Temporal Signal Research Articles

Two-Dimensional Deep Learning Frameworks for Drug-Induced Cardiotoxicity Detection.

The identification of drug-induced cardiotoxicity remains a pressing challenge with far-reaching clinical and economic ramifications, often leading to patient harm and resource-intensive drug recalls. Current methodologies, including in vivo and in vitro models, have severe limitations in accurate identification of cardiotoxic substances. Pioneering a paradigm shift from these conventional techniques, our study presents two deep learning-based frameworks, STFT-CNN and SST-CNN, to assess cardiotoxicity with markedly improved accuracy and reliability. Leveraging the power of induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) as a more human-relevant cell model, we record mechanical beating signals through impedance measurements. These temporal signals were converted into enriched two-dimensional representations through advanced transformation techniques, specifically short-time Fourier transform (STFT) and synchro-squeezing transform (SST). These transformed data are fed into the proposed frameworks for comprehensive analysis, including drug type classification, concentration classification, cardiotoxicity classification, and new drug identification. Compared to traditional models like recurrent neural network (RNN) and 1-dimensional convolutional neural network (1D-CNN), SST-CNN delivered an impressive test accuracy of 98.55% in drug type classification and 99% in distinguishing cardiotoxic and noncardiotoxic drugs. Its feasibility is further highlighted with a stellar 98.5% average accuracy for classification of various concentrations, and the superiority of our proposed frameworks underscores their promise in revolutionizing drug safety assessments. With a potential for scalability, they represent a major leap in drug safety assessments, offering a pathway to more robust, efficient, and human-relevant cardiotoxicity evaluations.

Understanding photoacoustic signal formation in the presence of transparent thin films

Strain-induced variation of the refractive index is the main mechanism of strain detection in photoacoustic experiments. However, weak strain-optic coupling in many materials limits the application of photoacoustics as an imaging tool. A straightforward deposition of a transparent thin film as a top layer has previously been shown to provide signal enhancement due to elastic boundary effects. In this paper, we study photoacoustic signal formation in metal covered by thin transparent films of different thicknesses and demonstrate that in addition to boundary effects, the photoacoustic response is affected by optical effects caused by the presence of the top layer. The interplay of optical effects leads to a complex temporal signal shape that strongly depends on the thickness of the thin film.

A dual threshold adaptation method to dynamic vision sensor pixel

This paper introduces a method for the dynamic vision sensor (DVS) pixel that achieves dual-threshold adaptation based on the interval between adjacent event spikes. The Time-to-Amplitude Converter (TAC) measures the adjacent spike interval per pixel. Based on the output of the TAC and two predefined time limits, the threshold selection circuit automatically chooses between the low threshold ΘL and the high threshold ΘH. The proposed pixel pitch is 33.2μm×34.2μm with 8.1% fill factor in a 0.11μm 1P4M CMOS process. Compared to the traditional pixel with fixed threshold temporal contrast Θ = 15%, the behavior model indicates that the proposed structure maintains almost equivalent detection ability while reducing event output by 30% when ΘH = 45% and ΘL = 15%. Compared to the traditional pixel with fixed Θ = 45%, the behavior model indicates that the average gradient of the proposed structure is improved by at least 3 times with ΘH = 45% and ΘL = 15%. This enables the detection of low temporal contrast signals while minimizing the quantity of output events.

CB-HDM: ECG signal based heart disease classification using convolutional block attention assisted hybrid deep Maxout network

Cardiovascular diseases (CVD) stand as the leading cause of death globally. Diagnosis of CVD relies on electrocardiograms (ECGs), which record the heart’s activity. Adequate labeled data is essential for effectively training machine learning techniques, which may also demand considerable time and memory resources. To address these existing issues, this research introduces an improved detection technique for predicting CVD. Initially, data is sourced from the MIT-BIH arrhythmia database, followed by pre-processing using the Modified FIR filter model (M-FIR) to filter ECG signals. It is used to enhance signal quality and eliminate unwanted noise. M-FIR is used to preserve the specific features of the signal. Feature extraction utilizes the Radial Hilbert Function Transform Network (RHFTN) technique. It can extract the temporal and spectral signal features for effective CVD detection. The Improved Bald Eagle Search Algorithm (IBES) is employed to select features and minimize irrelevant features. Detection and classification of heart disease from ECG signals are achieved using the Convolutional Block Attention Assisted Hybrid Deep Maxout Network model (CB-HDM). Losses in the network model are mitigated by the Gazelle Optimization Algorithm (GOA). The evaluation results are simulated using Python. The proposed model achieved 98.81% accuracy, 96.18% precision, 96.87% recall, and a 94.39% F1 score. The proposed model demonstrates superior efficiency compared to other classifiers.

