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Maslennikov O, Perc M, Nekorkin V. Topological features of spike trains in recurrent spiking neural networks that are trained to generate spatiotemporal patterns. Front Comput Neurosci 2024; 18:1363514. [PMID: 38463243 PMCID: PMC10920356 DOI: 10.3389/fncom.2024.1363514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Accepted: 02/06/2024] [Indexed: 03/12/2024] Open
Abstract
In this study, we focus on training recurrent spiking neural networks to generate spatiotemporal patterns in the form of closed two-dimensional trajectories. Spike trains in the trained networks are examined in terms of their dissimilarity using the Victor-Purpura distance. We apply algebraic topology methods to the matrices obtained by rank-ordering the entries of the distance matrices, specifically calculating the persistence barcodes and Betti curves. By comparing the features of different types of output patterns, we uncover the complex relations between low-dimensional target signals and the underlying multidimensional spike trains.
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Affiliation(s)
- Oleg Maslennikov
- Federal Research Center A.V. Gaponov-Grekhov Institute of Applied Physics of the Russian Academy of Sciences, Nizhny Novgorod, Russia
| | - Matjaž Perc
- Faculty of Natural Sciences and Mathematics, University of Maribor, Maribor, Slovenia
- Department of Medical Research, China Medical University Hospital, China Medical University, Taichung City, Taiwan
- Complexity Science Hub Vienna, Vienna, Austria
- Department of Physics, Kyung Hee University, Seoul, Republic of Korea
| | - Vladimir Nekorkin
- Federal Research Center A.V. Gaponov-Grekhov Institute of Applied Physics of the Russian Academy of Sciences, Nizhny Novgorod, Russia
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2
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Maldonado D, Cantudo A, Perez E, Romero-Zaliz R, Perez-Bosch Quesada E, Mahadevaiah MK, Jimenez-Molinos F, Wenger C, Roldan JB. TiN/Ti/HfO 2/TiN memristive devices for neuromorphic computing: from synaptic plasticity to stochastic resonance. Front Neurosci 2023; 17:1271956. [PMID: 37795180 PMCID: PMC10546015 DOI: 10.3389/fnins.2023.1271956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 09/01/2023] [Indexed: 10/06/2023] Open
Abstract
We characterize TiN/Ti/HfO2/TiN memristive devices for neuromorphic computing. We analyze different features that allow the devices to mimic biological synapses and present the models to reproduce analytically some of the data measured. In particular, we have measured the spike timing dependent plasticity behavior in our devices and later on we have modeled it. The spike timing dependent plasticity model was implemented as the learning rule of a spiking neural network that was trained to recognize the MNIST dataset. Variability is implemented and its influence on the network recognition accuracy is considered accounting for the number of neurons in the network and the number of training epochs. Finally, stochastic resonance is studied as another synaptic feature. It is shown that this effect is important and greatly depends on the noise statistical characteristics.
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Affiliation(s)
- David Maldonado
- Departamento de Electronica y Tecnologia de Computadores, Facultad de Ciencias, Universidad de Granada, Granada, Spain
| | - Antonio Cantudo
- Departamento de Electronica y Tecnologia de Computadores, Facultad de Ciencias, Universidad de Granada, Granada, Spain
| | - Eduardo Perez
- Materials Research Department, IHP-Leibniz-Institut fuer innovative Mikroelektronik, Frankfurt an der Oder, Germany
- Mathematics, Computer Science, Physics, Electrical Engineering and Information Technology Department, Brandenburg University of Technology Cottbus-Senftenberg (BTU), Cottbus, Germany
| | - Rocio Romero-Zaliz
- Center for Research in Information and Communication Technologies (CITIC), Andalusian Research Institute on Data Science and Computational intelligence (DaSCI), University of Granada, Granada, Spain
| | - Emilio Perez-Bosch Quesada
- Materials Research Department, IHP-Leibniz-Institut fuer innovative Mikroelektronik, Frankfurt an der Oder, Germany
| | | | - Francisco Jimenez-Molinos
- Departamento de Electronica y Tecnologia de Computadores, Facultad de Ciencias, Universidad de Granada, Granada, Spain
| | - Christian Wenger
- Materials Research Department, IHP-Leibniz-Institut fuer innovative Mikroelektronik, Frankfurt an der Oder, Germany
- Mathematics, Computer Science, Physics, Electrical Engineering and Information Technology Department, Brandenburg University of Technology Cottbus-Senftenberg (BTU), Cottbus, Germany
| | - Juan Bautista Roldan
- Departamento de Electronica y Tecnologia de Computadores, Facultad de Ciencias, Universidad de Granada, Granada, Spain
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3
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Xue X, Wimmer RD, Halassa MM, Chen ZS. Spiking Recurrent Neural Networks Represent Task-Relevant Neural Sequences in Rule-Dependent Computation. Cognit Comput 2023; 15:1167-1189. [PMID: 37771569 PMCID: PMC10530699 DOI: 10.1007/s12559-022-09994-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 01/13/2022] [Indexed: 11/28/2022]
Abstract
Background Prefrontal cortical neurons play essential roles in performing rule-dependent tasks and working memory-based decision making. Methods Motivated by PFG recordings of task-performing mice, we developed an excitatory-inhibitory spiking recurrent neural network (SRNN) to perform a rule-dependent two-alternative forced choice (2AFC) task. We imposed several important biological constraints onto the SRNN, and adapted spike frequency adaptation (SFA) and SuperSpike gradient methods to train the SRNN efficiently. Results The trained SRNN produced emergent rule-specific tunings in single-unit representations, showing rule-dependent population dynamics that resembled experimentally observed data. Under varying test conditions, we manipulated the SRNN parameters or configuration in computer simulations, and we investigated the impacts of rule-coding error, delay duration, recurrent weight connectivity and sparsity, and excitation/inhibition (E/I) balance on both task performance and neural representations. Conclusions Overall, our modeling study provides a computational framework to understand neuronal representations at a fine timescale during working memory and cognitive control, and provides new experimentally testable hypotheses in future experiments.
