Design and Analysis of a Neuromemristive Reservoir Computing Architecture for Biosignal Processing

Kudithipudi, Dhireesha; Saleh, Qutaiba; Merkel, Cory; Thesing, James; Wysocki, Bryant

doi:10.3389/fnins.2015.00502

Cited by 71 publications

(47 citation statements)

References 38 publications

(41 reference statements)

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“…We also show that the inherent device variations, which can pose significant challenges for some applications, become a benefit for RC systems, as they help make the reservoir states more separable (Supplementary Fig. 15 ) 11 , 29 .…”

Section: Discussionmentioning

confidence: 81%

Reservoir computing using dynamic memristors for temporal information processing

et al. 2017

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Reservoir computing systems utilize dynamic reservoirs having short-term memory to project features from the temporal inputs into a high-dimensional feature space. A readout function layer can then effectively analyze the projected features for tasks, such as classification and time-series analysis. The system can efficiently compute complex and temporal data with low-training cost, since only the readout function needs to be trained. Here we experimentally implement a reservoir computing system using a dynamic memristor array. We show that the internal ionic dynamic processes of memristors allow the memristor-based reservoir to directly process information in the temporal domain, and demonstrate that even a small hardware system with only 88 memristors can already be used for tasks, such as handwritten digit recognition. The system is also used to experimentally solve a second-order nonlinear task, and can successfully predict the expected output without knowing the form of the original dynamic transfer function.

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Section: Discussionmentioning

confidence: 81%

Reservoir computing using dynamic memristors for temporal information processing

et al. 2017

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“…[85][86][87][88] Because of the inherent nonlinearity and short-term memory features, memristors are identified as good candidates for the reservoir computing system in the pattern classification and signal processing. [89][90][91][92] Here, we introduce two reservoir computing systems based on volatile memristors for pattern recognition.…”

Section: Volatile Memristor As Artificial Synapsementioning

confidence: 99%

Recent Advances of Volatile Memristors: Devices, Mechanisms, and Applications

Wang

Yang

Mao

et al. 2020

Advanced Intelligent Systems

136

113

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Due to the rapid development of artificial intelligence (AI) and internet of things (IoTs), neuromorphic computing and hardware security are becoming more and more important. The volatile memristors, which feature spontaneous decay of device conductance, own the distinct combination of high similarity to the biological neurons and synapses and unique physical mechanisms. They are excellent candidates for mimicking the synaptic functions and ideal randomness source of the entropy for hardware‐based security. Herein, the recent advances of volatile memristors in devices, mechanisms, and application aspects are summarized. First, a brief introduction is presented to describe the switching type, materials, and temporal response of volatile memristors. Second, the volatile switching mechanisms are discussed and grouped into ion effects, thermal effects, and electrical effects. Third, attention is focused on the applications of volatile memristors for access devices, neuromorphic computing (artificial neurons and synapses), and hardware security (true random number generators and physical unclonable functions). Finally, major challenges and future outlook of volatile memristors for neuromorphic computing and hardware security are discussed.

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“…Li et al [ 24 ] embedded Kalman filter and RNN into the real-time functional electrical stimulation system for identification and estimation. Kudithipudi [ 25 ] proposed a neuromemristive reservoir computing architecture with doubly twisted toroidal structure that significantly improved RNN architecture with an accuracy of 90 and 84% for epileptic seizure detection and EMG prosthetic finger control, respectively. However, the challenge in using RNN is the very unstable relationship between parameters and the dynamics of hidden states, known as “fading or exploding gradients.” Therefore, LSTM and gate recurrent unit gating systems are proposed.…”

Section: Previous Workmentioning

confidence: 99%

sEMG-Based Neural Network Prediction Model Selection of Gesture Fatigue and Dataset Optimization

Ma¹,

Song

Niu³

2020

Computational Intelligence and Neuroscience

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The fatigue energy consumption of independent gestures can be obtained by calculating the power spectrum of surface electromyography (sEMG) signals. The existing research studies focus on the fatigue of independent gestures, while the research studies on integrated gestures are few. However, the actual gesture operation mode is usually integrated by multiple independent gestures, so the fatigue degree of integrated gestures can be predicted by training neural network of independent gestures. Three natural gestures including browsing information, playing games, and typing are divided into nine independent gestures in this paper, and the predicted model is established and trained by calculating the energy consumption of independent gestures. The artificial neural networks (ANNs) including backpropagation (BP) neural network, recurrent neural network (RNN), and long short-term memory (LSTM) are used to predict the fatigue of gesture. The support vector machine (SVM) is used to assist verification. Mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE) are utilized to evaluate the optimal prediction model. Furthermore, the different datasets of the processed sEMG signal and its decomposed wavelet coefficients are trained, respectively, and the changes of error functions of them are compared. The experimental results show that LSTM model is more suitable for gesture fatigue prediction. The processed sEMG signals are appropriate for using as the training set the fatigue degree of one-handed gesture. It is better to use wavelet decomposition coefficients as datasets to predict the high-dimensional sEMG signals of two-handed gestures. The experimental results can be applied to predict the fatigue degree of complex human-machine interactive gestures, help to avoid unreasonable gestures, and improve the user’s interactive experience.

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Design and Analysis of a Neuromemristive Reservoir Computing Architecture for Biosignal Processing

Cited by 71 publications

References 38 publications

Reservoir computing using dynamic memristors for temporal information processing

Reservoir computing using dynamic memristors for temporal information processing

Recent Advances of Volatile Memristors: Devices, Mechanisms, and Applications

sEMG-Based Neural Network Prediction Model Selection of Gesture Fatigue and Dataset Optimization

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