A proposal for a fast infrared bursts detector

The gravitational wave GW170817 from a binary neutron star merger and the simultaneous electromagnetic detection of the GRB170717A by Fermi Gamma-Ray Space Telescope, opened a new era in the multi-messenger astronomy. Furthermore, the GRBs (Gamma-Ray Bursts) and the mysterious FRBs (Fast Radio Bursts) have sparked interest in the development of new detectors and telescopes dedicated to the time-domain astronomy across the electromagnetic spectrum. Time-domain astronomy aims to acquire fast astronomical bursts in temporal range between a few seconds down to 1 ns. Fast InfraRed Bursts (FIRB's) have been relatively understudied, often due to the lack of appropriate tools for observation and analysis. In this scientific scenario, the present contribution proposes a new detection system for ground-based reflecting telescopes working in the mid-infrared (mid-IR) range to search for astronomical FIRB's. Experience developed in the diagnostics for lepton circular accelerators can be used to design temporal devices for astronomy. Longitudinal diagnostic instruments acquire bunch-by-bunch particle shifts in the direction of flight, that is equivalent to temporal. Transverse device integrates the beam signal in the horizontal and vertical coordinates, as standard telescopes. The proposed instrument aims to work in temporal mode. Feasibility study tests have been carried out at SINBAD, the infrared beam line of DAFNE, the e+e- collider of INFN. SINBAD releases pulsed infrared synchrotron light with 2.7 ns separation. The front-end detector system has been evaluated to detect temporal fast infrared signals with 2–12 μm wavelengths and 1 ns rise times. The present contribute aims to be a step toward a feasibility study report.

The emergence and circulation of human immunodeficiency virus (HIV)-1 subtype C.

Introduction. Human immunodeficiency virus (HIV)-1 subtype C is the most prevalent globally and is thought to have originated in non-human primates in the Democratic Republic of Congo.Hypothesis/Gap Statement. Although the global dominance of HIV-1 subtype C is well established, a thorough understanding of its evolutionary history and transmission dynamics across various risk populations remains elusive. The current knowledge is insufficient to fully capture the global diversification and dissemination of this subtype.Aim. We for the first time sought to investigate the global evolutionary history and spatiotemporal dynamics of HIV-1 subtype C using a selection of maximum-likelihood-based phylodynamic approaches on a total of 1210 near full-length genomic sequences sampled from 32 countries, collected in 4 continents, with sampling dates between 1986-2019 among various risk groups were analysed.Methodology. We subsampled the HIV-1 subtype C genomic datasets based on continent and risk group traits, and performed nucleotide substitution model selection analysis, maximum likelihood (ML) phylogenetic reconstruction, phylogenetic tree topology similarity analysis, temporal signal analysis and traced the timings of viral spread both geographically and by risk group.Results. Based on the phylodynamic analyses of four datasets (full1210, locrisk626, loc562 and risk393), we inferred the time to the most recent common ancestor (TMRCA) in the 1930s and an evolutionary rate of 0.0023 substitutions per site per year. The total number of introduction events of HIV-1 subtype C between continents and between risk groups is estimated to be 71 and 115, respectively. The largest number of introductions occurred from Africa to Europe (n=32), from not-recorded to heterosexual (n=40) and from heterosexual to not-recorded (n=51) risk groups.Conclusion. Our results emphasize that HIV subtype C has mainly spread from Africa to Europe, likely through heterosexual transmission.