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Affiliation(s)
- Xiaohe Xue
- Courant Institute of Mathematical Sciences, New York University, New York, NY, USA
| | - Ralf D. Wimmer
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Michael M. Halassa
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Zhe Sage Chen
- Department of Psychiatry, New York University School of Medicine, New York, NY, USA
- Department of Neuroscience and Physiology, New York University School of Medicine, New York, NY, USA
- Neuroscience Institute, New York University School of Medicine, New York, NY, USA
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4
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Guo Y, Huang X, Ma Z. Direct learning-based deep spiking neural networks: a review. Front Neurosci 2023; 17:1209795. [PMID: 37397460 PMCID: PMC10313197 DOI: 10.3389/fnins.2023.1209795] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 06/01/2023] [Indexed: 07/04/2023] Open
Abstract
The spiking neural network (SNN), as a promising brain-inspired computational model with binary spike information transmission mechanism, rich spatially-temporal dynamics, and event-driven characteristics, has received extensive attention. However, its intricately discontinuous spike mechanism brings difficulty to the optimization of the deep SNN. Since the surrogate gradient method can greatly mitigate the optimization difficulty and shows great potential in directly training deep SNNs, a variety of direct learning-based deep SNN works have been proposed and achieved satisfying progress in recent years. In this paper, we present a comprehensive survey of these direct learning-based deep SNN works, mainly categorized into accuracy improvement methods, efficiency improvement methods, and temporal dynamics utilization methods. In addition, we also divide these categorizations into finer granularities further to better organize and introduce them. Finally, the challenges and trends that may be faced in future research are prospected.
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Affiliation(s)
- Yufei Guo
- Intelligent Science & Technology Academy of CASIC, Beijing, China
- Scientific Research Laboratory of Aerospace Intelligent Systems and Technology, Beijing, China
| | - Xuhui Huang
- Intelligent Science & Technology Academy of CASIC, Beijing, China
- Scientific Research Laboratory of Aerospace Intelligent Systems and Technology, Beijing, China
| | - Zhe Ma
- Intelligent Science & Technology Academy of CASIC, Beijing, China
- Scientific Research Laboratory of Aerospace Intelligent Systems and Technology, Beijing, China
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Bauer FC, Lenz G, Haghighatshoar S, Sheik S. EXODUS: Stable and efficient training of spiking neural networks. Front Neurosci 2023; 17:1110444. [PMID: 36845419 PMCID: PMC9945199 DOI: 10.3389/fnins.2023.1110444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 01/09/2023] [Indexed: 02/10/2023] Open
Abstract
Introduction Spiking Neural Networks (SNNs) are gaining significant traction in machine learning tasks where energy-efficiency is of utmost importance. Training such networks using the state-of-the-art back-propagation through time (BPTT) is, however, very time-consuming. Previous work employs an efficient GPU-accelerated backpropagation algorithm called SLAYER, which speeds up training considerably. SLAYER, however, does not take into account the neuron reset mechanism while computing the gradients, which we argue to be the source of numerical instability. To counteract this, SLAYER introduces a gradient scale hyper parameter across layers, which needs manual tuning. Methods In this paper, we modify SLAYER and design an algorithm called EXODUS, that accounts for the neuron reset mechanism and applies the Implicit Function Theorem (IFT) to calculate the correct gradients (equivalent to those computed by BPTT). We furthermore eliminate the need for ad-hoc scaling of gradients, thus, reducing the training complexity tremendously. Results We demonstrate, via computer simulations, that EXODUS is numerically stable and achieves comparable or better performance than SLAYER especially in various tasks with SNNs that rely on temporal features.
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Guerrero-Criollo RJ, Castaño-López JA, Hurtado-López J, Ramirez-Moreno DF. Bio-inspired neural networks for decision-making mechanisms and neuromodulation for motor control in a differential robot. Front Neurorobot 2023; 17:1078074. [PMID: 36819006 PMCID: PMC9936153 DOI: 10.3389/fnbot.2023.1078074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 01/17/2023] [Indexed: 02/05/2023] Open
Abstract
The aim of this work is to propose bio-inspired neural networks for decision-making mechanisms and modulation of motor control of an automaton. In this work, we have adapted and applied cortical synaptic circuits, such as short-term memory circuits, winner-take-all (WTA) class competitive neural networks, modulation neural networks, and nonlinear oscillation circuits, in order to make the automaton able to avoid obstacles and explore simulated and real environments. The performance achieved by using biologically inspired neural networks to solve the task at hand is similar to that of several works mentioned in the specialized literature. Furthermore, this work contributed to bridging the fields of computational neuroscience and robotics.
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Affiliation(s)
- Roberto Jose Guerrero-Criollo
- Department of Engineering, Universidad Autónoma de Occidente, Cali, Colombia,*Correspondence: Roberto Jose Guerrero-Criollo ✉
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7
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Accelerating spiking neural networks using quantum algorithm with high success probability and high calculation accuracy. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.02.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
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8
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Liu J, Hua Y, Yang R, Luo Y, Lu H, Wang Y, Yang S, Ding X. Bio-Inspired Autonomous Learning Algorithm With Application to Mobile Robot Obstacle Avoidance. Front Neurosci 2022; 16:905596. [PMID: 35844210 PMCID: PMC9279938 DOI: 10.3389/fnins.2022.905596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Accepted: 06/08/2022] [Indexed: 11/23/2022] Open
Abstract
Spiking Neural Networks (SNNs) are often considered the third generation of Artificial Neural Networks (ANNs), owing to their high information processing capability and the accurate simulation of biological neural network behaviors. Though the research for SNNs has been quite active in recent years, there are still some challenges to applying SNNs to various potential applications, especially for robot control. In this study, a biologically inspired autonomous learning algorithm based on reward modulated spike-timing-dependent plasticity is proposed, where a novel rewarding generation mechanism is used to generate the reward signals for both learning and decision-making processes. The proposed learning algorithm is evaluated by a mobile robot obstacle avoidance task and experimental results show that the mobile robot with the proposed algorithm exhibits a good learning ability. The robot can successfully avoid obstacles in the environment after some learning trials. This provides an alternative method to design and apply the bio-inspired robot with autonomous learning capability in the typical robotic task scenario.
