced to 2 mW mm−2 (50% chance level accuracy). However, upon increasing to 4 mW mm−2, discrimination performance increased again (Fig. 6e). Detailed analysis of all animals is provided in Supplementary Fig. 15.

Methods
μLED array fabrication
The μLED arrays were fabricated on a commercial InGaN on patterned sapphire wafer before being diced, individualised, and released (Supplementary Fig. 1). The sapphire substrate is thinned to 150 μm before processing; after thinning, the wafer is optically polished (Valley Design Corp.). A palladium layer (Pd) – 100 nm – is deposited using an E-beam evaporator (Edwards 306), to define the 40 μm pixel shape and also act as a spreading layer. A thin silicon dioxide (SiO2) layer patterned by a photoresist (Oxford Plasmalab 80 Plus, Electronic Micro Systems 4000, Suss Microtec mjb4) is used as an etching mask to etch the Pd layer and p-GaN layer, forming the pixel structure. Another SiO2 layer is then used to mask the n-GaN etching, which forms the pads for wire bonding or flip-chip bonding as well as the n-type mesa, with 10 rows, each with 10 pixels per array, forming the 10x10 array. A lift-off process using a bi-layer of photoresist deposits a layer of Ti:Au (100 nm:300 nm) along the n-type mesa, the bond pads as well as a contact on top of each p-type pixel. Two insulation layers are formed over the n-metal tracks. Firstly, 800 nm of SiO2 is deposited and vias etched open using an RIE tool (Oxford Plasmalab 80 Plus). A layer of SU-8 TF 6005 photoresist is spun and patterned then hard baked at a temperature of 200 °C for 2 h. A lift-off process is then repeated on top of these insulation layers, depositing Ti:Au (100 nm:300 nm) in columns forming p-contacts to the pixels, as well as further depositing metal on the wire bonding and flip-chip bonding pads. A final layer of SU-8 TF 6005 is spun on top of these tracks, vias opened above the bond pads and hard baked. A protective layer of photoresist (SPR 4.5) is spun on top of the wafer, and individual arrays are released using a diamond saw dicing tool (Disco Hi-Tech DAD 3320).

μLED array packaging
The μLED array has 20 pads for interconnection, connecting to the 10 sets of anodes or cathodes, and allowing all 100 μLEDs to be matrix-addressed. There is an option to interconnect through wire bonding pads (on two edges of the device) or a ball-grid array (BGA), for flip-chip bonding.

For acute, head-fixed experimentation in the mouse model, the μLED arrays were packaged into a custom printed circuit board (PCB) system (Supplementary Fig. 4a). The acute system consists of two PCBs (0.2 mm thin) approximately 3 cm in length which are glued together, with the μLED array attached to a silicon spacer block (1.25x1.25x1 mm3) to allow placement in the cranial recess avoiding surrounding bone and tissue while remaining flat on the cortical window (Fig. 3a). Epoxy adhesive paste (MG Chemicals 9460TC) is used to glue the components together. Placing and gluing of components is achieved using a flip-chip bonder system (Finetech Picoma) and programmable adhesive dispenser (Metcal DX-350). A 1.27 mm pitch 20-pin rectangular connector is soldered to both PCBs, allowing electrical connection to the 10 p and n tracks. The μLED array is electrically connected to 75 nm thick ENIG-coated (electroless nickel immersion gold) tracks on each PCB through 25 μm diameter gold ball-wedge wire bonds (MicroPoint Pro i5000 Dual). The wire bonds are then potted, and the PCB shanks glued together (MG Chemicals 9510) (Fig. 1g). The system is coated with 7 μm of parylene-C (SCS PDS 2010).

The μLED array was integrated to a flexible system for chronic implantation and used in behavioural experiments (Supplementary Fig. 4b). The shank is 100 μm thin and 2 mm wide, breaking out 20 connections and allowing every μLED to be individually addressed. The system was developed by bumping 75 μm diameter gold balls onto a commercial flexible polyimide PCB (PCBway) and attaching the μLED array through the BGA pads using thermosonic flip-chip bonding. A medical-grade epoxy (EPO TEK MED-301-2) is then used to underfill the μLED array for electrical and mechanical protection. A miniature circular Omnetics connector is soldered at the opposite end, which is potted using the same epoxy. The system is coated with 15 μm of parylene-C (PaC). We further explored the encapsulation procedure in accelerated tests only by adding an additional layer of PaC (15 μm) as well as a medical-grade silicone dip coating (NuSil MED-2214). Supplementary Fig. 4c shows the steps involved in applying the silicone dip coating. For our devices, a silicone primer (NuSil MED-161) was firstly applied using a small bristle brush and allowed to dry for one hour. A small piece (3x3 mm2) of UV-sensitive dicing tape was attached to the sapphire surface of the device. The device was fully submerged in MED-2214 which had been decanted into a beaker. While submerged, the beaker was rotated slowly several times with the device held at different angles, before carefully lifting the device out and leaving it suspended on a drying rack for 10 min at room temperature until the silicone partially cured. A second coating was applied in the same manner as before and the device suspended on a drying rack, this time above a hot plate set to 50 °C, for one hour. The dicing tape was exposed to UV light and peeled off, so the sapphire surface was clear, but the rest of the device was encapsulated; this was necessary to minimise standoff distance between the μLEDs and the brain surface. A final cure was carried out at 150 °C until the silicone had fully cured.

Optical modelling and characterisation
The μLEDs were optically modelled using a custom ray tracing (Monte Carlo) model and simulation (Zemax OpticStudio 2021 in non-sequential mode). The model consists of a single 40 μm square GaN μLED containing a light source acting as the QW (quantum well) structure (Supplementary Fig. 3a). The μLED has a gold contact on the back, which is modelled as a mirror. The model includes a 2 mm square sapphire substrate (150 μm), parylene-C layer (7 μm for acute device with cortical window or 15 μm for chronic device directly on brain tissue), optional cortical window (150 μm) and auditory cortex. The auditory cortex was modelled as grey matter, with a 150 μm layer of white matter on the surface. The refractive index of water was used as it approximately matches the reported values for brain tissue48, while incorporating suitable scattering coefficients to approximate the optical characteristics of the brain; white matter: scattering coefficient, σs = 50 mm-1, absorption coefficient, σa = 0.14 mm-1, anisotropy index, g = 0.78; grey matter: σs = 11 mm-1, σa = 0.07 mm-1, g = 0.8829,30. The ray tracing simulation simulates 2.107 photons.

To characterise the μLED arrays, the laboratory setup consists of a source/measurement unit (Keysight B2911A) and multiplexer (Keithley 2000) for software-programmable addressing of individual μLEDs. The optical power of all 100 μLEDs was measured under a sweep of drive currents. Using a similar optical model but based on the geometry of our laboratory characterisation setup (Supplementary Fig. 3a right), the proportion of optical power from the μLED reaching the detector (detector scale factor, DSF) was estimated using a ray tracing simulation. The optical model in Supplementary Fig. 3a (left) was then used to estimate the proportion of power from the μLED entering the brain tissue (brain scale factor, BSF). Dividing by the area of the illuminated spot size on the cortical surface (approximate diameter 530 μm with window or 270 μm without window, Supplementary Fig. 3b) gives the cortical irradiance in mW mm−2. This gives a calibration between measured optical power and cortical irradiance and therefore, cortical irradiance as a function of μLED drive current. The measured optical power to irradiance calibration equation is given at the cortical surface.

CSI (mW mm−2) = (Measured optical power (mW) * BSF) / (DSF * πd2/4) (1)

Where CSI is the cortical surface irradiance, BSF the brain scale factor, DSF the detector scale factor and d the diameter of the illuminated spot on the cortical surface.

We generate regression fits for current-cortical irradiance for all 100 μLEDs in a device. The drive current can therefore be individually inferred for each μLED from the desired cortical surface irradiance during experimentation by software, allowing normalisation across the array (Supplementary Fig. 3c).

Optical model experimental validation
To validate the optical modelling experimentally, a pinhole (100 μm in diameter, 40 μm thick) is positioned in front of a photodetector (Supplementary Fig. 3d). The pinhole was positioned as close to the detector as possible to ensure that all light that passes through the pinhole is collected by the detector. The pinhole-photodetector is then translated through the μLED emission profile, measuring the light intensity at each point. The measurement is repeated at various μLED-pinhole distances. The light measurement at each point allows for the beam profile reconstruction with a resolution of approximately the pinhole size. This method is modified from ref. 49.

Optical interposer validation and modelling
We bonded a thin (80 μm) silicon interposer with 80 μm diameter holes onto the sapphire surface of a packaged device to investigate improving spatial resolution through collimation (Supplementary Fig. 16). Integration of the interposer and hole alignment to pixels was achieved using flip-chip bonding. Emission profiles of devices with and without the interposer were imaged with an SLR camera in 10 μM fluorescein solution (Supplementary Fig. 16a left). To compare the lateral light spread, raw green pixel data was plotted (Supplementary Fig. 16a right, blue lines - error bars computed from different shutter integration times), demonstrating a significant reduction in light spread with the interposer. Both images were compared with Monte Carlo models of equivalent geometry, modelling fluorescence by taking a cut through detectors at equivalent distance from the device (Zemax OpticStudio 2021) (Supplementary Fig. 16a right, orange lines). The experimentally validated optical interposer was modelled in brain tissue at equivalent delivered optical power to Fig. 4 (Supplementary Fig. 16c).

