Very-large-scale mimetic optogenetic synapses for physical reservoir computing

Domain: physics
Published: 2026-01-10
Editorial outcome: major_revision

Abstract
The scaling law of deep learning, which governs the relationship between model size and performance, has led to critical concerns regarding efficiency and sustainability. To address these challenges, this study presents a computational approach using self-organized submillimeter-long tungsten disulfide nanotube cluster as a 3D very-large-scale physical reservoir. The reservoir, with its 0D van der Waals interfaces on the order of 10^8, or 1.0 × 10^10 mm^-3, matches the synaptic quantity and density of the fruit fly’s brain. The reservoir demonstrates the capability to perform a wide range of tasks from monomodal challenges to multimodal endeavors such as speech-to-image and medical image generation. The photosensitive mimetic synaptic connections in the very large scale reservoir emulate the optogenetic modulation of neuron circuits in in-vivo biological systems. By integrating the principles of the scaling law, multimodal task capabilities, and mimetic optogenetic mechanisms, this research paves a path toward advanced computing architectures tailored for next-generation energy-efficient artificial intelligence.

Introduction
In the dynamically evolving realm of artificial intelligence, the scaling law of deep learning has entrenched itself as a foundational concept 1–4. This scalability has fueled remarkable performance advancements across diverse domains, including computer vision, natural language processing, and multimodal learning paradigms 5–9. Empirically, the model’s accuracy in tackling complex tasks exhibits a positive correlation with its architectural scale. However, larger models suffer from high computational costs 10,11, which lead to low model efficiency and pose significant challenges to environmental sustainability. These concerns have prompted the exploration of alternative computing paradigms beyond the von Neumann framework 12–33, with physical reservoir computing (PRC) emerging as a transformative paradigm. Unlike conventional digital systems rooted in sequential logic, PRC fundamentally redefines computation by embedding information processing within the analog dynamics of physical substrates, marking a paradigmatic shift from abstract instruction-based operations to physics-driven computation 34–63. The essence of PRC lies in its ability to transform input signals through the inherent complex dynamics to produce computationally relevant outputs, leveraging the physical system’s intrinsic computational capabilities 17,46,64–66.

On the other hand, optogenetics, an experimental method at the intersection of genetics and optics, has dramatically transformed the field of neuroscience 67–70. Optogenetics enables researchers to control and monitor the activity of neurons using light. The optogenetics approach involves the cells that express light-sensitive proteins that can be activated or inhibited by light of specific wavelengths. This allows for the precise control of cellular signaling and has been employed to probe complex biological questions regarding a variety of behaviors and physiological processes, including movement, learning, memory, and sensory processing 67–70. While traditional optogenetics depends on genetic modification to express the light sensitive proteins, the essence is fundamentally about light manipulation, and the scope of this field has expanded to include non-genetic methods 71–78. Therefore, the optoelectrical response of the WS2 nanotubes and their interface allow light to manipulate projections inside the WS2 nanotube cluster, translating the principles of optogenetics into the PRC paradigm.

In this context, our research introduces a convergence of very-large-scale (VLS) computational physical reservoir with the feasible control afforded by light. The 3D reservoir, composed of 0D van der Waals interfaces on the order of 10^8 or 10^10 mm^-3, not only emulates the synaptic quantity and density of fruit flies’ brains but also integrates photosensitive mimetic optogenetic connections. A key strength of the VLS PRC system is its ability to handle a diverse range of tasks. It effectively supports both monomodal and multimodal endeavors including speech-to-image, colorectal tissue image generation, and blood cell image generation, while maintaining high energy efficiency with 7.0 μW per input. The physical reservoir is designed to respond to light stimuli, effectively translating the principles of optogenetics into the PRC paradigm. By integrating the scale of PRC, the capability for monomodal/multimodal tasks, the exceptional energy efficiency and the mimetic optogenetics, this interdisciplinary research provides a perspective on how optoelectronics, neuroscience, material science, and computational science merge to shape a future pathway for the next-generation neuromorphic computing hardware platform.

Results
As shown in Fig. 1a, scaled artificial neural network models have set benchmarks in tasks of computer vision, natural language processing, robotics, autonomous driving, scientific discovery, etc. By scaling up the number of parameters, deep learning models advance across various domains and enable more intelligent systems 79. The parameter volume of deep learning models has increased significantly in the past few decades, from the representative LeNet (60,000 parameters) 80 to GPT-4 (around 1 trillion parameters) 81. The increased number of parameters enables enhanced capability to process tasks of increasing complexity, and even the emergence of unpredictable intelligence 82–85, as shown in Supplementary Fig. 1. A similar pattern of the scaling law is found in the neural systems of biological organisms. Figure 1b shows the number of neurons and synapses of Caenorhabditis elegans, fruit flies, mice, orangutans, and humans. Extensive networks of neurons and synapses support more complex functions, such as abstract reasoning, problem-solving, language-using, social interaction, self-awareness, and deep emotional experiences 86–89. The research on biological neural systems has led to the development of optogenetics. As shown in Fig. 1c, by implanting optical sensitive ion channel proteins, the operation of the neural system can be manipulated with an incident light beam. The exposure to light selectively modulates the active or suppressed status of ion channels, thereby altering the membrane potential of neuronal cell membranes. This precise manipulation of neural cells has the capacity to shape and direct the behavior of living organisms, transforming the level of control researchers can exert over neural networks.

Drawing inspiration from the scaled biological neural networks and optogenetics, we propose a computational paradigm using a self-organized, submillimeter-long, photoreactive tungsten disulfide (WS2) nanotube cluster as a dynamic physical reservoir. The bottom-up synthesis progress and material characterization of the WS2 nanotube cluster are described in Method and Supplementary Figs. 2–5 90. The length of the nanotube is measured in the transmission electron microscope (TEM) with a maximum length of 0.5 mm and an average diameter below 50 nm. As shown by the scanning electron microscope (SEM) image in Fig. 1d and Supplementary Fig. 2, the synthesized ultra-long nanotubes aggregate into bundles, each of which is approximately hundreds of micrometers in size. These bundles coalesce into a larger cluster, resulting in a bundle-cluster hierarchical structure. Such a bi-level structure is verified by the micro X-ray computed tomography (CT) with a resolution of 5 μm. Figure 1e shows the shape and the cross-section of the cluster by the micro X-ray CT, showing the sub-structure with interspace and filled bundle blocks. The porosity of the cluster Φ is 32%. The sub-structure aligns with the hierarchical characteristics of large deep learning models and the architecture of brain networks which are adept at capturing distinct levels of features 91–93. Figure 1f displays the WS2 nanotube cluster on a substrate with fabricated electrodes, while the complete setup is illustrated in Supplementary Fig. 6.