WITHDRAWN: FCDNet: Frequency-guided complementary dependency modeling for multivariate time-series forecasting

Multivariate time-series (MTS) forecasting is a challenging task in many real-world non-stationary dynamic scenarios. In addition to intra-series temporal signals, the inter-series dependency also plays a crucial role in shaping future trends. How to enable the model’s awareness of dependency information has raised substantial research attention. Previous approaches have either presupposed dependency constraints based on domain knowledge or imposed them using real-time feature similarity. However, MTS data often exhibit both enduring long-term stable relationships and transient short-term interactions, which mutually influence their evolving states. It is necessary to recognize and incorporate the complementary dependencies for more accurate MTS forecasting. The frequency information in time series reflects the evolutionary rules behind complex temporal dynamics, and different frequency components can be used to well construct interactive dependency structures with varying states between variables. To this end, we propose FCDNet, a concise yet effective complementary dependency modeling framework for multivariate time-series forecasting. Specifically, FCDNet overcomes the above limitations by applying two lightweight dependency constructors to help extract stable static and dynamic varying dependency information adaptively from long short-term multi-level frequency patterns. With the growth of input variables, the number of trainable parameters in FCDNet only increases linearly, which is conducive to the model’s scalability and avoids over-fitting. Additionally, adopting a frequency-based perspective can effectively mitigate the influence of noise within MTS data, which helps capture more genuine dependencies. The experimental results on six real-world datasets from multiple fields show that FCDNet significantly exceeds strong baselines, with an average improvement of 6.82% on MAE, 4.98% on RMSE, and 4.91% on MAPE. In addition, the ability of FCDNet to jointly learn high-quality static and dynamic graph structures is demonstrated empirically. Our source codes are publicly available at https://github.com/onceCWJ/FCDNet.

Enhancing Demodulation Performance of DCM Algorithm in φ-OTDR System Through Temporal Spline Interpolation

For expanding the amplitude-frequency response range of the differential cross-phase multiply (DCM) algorithm in the φ-OTDR system, a temporal spline interpolation (TSI) method is proposed to pre-process Rayleigh backscattering (RBS) signals. Through the TSI method, the discrete temporal signals characterizing RBS traces are subjected to interpolation, facilitating a reduction in differential approximation errors. This, in turn, establishes a heightened level of precision in phase demodulation, especially relevant across extensive sensing distances. By comparing the recovered time-domain waveforms and the corresponding power spectral densities without and with the TSI, the above improvement effect has been experimentally validated by utilizing the TSI. The results show that, with the TSI, the amplitude-frequency response range of the DCM algorithm is enlarged by 2.78 times, and the new relationship among fpulse, f, and D under the root mean square error (RMSE) tolerance less than 0.1 can be expressed as 1.9(D+1)f ≤ fpulse. This contribution underscores a substantial advancement in the capabilities of the DCM algorithm, holding promise for refined performance in optical fiber sensing applications.

Polymerase-Based Signal Delay for Temporally Regulating DNA Involved Reactions, Programming Dynamic Molecular Systems, and Biomimetic Sensing.

Complex temporal molecular signals play a pivotal role in the intricate biological pathways of living organisms, and cells exhibit the ability to transmit and receive information by intricately managing the temporal dynamics of their signaling molecules. Although biomimetic molecular networks are successfully engineered outside of cells, the capacity to precisely manipulate temporal behaviors remains limited. In this study, the catalysis activity of isothermal DNA polymerase (DNAP) through combined use of molecular dynamics simulation analysis and fluorescence assays is first characterized. DNAP-driven delay in signal strand release ranged from 100 to 102min, which is achieved through new strategies including the introduction of primer overhangs, utilization of inhibitory reagents, and alteration of DNA template lengths. The results provide a deeper insight into the underlying mechanisms of temporal control DNAP-mediated primer extension and DNA strand displacement reactions. Then, the regulated DNAP catalysis reactions are applied in temporal modulation of downstream DNA-involved reactions, the establishment of dynamic molecular signals, and the generation of barcodes for multiplexed detection of target genes. The utility of DNAP-based signal delay as a dynamic DNA nanotechnology extends beyond theoretical concepts and achieves practical applications in the fields of cell-free synthetic biology and bionic sensing.

SVD-Based Mind-Wandering Prediction from Facial Videos in Online Learning.

This paper presents a novel approach to mind-wandering prediction in the context of webcam-based online learning. We implemented a Singular Value Decomposition (SVD)-based 1D temporal eye-signal extraction method, which relies solely on eye landmark detection and eliminates the need for gaze tracking or specialized hardware, then extract suitable features from the signals to train the prediction model. Our thorough experimental framework facilitates the evaluation of our approach alongside baseline models, particularly in the analysis of temporal eye signals and the prediction of attentional states. Notably, our SVD-based signal captures both subtle and major eye movements, including changes in the eye boundary and pupil, surpassing the limited capabilities of eye aspect ratio (EAR)-based signals. Our proposed model exhibits a 2% improvement in the overall Area Under the Receiver Operating Characteristics curve (AUROC) metric and 7% in the F1-score metric for 'not-focus' prediction, compared to the combination of EAR-based and computationally intensive gaze-based models used in the baseline study These contributions have potential implications for enhancing the field of attentional state prediction in online learning, offering a practical and effective solution to benefit educational experiences.