Thermal characterisation
The μLED array was imaged using an infrared (IR) camera (FLIR SC7000-Series) focussed on the sapphire surface using an integration time of 1.5 ms and refresh rate of 100 Hz (Fig. 2a and Supplementary Fig. 6a). Since the camera returns a digital value proportional to the number of detected IR photons, a calibration is necessary to convert this value to actual temperature in degrees celcius. The calibration uses an E-type thermocouple fixed to the back surface of the device. The sapphire surface was brought into focus first by focussing on the mesa structures in the device, and then moving the stage back through the known thickness of the sapphire (150 μm) (Supplementary Fig. 6b). An area of sapphire, located close to the μLED being tested was used for calibration (Supplementary Fig. 6b). Using a heat gun, the device and thermocouple were simultaneously and uniformly heated to over 50 °C and allowed to cool down to room temperature, while both IR camera and thermocouple measurements were made at an interval of 100 ms. This process was repeated 5 times, and a second-order polynomial was fitted to the thermocouple measurements against camera counts (Supplementary Fig. 6c).

For the thermal measurement, the μLEDs were pulsed under several stimulation protocols. For verification of the model and to more easily assess the time constant of the thermal drop-off, a simple protocol was used; single μLED pulsed at 5 mA (1 Hz, 50% duty cycle) (Fig. 2a). The IR image in Fig. 2a shows a frame corresponding to the peak temperature from the IR camera calibrated using the thermocouple measurements. The calibration was carried out using a measurement from the sapphire area of the image with the remaining image scaled accordingly, however, this assumes linear emissivity between the materials and so is only indicative.

Thermal modelling used the COMSOL Multiphysics 5.6, heat transfer package. Two thermal models were designed; firstly, to simulate the IR imaging experiment (i.e., the device suspended in air) (Supplementary Fig. 7a) to verify the accurateness of the model, and secondly to simulate a chronic in vivo experiment (Supplementary Fig. 7b). Both are 2D rotationally-symmetric models and implement the device as a layer of gallium nitride (GaN) atop sapphire with parylene-C encapsulation. The device model also includes the polyimide PCB with a layer of copper on each side, gold bump bonds between the GaN and PCB, and an epoxy underfill. As the model is rotationally symmetric, care has been taken in designing the geometry such that the resulting 3D model has the correct volumes of each material, particularly the μLED itself, which acts as the heat source in the model. The chronic implantation was simulated by placing the device over a volume of brain tissue with encapsulating cement atop, in a modelled environment. Brain tissue was approximated using the thermal properties of water.

To input thermal power to the model, firstly the electrical and optical power were measured as a function of drive current for a set of 10 representative μLEDs on one of our devices (Supplementary Fig. 7c). The optical power generated by the μLED is calculated from the measured power using optical modelling (see Optical modelling Methods section). By subtracting the optical power from electrical power, thermal power was determined (Supplementary Fig. 7d). A second-order polynomial is fitted so thermal power can be calculated for any drive current, and this is then input to the model as a train of pulses (3 mA/5 mA, 20 Hz, 50% duty cycle, Supplementary Fig. 7e). Since the model is rotationally-symmetric, the μLED heat source cannot be implemented as a cuboid (the true geometry of the μLED). Instead, a cylinder of equivalent volume is used and IR imaging used to verify the accurateness of this assumption. To implement multiple μLEDs at the same time, a concentric ring is used with a radius similar to the distance between the rows of μLEDs. The volume of this ring is equivalent to the volume of 3 μLEDs or 8 μLEDs, with the centre μLED having a volume equivalent to a single μLED. A similar comparison was made between IR imaging with squares of μLEDs pulsed and the model with rings, and both cases were found to agree well (Supplementary Fig. 7g, h).

Accelerated ageing experiments
The accelerated testing setup uses a heated water bath to ensure a steady temperature and safety for running the experiment over a long timescale. A custom-machined aluminium lid is placed over the water bath with a hole to allow a polystyrene tank to protrude through (Fig. 2c and Supplementary Fig. 8a). This setup allows the device to be submerged in PBS within the tank while preventing water evaporating and escaping the chamber.

Several μLEDs across the array were pulsed constantly (approximately once each per minute) with a fixed current (5 mA, 10 pulses at 20 Hz, 50% duty cycle) and the voltage was measured to evaluate changes in the resistance of the device (Fig. 2d). A microscope camera images the device for the duration of the experiment. A PT100 sensor is also placed into the solution for accurate temperature measurements over the course of the experiment. 10 different μLEDs were tested in a repeated cycle for 337 h (approximately 14 days), which corresponds to an accelerated time of 1025 h (approximately 42.5 days) based on the Arrhenius relation for the accelerated testing of polymers35.

Animals and ethics
Every experiment involving animals has been carried out following a protocol approved by an ethical commission, in accordance with the French Ethical Committee (Direction Générale de la Recherche et de l’Innovation) and European legislation (2010/63/EU). Procedures were approved by the French Ministry of Education in Research after consultation with the ethical committee #59 (authorisation number APA-FIS#28757-2020122111048873).

Optogenetic experiments were conducted on adult transgenic mice (Emx1-IRES-CrexAi27D) expressing ChR2 in all excitatory neurons of the pallium37. Control experiments were performed on adult transgenic mice (Emx1-Cre) that did not express any opsin.

For freely-behaving experiments only, mice were instead transfected with AAV vectors encoding ChRmine under the control of the CaMKIIα promoter, which were injected to drive expression in pyramidal neurons of the auditory cortex (pAAV-CamKIIα-ChRmine-mScarlet-Kv2.1-WPRE (AAV8). Addgene number: 130991). ChRmine’s activation spectrum is red-shifted, but it is broad with less than 50% reduction of elicited current at the wavelength of the μLED arrays (λ=450 nm)42. Since ChRmine has much higher photocurrents than ChR2, blue light illumination was sufficient to elicit robust enough neuronal activation for behaviour.

Mice were group-housed in an animal facility (08:00-20:00 light) at a temperature of 22 ± 2 °C and humidity between 60–65%, in cages with access to enrichment (running wheel, cotton bedding and wooden logs).

Surgery: cranial window implantation
For chronic unilateral access to the auditory cortex (AC), a cranial window was introduced, and a metal post for head fixation was implanted on the opposite side. The procedure was performed on 8–12 week-old mice. Buprenorphine (Vertergesic, 0.05–0.1 mg/kg) was administered 30 min before anaesthesia with isoflurane (3%), maintained at 1–1.5% and the animal placed on a thermal blanket during the procedure. The eyes were covered using Ocry gel (TVM Lab), and Xylocaine 20 mg/ml (Aspen Pharma) was injected locally at the incision site. The right masseter was partially resected, and a large craniotomy (5 mm diameter) was made above the AC, guided by skull sutures. A 5 mm circular coverslip (150 μm thick) was sealed over the craniotomy using cyanolite glue and dental cement (Ortho-Jet, Lang). For the electrophysiology experiment, a grounding electrode (PlasticsOne MS303T/2-B) was implanted contralaterally.

Post-surgery, mice received a subcutaneous injection of glucose (G30) and Metacam (1 mg/kg). The mice were then housed for one week with metacam administered by drinking water or dietgel (Clear H20) without any manipulation. All animals were housed together before and after surgery without any degradation to the implanted grounding electrode.

Acute in vivo electrophysiology experiments
Electrophysiology recordings were conducted in mice implanted with a glass cortical window above the AC for 3-4 weeks. One hour prior to in vivo experiments, mice were anaesthetised with isoflurane (3% at induction), placed on a thermal blanket and maintained at 1–1.5%. Excess dental cement was trimmed to allow better access to the craniotomy. A 0.5 mm hole was carefully drilled by hand with diamond-coated drill tips in the glass window. Mice were then awakened, and Kwik-Cast silicone (World Precision Instrument) was applied on the cranial window to prevent drying.

Six recording sessions were conducted in five mice: four transgenic mice expressing ChR2 (ChR2+) and one not expressing ChR2 (ChR2-) as a control. A 64-channel linear recording electrode array (NeuroNexus A1×64 Poly2-6mm-23S-160) was inserted 1.4 mm at an angle of 28° with respect to the brain surface through the previously drilled hole. The electrode array extended to a vertical depth of ~680 μm corresponding to the electrode located furthest from the shank tip being positioned on the cortical surface. This allowed estimation of the cortical layer being recorded from and the lateral distance of the recording electrodes from stimulation sites (Fig. 3a, b, Supplementary Figs. 10a, 17). The μLED array was placed on top of the cortical window above the recording electrode array. Voltage traces were recorded at 20 kHz using an RHD2000 USB interface board (Intan Technologies).

The stimulation protocol cycled through each μLED in a pseudorandom sequence, pulsing 10 times (20 Hz, 50% duty cycle) for five cortical irradiance levels: 1.0 ± 0.3, 1.2 ± 0.6, 2.8 ± 0.5, 3.2 ± 0.8, and 4.6 ± 1.2 mW mm−2 every 500 ms. Each μLED-irradiance combination was repeated over 50, 20, or 10 trials, prioritising μLEDs near the electrode array, which was necessary due to head-fixed experimental time constraints. Eight μLEDs were tested at all five irradiances, while others were tested at three: 1.0 ± 0.3, 2.8 ± 0.5, and 4.6 ± 1.2 mW mm−2. Cortical irradiances quoted as pooled mean ± pooled standard deviation. Non-functional μLEDs or those with insufficient power were excluded. μLED drive currents corresponded to 2.4±0.2, 4.0±0.3, 5.8 ±0.5, 7.7 ±0.6 and 10.0 ±0.8 mA. Currents quoted as mean ± standard deviation. Each mouse underwent a 5030-trial experiment lasting over approximately 1 h.