The self-organized WS2 nanotube cluster provides intricate pathways through the numerous van der Waals interfaces. Here we evaluate the quantity of the van der Waals contacts by the WS2 nanotubes. The WS2 bundles are modeled using the random packing of 1D rods that form the numerous 0D interfaces, developed by Philipse 94,95 and refined in this study considering the hollow morphology and flexibility of the WS2 nanotubes. The model is elaborated in Supplementary Fig. 7 and Supplementary Note 1. The random packing volume fraction φ of 14% is derived using:

φ = m / (ρ V(1 - Φ)) (1)

where m and V are the mass and volume of the WS2 nanotube cluster, ρ is the density of WS2 (7.5 g·cm^-3), D and d are the outer and inner diameter of the hollow WS2 nanotube. According to the random contact model, we have

φ * L0 / D = <c> (2)

where L’ is the equivalent length of the nanotube with rigidness, D is the diameter of the nanotube, and <c> is the average contact per rod. Note that L’ is shorter than the actual length of the nanotube considering the long flexible nanotube as the combination of shorter rigid segments with an equivalent length of L’. For the thin segment, the average contact number is 5.4 94,95. From Eq. (1), the approximate value of L’ is obtained, which is 2.8 μm. We can further derive the contact density as 1.0 × 10^10 mm^-3 and the total contact quantity as 1.4 × 10^8 for the WS2 cluster in Fig. 1e with a volume of 1.4 × 10^-2 mm^3 extracted from the micro X-ray CT. Comparably, the brain of a fruit fly has a synapse density of 0.7 × 10^10 mm^-3, a volume of 1.4 × 10^-2 mm^3, and a total synapse number of ~10^8. The abundant connectivity in the WS2 nanotube cluster resembles the fruit fly’s synapses, providing the rich complexity needed for the scaled PRC. The electrodes and peripheral circuits are deliberately positioned outside the WS2 nanotube cluster to preserve the intrinsic density of the WS2 nanotubes’ cluster without disruption. This design ensures that the high-density van der Waals interfaces remain undisturbed while enabling electrical access to the reservoir. The electrodes, connecting gaps, and peripheral circuits in the system are constructed on the web surface and do not interfere with the number or functionality of the mimetic synapses. Importantly, the computational capability of our PRC system relies on the collective behavior of these interfaces rather than precise control of individual connections. This approach mirrors the statistical nature of biological neural networks, where global network properties dominate over single-synaptic precision. The inherent randomness and complexity within the cluster structure are the key features that enable it to mimic biological synaptic networks. This bioinspired design allows the system to maintain structural integrity while exhibiting robust computational performance.

Before utilizing the ultralong WS2 nanotube cluster with massive van der Waals 0D interfaces for PRC, we investigate the electrical and photoelectrical characteristics of the single 1D WS2 nanotube and single 0D van der Waals interfacial connection, respectively. In essence, the electrical path within the cluster, despite its complexity, is composed of the nanotubes themselves and the van der Waals contacts. The four-terminal devices of single and crossbred WS2 nanotubes were fabricated, as shown in Fig. 2a and b. The schematic and equivalent circuit diagrams of electrical measurement are shown in Fig. 2c. The conductivity of a single WS2 nanotube averages at 192 S·m^-1, as shown in Fig. 2d 90. The I-V curve presents high nonlinearity of the 0D van der Waals interface, as shown in Fig. 2e and f. As temperature decreases, the current exhibits a concomitant reduction, a phenomenon indicative of an energy barrier that charge carriers must surmount to traverse the 0D van der Waals interface as shown in Fig. 2g. An equivalent barrier height on the order of several hundred meV is derived from an Arrhenius analysis of how the current varies with temperature, as detailed in Supplementary Fig. 8. Therefore, the 0D van der Waals barriers provide the nonlinearity in the electrical dynamics of the self-aligned WS2 nanotube cluster, which is essential for the nonlinear projection in reservoir computing.

We further study the optoelectrical response of the single 1D nanotube and the single 0D van der Waals interface device. Upon light illumination, the WS2 nanotube presents an intrinsic photovoltaic effect in accordance with the report by Zhang et al. 96, as shown in Fig. 2h and Supplementary Fig. 9. This photovoltaic effect is programmable based on the history of the applied current and voltage, accompanied by interlayer sliding inside the nanotubes 97. The programmable photovoltaic response is also observed at the single van der Waals interface device due to the 0D sliding ferroelectric effect 98, as shown in Fig. 2i and Supplementary Fig. 10. The absorption spectrum of WS2 nanotubes covers most of the visible wavelength range, corresponding to a bandgap of 1.8 eV, as shown in the upper panel of Fig. 2j. This is in accordance with the photovoltaic current spectra of the 1D nanotube device and 0D van der Waals interface device, as shown in the middle and lower upper panels of Fig. 2j. The optoelectrical response of the WS2 nanotube and their 0D interface allow the intervention of light as a method for manipulating the projections inside the WS2 nanotube cluster, which imitates the optogenetic response of the neural ion channels 67–70,99–104.