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Affiliation(s)
- Junxiu Liu
- School of Electronic Engineering, Guangxi Normal University, Guilin, China
| | - Yifan Hua
- School of Electronic Engineering, Guangxi Normal University, Guilin, China
| | - Rixing Yang
- College of Innovation and Entrepreneurship, Guangxi Normal University, Guilin, China
- *Correspondence: Rixing Yang
| | - Yuling Luo
- School of Electronic Engineering, Guangxi Normal University, Guilin, China
| | - Hao Lu
- School of Electronic Engineering, Guangxi Normal University, Guilin, China
| | - Yanhu Wang
- School of Electronic Engineering, Guangxi Normal University, Guilin, China
| | - Su Yang
- Department of Computer Science, Swansea University, Swansea, United Kingdom
| | - Xuemei Ding
- School of Computing, Engineering and Intelligent Systems, Ulster University, Derry, United Kingdom
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Makarov VA, Lobov SA, Shchanikov S, Mikhaylov A, Kazantsev VB. Toward Reflective Spiking Neural Networks Exploiting Memristive Devices. Front Comput Neurosci 2022; 16:859874. [PMID: 35782090 PMCID: PMC9243340 DOI: 10.3389/fncom.2022.859874] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 05/10/2022] [Indexed: 11/29/2022] Open
Abstract
The design of modern convolutional artificial neural networks (ANNs) composed of formal neurons copies the architecture of the visual cortex. Signals proceed through a hierarchy, where receptive fields become increasingly more complex and coding sparse. Nowadays, ANNs outperform humans in controlled pattern recognition tasks yet remain far behind in cognition. In part, it happens due to limited knowledge about the higher echelons of the brain hierarchy, where neurons actively generate predictions about what will happen next, i.e., the information processing jumps from reflex to reflection. In this study, we forecast that spiking neural networks (SNNs) can achieve the next qualitative leap. Reflective SNNs may take advantage of their intrinsic dynamics and mimic complex, not reflex-based, brain actions. They also enable a significant reduction in energy consumption. However, the training of SNNs is a challenging problem, strongly limiting their deployment. We then briefly overview new insights provided by the concept of a high-dimensional brain, which has been put forward to explain the potential power of single neurons in higher brain stations and deep SNN layers. Finally, we discuss the prospect of implementing neural networks in memristive systems. Such systems can densely pack on a chip 2D or 3D arrays of plastic synaptic contacts directly processing analog information. Thus, memristive devices are a good candidate for implementing in-memory and in-sensor computing. Then, memristive SNNs can diverge from the development of ANNs and build their niche, cognitive, or reflective computations.
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Affiliation(s)
- Valeri A. Makarov
- Instituto de Matemática Interdisciplinar, Universidad Complutense de Madrid, Madrid, Spain
- Department of Neurotechnologies, Research Institute of Physics and Technology, Laboratory of Stochastic Multistable Systems, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Sergey A. Lobov
- Department of Neurotechnologies, Research Institute of Physics and Technology, Laboratory of Stochastic Multistable Systems, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
- Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, Innopolis, Russia
- Center For Neurotechnology and Machine Learning, Immanuel Kant Baltic Federal University, Kaliningrad, Russia
| | - Sergey Shchanikov
- Department of Neurotechnologies, Research Institute of Physics and Technology, Laboratory of Stochastic Multistable Systems, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
- Department of Information Technologies, Vladimir State University, Vladimir, Russia
| | - Alexey Mikhaylov
- Department of Neurotechnologies, Research Institute of Physics and Technology, Laboratory of Stochastic Multistable Systems, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Viktor B. Kazantsev
- Department of Neurotechnologies, Research Institute of Physics and Technology, Laboratory of Stochastic Multistable Systems, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
- Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, Innopolis, Russia
- Center For Neurotechnology and Machine Learning, Immanuel Kant Baltic Federal University, Kaliningrad, Russia
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10
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Walking motion real-time detection method based on walking stick, IoT, COPOD and improved LightGBM. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03264-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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11
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Chen L, Ren J, Chen P, Mao X, Zhao Q. Limited text speech synthesis with electroglottograph based on Bi-LSTM and modified Tacotron-2. APPL INTELL 2022. [DOI: 10.1007/s10489-021-03075-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
AbstractThis paper proposes a framework of applying only the EGG signal for speech synthesis in the limited categories of contents scenario. EGG is a sort of physiological signal which can reflect the trends of the vocal cord movement. Note that EGG’s different acquisition method contrasted with speech signals, we exploit its application in speech synthesis under the following two scenarios. (1) To synthesize speeches under high noise circumstances, where clean speech signals are unavailable. (2) To enable dumb people who retain vocal cord vibration to speak again. Our study consists of two stages, EGG to text and text to speech. The first is a text content recognition model based on Bi-LSTM, which converts each EGG signal sample into the corresponding text with a limited class of contents. This model achieves 91.12% accuracy on the validation set in a 20-class content recognition experiment. Then the second step synthesizes speeches with the corresponding text and the EGG signal. Based on modified Tacotron-2, our model gains the Mel cepstral distortion (MCD) of 5.877 and the mean opinion score (MOS) of 3.87, which is comparable with the state-of-the-art performance and achieves an improvement by 0.42 and a relatively smaller model size than the origin Tacotron-2. Considering to introduce the characteristics of speakers contained in EGG to the final synthesized speech, we put forward a fine-grained fundamental frequency modification method, which adjusts the fundamental frequency according to EGG signals and achieves a lower MCD of 5.781 and a higher MOS of 3.94 than that without modification.
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12
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Learning general temporal point processes based on dynamic weight generation. APPL INTELL 2022. [DOI: 10.1007/s10489-021-02590-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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13
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14
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Self-supervised representation learning for detection of ACL tear injury in knee MR videos. Pattern Recognit Lett 2022. [DOI: 10.1016/j.patrec.2022.01.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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15
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Liu Q, Cong Q. Kinematic and dynamic control model of wheeled mobile robot under internet of things and neural network. THE JOURNAL OF SUPERCOMPUTING 2022; 78:8678-8707. [PMID: 35035085 PMCID: PMC8752188 DOI: 10.1007/s11227-021-04160-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 10/19/2021] [Indexed: 06/14/2023]
Abstract
This study aims to solve the issues of nonlinearity, non-integrity constraints, under-actuated systems in mobile robots. The wheeled robot is selected as the research object, and a kinematic and dynamic control model based on Internet of Things (IoT) and neural network is proposed. With the help of IoT sensors, the proposed model can realize effective control of the mobile robot under the premise of ensuring safety using the model tracking scheme and the radial basis function adaptive control algorithm. The results show that the robot can be controlled effectively to break the speed and acceleration constraints using the strategy based on the model predictive control, thus realizing smooth movement under the premise of safety. The self-adapting algorithm based on the IoT and neural network shows notable advantages in parameter uncertainty and roller skidding well. The proposed model algorithm shows a fast convergence rate of about 2 s, which has effectively improved performances in trajectory tracking and robustness of the wheeled mobile robot, and can solve the difficulties of wheeled mobile robots in practical applications, showing reliable reference value for algorithm research in this field.