Electronic control system
A custom electronic control system was developed to power the μLED array and address all 100 μLEDs via software protocols (LabVIEW 2021, National Instruments) (Supplementary Fig. 9). The system includes an Arduino UNO R3 microcontroller for communication with the LabVIEW-equipped PC and programmable 10-channel current DACs using two DC2903A development boards (Analog Devices). A custom shield PCB connects to the Arduino UNO, allowing SPI communication with the current supplies. The PCB also houses a 10-channel solid-state switch array (Texas Instruments TM7211) controlled by the microcontroller. In addition, the microcontroller supplies a trigger signal to the Intan recording system to align recorded data with the μLED stimulation onset and offset.

Spike sorting and analysis of electrophysiology data
Raw electrophysiology data was captured with Intan RHX and filtered using a band pass filter with cutoffs at 300 Hz and 5.5 kHz followed by a common average reference subtraction across recording channels (Supplementary Fig. 10b). Pre-processed electrophysiology data was then automatically spike sorted using the SpikeInterface package and Kilosort 2.5 (Python 3.10). Manual curation was carried out with Phy to remove incorrectly identified clusters. Stimulation artefacts were also manually identified and excluded from analysis.

A custom Python pipeline was used to aggregate spike counts based on trigger signals recorded by the Intan recording controller and to perform all subsequent data analysis. Spike counts were binned into 5 ms intervals, with each trial consisting of 100 bins (500 ms) of stimulus, 40 bins (200 ms) before stimulus onset, and 40 bins (200 ms) after stimulus offset, totalling 180 bins (900 ms). For simplicity, further binning into 25 ms bins was carried out to coincide with the stimulation protocol (20 Hz, 50% duty cycle).

The pre-stimulus and post-stimulus bins were used to calculate mean background spike counts, which were subtracted from the primary data recorded during the 500 ms stimulus. This allowed determination of which neurons were time-locked to the stimulus and the magnitude of their responses. Data visualisation was carried out using the Matplotlib and Plotly packages in Python 3.

The dose responses (Fig. 3e and Supplementary Fig. 11a) show pooled mean, with error bars as pooled SEM of average spike counts per trial (background subtracted); ChR2+ (optogenetic): n=13, 14, 16, 16 time-locked neurons; ChR2- (control): n=51 neurons. Sigmoid curves were fitted to each set of points to aid visualisation.

Spatial resolution plots (Fig. 4b) show error bars as pooled SEM of standard deviations of average spike counts per trial (background subtracted); n=13, 16 time-locked neurons. Gaussian curves were fitted to each row using data points, x, weighted by the error bars.

y = y0 + A * exp(-(x-μ)2 / 2σ2) (2)

Initial values for (y0, μ, A, σ) were (0, -500, 50, 200) with bounds as ([0, 2], [-900, 0], [0, 200], [100, 1000]). The x-axis is in μm. The standard error of the fitted parameters defined the shaded region. Reduced χ2 values were 0.56 and 1.40 for rows 3 and 4 of Experiment 1, and 1.68 and 4.43 for rows 3 and 4 of Experiment 3.

One experiment was omitted (Supplementary Fig. 11): a repeat on the same animal as Experiment 1, carried out a day after a recording probe broke and was manually removed. This data was considered contaminated and excluded from Figure 3.

Surgery: chronic implantation of flexible packaged μLED array
The procedure was performed on 8–12 week-old mice. Buprenorphine (Vertergesic, 0,05-0,1 mg/kg) was administered 30 minutes before anaesthesia with isoflurane (3%), maintained at 1–1.5% and the animal placed on a thermal blanket during the procedure. The eyes were covered using Ocrygel (TVM Lab), and Xylocaine 20 mg/ml (Aspen Pharma) was injected locally at the incision site. A square craniotomy of 2.4x2.4 mm2 was performed above the auditory cortex using skull bone sutures as a landmark. The μLED array was placed directly on the cortical surface after removal of the dura mater, and the edges were glued to the skull using Super Glue (Loctite). The flexible PCB packaging was fixed with dental cement (Ortho-Jet, Lang) and an MFB electrode was implanted.

Post-surgery, mice received subcutaneous glucose (G30) and Metacam (1 mg/kg). They were housed for one week with Metacam provided via drinking water or Diet Gel (Clear H2O) without manipulation. All animals were co-housed before and after surgery without damage to the implanted grounding electrode. However, occasional temporary isolation occurred when co-housed mice chewed on the connectors of implanted devices.

MFB stimulation
All mice used in the head-fixed behavioural experiments were implanted with an intracranial electrode stereotaxically targeting the medial forebrain bundle (MFB) to deliver a stable reward40.

The MFB electrode was implanted using stereotaxic coordinates (AP -1.4, ML +1.2, DV +4.8) using a NeuroStar stereotaxic device (Stereodrive 018.117). The electrode and head plate were secured to the skull using dental cement (Ortho-Jet, Lang).

MFB stimulation was provided with a pulse train generator (PulsePal V2, Sanworks) delivering 2 ms biphasic pulses for 100 ms at 50 Hz.

Behavioural experimental procedure
The experimental setup is shown in Supplementary Fig. 12. The μLED array is controlled by the Arduino-based electronic control system, while the MFB stimulator is connected to a pulse train generator. A PC running LabVIEW 2023 (for μLEDs) and MATLAB 2024 routines manages the overall experiment. A National Instruments DAQ detects licks, applies MFB stimulation as a reward, and triggers the LabVIEW routine to apply the appropriate μLED array illumination pattern.

Mice performed behavioural tasks five days per week (Monday to Friday). Throughout the behavioural training period, food and water were provided ad libitum. Habituation: Mice were habituated to head fixation over two days by remaining in the contention tube without reward for 20 min on the first day and 40 minutes on the second day. On the third day, mice received a systematic reward for licking the spout, with a maximum reward rate of 1 reward per second for 500 trials to establish the association between licking and reward. To encourage licking in non-water-deprived mice, the spout was positioned very close to their mouths. During this initial phase, before calibration, MFB stimulation was set at 3 V and reduced by the experimenter if it caused a motor reaction.

Go training (association): Once animals were spontaneously licking, the association experiment was conducted. Go trials were presented with 90% probability, while the remaining trials were blank trials (no illumination and no reward/time-out period). A trial consisted of a random inter-trial interval between 1 and 2 seconds, a ‘no lick’ period between 3 and 5 seconds (these timings were randomised to avoid prediction of the illumination based on timing), and a fixed response window of 2300 ms. A single lick was registered as a response. Licks during the response window on a go trial was scored as a ‘hit’ and triggered an immediate reward via MFB stimulation. No lick was scored as a ‘miss’. Successful learning of the association task comprised the mouse reaching an accuracy of 80% over an experimental session. Mouse 3 struggled to reach 80% but was moved onto the discrimination task anyway.

Go/no-go training (discrimination): Licks for go trials were rewarded the same as the association experiment. During presentation of the no-go pattern, the absence of a lick was scored as a ‘correct rejection’, and the next trial immediately followed. Any licking during the response window of the no-go trials was scored as a ‘false alarm’; no reward was issued, and a random time-out penalty (between 5 and 7 seconds) was incurred. Each session contained 47.5% probability of a go trial, 47.5% probability of a no-go trial and 5% probability of a blank trial. Due to initially high performance, discrimination tasks were terminated after a set number of trials, dependent on time available for experimentation.

All parameters for the behavioural experiments are given in Supplementary Tables 3 and 4.

Behavioural data analysis
The voltage on the lick detector was recorded using a National Instruments DAQ. The timing of individual licks was determined from the activation and deactivation of the sensor (a single lick was registered when the mouse’s tongue made and then broke contact with the sensor). Licks were recorded throughout the experiment, including during the inter-trial period.

Freely-behaving experimental setup and procedure
Freely-behaving experiments utilised devices identical to those used for head-fixed behaviour with 15 μm parylene-C encaspulation, with the exception of Mouse 2 which was implanted with a device with enhanced encapsulation (30 μm parylene-C and silicone). Freely-behaving experiments were carried out in a transparent polypropylene box (27x25x22) cm3 (Fig. 6a, b and Supplementary Fig 14b). The box was elevated on 3D-printed supports fixed to a metal base, facilitating component installation and removal between experimental sessions while ensuring stability during testing. The experimental apparatus featured a dedicated reward delivery zone along one edge of the box. Two rectangular holes in the floor accommodated an infrared fork sensor for monitoring time spent in the “reward zone”, while adjacent small holes in the side wall held the lick port (syringe) connected to a solenoid valve for precise reward delivery. The box lid included an opening for the cable connecting the implanted μLED array to the control electronics, with a 3D-printed cable holder. A lamp provided consistent ambient illumination throughout experiments, preventing complete darkness and ensuring that light emitted from the μLEDs would not influence experimental outcomes or animal behaviour.

Association sessions were conducted before the main task to familiarise the animals with the experimental environment, including the presence of a reward zone, optogenetic stimulation delivered via the μLED array and wearing the connection cable. During this phase, the cortical surface irradiance was set to 10 mW mm−2 per μLED in a 3×3 pattern (20 Hz, 50% duty cycle, 10 pulses). In the beginning, the rewards were automatically delivered when the mouse broke the IR beam. Then, the required cumulative beam break duration before reward delivery was progressively increased up to 75% of the response window (1 s). Training continued until animals achieved 80% performance accuracy.

Mice advanced to the discrimination experiment employing spatially opposed go and no-go stimuli. These consisted of 3×3 μLEDs patterns (20 Hz, 50% duty cycle, 10 pulses) positioned at opposite corners of the 10 × 10 matrix (Fig. 6c). During go trials, IR beam interruption triggered go stimulus presentation and initiated the 1 s response window. Successful performance (“hit”) required maintaining IR beam break for a cumulative time of ≥75% of the response window, resulting in immediate reward delivery (4 μL water). Shorter interruptions were recorded as “misses” with no reward delivered. During no-go trials, correct performance ("correct rejection”) required exiting the beam zone before reaching the 75% threshold, with no reward delivered. Cumulative beam break times exceeding the threshold were classified as “false alarms”, triggering a randomised 3-4 second time-out penalty. All trials were followed by a randomised 1-2 second refractory period during which the beam must remain unbroken before the next trial begins (Supplementary Fig. 14a). Discrimination experiments were terminated after a set number of trials, dependent on time available for experimentation.