We further study the dynamics of the 3D nanotube cluster as the physical reservoir. The electrochemical impedance spectroscopy of the 3D nanotube cluster is shown in Fig. 2k and in Supplementary Fig. 11. The cluster shows a capacitive impedance, potentially resulting from the combination of interlayer capacitance 105 and nanobattery effect 106. The capacitance includes the intrinsic capacitance determined by the complex network structure of the cluster, which is formed by the geometric coupling of the nanotubes and does not change with the voltage bias, and the parasitic capacitance that is sensitive to the electrical dynamics of nanotubes, whose origins is from the nanobattery effect. The capacitive character of the 3D nanotube cluster enables temporal information storage. By applying the voltage pulse stimulation, the responding current reduces with a relaxation time of 0.06 ms, as shown in Supplementary Fig. 12. The cluster was then subjected to different input pulse streams, as shown in Fig. 2l. The read states collected with the input of a 4-bit temporal binary series can be well separated at distinguishable levels. The classification accuracy for distinct pulse signals remains unaffected by readout voltage fluctuations due to the high-dimensional projection property in the reservoir space, which ensures different temporal patterns are mapped to non-overlapping regions. Moreover, the system exhibits intrinsic tolerance to minor perturbations through its nonlinear dynamics and short-term memory properties, while the trained readout layer effectively adapts to such fluctuations by extracting the fundamental statistical features of reservoir responses. The WS2 nanotube cluster exhibits synaptic behaviors, transitioning from short-term plasticity (STP) to long-term plasticity (LTP) under repeated voltage pulses (0.5 V, 100 ms, 200 ms intervals). As shown in Supplementary Fig. 13a-c, with increasing pulse counts (N = 2, 5, 10), the device shifts from transient STP to persistent LTP, mimicking biological synaptic potentiation. Additionally, the paired-pulse facilitation (PPF) ratio, defined as (A2 - A1) / A1 × 100%, was measured. Supplementary Fig. 13d shows the PPF ratio decreasing with longer pulse intervals, consistent with biological synapses. The ability to store and process temporal information lay the foundation for its application as a computing reservoir for various tasks, as presented below.

We demonstrate the paradigm of VLS PRC based on the 3D WS2 nanotube cluster by implementing various monomodal tasks. For the handwritten digit recognition task, the digit images from 0 to 9 were binarized and transformed into spatiotemporal sequences, represented by low (0 V) and high voltage (5 V) electrical pulses with eight input channels, as depicted in Fig. 3a. The handwritten digit recognition task was performed by feeding voltage pulses into the 3D WS2 nanotube network, where the elicited responses were recorded as inputs for the next-level artificial neural network. This network was subsequently fine-tuned through standard backpropagation training to achieve digit classification. As shown in Fig. 3b, classification accuracies were tracked over 200 training epochs, with input data augmented by adding Gaussian noise (σ = 0.05, 0.1, …, 0.4) to evaluate system robustness. In Fig. 3c the confusion matrix was generated for the data with a noise level of σ = 0.05. Despite a gradual decrease in accuracy on the test set with increasing noise levels, the VLS PRC model retained its recognition ability, showcasing the robust image recognition capabilities of the 3D VLS PRC platform. Supplementary Fig. 14 and Supplementary Table 1 compare the performance of the computation using different sizes of the reservoir. It can be seen that the accuracy of the PRC was improved with increasing cluster size. This may be attributed to the fact that projections within a smaller reservoir occur in a lower-dimensional space, which limits the performance of the PRC.

To further assess the capability of the 3D VLS PRC platform in temporal information classification, we conducted a benchmark test focusing on speech recognition. As shown in Fig. 3d, the audio waveforms of isolated spoken digits (zero to nine in English), sourced from the NIST TI-46 database, were resampled at a rate of 12.5 kHz. The waveforms were preprocessed into 8 frequency channels using the Lyon cochlear model 107. The Lyon passive cochlear model is elaborated in Supplementary Fig. 15 and Supplementary Note 2. The preprocessed data were then fed into the reservoir. As shown in Fig. 3e and f, we used 200 samples to form the test set and the accuracies increase with the training epochs for all noise levels and reach a satisfying recognition ability for different noise levels. This demonstrates the 3D VLS PRC’s capability to conduct speech classification task even in chaotic auditory environments.

For the biological signal recognition, electrocardiogram (ECG) signals of normal sinus and arrhythmic rhythm—coronary artery disease, myocardial infarction, and complete atrioventricular block 108 were used as input signals for the reservoir in Fig. 3g. The signal was amplified and fed into the reservoir via a signal generator. The output was collected and processed with the tunable artificial neural network. The 3D VLS PRC exhibited excellent performance in the recognition task and can do the accurate classification of the normal and the arrhythmic electrocardiogram signals, as shown in Fig. 3h.

Besides the classification tasks, this work explored the prediction task of nonlinear time-series using the 3D VLS PRC. The research builds upon the nonlinear equation of autoregressive moving average with exogenous inputs established in prior studies 109, aiming to evaluate the capability of the 3D VLS PRC to process dynamic input time signals and its predictive performance. The dynamic equation is given by

y(k) = 0.4y(k-1) + 0.4(k-1)y(k-2) + 0.6u^3(k) + 0.1 (3)

The primary objective of the task is to predict the value y(k), which is dependent on the current input u(k) as well as the recent outputs y(k-1) and y(k-2). The results depend on the system’s response to the current input and its short-term memory capabilities in relation to historical outputs. The input signal u(k), which is represented by the electrical voltage, was subsequently fed into the VLS PRC, while dynamic response was collected and transferred to the readout layer. By training the weights of the readout layer, the mean squared error decreased with increasing number of epochs, as shown in Fig. 3i. The ground truth time-series values and the prediction through the 3D VLS PRC exhibited a strong correlation, as illustrated in Fig. 3j.

The self-organized, submillimeter-long WS2 nanotube cluster functions as a 3D physical reservoir for very-large-scale computation. The abundance of van der Waals interfaces (10^10 mm^-3) provides a high-dimensional state space for capturing input patterns. The density of the 3D WS2 nanotube cluster serves as a critical design parameter for optimizing reservoir computing performance, providing essential guidance for future neuromorphic hardware development. Even as the reservoir’s scale and complexity expand, internal information mapping occurs simultaneously. Thus, differently from neural networks implanted in very-large-scale logic circuits, here the power consumption and computation time are not sensitive to the scale. We analyzed the energy consumption of the 3D VLS PRC. The input voltage (V) pulses to the 3D WS2 nanotube cluster reservoir are set to 5 V, and the average operating current (I) is approximately 1.4 μA, attributed to the large impedance of the 3D WS2 nanotube cluster. The power consumption of each input node is P = V × I = 5 V × 1.4 μA = 7.0 μW. The power can be further lowered by decreasing the amplitude of the input voltage, for example, the input voltage of 2.5 V corresponds to an operating current of 0.22 μA and a power of 0.6 μW. For the readout layer, a digital implementation using analog-to-digital converters and an FPGA consumes approximately 81 mW in total. Additional details are provided in the Supplementary Note 3 and Supplementary Table 2. Therefore, the repeatable performance of the entire reservoir computing system has been demonstrated, with additional specific details provided in Supplementary Table 3.