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Affiliation(s)
- Qiang Liu
- Shanghai Polytechnic University, Shanghai, 201209 China
| | - Qun Cong
- Shanghai Polytechnic University, Shanghai, 201209 China
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16
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Enhancing cooperation by cognition differences and consistent representation in multi-agent reinforcement learning. APPL INTELL 2022. [DOI: 10.1007/s10489-021-02873-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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17
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Huang Y, Yu Z, Guo J, Xiang Y, Yu Z, Xian Y. Abstractive document summarization via multi-template decoding. APPL INTELL 2022. [DOI: 10.1007/s10489-021-02607-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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18
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19
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Zou C, Cui X, Kuang Y, Liu K, Wang Y, Wang X, Huang R. A Scatter-and-Gather Spiking Convolutional Neural Network on a Reconfigurable Neuromorphic Hardware. Front Neurosci 2021; 15:694170. [PMID: 34867142 PMCID: PMC8636746 DOI: 10.3389/fnins.2021.694170] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 10/15/2021] [Indexed: 11/13/2022] Open
Abstract
Artificial neural networks (ANNs), like convolutional neural networks (CNNs), have achieved the state-of-the-art results for many machine learning tasks. However, inference with large-scale full-precision CNNs must cause substantial energy consumption and memory occupation, which seriously hinders their deployment on mobile and embedded systems. Highly inspired from biological brain, spiking neural networks (SNNs) are emerging as new solutions because of natural superiority in brain-like learning and great energy efficiency with event-driven communication and computation. Nevertheless, training a deep SNN remains a main challenge and there is usually a big accuracy gap between ANNs and SNNs. In this paper, we introduce a hardware-friendly conversion algorithm called "scatter-and-gather" to convert quantized ANNs to lossless SNNs, where neurons are connected with ternary {-1,0,1} synaptic weights. Each spiking neuron is stateless and more like original McCulloch and Pitts model, because it fires at most one spike and need be reset at each time step. Furthermore, we develop an incremental mapping framework to demonstrate efficient network deployments on a reconfigurable neuromorphic chip. Experimental results show our spiking LeNet on MNIST and VGG-Net on CIFAR-10 datasetobtain 99.37% and 91.91% classification accuracy, respectively. Besides, the presented mapping algorithm manages network deployment on our neuromorphic chip with maximum resource efficiency and excellent flexibility. Our four-spike LeNet and VGG-Net on chip can achieve respective real-time inference speed of 0.38 ms/image, 3.24 ms/image, and an average power consumption of 0.28 mJ/image and 2.3 mJ/image at 0.9 V, 252 MHz, which is nearly two orders of magnitude more efficient than traditional GPUs.
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Affiliation(s)
- Chenglong Zou
- Institute of Microelectronics, Peking University, Beijing, China.,School of ECE, Peking University Shenzhen Graduate School, Shenzhen, China
| | - Xiaoxin Cui
- Institute of Microelectronics, Peking University, Beijing, China
| | - Yisong Kuang
- Institute of Microelectronics, Peking University, Beijing, China
| | - Kefei Liu
- Institute of Microelectronics, Peking University, Beijing, China
| | - Yuan Wang
- Institute of Microelectronics, Peking University, Beijing, China
| | - Xinan Wang
- School of ECE, Peking University Shenzhen Graduate School, Shenzhen, China
| | - Ru Huang
- Institute of Microelectronics, Peking University, Beijing, China
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20
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Xu C, Liu Q. An inertial neural network approach for robust time-of-arrival localization considering clock asynchronization. Neural Netw 2021; 146:98-106. [PMID: 34852299 DOI: 10.1016/j.neunet.2021.11.012] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Revised: 07/21/2021] [Accepted: 11/09/2021] [Indexed: 12/01/2022]
Abstract
This paper presents an inertial neural network to solve the source localization optimization problem with l1-norm objective function based on the time of arrival (TOA) localization technique. The convergence and stability of the inertial neural network are analyzed by the Lyapunov function method. An inertial neural network iterative approach is further used to find a better solution among the solutions with different inertial parameters. Furthermore, the clock asynchronization is considered in the TOA l1-norm model for more general real applications, and the corresponding inertial neural network iterative approach is addressed. The numerical simulations and real data are both considered in the experiments. In the simulation experiments, the noise contains uncorrelated zero-mean Gaussian noise and uniform distributed outliers. In the real experiments, the data is obtained by using the ultra wide band (UWB) technology hardware modules. Whether or not there is clock asynchronization, the results show that the proposed approach always can find a more accurate source position compared with some of the existing algorithms, which implies that the proposed approach is more effective than the compared ones.
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Affiliation(s)
- Chentao Xu
- School of Cyber Science and Engineering, Frontiers Science Center for Mobile Information Communication and Security, Southeast University, Nanjing 210096, China; Purple Mountain Laboratories, Nanjing 211111, China.
| | - Qingshan Liu
- School of Mathematics, Frontiers Science Center for Mobile Information Communication and Security, Southeast University, Nanjing 210096, China; Purple Mountain Laboratories, Nanjing 211111, China.