Histology
Tissue preparation: Tissue samples were obtained and analysed from mice chronically implanted with the μLED array between 83–101 days; post-surgery recovery: 30 days, behavioural training: 50–70 days. At the end of the experiments, mice were deeply anaesthetised with an overdose of ketamine/xylazine administered intraperitoneally (100 mg/kg and 10 mg/kg i.p. respectively) and perfused transcardially with 1X phosphate-buffered saline (PBS 1X) followed by 4% paraformaldehyde solution. Brains were extracted from the skull and kept immersed overnight in 4% paraformaldehyde solution at 4 °C. 40 μm-thick coronal sections were obtained using a vibratome (Leica VT1200S).

Immunofluorescence: Slices underwent blocking using a 10% normal goat serum (NGS) (Abcam AB7481) −0.5% Triton X (Merck, 648462) in PBS 1X solution for 1 h at room temperature. Slices were then incubated with primary antibody rabbit anti-Iba1 (1/500) (Anti-Iba1 antibody, monoclonal, Clone #: EPR16588, Catalogue #: ab178846 https://www.abcam.com/en-us/products/primary-antibodies/iba1-antibody-epr16588-ab178846) in a 3% NGS-PBS 1X solution overnight at 4 °C. 3% NGS in PBS 1X was used as a negative control. The following day, slices were washed with PBS 1X for 5 min 3 times prior to an incubation with secondary antibody goat anti-rabbit Alexa Fluor 488 (1/500) (Abcam AB150089) for 2 h at room temperature. DAPI (Invitrogen Hoechst 33342 H3570) (1/5000) was applied as a nuclear stain to reveal the general anatomy of the preparation. Slices were mounted using FluorSave reagent (Merck, 345789). Fluorescence images were acquired using a Nikon A1HD25 confocal microscope and analysed using ImageJ.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This study by Greer, Mathieson and co-workers presents the development and in-vivo use of a 100-element micro-LED (µLED) array for optogenetic stimulation of the mouse auditory cortex. The authors fabricate a 2×2 mm² array of GaN-on-sapphire µLEDs with 200 µm pitch, integrated into a chronically implantable, flexible system. Tests indicate that the device can enable stable, spatially confined stimulation over weeks, with manageable thermal impact. Electrophysiological recordings in transgenic mice expressing ChR2 confirm neuronal activation; spatial resolution is estimated to be 600 to 800 µm, consistent with optical modelling. Chronic behavioural experiments demonstrate that mice can learn to discriminate spatial patterns of cortical activation using these arrays.\n\nThe goal of achieving high-resolution, minimally invasive optogenetic stimulation in freely behaving animals has motivated the development of many implantable µLED systems. While this work is fundamentally different from earlier demonstrations, it convincingly demonstrates a technically robust and scalable platform that advances that goal. I believe that this report will be of interest to the broad optogenetics and neuroimplants communities. However, the manuscript needs to be revised to address a few shortcomings and to make the discussion more accessible to a broad audience.\n\nAs the authors explain, resolution is an important aspect of any optical stimulation device. The Lambertian emission profile of the µLEDs and the separation between µLED and target tissue lead to a much lower spatial resolution than one might have aimed for. The authors should comment in more detail on future strategies to improve this situation. There is a brief discussion on a potential “optical interposer”. What about reducing the separation between light-source and tissue surface? At present there is a 150µm thick sapphire substrate, adding very significant distance between the µLED and the target region in Layer 2/3. (Depending on how one counts, essentially doubling the intrinsic distance given by Layer 1.) Are there device designs or LED variants where this substrate could be thinned further or avoided entirely?\n\nRelated to the prior point, the simulation and schematics always appear to show the bare device, without encapsulation. In the methods, we learn that the parylene coating is 15-30µm in thickness, and there is a further silicone coating of unspecified thickness. The authors need to specify the exact geometry and should show this in the schematics as well.\n\nIn Figure 3e and other data, the variation of neuronal response to the µLED stimulation appears very large (the average spike counts between experiment #3 and #4 appear to differ by almost a factor of four). Is this to be expected? Would the variation be similar for external stimulation, e.g. with a DMD or is this the result of heterogeneity from the device? Likewise, the variation in average spike count for different stimulation regions across the device is dramatic, with some regions showing nearly no response. The authors should elaborate more on the origin of this effect. It remains unclear if this is related to the location of the recording device or intrinsic to either the illumination device or the animal model.\n\nRelated to this point, given the broad audience at Nature Communications, the methods used to pinpoint the location of active neurons from the limited spatial data provided by the ephys shank, which only intersects Layers 2/3 in a narrow region, should be explained, at least briefly.\n\nFinally, how would the approach described here be scaled in the future, both in terms of device size (for work with larger animal models, a 2x2mm2 device will not be useful) and in terms of achievable resolution (where in addition to the challenge of getting the light sources closer to the target region or finding other means of minimizing light spreading, see above, the number of µLEDs will need to be scaled dramatically). For instance, keeping the same pitch on a 10x10mm2 device would require a 25-fold increase in number of addressable LEDs and substantially larger InGaN on sapphire samples. Would this be practical at some point?", "reviewer_2": "In this manuscript, Greer et al. describe a microfabricated µLED array designed for chronic implantation to interface with superficial cortical regions in mice. The authors detail the fabrication process, characterize optical transmission and heat generation during device operation, and validate functionality by demonstrating neuronal activation and behavioral discrimination experiments in the auditory cortex. While the study addresses an interesting technological area, I find significant shortcomings that substantially limit its novelty, impact, and overall contribution to the field. Consequently, I cannot support publication of this manuscript in its current form. The major concerns are detailed below:\n\nMajor concerns:\n1. While the technology itself appears robust, the authors fail to adequately compare and contextualize their approach relative to recently published technologies, including the group's previous work. For example, the Utah optrode array reported by McAlinden et al. (2024) is likely more useful for cortical neuromodulation, as it separates the LED source from neural tissue and offers beam control capabilities. Pollmann et al. (2024) reported a system (SCOPe) using a similar cortical arraying idea for all-optical bidirectional neural interfacing, providing both recording and modulation with greater controllability. Shin et al. (2024) demonstrated a wireless operation solution. Taal et al. (2024) achieved an array with 1024 OLEDs, providing single-neuron resolution. These works, although cited in the manuscript, are not adequately contrasted or acknowledged.\n\n2. The presented µLED array is proposed as a tool for behavioral neuroscience; however, existing methods such as holographic optogenetics (see Adesnik & Abdeladim, 2021), multi-fiber approaches (e.g., Sych et al., 2019), and single-photon scanning optogenetics (e.g., Pinto et al., 2019) are not discussed. These systems, could enable experiments with much higher spatiotemporal resolution in the same neuronal ensembles, over extended periods, and with far less concern about off-target heating effects (at least in head-fixed setups). Additionally, the complexity of clean-room fabrication processes severely limits accessibility and ease of adoption by typical neuroscience laboratories, especially without clear practical advantages demonstrated by the current study.\n\n3. The authors claim that the potential advantage of this system lies in its use with freely behaving animals (albeit at the cost of spatial resolution and deep-layer access). However, this supposed advantage is unsubstantiated in the current work, as it is not demonstrated experimentally in the paper.\n\n4. The Lambertian radiation pattern of micro-LEDs is generally not precise. The spatial separation between the neuronal ensembles and the LEDs—introduced by the packaging process—further worsens this issue. Thus, the spatial resolution is intrinsically limited even if the LED array is made with a high density of emitters. The authors propose a beam-shaping method in supplementary information, but this approach is not experimentally validated.\n\n5. In terms of optical efficiency, the authors demonstrate that about 90% of the electrical power is converted to heat. Although the modeling results suggest the resulting temperature rise is physiologically safe, this claim must be validated experimentally in vivo. How was this modeled in air, and what conditions were assumed in the brain tissue model? Is a 3–5 mA drive current sufficient to recruit neuronal activation, given that the authors used 10 mA in their in vivo electrophysiology experiments (which would correspond to roughly a one- to two-fold higher temperature increase)? Additionally, the absorption of blue light by tissue also contributes to heat generation, and it is unclear why this was not taken into account.\n\n6. The flexibility claims of the device are difficult to reconcile with the use of a 150-µm sapphire substrate. The authors must clarify this discrepancy or provide additional experimental support.\n\n7. Blue light penetrates poorly in living brain tissue. Therefore, the authors must validate the light transmission profile predicted by the Monte Carlo simulation with experimental measurements in actual brain tissue.\n\n8. Given the relatively large size of the head plate shown in Figure 1c, it is unclear whether this surface-interfacing arrangement will outperform the authors’ previous optrode array approach. That earlier approach avoids heat generation at the neural interface and offers beam-regulating (beam-shaping) capabilities. The advantages of the new surface array over the established optrode array are not convincingly demonstrated.\n\n9. It is unclear why the authors used a 20 Hz stimulation with a 50% duty cycle for their electrophysiology experiments, as this protocol is likely to induce a sustained cation influx through ChR2 and could result in multiple spikes per neuron per 25 ms. This protocol also generates substantial heat. In Figure 3c, the authors identify single-unit activity among multiple spikes, but it is not clear what these spikes are time-locked to – the onset of the stimulation or the offset of the stimulation. The authors also repeatedly mention using a “low” drive current; however, in their experiments they used 8.0 mA and 10.4 mA, which are relatively high currents and contradict the claim of a low-current drive. Additionally, it is unclear how the electrophysiological data were filtered and preprocessed, since one would expect evoked local field potentials given this optogenetic protocol, yet none are shown in their traces.