Optogenetics offers the second sight that uses light to modulate the organism’s activity or behavior. Here, the mimetic optogenetic behavior is demonstrated using the light-sensitive 3D VLS PRC system. The WS2 nanotube reservoir is exposed to a light beam with an intensity of 3 × 10^-4 W·cm^-2. As shown in Fig. 4a, upon illumination, the accuracy of handwritten digit recognition notably decreases, with a reduction of 32% at a noise level of σ = 0.05. The confusion matrix from the illuminated reservoir is presented in Fig. 4b. The increased confusion is caused by the photoconductive and photovoltaic effect in the 1D WS2 nanotube and the 0D van der Waal interfaces, which changes the projection inside the reservoir and disables the trained PRC model. However, this disability can be mitigated by a second-time training of the fully connected layers in the PRC model, as shown by the confusion matrix in Fig. 4c and the learning curves in Supplementary Fig. 16. Similar mimetic optogenetic change is also observed in the speech recognition task (Figs. 4d-f, Supplementary Fig. 17), the electrocardiogram signal recognition task (Figs. 4g-i) and the time-series prediction task (Figs. 4j-l). The light exposure impacts the feature vectors extracted from the WS2 nanotube-network reservoir, confuses the previously learned model, and lowers the recognition or prediction accuracies. The damaged function can be repaired by readjusting the weights in the fully connected layers through the second-time training.

In prior research, Cai et al. established that electrical stimulation of neural network reservoirs significantly enhances computational accuracy through augmented neuronal interconnectivity within the reservoir architecture 34. Our experimental findings reveal analogous precision enhancement in photo-stimulated reservoir computing systems, potentially stemming from the optimized 3D connectivity established within the nanotube matrix. This structural evolution in the WS2 nanocluster configuration appears to facilitate strengthened inter-nanotube signal transduction pathways, mirroring the neural network optimization observed in bioelectrical systems. Therefore, the optoelectrical response of the WS2 nanotubes and their interface allow light to manipulate projections inside the WS2 nanotube cluster, translating the principles of optogenetics into the PRC paradigm.

The development of multimodal models is regarded as an essential step towards general artificial intelligence. Current deep learning models still struggle with multimodal tasks that humans can easily accomplish. In this work, we utilize the 3D VLS PRC to demonstrate a speech-to-image multimodal generative task which exceeds previous reservoir computing systems, as shown in Supplementary Table 4. Gaussian noises are introduced to an MNIST handwritten digit image dataset over time steps and a diffusion model is trained to denoise for the reverse process. The 3D VLS PRC device, demonstrated in this work, projects the speech input signals into higher dimensional vectors as the latent conditions to guide the generation of images via the cross-attention mechanism, as illustrated in Fig. 5a. The device performance is reflected in the algorithm through the quality of these projections, as higher nonlinearity and richer dynamics enable separation of features in the latent space, directly influencing the accuracy and robustness of the multimodal output. The loss function curves for generating digital images are shown in Fig. 5b. The generated digit images, guided by the latent vectors from the reservoir, closely resembled their original counterparts, despite a small portion of irregularities highlighted by the blue box, as shown in Fig. 5c. Detailed information about the generative denoising process is depicted in Supplementary Fig. 18. It can be seen that the latent vectors, from the projection of the reservoir, are qualified to condition the generation of the handwriting digits.

We further conduct the mimetic optogenetic test on the multimodal model. In the biological system, the disorder in neural and synaptic activity causes cognitive dissonance and intellectual dysfunction. In this work, we used a 532 nm laser with adjustable intensity to modulate reservoir projections, mimicking neural and synaptic activity dynamics in biological intelligence systems. As the 3D VLS reservoir was exposed to weak illumination (1.6 × 10^-4 W·cm^-2), there was a noticeable degeneration of the output image, as depicted by the blue and red boxes in Fig. 5d and Supplementary Fig. 19. These annotations, generated by an ANN-based detection algorithm 110, classify errors as either critical (red boxes) or partial defects (blue boxes). These results are consistent with our manual observations. Interestingly, we note that the generated images for “7” look more like the shape of “5”. Despite the general lack of explainability of neuromorphic computing, the latent space from the reservoir provides interpretability to a certain extent that the projected vectors of “7” merge with that of “5”, as indicated in the right panel of Fig. 5d. Furthermore, increasing the light intensity (9.3 × 10^-4 W·cm^-2) resulted in a more significant degeneration, in which case, the model even fails to generate the shape of a digit or generates a digit image with a wrong label (for example, image “8” is generated with an input speech of “0”), as shown in Fig. 5e, Supplementary Fig. 20, Supplementary Movie 1 and Supplementary Movie 2. The optogenetic perturbation, by modulating the reservoir states through light illumination, further demonstrates the device’s functional role. Using a second-time training with adjustments of weights, the multimodal diffusion model regains the ability of speech-to-image generation, as shown in Fig. 5f and Supplementary Fig. 21. Interestingly, the second-time training enhances the accuracies of the 3D VLS PRC under high-illumination conditions. The enhanced performance can be attributed to the changes in functional connectivity, which enables the dynamic reshaping of the reservoir 34. We further extended the 3D VLS PRC multimodal generative paradigm to biomedical domains. The 3D WS2 nanotube reservoir successfully performed colorectal tissue image and blood cell image generation tasks in Fig. 5g-j and Supplementary Figs. 22-25, demonstrating its capability to process complex medical imaging data. The system maintained robust performance despite the increased complexity of the biomedical features, while still exhibiting the characteristic optogenetic modulation under light illumination. This expansion to clinically significant imaging modalities underscores the system’s potential for medical applications in healthcare settings.

This study introduces a computational framework that leverages a self-organized 3D physical reservoir and verifies the feasibility and potential of this concept. The reservoir utilizes the abundant 0D van der Waals interfaces within a WS2 nanotube cluster as mimetic synaptic connections, resembling the neural system of a fruit fly (with a synaptic quantity of ~10^8 and synaptic density of ~10^10 mm^-3). Such a high-density synaptic connection enables the system to handle complex sequential tasks and endows it with a high degree of parallel computing ability. The 3D VLS PRC exhibits outstanding performance in both monomodal tasks, such as handwritten digit recognition, speech recognition, biological signal recognition, time-series prediction, and multimodal tasks like speech-to-image generation, highlighting its potential for complex applications. By exploiting the photoelectrical response of the 1D semiconducting nanotubes and their 0D interfaces, the study demonstrates a mimetic optogenetic behavior, enabling the manipulation of the VLS PRC dynamics and projections through light stimulation. This work investigates the principles of the scaling law, the capability to perform multimodal tasks, and the mimetic optogenetic mechanisms in the VLS physical reservoir, providing an innovative perspective on how computer science, nanotechnology, optoelectronics, and neuroscience merge to shape the next-generation artificial intelligence paradigm. Looking ahead, the development of all-hardware neuromorphic systems represents a critical frontier for future research. Future efforts will focus on bridging the gap between software-based training and hardware implementations, leveraging the unique properties of WS2 nanotube cluster and their optoelectronic responses to create more robust and efficient neuromorphic computing platforms.