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21
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Imitation and mirror systems in robots through Deep Modality Blending Networks. Neural Netw 2021; 146:22-35. [PMID: 34839090 DOI: 10.1016/j.neunet.2021.11.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 09/29/2021] [Accepted: 11/04/2021] [Indexed: 11/23/2022]
Abstract
Learning to interact with the environment not only empowers the agent with manipulation capability but also generates information to facilitate building of action understanding and imitation capabilities. This seems to be a strategy adopted by biological systems, in particular primates, as evidenced by the existence of mirror neurons that seem to be involved in multi-modal action understanding. How to benefit from the interaction experience of the robots to enable understanding actions and goals of other agents is still a challenging question. In this study, we propose a novel method, deep modality blending networks (DMBN), that creates a common latent space from multi-modal experience of a robot by blending multi-modal signals with a stochastic weighting mechanism. We show for the first time that deep learning, when combined with a novel modality blending scheme, can facilitate action recognition and produce structures to sustain anatomical and effect-based imitation capabilities. Our proposed system, which is based on conditional neural processes, can be conditioned on any desired sensory/motor value at any time step, and can generate a complete multi-modal trajectory consistent with the desired conditioning in one-shot by querying the network for all the sampled time points in parallel avoiding the accumulation of prediction errors. Based on simulation experiments with an arm-gripper robot and an RGB camera, we showed that DMBN could make accurate predictions about any missing modality (camera or joint angles) given the available ones outperforming recent multimodal variational autoencoder models in terms of long-horizon high-dimensional trajectory predictions. We further showed that given desired images from different perspectives, i.e. images generated by the observation of other robots placed on different sides of the table, our system could generate image and joint angle sequences that correspond to either anatomical or effect-based imitation behavior. To achieve this mirror-like behavior, our system does not perform a pixel-based template matching but rather benefits from and relies on the common latent space constructed by using both joint and image modalities, as shown by additional experiments. Moreover, we showed that mirror learning (in our system) does not only depend on visual experience and cannot be achieved without proprioceptive experience. Our experiments showed that out of ten training scenarios with different initial configurations, the proposed DMBN model could achieve mirror learning in all of the cases where the model that only uses visual information failed in half of them. Overall, the proposed DMBN architecture not only serves as a computational model for sustaining mirror neuron-like capabilities, but also stands as a powerful machine learning architecture for high-dimensional multi-modal temporal data with robust retrieval capabilities operating with partial information in one or multiple modalities.
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Datta S, Boulgouris NV. Recognition of grammatical class of imagined words from EEG signals using convolutional neural network. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2021.08.035] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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23
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Leveraging label hierarchy using transfer and multi-task learning: A case study on patent classification. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2021.07.057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Hadri A, Laghrib A, Oummi H. An optimal variable exponent model for Magnetic Resonance Images denoising. Pattern Recognit Lett 2021. [DOI: 10.1016/j.patrec.2021.08.031] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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25
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Time delay system identification using controlled recurrent neural network and discrete bayesian optimization. APPL INTELL 2021. [DOI: 10.1007/s10489-021-02823-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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26
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Defect classification on limited labeled samples with multiscale feature fusion and semi-supervised learning. APPL INTELL 2021. [DOI: 10.1007/s10489-021-02917-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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27
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Chen J, Wang L, Duan S. A mixed-kernel, variable-dimension memristive CNN for electronic nose recognition. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2021.07.009] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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28
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Gu L, Pang C, Zheng Y, Lyu C, Lyu L. Context-aware pyramid attention network for crowd counting. APPL INTELL 2021. [DOI: 10.1007/s10489-021-02639-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
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Thermal-based early breast cancer detection using inception V3, inception V4 and modified inception MV4. Neural Comput Appl 2021; 34:333-348. [PMID: 34393379 PMCID: PMC8349135 DOI: 10.1007/s00521-021-06372-1] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Accepted: 07/26/2021] [Indexed: 11/17/2022]
Abstract
Breast cancer is one of the most significant causes of death for women around the world. Breast thermography supported by deep convolutional neural networks is expected to contribute significantly to early detection and facilitate treatment at an early stage. The goal of this study is to investigate the behavior of different recent deep learning methods for identifying breast disorders. To evaluate our proposal, we built classifiers based on deep convolutional neural networks modelling inception V3, inception V4, and a modified version of the latter called inception MV4. MV4 was introduced to maintain the computational cost across all layers by making the resultant number of features and the number of pixel positions equal. DMR database was used for these deep learning models in classifying thermal images of healthy and sick patients. A set of epochs 3–30 were used in conjunction with learning rates 1 × 10–3, 1 × 10–4 and 1 × 10–5, Minibatch 10 and different optimization methods. The training results showed that inception V4 and MV4 with color images, a learning rate of 1 × 10–4, and SGDM optimization method, reached very high accuracy, verified through several experimental repetitions. With grayscale images, inception V3 outperforms V4 and MV4 by a considerable accuracy margin, for any optimization methods. In fact, the inception V3 (grayscale) performance is almost comparable to inception V4 and MV4 (color) performance but only after 20–30 epochs. inception MV4 achieved 7% faster classification response time compared to V4. The use of MV4 model is found to contribute to saving energy consumed and fluidity in arithmetic operations for the graphic processor. The results also indicate that increasing the number of layers may not necessarily be useful in improving the performance.
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Yang J, Cho H, Ryu H, Ismail M, Mahata C, Kim S. Tunable Synaptic Characteristics of a Ti/TiO 2/Si Memory Device for Reservoir Computing. ACS APPLIED MATERIALS & INTERFACES 2021; 13:33244-33252. [PMID: 34251796 DOI: 10.1021/acsami.1c06618] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
In this study, we fabricate and characterize a Ti/TiO2/Si device with different dopant concentrations on a silicon surface for neuromorphic systems. We verify the device stack using transmission electron microscopy (TEM). The Ti/TiO2/p++Si device exhibits interface-type bipolar resistive switching with long-term memory. The potentiation and depression by the pulses of various amplitudes are demonstrated using gradual resistive switching. Moreover, pattern-recognition accuracy (>85%) is obtained in the neuromorphic system simulation when conductance is used as the weight in the network. Next, we investigate the short-term memory characteristics of the Ti/TiO2/p+Si device. The dynamic range is well-controlled by the pulse amplitude, and the conductance decay depends on the interval between the pulses. Finally, we build a reservoir computing system using the short-term effect of the Ti/TiO2/p+Si device, in which 4 bits (16 states) are differentiated by various pulse streams through the device that can be used for pattern recognition.