\n\n10. The experiment in Figure 3f demonstrating spatial resolution is a valuable addition. However, it is not justified to compare the neuronal responses directly to the Monte Carlo simulation results to derive an “activation threshold.” This approach ignores important morphological considerations of pyramidal neurons: activation of ChR2 in neuronal processes (such as apical dendrites) can contribute to action potential generation at cell bodies, especially since the viral construct used here does not have subcellular specificity. Consequently, the large apparent depth of neuronal activation shown in Figure 4g is likely due to the activation of ChR2 in apical dendrites in the superficial layers. Thus, it is very misleading that the authors’ figure suggests ~1 mW of 450 nm light can reach approximately 500 µm deep into layer 4, as this depiction overlooks the contribution of dendritic activation in superficial layers.\n\n11. The behavioral experiments presented here do not clearly showcase the capabilities of the technology. They still require a head-fixed setup, a restraint tube, and a medial forebrain bundle (MFB) stimulator to motivate the animals during the tasks. Again, the authors used a 20 Hz stimulation with a 50% duty cycle for these behavioral experiments, which compromises the inherently high temporal resolution advantage of optogenetics. In summary, the behavioral results do not demonstrate a clear benefit of the micro-LED array approach compared to existing optogenetic methodologies for similar experiments.", "reviewer_3": "General comment\nGreer et al. present a μLED-based optogenetic stimulation system designed for chronic cortical surface implantation in mice. This study is notable for its comprehensive in vivo validation, integrating acute electrophysiology to assess neuronal activation and chronic implantation to evaluate long-term functionality and behavioral effects. The dual experimental approach strengthens the study’s relevance in neural interface research.\nWhile the μLED array is designed for high spatial resolution, the reported optical spread raises questions about the precision of neuronal targeting. Additionally, electrophysiological recordings were limited to a single probe insertion, making it unclear whether the findings generalize across the full μLED array. Lastly, despite being intended for chronic use, the study does not include direct biocompatibility testing such as histological analysis of immune response or tissue damage. Addressing these aspects would further strengthen the validation of the system. Therefore, the following points should be considered before publication.\n\nMajor comment\n1. As highlighted in the abstract and title, one of the main claimed advantages of this paper is the device's suitability for chronic implantation due to its flexibility. However, the device presented in the manuscript utilizes a sapphire wafer with a stated thickness of 150 µm for the implant device. Given the rigidity of sapphire, there is concern whether sufficient flexibility has truly been achieved to justify this claim.\n\n2. Additionally, given the expected high Young’s modulus of the described sapphire-based device, it likely exhibits limited flexibility, resulting in poor conformal contact with the brain's surface. Given this rigidity, the presence of wrinkles or folding—necessary for flexibility—seems unlikely. Therefore, it raises concerns regarding the precision and accuracy when claiming targeted stimulation in specific brain regions. Clarification and supporting data on the device's ability to accurately target stimulation despite potential mechanical mismatch would be beneficial.\n\n3. Similarly, due to the rigidity of the device, issues related to micro-motion after implantation are likely to arise. It is expected that prolonged implantation of this rigid device may lead to damage in the brain tissue. Although the authors have confirmed device functionality post-explantation, the manuscript lacks information regarding potential damage to the brain. Therefore, histological data demonstrating the degree of tissue damage following device implantation should be provided. Additionally, despite describing an implantable device, the authors have not included data regarding immune responses, which should also be addressed.\n\n4. Further data related to temperature management appear necessary. Duty cycle and frequency can significantly vary depending on the experimental conditions employed. Additional data specifically demonstrating temperature variations under different experimental conditions, such as varying duty cycles and frequencies, would enhance the robustness of the results and the claims made in this study.\n\n5. In the current experimental setup, the authors investigated temperature changes with only 10 pulses of LED activation. However, if prolonged, continuous operation occurs beyond these conditions, it is expected that temperature would continually increase. Therefore, it remains unclear whether the device can safely operate for extended periods, such as 100 seconds, without encountering significant thermal issues. Additional data evaluating the device under prolonged LED activation conditions should be provided.\n\n6. In the presented lifetime data, the sequential activation of ten LEDs, each with a 1-minute interval between activations, does not adequately reflect typical in vivo scenarios where multiple LEDs might operate simultaneously or with shorter intervals. Consequently, the reported lifetime results may not accurately represent practical usage conditions. Additionally, simultaneous use of multiple LEDs could significantly raise local temperatures, potentially decreasing device lifetime. A detailed discussion addressing this scenario and its implications on device performance and longevity is warranted.\n\n7. Following the above points, the manuscript does not adequately address the implications of the inability to simultaneously operate all LEDs. If simultaneous activation is restricted due to thermal or other limitations, this undermines the claim of effectively covering a large area. Thus, further discussion and evidence are required to substantiate how the device achieves broad-area coverage when limitations on simultaneous LED operation are considered.\n\n8. The study presents compelling evidence that μLED-based optogenetic stimulation can modulate behavior through artificial perception. However, the exact duration of the behavioral training period is not clearly stated in the manuscript. Given that Figure 5(d) shows a learning curve spanning multiple sessions, it would be helpful to specify how many days or weeks the behavioral training lasted. Please clarify the total duration of the behavioral training in the Methods section, including the number of sessions per day and whether any criteria were used to determine when training was complete. This information will help readers better understand the timescale required for optogenetic learning and its potential for long-term applications.\n\n9. The manuscript claims that the μLED array provides high spatial resolution, with a 200 μm pitch enabling precise neuronal targeting. However, in Figure 4, the reported light spread (600–800 μm) suggests that individual μLEDs may activate overlapping neuronal populations. This raises the question of whether the effective resolution is truly determined by the μLED pitch, or if light scattering imposes limitations on selectivity. Additionally, while the study provides an estimate of spatial resolution based on electrophysiological data and optical modeling, a direct comparison with previous optogenetic stimulation technologies (e.g., fiber-optic stimulation, lower-density μLED arrays) is not clearly presented. Given that one of the key claims of this work is improved spatial precision, it would be beneficial to include a quantitative comparison of spatial resolution with prior studies using different optogenetic stimulation methods.\n\n10. The authors highlight the high spatial resolution enabled by the 10×10 μLED array. However, if neuronal responses were recorded from only a single insertion site using a single 64-channel electrode probe, this significantly limits the validation of the claimed spatial resolution. The potential advantage of the μLED array—precisely targeting different neuronal populations—cannot be fully demonstrated if only a small region is sampled. Clarifying whether multiple insertion sites were used across experiments or discussing how the recorded neuronal responses generalize across the full μLED array would provide stronger evidence supporting the advantages of the high-density μLED system.\n\nMinor comment\n1. The study demonstrates that μLED stimulation can modulate neuronal activity and behavior. However, it remains unclear whether the frequency or temporal structure of the μLED pulses influences neuronal responses differently. Further discussion on how different stimulation frequencies affect neuronal activation and behavioral performance would provide deeper insight into the functional implications of the stimulation paradigm.\n\n2. The manuscript outlines the positioning of the μLED array on the cortical surface and the insertion of a 64-channel electrode probe at a 28° angle. However, it does not provide sufficient detail on how the electrode was aligned relative to the μLED array to ensure accurate mapping of neuronal responses to specific stimulation sites. Given the study's emphasis on spatial resolution, further clarification on the alignment strategy would strengthen the interpretation of the recorded neuronal activity.\n\n3. The authors describe the use of Parylene-C, Polyimide, and Silicone for chronic implantation, as these materials are commonly used in neural interfaces. However, the manuscript does not provide any direct biocompatibility testing or histological analysis to confirm the absence of immune responses or tissue damage over time. Given that the μLED array is designed for long-term implantation, including supporting data on tissue response or referencing previous biocompatibility studies using similar packaging materials would strengthen the validation of its chronic use.\n\n4. The multi-unit analysis in Supplementary Figure 10 effectively demonstrates how μLED stimulation modulates neuronal activity across different cortical depths. This analysis provides valuable insight into the spatial extent of optogenetic activation, which many readers may find crucial for understanding depth-dependent electrophysiological responses. Given its importance, incorporating this data into a main figure would improve accessibility and further strengthen the discussion on the spatial resolution of μLED-driven neuronal activation."}, "author_response": "Thank you for highlighting these aspects on the spatial resolution. We have expanded the discussion to include approaches that improve the spatial resolution through the removal of the substrate material (reducing the source-target distance). The paragraph has also been restructured to better contextualise our approach with recent literature.\n\n“The µLEDs are fabricated on a thinned sapphire substrate, therefore maintaining stress-matching to the µLED quantum well structures, resulting in high-intensity sources capable of driving robust optogenetic responses in mouse cortex from a single site or spatiotemporal patterns. The trade-off is a reduction in spatial resolution due to the 150 µm standoff between source and brain surface. This can be reduced by using top-emitting devices or removing the LED structures from their substrates, either through laser lift-off processes or through selective chemical etching (Klein et al. 2019). The latter offers fully flexible interfaces and can both deliver reduced source-tissue proximity and similar stimulation site spacing (Sekiguchi et al. 2022, Gu et al. 2024). However, device quantum efficiency is compromised and further in-vivo validation of these approaches need to confirm the µLEDs can achieve sufficient intensity for behavioural outcomes as well as long-term, chronic stability.”\n\nComment 2\nRelated to the prior point, the simulation and schematics always appear to show the bare device, without encapsulation. In the methods, we learn that the parylene coating is 15-30µm in thickness, and there is a further silicone coating of unspecified thickness. The authors need to specify the exact geometry and should show this in the schematics as well.