Methods
Materials
WS2 nanotubes were synthesized through two steps: firstly, the ultra-long WO3-x nano whiskers were obtained by the high-temperature annealing of H2WO4 in a sealed quartz ampoule environment. These nanowhiskers were then used as precursors and subjected to a sulfidation process at 845 °C for 6 h in a flowing mixture of H2S and H2 gases, ultimately yielding almost pure ultralong (0.1−0.5 mm) WS2 nanotubes. The nanotubes are stable, retaining their structural and functional properties for over 25 years, making them ideal for long-term applications in neuromorphic computing. The final WS2 nanotube crystals consist of a complex, bottom-up randomly stacked network of WS2 nanotubes, providing the material foundation for this research. The nanotube sample was then transferred for X-ray diffraction, SEM, TEM, micro X-ray CT, Raman spectroscopy, and absorption spectroscopy characterization.

Devices
WS2 nanotubes were dispersed on a silicon substrate coated with 500 nm of silicon dioxide. The dispersion density of WS2 on the silicon wafer was examined using an optical microscope, which allowed for the selection of suitable single nanotubes and their crossbars. Subsequently, a thin film of PMMA photoresist was deposited via spin-coating, followed by thermal treatment to remove residual solvent. Electron beam lithography was then employed to pattern the electrode geometry. After the development of the pattern, Cr (20 nm)/Au (120 nm) metal layers were deposited as the electrode via electron beam evaporation. The lift-off process was done by dissolving the PMMA in acetone. Subsequently, electrical and optoelectrical measurement of the device were conducted with a semiconductor characterization system with a probe station. The incident lights were introduced by lasers with wavelengths of 488, 532, 633 nm and a supercontinuum laser with a range between 655 nm to 1,000 nm.

Physical reservoir computing framework
The WS2 nanotube cluster was integrated into a reservoir computing framework. A microcontroller or DG1032Z signal generator was used to transmit digital or analog signals (handwritten digits, spoken digits, bio-signals, time-series signals) to the WS2 nanotube cluster in the form of electrical voltage pulses, which transformed them into a high-dimensional computational space. The output voltage signals were collected by an oscilloscope and fed into a layer of fully connected artificial neural networks, which delivered the final results for classification and prediction.

Dataset
The handwriting images were from the MNIST dataset, which at 28 × 28 pixels were standard and scaled to 8 × 8 binary images that can be represented by the binary high and low voltages. All images are labeled with the corresponding digit they represent, ranging from 0 to 9. The speech recognition data were from the TI-46 dataset, which consists of read utterances including the numbers 0 to 9. The sound was collected using an Electro-Voice RE-16 Dynamic Cardioid microphone at a 12.5 kHz sample rate with 12-bit quantization. The waveforms of the speeches were fed into 8 frequency channels using the Lyon cochlear model, and then into the reservoir. Biological signals were sampled from the built-in electrocardiogram waveforms of the signal generator (one normal rhythm and three arrhythmias) for the task of arrhythmia detection. The input for time-series prediction was derived from the random discontinuous waveforms built into the signal generator.

Monomodal task training
For classification tasks such as handwritten digit recognition, speech recognition, and biological signal recognition, the feature vector from the reservoir serves as the input to the readout layer. Using the states of the reservoir at the final time step as input x, and the output ^y was obtained through the softmax layer.

^y_j = P_j = exp(z_j) / Σ exp(z_k), z_j = w_ij * x_i + b (4)

where w_ij refers to the synaptic weight between the reservoir and the softmax layer, b is the bias.

The cross-entropy loss function was used to measure the difference between the read-out prediction ^y_j and the desired target y_j

L = - 1/n Σ Σ y^(m)_k log(^y^(m)_k) (5)

where n is the total number of samples.

Linear regression algorithm was used to train the readout function for nonlinear time-series prediction task. Assume the state of the reservoir is x, which is fed to the readout network as the input. The cost function is defined as

J(w) = 1/2n Σ (w^T * x^(i) - y^(i))^2 (6)

where n is the total number of samples, y^(i) is the desired value of the input x^(i), w refers to the weight matrix.

Gradient descent was used to find the parameters that correspond to the minimum value of the cost function J.

Multimodal task training
In the time-series signal forecasting task, the input signal feature vector generated by the VLS PRC is utilized through the cross-attention mechanism as a conditional vector to guide the process from Gaussian noise to the target images. The equation for adding Gaussian noise ϵ to an original image x_0 is given by

x_t = sqrt(Π (1-β_s)) * x_0 + sqrt(1 - Π (1-β_s)) * ϵ (7)

where x_t refers to the generated noisy image, β_s refers to the noise variance added at time steps.

The mean square error (MSE) loss function was used to measure the difference between the predicted noise and the actual noise. The MSE loss is defined as

L_2 = 1/n Σ (ϵ_i - ^ϵ_i)^2 (8)