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Affiliation(s)
- Jinwoong Yang
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, South Korea
| | - Hyojong Cho
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, South Korea
| | - Hojeong Ryu
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, South Korea
| | - Muhammad Ismail
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, South Korea
| | - Chandreswar Mahata
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, South Korea
| | - Sungjun Kim
- Division of Electronics and Electrical Engineering, Dongguk University, Seoul 04620, South Korea
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Adaptive SNN for Anthropomorphic Finger Control. SENSORS 2021; 21:s21082730. [PMID: 33924453 PMCID: PMC8069700 DOI: 10.3390/s21082730] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 04/09/2021] [Accepted: 04/10/2021] [Indexed: 11/16/2022]
Abstract
Anthropomorphic hands that mimic the smoothness of human hand motions should be controlled by artificial units of high biological plausibility. Adaptability is among the characteristics of such control units, which provides the anthropomorphic hand with the ability to learn motions. This paper presents a simple structure of an adaptive spiking neural network implemented in analogue hardware that can be trained using Hebbian learning mechanisms to rotate the metacarpophalangeal joint of a robotic finger towards targeted angle intervals. Being bioinspired, the spiking neural network drives actuators made of shape memory alloy and receives feedback from neuromorphic sensors that convert the joint rotation angle and compression force into the spiking frequency. The adaptive SNN activates independent neural paths that correspond to angle intervals and learns in which of these intervals the rotation the finger rotation is stopped by an external force. Learning occurs when angle-specific neural paths are stimulated concurrently with the supraliminar stimulus that activates all the neurons that inhibit the SNN output stopping the finger. The results showed that after learning, the finger stopped in the angle interval in which the angle-specific neural path was active, without the activation of the supraliminar stimulus. The proposed concept can be used to implement control units for anthropomorphic robots that are able to learn motions unsupervised, based on principles of high biological plausibility.
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Lobov SA, Zharinov AI, Makarov VA, Kazantsev VB. Spatial Memory in a Spiking Neural Network with Robot Embodiment. SENSORS 2021; 21:s21082678. [PMID: 33920246 PMCID: PMC8070389 DOI: 10.3390/s21082678] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 04/06/2021] [Accepted: 04/07/2021] [Indexed: 11/16/2022]
Abstract
Cognitive maps and spatial memory are fundamental paradigms of brain functioning. Here, we present a spiking neural network (SNN) capable of generating an internal representation of the external environment and implementing spatial memory. The SNN initially has a non-specific architecture, which is then shaped by Hebbian-type synaptic plasticity. The network receives stimuli at specific loci, while the memory retrieval operates as a functional SNN response in the form of population bursts. The SNN function is explored through its embodiment in a robot moving in an arena with safe and dangerous zones. We propose a measure of the global network memory using the synaptic vector field approach to validate results and calculate information characteristics, including learning curves. We show that after training, the SNN can effectively control the robot’s cognitive behavior, allowing it to avoid dangerous regions in the arena. However, the learning is not perfect. The robot eventually visits dangerous areas. Such behavior, also observed in animals, enables relearning in time-evolving environments. If a dangerous zone moves into another place, the SNN remaps positive and negative areas, allowing escaping the catastrophic interference phenomenon known for some AI architectures. Thus, the robot adapts to changing world.
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Affiliation(s)
- Sergey A. Lobov
- Neurotechnology Department, Lobachevsky State University of Nizhny Novgorod, 23 Gagarin Ave., 603950 Nizhny Novgorod, Russia; (A.I.Z.); (V.A.M.); (V.B.K.)
- Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, 1 Universitetskaya Str., 420500 Innopolis, Russia
- Center For Neurotechnology and Machine Learning, Immanuel Kant Baltic Federal University, 14 Nevsky Str., 236016 Kaliningrad, Russia
- Correspondence:
| | - Alexey I. Zharinov
- Neurotechnology Department, Lobachevsky State University of Nizhny Novgorod, 23 Gagarin Ave., 603950 Nizhny Novgorod, Russia; (A.I.Z.); (V.A.M.); (V.B.K.)
| | - Valeri A. Makarov
- Neurotechnology Department, Lobachevsky State University of Nizhny Novgorod, 23 Gagarin Ave., 603950 Nizhny Novgorod, Russia; (A.I.Z.); (V.A.M.); (V.B.K.)
- Instituto de Matemática Interdisciplinar, Facultad de Ciencias Matemáticas, Universidad Complutense de Madrid, 28040 Madrid, Spain
| | - Victor B. Kazantsev
- Neurotechnology Department, Lobachevsky State University of Nizhny Novgorod, 23 Gagarin Ave., 603950 Nizhny Novgorod, Russia; (A.I.Z.); (V.A.M.); (V.B.K.)
- Neuroscience and Cognitive Technology Laboratory, Center for Technologies in Robotics and Mechatronics Components, Innopolis University, 1 Universitetskaya Str., 420500 Innopolis, Russia
- Center For Neurotechnology and Machine Learning, Immanuel Kant Baltic Federal University, 14 Nevsky Str., 236016 Kaliningrad, Russia
- Lab of Neurocybernetics, Russian State Scientific Center for Robotics and Technical Cybernetics, 21 Tikhoretsky Ave., St., 194064 Petersburg, Russia
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Demin VA, Nekhaev DV, Surazhevsky IA, Nikiruy KE, Emelyanov AV, Nikolaev SN, Rylkov VV, Kovalchuk MV. Necessary conditions for STDP-based pattern recognition learning in a memristive spiking neural network. Neural Netw 2020; 134:64-75. [PMID: 33291017 DOI: 10.1016/j.neunet.2020.11.005] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Revised: 09/19/2020] [Accepted: 11/12/2020] [Indexed: 11/28/2022]
Abstract
This work is aimed to study experimental and theoretical approaches for searching effective local training rules for unsupervised pattern recognition by high-performance memristor-based Spiking Neural Networks (SNNs). First, the possibility of weight change using Spike-Timing-Dependent Plasticity (STDP) is demonstrated with a pair of hardware analog neurons connected through a (CoFeB)x(LiNbO3)1-x nanocomposite memristor. Next, the learning convergence to a solution of binary clusterization task is analyzed in a wide range of memristive STDP parameters for a single-layer fully connected feedforward SNN. The memristive STDP behavior supplying convergence in this simple task is shown also to provide it in the handwritten digit recognition domain by the more complex SNN architecture with a Winner-Take-All competition between neurons. To investigate basic conditions necessary for training convergence, an original probabilistic generative model of a rate-based single-layer network with independent or competing neurons is built and thoroughly analyzed. The main result is a statement of "correlation growth-anticorrelation decay" principle which prompts near-optimal policy to configure model parameters. This principle is in line with requiring the binary clusterization convergence which can be defined as the necessary condition for optimal learning and used as the simple benchmark for tuning parameters of various neural network realizations with population-rate information coding. At last, a heuristic algorithm is described to experimentally find out the convergence conditions in a memristive SNN, including robustness to a device variability. Due to the generality of the proposed approach, it can be applied to a wide range of memristors and neurons of software- or hardware-based rate-coding single-layer SNNs when searching for local rules that ensure their unsupervised learning convergence in a pattern recognition task domain.