\n\nThank you for highlighting this. We did vary the encapsulation depending on the device configuration and experiment, which was not fully explained in the manuscript. For the acute device used for electrophysiology experiments, we used a 7 rim parylene-C (PaC) coating. For the chronic devices which were implanted directly on the brain surface without a cortical window, we increased the PaC thickness to 15 rim. We then explored how to further improve the encapsulation of the chronic devices by using two 15 rim PaC coatings (totalling 30 rim) and a silicone dip coating, which were evaluated in accelerated testing only. With the exception of Mouse 2 in the freely-behaving experiments, which had a chronic implant with the improved encapsulation (30 rim PaC + silicone), all in-vivo behavioural experiments used chronic devices with the standard 15 rim PaC and no silicone. As a result, the optical models were designed to reflect the two main encapsulant designs: 1) a 15 rim PaC layer in front of the sapphire for chronic experiments (e.g. Fig. 4e, Fig. 4g); 2) a 7 rim PaC used in the acute device for the electrophysiology experiment, which included a cortical window (Fig. 4c). The silicone encapsulation process (now properly detailed in the methods) was designed to avoid silicone encapsulation on the sapphire (emitting) surface and so plays no role in the light transmission to the brain surface and is not included in the optical models. We address this in the manuscript as follows:\n\nSchematics of device cross-section have been updated throughout the report (Fig. 1e, Fig. 4c, Fig. 4e, Fig. 4g, Supplementary Fig. 3a, Supplementary Fig. 16c) to include the 7 rim or 15 rim PaC used in optical models.\n\nFig. 1e caption has been updated to include more detail on model.\n“Model reflects chronic implantation with 150 iim sapphire and 15 iim parylene-C (PaC).”\n\nFig. 4c and Fig. 4d captions updated to clarify different PaC thicknesses.\n“(acute device model with 7 iim parylene-C)” and “(chronic device model with 15 iim parylene-C)” respectively.\n\nSupplementary Fig. 3a caption also updated.\n“7 μm parylene-C (PaC) modelled for acute device (with cortical window) and 15 μm PaC modelled for chronic device (implanted directly on brain tissue).”\n\nText in Results relating to device lifetime testing has been updated to improve clarity.\n“Subsequent packaging steps include a polyimide PCB, medical-grade epoxy underfill and 15 iim parylene-C (PaC) conformal coating. For accelerated testing, an enhanced encapsulation strategy consisting of an additional 15 iim PaC coating and medical-grade silicone dip coating was evaluated.”\n\n“Other encapsulation strategies were evaluated (Supplementary Fig. 8b), with 30 iim of PaC and silicone providing the best performance.”\n\nMethods (riLED array packaging) text has also been updated to clarify the different encapsulations as well as to detail the silicone dip coating procedure fully along with an additional panel (Supplementary Fig. 4c).\n\n“The system is coated with 15 iim of parylene-C (PaC). We further explored the encapsulation procedure in accelerated tests only by adding an additional layer of PaC (15 iim) as well as a medical-grade silicone dip coating (NuSil MED-2214). Supplementary Fig. 4c shows the steps involved in applying the silicone dip coating. For our devices, a silicone primer (NuSil MED-161) was firstly applied using a small bristle brush and allowed to dry for one hour. A small piece (3 x 3 mm2) of UV-sensitive dicing tape was attached to the sapphire surface of the device. The device was fully submerged in MED-2214 which had been decanted into a beaker. While submerged, the beaker was rotated slowly several times with the device held at different angles, before carefully lifting the device out and leaving it suspended on a drying rack for 10 minutes at room temperature until the silicone partially cured. A second coating was applied in the same manner as before and the device suspended on a drying rack, this time above a hotplate set to 50 °C, for one hour. The dicing tape was exposed to UV light and peeled off, so the sapphire surface was clear but the rest of the device was encapsulated; this was necessary to minimise standoff distance between the µLEDs and the brain surface. A final cure was carried out at 150 °C until the silicone had fully cured.”\n\nSupplementary Figure 2 (c) Steps for medical-grade silicone dip coating encapsulation. Applying UV-sensitive dicing tape (i). Dip 1 + beaker rotation (ii). Dry 1 (10 mins, room temp) (iii). Dip 2 + beaker rotation (iv). Dry 2 (1 hour, 50 °C) (v). UV exposure and peel back dicing tape (vi). Final dry (150 °C, until rubber) (vii).\n\nMethods (Optical modelling and characterisation) text has been updated to clarify parylene-C thicknesses.\n“The model includes a 2 mm square sapphire substrate (150 µm), parylene-C layer (7 µm for acute device with cortical window or 15 µm for chronic device directly on brain tissue), optional cortical window (150 µm) and auditory cortex.”\n\nComment 3\nIn Figure 3e and other data, the variation of neuronal response to the µLED stimulation appears very large (the average spike counts between experiment #3 and #4 appear to differ by almost a factor of four). Is this to be expected? Would the variation be similar for external stimulation, e.g. with a DMD or is this the result of heterogeneity from the device?\n\nThe variability of neuronal responses in Fig. 3e is indeed large, but this is expected because extracellular electrophysiology recordings are intrinsically variable and because the distance between the electrode insertion site and the µLED array varied from experiment to experiment by roughly ±200 µm (Supplementary Fig. 10a), leading to variance in the µLED to electrodes distances. The sources of variability in population firing rates in this figure are therefore: the health of the tissue which depends on multiple factors of the preparation (quality of the craniotomy, fresh insertion or after a few days) and the variation in position of the electrode array with respect to the µLED array. We observed similar variability in recordings made with DMDs in the Bathellier lab. The same device was utilised throughout all electrophysiology experiments and experienced some degradation in optical performance, however we quantified this by monitoring light output versus drive current before and after the experiments and factored this into the error bars shown on, for example, Fig. 3e. Also, the device was current-driven during experiments, which means even if there are fluctuations in electrical performance the voltage is modulated to ensure consistent optical power is delivered to the brain.\n\nThe exact horizontal alignment of the array with the cranial window varied slightly also. We updated the results to explain this variability:\n\n“Variation in the neuronal response between experiments could be due to variation in the relative positioning of the recording electrode array to the µLED stimulation site (Supplementary Fig. 10a), the flatness of the µLED array placement on the cortical window, or the overall electrode array insertion quality and surrounding tissue health. Improved consistency between experiments is possible with removal of the cortical window and with our chronically implantable devices, where the supporting polyimide PCB is flexible, making it easier to fix into position.”\n\nComment 4\nLikewise, the variation in average spike count for different stimulation regions across the device is dramatic, with some regions showing nearly no response. The authors should elaborate more on the origin of this effect. It remains unclear if this is related to the location of the recording device or intrinsic to either the illumination device or the animal model.\n\nIn our electrophysiology experiments, we use a single shank linear recording electrode array to measure neuronal responses under stimulation from individual µLEDs across the device. Despite driving responses across the region underneath the µLED array, we were only able to detect responses from a subset of neurons and ensembles clustered nearby the recording electrodes (see Supplementary Fig. 10a), giving rise to the non-homogeneous single-unit spiking response shown in Fig. 4a, which is averaged from the activity of all detected neurons when each µLED is illuminated individually.\n\nThese experiments allowed us to record individual neuron response being driven as a function of space, thereby establishing a spatial resolution, or width of activation by an individual µLED. For this study a single electrode shank configuration was sufficient. Thank you for highlighting this, we have clarified this point within the Results section:\n\n“The spatial resolution was inferred from the full width at half maximum (FWHM) of these fits. Due to the use of a single shank recording electrode device with fixed position, spatial resolution estimates could only be obtained from µLEDs located in rows near the edge where the electrode array was inserted.”\n\nComment 5\nRelated to this point, given the broad audience at Nature Communications, the methods used to pinpoint the location of active neurons from the limited spatial data provided by the ephys shank, which only intersects Layers 2/3 in a narrow region, should be explained, at least briefly.\n\nThroughout electrophysiology experiments, we used transgenic mice (Emx-IRES-Cre x Ai27d) which express ChR2 in all excitatory neurons, most of which are pyramidal neurons. There are a high density of pyramidal neurons and dendrites in layer 2/3 of the auditory cortex where we targeted our optogenetic stimulation and neuronal recordings. The auditory cortex was identified using anatomical landmarks for surgical implantation of the cortical window, which a hole was inserted in and the recording electrode array shank inserted for electrophysiology. We inserted the shank at an angle of 28° to the surface of the cortex, with the probe extending to a vertical depth of approximately 680 µm. Using micrographs, the lateral position of the recording electrodes with respect to the stimulation sites in each experiment was also approximated (Fig. 3a, Fig. 3b, Supplementary Fig. 10a). Therefore, analyses surrounding thresholds and spatial resolution focussed on µLEDs located nearby the recording electrodes. We have updated the methods (Acute in-vivo electrophysiology experiments) to provide more detail on this.\n\n“The electrode array extended to a vertical depth of ~680 µm corresponding to the electrode located furthest from the shank tip being positioned on the cortical surface. This allowed estimation of the cortical layer being recorded from and the lateral distance of the recording electrodes from stimulation sites (Fig. 3a, Fig. 3b, Supplementary Fig. 10a, Supplementary Fig. 17).”\n\nWe then carried out spike sorting to attribute the recorded electrical activity to individual neurons, and determine whether responses were time-locked to the optogenetic stimulus through a statistical threshold. The spiking activity of these neurons (average spikes counted per trial) allowed quantification of neuronal activity as a function of µLED drive current and, by assuming the neuron as a single point, the spatial extent of activation by a single µLED. We have further elaborated on spike sorting within the results.\n\n“Spike sorting was used to attribute the recorded electrophysiology signal to the neuronal firing activity of individual neurons (Fig. 3c right) allowing us to determine if neuronal responses were time-locked to the optogenetic stimulus and under what conditions. For the following analyses, only single unit activity is considered.”