DenRAM: neuromorphic dendritic architecture with RRAM for efficient temporal processing with delays

Neuroscience findings emphasize the role of dendritic branching in neocortical pyramidal neurons for non-linear computations and signal processing. Dendritic branches facilitate temporal feature detection via synaptic delays that enable coincidence detection (CD) mechanisms. Spiking neural networks highlight the significance of delays for spatio-temporal pattern recognition in feed-forward networks, eliminating the need for recurrent structures. Here, we introduce DenRAM, a novel analog electronic feed-forward spiking neural network with dendritic compartments. Utilizing 130 nm technology integrated with resistive RAM (RRAM), DenRAM incorporates both delays and synaptic weights. By configuring RRAMs to emulate bio-realistic delays and exploiting their heterogeneity, DenRAM mimics synaptic delays and efficiently performs CD for pattern recognition. Hardware-aware simulations on temporal benchmarks show DenRAM’s robustness against hardware noise, and its higher accuracy over recurrent networks. DenRAM advances temporal processing in neuromorphic computing, optimizes memory usage, and marks progress in low-power, real-time signal processing

Bayesian geochemical correlation and tomography

To accurately reconstruct palaeoenvironmental change through time it is important to determine which rock samples were deposited contemporaneously at different sites or transects, as erroneous correlation may lead to incorrectly inferred processes and rates. To correlate samples, current practice interpolates geological age between datable units along each transect, then temporal signatures observed in geochemical logs are matched between transects. Unfortunately spatiotemporally variable and unknown rates of sedimentary deposition create highly nonlinear space-time transforms, significantly altering apparent geochemical signatures. The resulting correlational hypotheses are also untestable against independent transects, because correlations have no spatially-predictive power. Here we use geological process information stored within neural networks to correlate spatially offset logs nonlinearly and geologically. The same method creates tomographic images of geological age and geochemical signature across intervening rock volumes. Posterior tomographic images closely resemble the true depositional age throughout the inter-transect volume, even for scenarios with long hiatuses in preserved geochemical signals. Bayesian probability distributions describe data-consistent variations in the results, showing that centred summary statistics such as mean and variance do not adequately describe correlational uncertainties. Tomographic images demonstrate spatially predictive power away from geochemical transects, creating novel hypotheses attributable to each geochemical correlation which are testable against independent data.

Study of an Optimization Tool Avoided Bias for Brain-Computer Interfaces Using a Hybrid Deep Learning Model

ObjectiveThis study addresses the challenge of user-specific bias in Brain-Computer Interfaces (BCIs) by proposing a novel methodology. The primary objective is to employ a hybrid deep learning model, combining 2D Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) layers, to analyze EEG signals and classify imagined tasks. The overarching goal is to create a generalized model that is applicable to a broader population and mitigates user-specific biases. Materials and MethodsEEG signals from imagined motor tasks in the public dataset Physionet form the basis of the study. This is due to the need to use other databases in addition to the BCI competition. A model of arrays emulating the electrode arrangement in the head is proposed to capture spatial information using CNN, and LSTM algorithms are used to capture temporal information, followed by signal classification. ResultsThe hybrid model is implemented to achieve a high classification rate, reaching up to 90% for specific users and averaging 74.54%. Error detection thresholds are set to eliminate subjects with low task affinity, resulting in a significant improvement in classification accuracy of up to 21.34%. ConclusionThe proposed methodology makes a significant contribution to the BCI field by providing a generalized system trained on diverse user data that effectively captures spatial and temporal EEG signal features. This study emphasizes the value of the hybrid model in advancing BCIs, highlighting its potential for improved reliability and accuracy in human-computer interaction. It also suggests the exploration of additional advanced layers, such as transformers, to further enhance the proposed methodology.