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Affiliation(s)
- V A Demin
- National Research Center "Kurchatov Institute", Moscow, Russia.
| | - D V Nekhaev
- National Research Center "Kurchatov Institute", Moscow, Russia
| | - I A Surazhevsky
- National Research Center "Kurchatov Institute", Moscow, Russia
| | - K E Nikiruy
- National Research Center "Kurchatov Institute", Moscow, Russia
| | - A V Emelyanov
- National Research Center "Kurchatov Institute", Moscow, Russia; Moscow Institute of Physics and Technology, Dolgoprudny, Russia
| | - S N Nikolaev
- National Research Center "Kurchatov Institute", Moscow, Russia
| | - V V Rylkov
- National Research Center "Kurchatov Institute", Moscow, Russia; Kotel'nikov Institute of Radio Engineering and Electronics RAS, 141190 Fryazino, Moscow Region, Russia
| | - M V Kovalchuk
- National Research Center "Kurchatov Institute", Moscow, Russia; Moscow Institute of Physics and Technology, Dolgoprudny, Russia; Lomonosov Moscow State University, Moscow, Russia
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Kanakov O, Gordleeva S, Zaikin A. Integrated Information in the Spiking-Bursting Stochastic Model. ENTROPY (BASEL, SWITZERLAND) 2020; 22:E1334. [PMID: 33266518 PMCID: PMC7761117 DOI: 10.3390/e22121334] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 11/18/2020] [Accepted: 11/20/2020] [Indexed: 11/17/2022]
Abstract
Integrated information has been recently suggested as a possible measure to identify a necessary condition for a system to display conscious features. Recently, we have shown that astrocytes contribute to the generation of integrated information through the complex behavior of neuron-astrocyte networks. Still, it remained unclear which underlying mechanisms governing the complex behavior of a neuron-astrocyte network are essential to generating positive integrated information. This study presents an analytic consideration of this question based on exact and asymptotic expressions for integrated information in terms of exactly known probability distributions for a reduced mathematical model (discrete-time, discrete-state stochastic model) reflecting the main features of the "spiking-bursting" dynamics of a neuron-astrocyte network. The analysis was performed in terms of the empirical "whole minus sum" version of integrated information in comparison to the "decoder based" version. The "whole minus sum" information may change sign, and an interpretation of this transition in terms of "net synergy" is available in the literature. This motivated our particular interest in the sign of the "whole minus sum" information in our analytical considerations. The behaviors of the "whole minus sum" and "decoder based" information measures are found to bear a lot of similarity-they have mutual asymptotic convergence as time-uncorrelated activity increases, and the sign transition of the "whole minus sum" information is associated with a rapid growth in the "decoder based" information. The study aims at creating a theoretical framework for using the spiking-bursting model as an analytically tractable reference point for applying integrated information concepts to systems exhibiting similar bursting behavior. The model can also be of interest as a new discrete-state test bench for different formulations of integrated information.
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Affiliation(s)
- Oleg Kanakov
- Faculty of Radiophysics, Lobachevsky State University of Nizhny Novgorod, 603950 Nizhny Novgorod, Russia;
| | - Susanna Gordleeva
- Institute of Biology and Biomedicine, Lobachevsky State University of Nizhny Novgorod, 603950 Nizhny Novgorod, Russia;
- Center for Technologies in Robotics and Mechatronics Components, Innopolis University, 420500 Innopolis, Russia
| | - Alexey Zaikin
- Institute of Information Technology, Mathematics and Mechanics, Lobachevsky State University of Nizhny Novgorod, 603950 Nizhny Novgorod, Russia
- Institute for Women’s Health and Department of Mathematics, University College London, London WC1E 6BT, UK
- Centre for Analysis of Complex Systems, Sechenov University, 119991 Moscow, Russia
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36
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Spaeth A, Tebyani M, Haussler D, Teodorescu M. Spiking neural state machine for gait frequency entrainment in a flexible modular robot. PLoS One 2020; 15:e0240267. [PMID: 33085673 PMCID: PMC7577446 DOI: 10.1371/journal.pone.0240267] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Accepted: 09/22/2020] [Indexed: 12/02/2022] Open
Abstract
We propose a modular architecture for neuromorphic closed-loop control based on bistable relaxation oscillator modules consisting of three spiking neurons each. Like its biological prototypes, this basic component is robust to parameter variation but can be modulated by external inputs. By combining these modules, we can construct a neural state machine capable of generating the cyclic or repetitive behaviors necessary for legged locomotion. A concrete case study for the approach is provided by a modular robot constructed from flexible plastic volumetric pixels, in which we produce a forward crawling gait entrained to the natural frequency of the robot by a minimal system of twelve neurons organized into four modules.