where n refers to the total number of samples, ϵ_i represents the actual noise, and ^ϵ_i represents the predicted noise.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In this paper, the author introduced a convergence of very-large-scale computational physical reservoir with the feasible control afforded by light. The 3D reservoir, composed of 0D van der Waals interfaces on the order of 10^8 or 10^10/mm^3, not only emulates the synaptic quantity and density of fruit flies’ brains but also integrates photosensitive mimetic optogenetic connections. The resulting VLS PRC system can handle a wide variety of tasks and manages both monomodal challenges and multimodal endeavors such as speech-to-image generation based on the cross attention mechanism. The physical reservoir is designed to respond to light stimuli, effectively translating the principles of optogenetics into the PRC paradigm. There are several major concerns about this work:\n(1) The relationship between the principles of optogenetics and WS2 nanotube is unconvincing. The optogenetics approach involves the genetic manipulation of cells to express light-sensitive proteins that can be activated or inhibited by light of specific wavelengths, as stated by the author. However, WS2 inherently responds to light of specific wavelengths without need of any “genetic manipulation”. It is difficult to establish a connection between optogenetics and WS2 nanotube. For the reviewer, WS2 is a well studied optical and electrical active materials, like many other optoelectrical materials, and does show non-linear responses to light pulse streams, which is a well-known phenomenon. It is not clear why the author made such claim.\n(2) Generally, the learning of a RC is limited to the readout layer, the training cost is significantly reduced compared with that of conventional deep-learning methods. The question here is: does the very-large-scale computation contradict with the essence of RC?\n(3) The contact or the connection between WS2 wires in the cluster are rather complicated. However, the author only showed two type of device architectures in Figure 2a-2c. How to fully mimic the photo response of the WS2 wires in the cluster? How the author harness the WS2 connection and the weight of each “synapse“ in the physical device made out of the WS2 cluster? Also, please show the image of the physical device of WS2 cluster, with electrode connections denoted.\n(4) The author stated that the size of the model is proportional to the accuracy achieved by the model on complex tasks. However, no experimental data shows the claimed relationship between model size and accuracy. It is recommended to add this experiments, for example, using WS2 cluster with different synaptic quantity and density for multimodel tasks, and detail the relationship between WS2 contact density and accuracy.", "reviewer_2": "The paper titled “Very-large-scale mimetic optogenetic synapses for reservoir computing” develops the WS2 nanotube cluster-based optogenetic synapses for reservoir computing. The authors demonstrate the paradigm of VLS PRC based on the 3D WS2 nanotube cluster by implanting various tasks. Although the authors propose a novel concept in the field of neuromorphic computing, the key recognition results are all based on algorithm simulation, which can be replicated by other reported synaptic devices. Basic synaptic behaviors and uniformity tests are missing in this article. More importantly, the mentioned integration density of synapses and recognition results are not reliable enough because the authors ignored the areas occupied by the electrodes and peripheral circuits. Also, the number of selected samples for recognition was seriously insufficient. Overall, the current results presented in this article are limited, which are not enough for publication in “Nature Communications”. Furthermore, I still have specific concerns about this work, shown below：\n1. In introduction, many cutting-edge technologies based on synapses are mentioned, such as mimicking optogenetic or neuroscience, and building the next-generation neuromorphic hardware computing platform. However, the WS2 nanotube cluster device did not show advantages over other reported optoelectronic synaptic devices, either in terms of pulse encoding functions as a reservoir or building the fully-connected neural networks. In other words, the simulation results in this work can be replicated with other reported synaptic devices, such as Front. Electron. 2022, 3, 869013, and Adv. Sci. 2022, 9, 2106092.\n2. According to the circuit diagram and photo response behavior in Figure 2h, 2i, the optical gain of devices is entirely due to the photosensitivity of WS2 nanotube cluster itself, which is much lower than the optical gain of traditional three-terminal transistors. In addition, the electronic transport properties of devices are not suitable for bio-integration applications. Therefore, I do not think the designed device has advantages in mimetic optogenetics over previously reported organic photoelectrochemical transistors such as Nat. Commun. 2023, 14, 5481, and Adv. Mater. 2024, 36, 2407654.\n3. In line 20 of page 6, the authors highlighted the integrated synapse number was over 108, which resembled the fruit fly’s synapses. However, I think this result is not reliable because the authors ignored the areas occupied by the electrodes and the connecting gaps. Moreover, the construction of neuromorphic computing hardware requires numerous peripheral circuit designs. Obviously, the authors did not take these factors into account.\n4. Many important measurements are missing in this article, including the uniformity tests of designed large-scale synapse array and repeated tests in Figure 2i.\n5. As optoelectronic synapses, the WS2 nanotube cluster devices did not demonstrate traditional synaptic behaviors, such as EPSC, PPF, STP, and LTP. Therefore, the authors are suggested to conduct further research.\n6. In line 2 of page 8, the authors attributed the capacitive impedance behavior of cluster to the influence of many factors. Have the authors considered designing relevant measurements to support these assumptions.\n7. In this article, the authors introduced the paradigm of VLS PRC to achieve multi-task classification, but all results are based on algorithm simulation display. Could the authors further build neuromorphic hardware based on WS2 nanotube cluster to reproduce the simulation results.\n8. The maximum value of the color label in confusion matrix is only 20, which means the number of selected samples for classification tasks is very small. Therefore, I doubt the reliability of the recognition results in the article.\n9. In demonstrations of Mimetic optogenetics in 3D VLS PRC, the authors mentioned that illumination would reduce the accuracy of the first simulation. In my opinion, this result is obvious. After the second simulation, the enhancements of accuracies are benefited from the training of software-based fully connected neural networks, which had no connection to the designed devices.\n10. In view of the presented recognition results, I do not clearly perceive the advantages of proposed 3D VLS PRC for multi-task classification over previously reported works, such as Nat. Commun. 2023, 14, 8489, and Nat. Commun. 2023, 14, 7655.\n11. This device is rely on the charge transportation of single nanowire, it would be very difficult to fabricate multiple devices with similar performance. Actually for the potential applications proposed by this work, an array with a large number of devices with consistent performance will be needed. Devices as shown in figure 1 can not achieve the requirement.\n12. long-term stability of this type of device is not good enough for the potential applications proposed by this work.", "reviewer_3": "In response to concerns regarding the scaling law in rapidly advancing deep learning, this manuscript by Han et al. introduces an innovative physical reservoir computing approach by developing a very-large-scale mimetic optogenetic synapse system based on self-organized WS2 nanotube clusters. The integration of physical reservoir computing with optogenetic principles is particularly noteworthy, with the detailed characterization of the WS2 nanotube cluster and its electrical and optoelectrical properties serving as a foundation for the proposed computational paradigm. The capability in handling multimodal challenges such as voice-to-image generation is impressive and highlights the versatility and potential of this approach. This research is intriguing and merits publication in Nature Communications. The authors need to address the comments provided below.