\n\nComment 6\nFinally, how would the approach described here be scaled in the future, both in terms of device size (for work with larger animal models, a 2x2mm2 device will not be useful) and in terms of achievable resolution (where in addition to the challenge of getting the light sources closer to the target region or finding other means of minimizing light spreading, see above, the number of µLEDs will need to be scaled dramatically). For instance, keeping the same pitch on a 10x10mm2 device would require a 25-fold increase in number of addressable LEDs and substantially larger InGaN on sapphire samples. Would this be practical at some point?\n\nTo demonstrate how our µLED array-based optogenetic stimulation approach could be scaled, we integrated four µLED arrays onto a flexible PCB arranged in a line, separated by 1 mm gaps (Supplementary Fig. 5).\n\nSupplementary Figure 5: Flexible, chronically implantable device integrating 4 µLED arrays arranged in a line, separated by approx. 1 mm gaps.\n\nWe fabricate the µLED arrays at scale (220 arrays per 2” wafer) (Supplementary Fig. 1b) and our flip-chip bonding process allows several µLED arrays to be packaged to a single flexible PCB, where the geometry could be custom-designed for a particular neuroscience experiment or animal model. This is a clear advantage of our packaging approach which allows a number of 10x10 µLED array chips to be tiled such as the one demonstrated (Supplementary Fig. 5). While each 2x2 mm2 chip is rigid, the PCB is flexible and could allow conformability over a larger curved/contoured surface area. In the 10x10 mm2 example, we would integrate 25 µLED arrays to a flexible PCB and interconnection scalability could be addressed by multiplexing circuitry on the PCB. We have expanded the discussion to re-iterate this advantage and refer to the new device shown.\n\n“Furthermore, flexible, biocompatible packaging enabled chronic implantation in the mouse model and scalability through potential integration of several µLED arrays tiled on a single device (Supplementary Fig. 5). This could facilitate experimentation across brain regions or in larger animal models such as non-human primates. This potential for rapid scalability and device customisation, along with robustly established stimulation parameters and in-vivo stability makes this an ideal platform for chronic studies into cortical function and the targeted transfer of information to the brain.”\n\nRegarding minimising light spreading, we have discussed different approaches with the most promising being the optical interposer, which we had modelled (and since experimentally validated – see response to Reviewer 2, comment 4). These interposers are simple to fabricate at scale as they rely on standard silicon processing techniques. They can then be individually integrated to each µLED array across a tiled device."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have provided a carefully revised manuscript which discusses and as best as could be expected addresses the issues I brought up in my previous report. I would only ask that, for clarity, the following part is amended as follows to avoid any ambiguity about whether these additional steps have already been implemented (line 377):\n\n“The trade-off is a reduction in spatial resolution due to the 150 µm standoff between source and brain surface. IN THE FUTURE, this COULD be reduced by using …” (or of course similar wording that clarifies this point)\n\nApart from this minor change (that will not require me to look at the manuscript again), I recommend publication of the study as is.", "reviewer_2": "In the revised manuscript, the authors carried out new experiments demonstrating the use of their device in freely behaving behavioral paradigms, and they expanded the discussion to better position their work relative to other existing technologies. From these perspectives, the revised document is appropriate in terms of significance. The authors have now addressed what they originally claimed as a major technical advantage (freely-behaving experiments) and have more clearly contextualized their contributions within the broader literature.\n\nFurthermore, the authors included experimental validations of their prior optical and thermal simulations. While these validations remain indirect — for example, relying on IR imaging in air or water rather than in vivo, or optical measurements without considering tissue scattering and absorption — they are reasonable given the technical challenges of in vivo validation and the supporting role these data play for the core technological contributions.\n\nOverall, I am positive about this work. Many of the previous major points have been addressed, and the technical advancement has strong potential for broad impact in the field. However, I have several remaining concerns regarding specific experiments and internal consistency in the revised manuscript. These issues should be clarified to improve scientific rigor and highlight important caveats.\n\nRemaining Concerns\n1. The addition of freely behaving behavioral experiments is exciting. However, it is unclear whether opsin-negative control mice were included. If these experiments were conducted, the results should be shown; if not, please justify their omission (noting that some other experiments already included controls).\n\n2. Most behavioral tasks used 3×3 LED activation patterns rather than single-pixel stimulation. Is this because individual LEDs within a 3×3 block cannot be independently addressed simultaneously due to PN junction wiring? If so, this limitation undercuts the repeated claim of spatial precision. Please clarify and justify this when presenting the behavioral experiments.\n\n3. Most in vivo experiments were conducted at ~10 mW/mm² at the cortical surface, which would yield >1 °C heating based on the authors’ own simulations. The rebuttal argues that because cortical windows are absent in chronic implants, the same irradiance can be achieved at <3 mA per LED (Supplementary Fig. 1d), implying this is safe. This reasoning is flawed: removal of the window decreases the source–tissue distance and can increase heating at the interface, even if the driving current is lower. This caveat should be explicitly acknowledged.\n\n4. Throughout the manuscript, the authors emphasize the accuracy of their optical and thermal simulations. Given the simplifying assumptions (water model, omission of absorption, dendritic contributions, etc.), it would be more accurate to present these as approximations that may over- or under-estimate in vivo outcomes. Please moderate claims of “accuracy” and frame results as approximate guides with caveats.\n\n5. The histological analysis raises questions. When layer 1 is included, the implanted side shows only a modest increase in Iba-1+ cells (228 ± 24 vs. 181 ± 18 per mm²), albeit statistically significant. Excluding layer 1 increases the numerical difference but eliminates statistical significance. The use of a non-parametric Wilcoxon test may not capture this nuance appropriately.\n\nThe overall Iba-1 signal appears surprisingly low given that the dura was removed and the device directly contacted cortex. Was tissue collected long after implantation, potentially missing acute responses? Please indicate the interval between surgery and tissue harvest.\n\nAdding GFAP staining (astrocyte marker) would provide an important complementary measure, particularly given the low microglial response reported.\n\n6. Supplementary Fig. 10b shows that filtering introduces potential artifacts, and the apparent spiking activity may in part reflect residual LFP responses after filtering. The single-unit spike sorting (Fig. 3c, right) is therefore not fully convincing. It would strengthen confidence if the authors showed examples of clearly identified spikes in raw, unfiltered traces at higher temporal resolution (e.g., 10 ms windows), even if superimposed on LFPs. While the LFP responses themselves suggest successful opsin activation, the claims of single-unit resolution need clearer support.\n\n7. The choice of 20 Hz at 50% duty cycle is not ideal: it imposes a higher thermal load and diminishes the temporal precision that is a key advantage of optogenetics. Please explicitly note that this protocol was chosen as a proof-of-concept for behavioral salience, but is not optimal for optogenetic control.\n\n8. It remains difficult to track which encapsulation scheme (material, thickness, coatings) was used in each experiment. Please provide a supplementary table summarizing encapsulation conditions across all experimental contexts for clarity.", "reviewer_3": "The concerns regarding the behavioral experiments, LED operational parameters, and temperature increase appear to be well addressed. However, my primary and most significant concern, which was initially raised regarding the device's suitability for chronic applications, remains. The rigidity of a device is largely determined by its thickness and the Young's modulus of its constituent materials. In our view, the device's thickness and materials are not suitable for it to be considered a chronic implant. Furthermore, it is difficult to obtain precise information about the chronic in-vivo experiments conducted to resolve this issue, and we have reservations about whether the immune response data has been interpreted correctly.\n\nFurther discussion on these points is required.\n\nRegarding Comment #1\nOur concern regarding the device's lack of 'flexibility' from its rigid sapphire substrate was misunderstood. The response incorrectly addresses surgical 'positioning' instead of this fundamental material limitation. The use of a rigid sapphire substrate makes any discussion of flexibility meaningless. Because the justification provided does not resolve this issue, the response is insufficient. Please provide a revised answer focused on the material's rigidity.\n\nRegarding Comment #2\nWe question the authors' assertion that the 2x2 mm² cortical area is 'flat.' This claim is directly refuted by the authors' own data in Supplementary Figure 2(a), which clearly shows tissue deformation at the implant site caused by the rigid device. A visible difference in compression exists between the implanted and non-implanted hemispheres, which would not occur if the surface were truly flat. Conformal contact with soft tissue is governed by bending stiffness, not the topography of the target area. Given the high stiffness imparted by the sapphire and the device's thickness, the claim that it can achieve conformal contact on the soft cortical surface is not well-supported. This comment has not been satisfactorily addressed.\n\nRegarding Comment #3\nThe response lacks information on the total implantation period for the histology study, a critical parameter for the authors' claims. To validate the device as a 'chronic implant,' the immune response must be assessed following a genuinely long-term implantation. This essential information has not been provided, which weakens the claims of chronic biocompatibility. The duration needs to be explicitly stated."}, "author_response": "The authors thank you for reviewing our rebuttal letter and for your feedback on our revised manuscript titled “Chronically implantable µLED arrays for optogenetic cortical surface stimulation in mice” for publication in Nature Communications. We were encouraged by the feedback and have addressed the remaining concerns in this document and in a revised version of the manuscript and supplementary material document. Please do not hesitate to reach out to the corresponding authors (Ryan Greer or Keith Mathieson) if you have any further questions or comments.