Robust compression and detection of epileptiform patterns in ECoG using a real-time spiking neural network hardware framework

Interictal Epileptiform Discharges (IED) and High Frequency Oscillations (HFO) in intraoperative electrocorticography (ECoG) may guide the surgeon by delineating the epileptogenic zone. We designed a modular spiking neural network (SNN) in a mixed-signal neuromorphic device to process the ECoG in real-time. We exploit the variability of the inhomogeneous silicon neurons to achieve efficient sparse and decorrelated temporal signal encoding. We interface the full-custom SNN device to the BCI2000 real-time framework and configure the setup to detect HFO and IED co-occurring with HFO (IED-HFO). We validate the setup on pre-recorded data and obtain HFO rates that are concordant with a previously validated offline algorithm (Spearman’s ρ = 0.75, p = 1e-4), achieving the same postsurgical seizure freedom predictions for all patients. In a remote on-line analysis, intraoperative ECoG recorded in Utrecht was compressed and transferred to Zurich for SNN processing and successful IED-HFO detection in real-time. These results further demonstrate how automated remote real-time detection may enable the use of HFO in clinical practice.

A learnable continuous wavelet-based multi-branch attentive convolutional neural network for spatio–spectral–temporal EEG signal decoding

Deep learning methods, particularly convolutional neural networks (CNNs), have advanced the field of motor imagery (MI) by effectively extracting spatio-spectral–temporal (SST) features from electroencephalography (EEG) signals. Image-based CNNs using time–frequency analysis such as wavelet transform have outperformed signal-based CNNs in MI-EEG classification. However, the spatial characteristics of EEG signals can be neglected due to the high dimensionality of spectral–temporal feature space. Moreover, the performance hinges on whether the predefined transformation can capture the class-discriminative spectral–temporal representations of EEG signals well. Despite attempts to alleviate these challenges by selecting EEG electrodes or restricting spectral–temporal feature space, large inter-subject variabilities have impeded successful MI-EEG decoding. In this paper, we propose a learnable continuous wavelet-based multi-branch attentive CNN framework for decoding MI-EEG signals. Our method is designed to automatically generate class-discriminative SST representations of EEG signals within subject-specific sub-bands by utilizing learnable wavelet-based convolutions. Additionally, it employs multi-branch attentive CNNs for the effective and efficient extraction of local and global SST features. Our comprehensive evaluation on two public datasets demonstrates that the proposed method significantly outperformed state-of-the-art methods. We also conduct an ablation study under various configuration settings within the proposed framework to demonstrate the effectiveness of our method. The proposed method provides a powerful and innovative approach to MI-EEG signal decoding, with implications for personalized EEG decoding.

The development and testing of a smart sensorized guide wire for catheterization in a "blood" vessel phantom to support aortic valve implementation.

Heart valve disease is commonly treated by minimally invasive procedures with guide wires and catheterization. The main purpose of this study is to find out whether an extension of the guide wire with a sensor can support the surgeon within the blood vessel to reduce X-ray necessity. A smart guide wire is developed by an extension with a flex-bending sensor to evaluate the sensor signal with and without "blood" flow at a constant compression force. Various surgically relevant investigations are performed. For assessment, the mean temporal average of the moving averaged filtered ADC signal and a subsequent FFT are carried out. Results show that there is a smaller sensor signal when the applied force or bending at the sensor is higher. In all investigations, there was a different sensor signal. The flex-bending sensor can detect the effect of pulsatile flow. The smallest temporal averaged signal difference between reference and clamp in the front wire's tip is 1.09%. For example, the mean temporal average of the filtered ADC signal for different clinically relevant scenarios is between 2550 and 2900. The results show that the sensorized guide wire developed for catheterization can support aortic valve implementation. The sensor sensitivity is sufficient to detect even very small variations within the blood vessel and therefore is promising to support catheterization heart valve surgeries in future.