\n1. While the manuscript provides an overview of the PRC system, a clear illustration or diagram of the system setup is missing. The authors should include a detailed illustration to clarify the arrangement and interaction of the various components, making it easier for readers to understand how the system functions as a whole and processes different types of tasks.\n2. In Figure 5, the PRC-generated figures are labeled with orange and red boxes to indicate irregular images and errors. It would be helpful to know whether these labels were subjectively determined by the authors based on visual inspection or if there was a specific standard or algorithm used to identify and categorize these errors.\n3. The authors used a light beam in their mimetic optogenetic experiments, but there seems to be limited information provided about the specific characteristics and settings of the light source. Details such as the wavelength range of the light exposure could be crucial for understanding the optogenetic effects observed in the system and for replicating the experiments in future studies.\n4. The X-ray diffraction data presented in Supplementary Fig. 4 shows a few crystal plane marks that seem to be slightly off the peaks. It would be important to clarify whether this is due to experimental errors, sample preparation issues, or if it indicates the presence of impurities or defects in the WS2 nanotube cluster. Understanding the cause of these discrepancies would help ensure the accuracy and reproducibility of the material characterization results.\n5. The Raman characterization of the WS2 nanotube cluster is a part of the material characterization process. However, the manuscript does not specify the laser wavelength used for the Raman measurements. Additionally, there are minor peaks observed at positions around 325 cm-1 and 380 cm-1. It would be helpful to provide more information about the origin of these peaks, whether they are related to the WS2 nanotubes themselves or if they indicate the presence of other phases or impurities in the sample\n6. Figure 4 Labels: In Figure 4 k, l, the authors compare predicted values from the VLS PRC with ground truth under illumination and after second-time training. It would be clearer to readers if the authors could label the conditions as \"illumination\" and \"second-time training.\"\n7. On line 330, \"The final WS2 nanotube crystals consist of a complex, bottom-up randomly stacked network of WS2 nanotubes, providing the material fundamental for this research.\" should be \"the material foundation.\" The authors are suggested to double-check for similar typos."}, "author_response": "Thank you for your valuable comments on this work. Your feedback is highly appreciated and has been instrumental in improving the quality of our research.\n(1) The relationship between the principles of optogenetics and WS nanotube is unconvincing. ... Thank you for raising the concern about the relationship between optogenetics and this 3D photosensitive physical reservoir computing system. ... In light of your insightful comment, we have carefully revised the manuscript accordingly: ...\n(2) Generally, the learning of a RC is limited to the readout layer, the training cost is significantly reduced compared with that of conventional deep-learning methods. The question here is: does the very-large-scale computation contradict with the essence of RC? ... Thank you for your question regarding the compatibility of very-large-scale computation with the essence of RC. ... In light of your comments, we have carefully revised the manuscript accordingly: ...\n(3) The contact or the connection between WS wires in the cluster are rather complicated. ... Thank you for your question. ... In light of the comment, we have revised the manuscript accordingly: ...\n(4) The author stated that the size of the model is proportional to the accuracy achieved by the model on complex tasks. ... Thank you for your suggestion regarding the relationship between model size and accuracy. ... In light of your comment, we have revised the manuscript accordingly: ...\nReviewer #2: ... Thank you very much for both your positive comments and your constructive suggestions, which help us to strengthen our work. ...\n1. ... Thank you for your valuable comment. ... In light of your valuable comment, we have revised the manuscript accordingly: ...\n2. ... Thank you for your valuable comment. ... In light of your comment, we have revised the manuscript accordingly: ...\n3. ... Thank you for the comment. ... In light of your comment, we have revised the manuscript accordingly: ...\n4. ... Thank you for the valuable comment. ... We have revised the manuscript accordingly: ...\n5. ... Thank you for your valuable suggestion. ... We have revised the manuscript accordingly: ...\n6. ... Thank you for your valuable suggestion. ... We have revised the manuscript accordingly: ...\n7. ... Thank you for the valuable comments. ... In light of the comment, we have revised the manuscript accordingly: ...\n8. ... Thank you for your valuable comment. ... In light of the comment, we have revised the manuscript accordingly: ...\n9. ... Thank you for your valuable comments. ... In light of your comment, we have revised the manuscript accordingly: ...\n10. ... Thank you for your insightful questions. ... In light of your comment, we have revised the manuscript accordingly: ...\n11. ... Thank you for your valuable suggestion. ... In light of your comment, we have revised the manuscript accordingly: ...\n12. ... Thank you very much for your invaluable suggestion. ... We have revised the manuscript accordingly: ...\nReviewer #3: ... 1. ... Thank you for your valuable suggestion. ... In light of the comment, we have revised the manuscript accordingly: ...\n2. ... Thank you for your valuable suggestion. ... We have revised the manuscript accordingly: ...\n3. ... Thank you for your valuable feedback regarding the light source used in our mimetic optogenetic experiments. ... In light of your comment, we have revised the manuscript accordingly: ...\n4. ... Thank you for your insightful observation regarding the X-ray diffraction data presented in Supplementary Fig. 4. ... We have revised our supplementary information accordingly: ...\n5. ... Thank you for your insightful comments regarding the Raman characterization of the WS2 nanotube cluster. ... In light of your comment, we have revised the supplementary information accordingly: ...\n6. ... Thank you for your suggestion, which has helped improve the clarity of our manuscript. ... In light of your comment, we have revised the manuscript accordingly: ...\n7. ... Thank you for your suggestion. ... In light of your comment, we have revised the manuscript accordingly: ..."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The author has addressed my concern. It could be published as it is now.", "reviewer_2": "In the first round of revision, the manuscript of Han et al. has been revised in some details based on the reviewers' comments. The innovation of the article is re-emphasized. The 3D large-scale PRC system based on 0D van der Waals interfaces is a new computing paradigm. However, the article does not well demonstrate the advantages of such a large connection weight in performing multimodal tasks compared with previous PRC systems in terms of energy consumption and efficiency. In addition, the drawbacks in scalable manufacturing and randomness of repeated testing are presented, which will greatly affect the calculation performance of PRC system. This work is still limited to the conceptual level. There are still some concerns for this work.\n1. In comment 1, the authors did not demonstrate the advantages of proposed PRC system based on 0D van der Waals interfaces in the order of 108 over others in terms of the energy consumption and efficiency. In addition, the fabrication of WS2 nanotubes involves a multi-step, high-temperature process, which is more complicated than organic devices that can be spin-coated in low temperature. Therefore, the merits of proposed PRC system are limited, which are difficult to provide a practical idea for the future development of reservoir computing.