\n\nReviewer #1\nThank you for highlighting this, the discussion text has been updated as follows:\n“The trade-off is a reduction in spatial resolution due to the 150 µm standoff between source and brain surface. In the future, this could be reduced by using top-emitting devices or removing the LED structures from their substrates, either through laser lift-off processes or through selective chemical etching”\n\nReviewer #2\nComment 1\nWe did not include control mice for the new freely-behaving experiments as we had confirmed there were no confounding effects (such as thermally-induced neuronal firing) influencing behavior in the head-fixed experiment. Since the implantation and stimulation protocol were very similar (3x3 patterns up to 10 mW/mm2), and to minimise the number of animals used, we decided that further control experiments were not essential. However, we did improve the controls within the experiments by using a masking light (external light providing bright conditions that desensitizes the retina) in these experiments, improving upon the head-fixed experiment and mitigating the possibility of the mouse visually perceiving the light from the device. We also note that the experiment is a discrimination task with identical illumination patterns activating spatially separated locations on the cortex, which also provides an element of inherent control within the experiment.\n\nThank you for highlighting this. We have now included the necessary detail in the results.\n“Control (opsin-negative) mice were not included as the stimulation protocol used here (Fig. 6c) was identical to the head-fixed behavioural experiments - these already confirmed successful optogenetic activation and no confounding effects influencing behaviour. Per-µLED cortical irradiance was set at 10 mW/mm2 and progressively reduced to 1 mW/mm2 in the cases where the behavioural outcome accuracy was maintained at a strong level (typically >80%) across sessions.”\n\nComment 2\nFor all behavioural experiments reported, we utilised 3x3 µLED activation patterns. This was not due to any limitation in the device wiring, as individual µLEDs can be illuminated; although diagonal patterns require more complex control due to matrix addressing (Supplementary fig. 9d). We chose 3x3 patterns to activate a broad region, replicating previous work in the Bathellier lab where a Digital Light Projector (DLP), illuminating spots of 400 µm diameter, was used for optogenetic activation of the mouse auditory cortex to drive behavioural outcomes (Ceballo, Bourg et al. 2019; Ceballo, Piwkowska et al. 2019). Here, we adopted a similar approach by activating a broad cortical surface area using a 3x3 pattern instead of individual pixels. We felt that this was correct approach to start with, as these were our first studies using the µLED arrays for behaviour. We have provided additional clarification on this when introducing the behavioural experiments.\n\n“Although the device facilitates individual pixel operation, a 3x3 square of µLEDs was used to illuminate one corner of the diagonal depending on the trial type (go or no-go). This achieved a broad area of illumination and was based on previous experiments using a digital light projector (DLP) system to illuminate 400 µm diameter regions of the AC, to drive behavioural outcomes in ChR2-expressing mice (Ceballo, Bourg et al. 2019; Ceballo, Piwkowska et al. 2019).”\n\nComment 3\nWe absolutely agree with the referee’s comment that reducing the source-tissue distance increases the thermal effects from the implant, and at no point did we mean to suggest otherwise. We perhaps did not make this point clear in the text and rebuttal and apologise for any confusion. We have clarified our reasoning as follows.\n\nThe cortical window experiments used single pixel illumination, which requires significantly less power than patterned stimulation. As can be seen in Fig 2b, single pixel illumination for all currents used never heats the tissue interface by a ∆T of 0.5 °C. This thermal modelling (and all of the thermal modelling we conduct) is for our worst-case scenario, which is direct implantation on the cortical surface without any cortical window. We only modelled direct implantation because we were also worried about the greater thermal risk in this case. Conclusions drawn from thermal modelling in the context of cortical window experiments simply ignore effect of the window as a thermal insulator. Therefore to improve the clarity of the text, we remove the mention of the cortical window in the sections relating to thermal effects.\n\nComment 4\nThank you for highlighting this. For our thermal analysis, we apply simplifications in our model of the brain, using the thermal coefficients for water, and have updated the text to emphasise this.\n\n“Appropriate material thermal properties were applied, with the brain approximated as water. To verify the accuracy of the modelled device, the sapphire surface of the µLED array was imaged using an IR camera in air (Supplementary Fig. 6), which agreed closely with the modelled output (Fig. 2a). These experiments, along with previous work demonstrating the accuracy of this model architecture with in-vivo IR imaging (McAlinden et al. 2024), gave us confidence in the thermal models to provide a good approximation of experimental conditions.”\n\nComment 5\nWe agree with the referee that it is difficult to capture the statistical nuances in the histological analysis. However, we used a non-parametric test because the data did not pass commonly used normality tests (Lilliefors, and Cramer-Von Mises tests), possibly due to the low sample size. In this case, the standard and conservative procedure is to use a non-parametric test such as the paired Wilcoxon signed rank test, which was not significant when excluding layer 1 from the analysis. We agree with the referee that there are two measurements in the “excludes layer 1” panel, which indicate a significantly larger lba-1+ cell count on the implanted side. However, the data does not all trend in the same direction when the implanted side is compared with the contralateral side, which reduces statistical significance across the sample population. There is also variability in staining across the slice (visible in the example shown), which is likely to complicate interpretation further.\n\nThe lba-1 signal is low because the tissue samples were collected between 83 and 101 days after implantation since we completed an extensive series of behavioural tasks. In summary, after implantation, mice were allowed to recover for 1 month and then behavioural training/experiments (5 days per week) lasted between ~50-70 days. Our measurements reflect the long-term impact of the µLED array after full operation, which we feel is most relevant for future users. Note also that the behavioural performance was maintained at a high level until the tissue was harvested, indicating the excellent stability of the cortical network over more than 2 months of training with the µLED arrays. We edited the manuscript to provide this important piece of information:\n\nComment 6\nPrior to spike sorting, we use a standard approach, applying a filter with a wide passband [300 Hz, 5.5 kHz] which removes the LFP component which we do not analyse, but the small spiking signals (~1-3 ms in length) should remain. This can be seen in the raw, unfiltered trace which we added (Supplementary fig. 10b) which corresponds to the same recording channel and time window as Fig. 3c where we highlight single unit spike times identified from spike sorting, and their average waveforms. However, it should be noted that spike sorting algorithms identify spikes by considering the signal across several channels nearby to the neuron and typically using a principal component analysis to extract the salient features. Supplementary fig. 10b has been updated to show a zoomed inset, and a further zoom to a 10 ms time window to clearly show spikes embedded in the unfiltered trace.\n\nComment 7\nThank you for highlighting this. We appreciate that the chosen optogenetic stimulation protocol is not ideal and provided a detailed justification for this in our response to comment #9 from the initial review, but we did not add this to the manuscript. Stimulation protocol timing was based on parameters previously established in the Bathellier lab to achieve robust neuronal signalling and behavioural responses (Ceballo, Bourg et al. 2019; Bagur et al. 2025). We have now provided this justification in the manuscript when introducing the experimental protocol in both electrophysiology and behaviour sections, and re-emphasised this in the discussion.\n\nComment 8\nWe have now included Supplementary table 5 which summarises the packaging details for devices used in all in-vivo experiments, and refer to this in the text. Thank you for highlighting this.\n\nReviewer #3\nComment 1\nThank you for highlighting this point again. We agree that due to the material and thickness of the µLED substrate (150 µm sapphire), the stimulation region of the device cannot achieve flexible, conformable contact with the brain surface. Previously, we updated the text to clarify that the stimulation area of the µLED array remains rigid, constrained by the GaN-on-sapphire fabrication process, but the supporting polyimide PCB packaging is flexible. The flexibility of the packaging is a valuable attribute of our devices for the surgical positioning and skull fixation, allowing minimally-invasive positioning of the µLED array within a compact craniotomy (equivalent to the size of the device, ~2.2 x 2.2 mm2), and helping to facilitate chronic implantation and studies in behaving mice. We understand the referees point around possible confusion with devices that utilise fully flexible components as the neural interface, which is a distinct approach that is rightly gathering much attention in the field. As such, we agree that some readers may miss the fact that our µLED array is rigid, especially since the detailed device description of device material is after the introduction. Therefore, we have updated several parts of the text where the term “flexible” is mentioned, and either removed its use, or fully clarified that we refer to the package and not the µLED array itself.\n\nComment 2\nThank you for highlighting this. It was not our intention in the rebuttal to suggest that the cortical surface is flat; rather the small size of the µLED array (2x2 mm²) and its planar geometry mean that the curvature mismatch with the brain is reduced. When introducing the histology data, we do mention that there is plastic deformation around the implant site comparable to the geometry of the device. We rely on the device's stiffness to maintain its shape against the brain’s soft tissue. From a surgical standpoint, we believe that the discrepancies between the curvature of the brain and the rigid µLED array at this scale are small enough that it can be resolved by applying reasonable amounts of pressure on the implant during surgery, as is the case with cranial window implantations (Holtmatt et al. 2009). This rigidity thus allows for robust contact between the cortical surface and the µLED arrays as well as ensuring that the implant geometry is not modified by the brain tissue. Within the manuscript, we do not describe this as conformal contact, and indeed the word “conformal” never appears in our manuscript in this context. We have expanded the text to better explain this constraint and further distinguish our device from flexible neural interfaces.\n\nComment 3\nThe tissue samples were collected between 83 and 101 days after implantation in three animals that underwent behavioural training with the device. After implantation, mice were allowed to recover for 1 month (necessary for AAV virus expression) and then behavioural training (5 days per week) lasted between ~50-70 days. Our measurements reflect the long-term impact of the µLED array after full operation, which is most relevant for future users. Note also that the behavioral performance was maintained at a high level until the tissue was harvested, indicating the excellent stability of the cortical network over more than 2 months of training with the µLED arrays. We edited the manuscript accordingly as discussed in response to reviewer 2, comment 5."}]

Decision letter