Fingerprinting the geochemical signals of episodic arc activity in the Sierra Nevada batholith in space and time

Although subduction is a continuous process, arc system behavior is non-steady-state, leading to uncertainty surrounding the composite spatial and temporal evolution of transcrustal arc magma plumbing systems. This study integrates field, geochronologic, and geochemical data sets from the central Sierra Nevada arc section to investigate the extent to which spatial inheritance is recorded in arc geochemical compositions, and how these signals may be modified by dynamic arc behaviors through time, from arc-wide flare-ups, migration, and crustal thickening to regional magma focusing. Geochemical patterns across Mesozoic arc rocks characterize persistent spatial signals of inheritance, whereas geochemical trends during Cretaceous arc activity provide the temporal component of simultaneous dynamic processes. Distinct bulk-rock isotopic signals define each of the three Mesozoic magmatic flare-ups, which, during Cretaceous arc magmatism, is coupled with eastward arc migration. Additionally, Cretaceous magmatic and tectonic thickening doubled the thickness of arc crust, and magmatism was focused toward a central zone, culminating in the formation of the ∼1100 km2 Tuolumne Intrusive Complex. During magma focusing, temporal signals of magma mixing outweighed the previously pervasive signal of spatial inheritance. Distinct dynamic behaviors effectively primed the arc by the Late Cretaceous, generating transcrustal hot zones of increased magma mixing, recycling, long-term storage, and homogenization. Non-steady-state behavior in the Sierra Nevada resulted in mountain building and voluminous continental crust formation by transforming the physical, thermal, and chemical properties of the lithosphere over tens of millions of years.

Quantitative blood glucose detection influenced by various factors based on the fusion of photoacoustic temporal spectroscopy with deep convolutional neural networks

In order to efficiently and accurately monitor blood glucose concentration (BGC) synthetically influenced by various factors, quantitative blood glucose in vitro detection was studied using photoacoustic temporal spectroscopy (PTS) combined with a fusion deep neural network (fDNN). Meanwhile, a photoacoustic detection system influenced by five factors was set up, and 625 time-resolved photoacoustic signals of rabbit blood were collected under different influencing factors.In view of the sequence property for temporal signals, a dimension convolutional neural network (1DCNN) was established to extract features containing BGC. Through the parameters optimization and adjusting, the mean square error (MSE) of BGC was 0.51001 mmol/L for 125 testing sets. Then, due to the long-term dependence on temporal signals, a long short-term memory (LSTM) module was connected to enhance the prediction accuracy of BGC. With the optimal LSTM layers, the MSE of BGC decreased to 0.32104 mmol/L. To further improve prediction accuracy, a self-attention mechanism (SAM) module was coupled into and formed an fDNN model, i.e., 1DCNN-SAM-LSTM. The fDNN model not only combines the advantages of temporal expansion of 1DCNN and data long-term memory of LSTM, but also focuses on the learning of more important features of BGC. Comparison results show that the fDNN model outperforms the other six models. The determination coefficient of BGC for the testing set was 0.990, and the MSE reached 0.1432 mmol/L. Results demonstrate that PTS combined with 1DCNN-SAM-LSTM ensures higher accuracy of BGC under the synthetical influence of various factors, as well as greatly enhances the detection efficiency.

Temporal and spectral signatures of the interaction between ultrashort laser pulses and Bloch surface waves

The resonant excitation of Bloch Surface Waves (BSWs) in dielectric one-dimensional photonic crystals is becoming a realistic photonic solution for surface integration in many domains, from spectroscopy to local field management. Bringing BSWs to ultrafast and nonlinear regimes requires a deep knowledge of the effects that the photonic crystal dispersion and the resonant surface wave excitation have on the ultrashort laser pulses. We report on the experimental evidence of spectral and temporal modifications of the radiation leaving a planar one-dimensional photonic crystal after coupling to BSWs. In such a resonant condition, a characteristic long temporal tail is observed in the outgoing pulses. Observations are performed by employing both frequency-resolved optical gating and field cross-correlation techniques.

Memristive tonotopic mapping with volatile resistive switching memory devices

To reach the energy efficiency and the computing capability of biological neural networks, novel hardware systems and paradigms are required where the information needs to be processed in both spatial and temporal domains. Resistive switching memory (RRAM) devices appear as key enablers for the implementation of large-scale neuromorphic computing systems with high energy efficiency and extended scalability. Demonstrating a full set of spatiotemporal primitives with RRAM-based circuits remains an open challenge. By taking inspiration from the neurobiological processes in the human auditory systems, we develop neuromorphic circuits for memristive tonotopic mapping via volatile RRAM devices. Based on a generalized stochastic device-level approach, we demonstrate the main features of signal processing of cochlea, namely logarithmic integration and tonotopic mapping of signals. We also show that our tonotopic classification is suitable for speech recognition. These results support memristive devices for physical processing of temporal signals, thus paving the way for energy efficient, high density neuromorphic systems.