\n2. In comment 3, the authors explain that the electrodes and peripheral circuits are located outside the nanoclusters, which does not affect the packing density of the PRC system. However, the information writing and reading process of the massive 0D van der Waals interfaces is very complicated, which is difficult to achieve precise regulation of a certain interface. Also, the above packing density is derived from the theoretical model, and the role of this packing density in future reservoir computing has not yet been well reflected.\n3. In comment 4, there are some deviations in the repeated testing of the PRC system, which means that the values of read voltage in Fig. 2l change with repeated measurements, which affects the output information of reservoir layer based on PRC system. The authors claimed that the statistical robustness and redundancy of WS₂ nanotube reservoir addressed the scalability of PRC system, which lacked a detailed explanation. The operation performance after 90 days only presented the long-term stability of devices, while the computational efficiency of the system components was not well demonstrated.\n4. In comment 7, what’s the role of devices in performing multimodal tasks. How device performance is reflected in the algorithm. These issues lack detailed explanations.\n5. In comment 8, the authors performed the handwritten digit recognition tasks with 200 test samples, which are far less than that of MNIST dataset (60000 train samples and 10000 test samples). What is the rule for selecting the number of samples?\n6. The PRC system is efficient for addressing multimodal tasks. In view of the massive connection in the 3D VLS PRC system, how is the efficiency compared to other computing paradigms? Is there any quantitative data to reflect this advantage?\n7. The simulation of multimodal tasks in terms of handwritten digits and speech signals fall short of practical application requirements (e.g., biomedical imaging or complex environmental image processing).", "reviewer_3": "In the revised manuscript, the authors have meticulously addressed the reviewers' comments, effectively enhancing the overall clarity, rigor, and impact of their work. This work presents the integration of the very-large-scale physical reservoir computing system with mimetic optogenetics, leveraging the self-assembled bundle-cluster hierarchical 3D structure to achieve synaptic densities rivaling biological systems. The revisions have strengthened the manuscript's conceptual elaboration, experimental validation, and technical transparency.\nReviewer #1's concerns primarily centered on the relationship between optogenetics and WS2 nanotube reservoir, the compatibility of very-large-scale computation with the essence of reservoir computing, and the complexity of the WS2 nanotube cluster connections. The authors have addressed these concerns by providing detailed explanations and additional experimental data. Reviewer #2 put forward several valid technical points. However, a few of the critiques may have partially stemmed from a somewhat differing viewpoint on the central focus related to PRC computational paradigms. The authors have taken these comments into careful consideration and have made appropriate revisions to enhance the clarity and coherence of the manuscript in this regard. Reviewer #3's (my) questions focused on the clarity of the system setup illustration, the criteria for labeling the generated images, the characterization of the materials, and the experimental conditions. The authors have made appropriate revisions in response to these comments. The authors added detailed illustrations of the physical devices of the WS cluster and the experimental setups, clarified the use of an ANN-based detection algorithm for labeling, elaborated the material characteristics, and specified the conditions for the mimetic optogenetic experiments.\nIn summary, the revisions have comprehensively addressed the reviewers' questions, with enhanced clarity, completeness, and persuasiveness of the research. The study presents an innovative approach by integrating the scaling law, multimodal task capabilities, and mimetic optogenetic mechanisms into a computational framework. It advances the field of physical reservoir computing and the development of next-generation artificial intelligence paradigms. Therefore, I recommend its publication in Nature Communications."}, "author_response": "Thank you very much for your positive feedback and support for the publication of our study. We greatly appreciate your time and insightful comments, which have contributed substantially to enhancing the quality of our manuscript. ... Thank you very much for your valuable feedback during this round of review. ... 1. ... Thank you for your valuable comment. ... In light of your comment, we have revised the manuscript and supplementary information accordingly: ... 2. ... Thank you very much for the comment, which leads to considerations regarding the precision regulation of individual interfaces in the reservoir, and the theoretical grounding and significance of the density of 0D van der Waal interfaces. ... In light of your comment, we have revised the manuscript accordingly: ... 3. ... Thank you for your comment. ... In light of your comment, we have revised the manuscript accordingly: ... 4. ... Thank you for your question. ... In light of your comment, we have revised the manuscript accordingly: ... 5. ... Thank you for your valuable comment. ... In light of your comment, we have revised the manuscript and supplementary information accordingly: ... 6. ... Thank you for your comment. ... In light of your comment, we have revised the manuscript and supplementary information accordingly: ... 7. ... Thank you for your valuable comment. ... In light of your comment, we have revised the manuscript and supplementary information accordingly: ... Thank you very much for your constructive feedback. We are also deeply grateful for your constructive suggestions, which were instrumental in refining our arguments and significantly enhancing the overall impact of the work."}, {"round": 3, "reviewer_comments": {"reviewer_2": "The revision answered my questions.", "reviewer_3": "As noted in our previous comments, Reviewer #2’s comments in the prior round of review raised concerns regarding energy efficiency [comments 1, 3, 6], fabrication process [comment 1], regulation of massive 0D interfaces [comment 2], packing density [comment 2], voltage deviations [comment 3], function of the reservoir in the multimodal tasks [comment 4], test sample size [comment 5], and practicality of multimodal tasks [comment 7]. Again, while certain concerns [energy efficiency, comments 1, 3, 6] were valid, most of the criticisms were either secondary or appeared to stem from misunderstandings.\nIn this revision, the authors have provided revision accompanied by new experiments of biomedical image generation, enlarged test datasets, quantitative metrics of energy efficiency, and clarifications of concepts. Specifically, the authors have:\n- quantified the system’s advantageous energy efficiency [comments 1, 3, 6],\n- clarified nanotube fabrication’s scalability [comment 1],\n- explained the collective behavior of the reservoir [comment 2],\n- verified packing density [comment 2],\n- noted voltage deviations as the noise to the reservoir [comment 3],\n- clarified the generation of high-dimensional latent vectors to guide multimodal tasks [comment 4],\n- expanded datasets [comment 5],\n- quantified multimodal task energy consumption [comment 6],\n- and added tissue/blood cell image generation to extend to biomedical applications [comment 7].\nGiven the prior rounds of reviews and revisions, the authors have fully addressed the Reviewers’ concerns. The manuscript now presents an innovative approach integrating the scaling law, multimodal task capabilities, and mimetic optogenetic mechanisms into one computational framework. I therefore recommend accepting the manuscript for publication, which aligns with the prior endorsements of Reviewer #1 and Reviewer #3."}, "author_response": ""}]

Decision letter