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Affiliation(s)
- Alex Spaeth
- Department of Electrical and Computer Engineering, University of California, Santa Cruz, Santa Cruz, California, United States of America
- Genomics Institute, University of California, Santa Cruz, Santa Cruz, California, United States of America
- * E-mail:
| | - Maryam Tebyani
- Department of Electrical and Computer Engineering, University of California, Santa Cruz, Santa Cruz, California, United States of America
| | - David Haussler
- Genomics Institute, University of California, Santa Cruz, Santa Cruz, California, United States of America
- Howard Hughes Medical Institute, University of California, Santa Cruz, Santa Cruz, California, United States of America
| | - Mircea Teodorescu
- Department of Electrical and Computer Engineering, University of California, Santa Cruz, Santa Cruz, California, United States of America
- Genomics Institute, University of California, Santa Cruz, Santa Cruz, California, United States of America
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Shen Z, Zhao C, Qi Y, Xu W, Liu Y, Mitrovic IZ, Yang L, Zhao C. Advances of RRAM Devices: Resistive Switching Mechanisms, Materials and Bionic Synaptic Application. NANOMATERIALS (BASEL, SWITZERLAND) 2020; 10:E1437. [PMID: 32717952 PMCID: PMC7466260 DOI: 10.3390/nano10081437] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Revised: 07/15/2020] [Accepted: 07/19/2020] [Indexed: 11/24/2022]
Abstract
Resistive random access memory (RRAM) devices are receiving increasing extensive attention due to their enhanced properties such as fast operation speed, simple device structure, low power consumption, good scalability potential and so on, and are currently considered to be one of the next-generation alternatives to traditional memory. In this review, an overview of RRAM devices is demonstrated in terms of thin film materials investigation on electrode and function layer, switching mechanisms and artificial intelligence applications. Compared with the well-developed application of inorganic thin film materials (oxides, solid electrolyte and two-dimensional (2D) materials) in RRAM devices, organic thin film materials (biological and polymer materials) application is considered to be the candidate with significant potential. The performance of RRAM devices is closely related to the investigation of switching mechanisms in this review, including thermal-chemical mechanism (TCM), valance change mechanism (VCM) and electrochemical metallization (ECM). Finally, the bionic synaptic application of RRAM devices is under intensive consideration, its main characteristics such as potentiation/depression response, short-/long-term plasticity (STP/LTP), transition from short-term memory to long-term memory (STM to LTM) and spike-time-dependent plasticity (STDP) reveal the great potential of RRAM devices in the field of neuromorphic application.
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Affiliation(s)
- Zongjie Shen
- Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China; (Z.S.); (Y.Q.); (C.Z.)
- Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool L69 3BX, UK;
| | - Chun Zhao
- Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China; (Z.S.); (Y.Q.); (C.Z.)
- Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool L69 3BX, UK;
| | - Yanfei Qi
- Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China; (Z.S.); (Y.Q.); (C.Z.)
- School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an 710061, China
| | - Wangying Xu
- College of Materials Science and Engineering, Shenzhen University, Shenzhen 518060, China;
| | - Yina Liu
- Department of Mathematical Sciences, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China;
| | - Ivona Z. Mitrovic
- Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool L69 3BX, UK;
| | - Li Yang
- Department of Chemistry, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China;
| | - Cezhou Zhao
- Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China; (Z.S.); (Y.Q.); (C.Z.)
- Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool L69 3BX, UK;
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Mikhaylov A, Pimashkin A, Pigareva Y, Gerasimova S, Gryaznov E, Shchanikov S, Zuev A, Talanov M, Lavrov I, Demin V, Erokhin V, Lobov S, Mukhina I, Kazantsev V, Wu H, Spagnolo B. Neurohybrid Memristive CMOS-Integrated Systems for Biosensors and Neuroprosthetics. Front Neurosci 2020; 14:358. [PMID: 32410943 PMCID: PMC7199501 DOI: 10.3389/fnins.2020.00358] [Citation(s) in RCA: 110] [Impact Index Per Article: 27.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Accepted: 03/24/2020] [Indexed: 11/18/2022] Open
Abstract
Here we provide a perspective concept of neurohybrid memristive chip based on the combination of living neural networks cultivated in microfluidic/microelectrode system, metal-oxide memristive devices or arrays integrated with mixed-signal CMOS layer to control the analog memristive circuits, process the decoded information, and arrange a feedback stimulation of biological culture as parts of a bidirectional neurointerface. Our main focus is on the state-of-the-art approaches for cultivation and spatial ordering of the network of dissociated hippocampal neuron cells, fabrication of a large-scale cross-bar array of memristive devices tailored using device engineering, resistive state programming, or non-linear dynamics, as well as hardware implementation of spiking neural networks (SNNs) based on the arrays of memristive devices and integrated CMOS electronics. The concept represents an example of a brain-on-chip system belonging to a more general class of memristive neurohybrid systems for a new-generation robotics, artificial intelligence, and personalized medicine, discussed in the framework of the proposed roadmap for the next decade period.
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Affiliation(s)
- Alexey Mikhaylov
- Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Alexey Pimashkin
- Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Yana Pigareva
- Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | | | - Evgeny Gryaznov
- Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Sergey Shchanikov
- Department of Information Technologies, Vladimir State University, Murom, Russia
| | - Anton Zuev
- Department of Information Technologies, Vladimir State University, Murom, Russia
| | - Max Talanov
- Neuroscience Laboratory, Kazan Federal University, Kazan, Russia
| | - Igor Lavrov
- Department of Neurologic Surgery, Mayo Clinic, Rochester, MN, United States
- Laboratory of Motor Neurorehabilitation, Kazan Federal University, Kazan, Russia
| | | | - Victor Erokhin
- Neuroscience Laboratory, Kazan Federal University, Kazan, Russia
- Kurchatov Institute, Moscow, Russia
- CNR-Institute of Materials for Electronics and Magnetism, Italian National Research Council, Parma, Italy
| | - Sergey Lobov
- Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
- Center for Technologies in Robotics and Mechatronics Components, Innopolis University, Innopolis, Russia
| | - Irina Mukhina
- Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
- Cell Technology Group, Privolzhsky Research Medical University, Nizhny Novgorod, Russia
| | - Victor Kazantsev
- Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
- Center for Technologies in Robotics and Mechatronics Components, Innopolis University, Innopolis, Russia
| | - Huaqiang Wu
- Institute of Microelectronics, Tsinghua University, Beijing, China
| | - Bernardo Spagnolo
- Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
- Dipartimento di Fisica e Chimica-Emilio Segrè, Group of Interdisciplinary Theoretical Physics, Università di Palermo and CNISM, Unità di Palermo, Palermo, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione di Catania, Catania, Italy
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