1
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Manninen T, Aćimović J, Linne ML. Analysis of Network Models with Neuron-Astrocyte Interactions. Neuroinformatics 2023; 21:375-406. [PMID: 36959372 PMCID: PMC10085960 DOI: 10.1007/s12021-023-09622-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/01/2023] [Indexed: 03/25/2023]
Abstract
Neural networks, composed of many neurons and governed by complex interactions between them, are a widely accepted formalism for modeling and exploring global dynamics and emergent properties in brain systems. In the past decades, experimental evidence of computationally relevant neuron-astrocyte interactions, as well as the astrocytic modulation of global neural dynamics, have accumulated. These findings motivated advances in computational glioscience and inspired several models integrating mechanisms of neuron-astrocyte interactions into the standard neural network formalism. These models were developed to study, for example, synchronization, information transfer, synaptic plasticity, and hyperexcitability, as well as classification tasks and hardware implementations. We here focus on network models of at least two neurons interacting bidirectionally with at least two astrocytes that include explicitly modeled astrocytic calcium dynamics. In this study, we analyze the evolution of these models and the biophysical, biochemical, cellular, and network mechanisms used to construct them. Based on our analysis, we propose how to systematically describe and categorize interaction schemes between cells in neuron-astrocyte networks. We additionally study the models in view of the existing experimental data and present future perspectives. Our analysis is an important first step towards understanding astrocytic contribution to brain functions. However, more advances are needed to collect comprehensive data about astrocyte morphology and physiology in vivo and to better integrate them in data-driven computational models. Broadening the discussion about theoretical approaches and expanding the computational tools is necessary to better understand astrocytes' roles in brain functions.
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Affiliation(s)
- Tiina Manninen
- Faculty of Medicine and Health Technology, Tampere University, Korkeakoulunkatu 3, FI-33720, Tampere, Finland.
| | - Jugoslava Aćimović
- Faculty of Medicine and Health Technology, Tampere University, Korkeakoulunkatu 3, FI-33720, Tampere, Finland
| | - Marja-Leena Linne
- Faculty of Medicine and Health Technology, Tampere University, Korkeakoulunkatu 3, FI-33720, Tampere, Finland.
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2
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Veisi N, Karimi G, Ranjbar M, Abbott D. Role of astrocytes in the self-repairing characteristics of analog neural networks. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.01.077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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3
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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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4
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Rahiminejad E, Azad F, Parvizi-Fard A, Amiri M, Linares-Barranco B. A Neuromorphic CMOS Circuit With Self-Repairing Capability. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022; 33:2246-2258. [PMID: 33417568 DOI: 10.1109/tnnls.2020.3045019] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Neurophysiological observations confirm that the brain not only is able to detect the impaired synapses (in brain damage) but also it is relatively capable of repairing faulty synapses. It has been shown that retrograde signaling by astrocytes leads to the modulation of synaptic transmission and thus bidirectional collaboration of astrocyte with nearby neurons is an important aspect of self-repairing mechanism. Specifically, the retrograde signaling via astrocyte can increase the transmission probability of the healthy synapses linked to the neuron. Motivated by these findings, in the present research, a CMOS neuromorphic circuit with self-repairing capabilities is proposed based on astrocyte signaling. In this way, the computational model of self-repairing process is hired as a basis for designing a novel analog integrated circuit in the 180-nm CMOS technology. It is illustrated that the proposed analog circuit is able to successfully recompense the damaged synapses by appropriately modifying the voltage signals of the remaining healthy synapses in the wide range of frequency. The proposed circuit occupies 7500- [Formula: see text] silicon area and its power consumption is about [Formula: see text]. This neuromorphic fault-tolerant circuit can be considered as a key candidate for future silicon neuronal systems and implementation of neurorobotic and neuro-inspired circuits.
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5
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Hong Q, Chen H, Sun J, Wang C. Memristive Circuit Implementation of a Self-Repairing Network Based on Biological Astrocytes in Robot Application. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022; 33:2106-2120. [PMID: 33382661 DOI: 10.1109/tnnls.2020.3041624] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
A large number of studies have shown that astrocytes can be combined with the presynaptic terminals and postsynaptic spines of neurons to constitute a triple synapse via an endocannabinoid retrograde messenger to achieve a self-repair ability in the human brain. Inspired by the biological self-repair mechanism of astrocytes, this work proposes a self-repairing neuron network circuit that utilizes a memristor to simulate changes in neurotransmitters when a set threshold is reached. The proposed circuit simulates an astrocyte-neuron network and comprises the following: 1) a single-astrocyte-neuron circuit module; 2) an astrocyte-neuron network circuit; 3) a module to detect malfunctions; and 4) a neuron PR (release probability of synaptic transmission) enhancement module. When faults occur in a synapse, the neuron module becomes silent or near silent because of the low PR of the synapses. The circuit can detect faults automatically. The damaged neuron can be repaired by enhancing the PR of other healthy neurons, analogous to the biological repair mechanism of astrocytes. This mechanism helps to repair the damaged circuit. A simulation of the circuit revealed the following: 1) as the number of neurons in the circuit increases, the self-repair ability strengthens and 2) as the number of damaged neurons in the astrocyte-neuron network increases, the self-repair ability weakens, and there is a significant degradation in the performance of the circuit. The self-repairing circuit was used for a robot, and it effectively improved the robots' performance and reliability.
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6
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Controlling synchronization of gamma oscillations by astrocytic modulation in a model hippocampal neural network. Sci Rep 2022; 12:6970. [PMID: 35484169 PMCID: PMC9050920 DOI: 10.1038/s41598-022-10649-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 04/11/2022] [Indexed: 12/13/2022] Open
Abstract
Recent in vitro and in vivo experiments demonstrate that astrocytes participate in the maintenance of cortical gamma oscillations and recognition memory. However, the mathematical understanding of the underlying dynamical mechanisms remains largely incomplete. Here we investigate how the interplay of slow modulatory astrocytic signaling with fast synaptic transmission controls coherent oscillations in the network of hippocampal interneurons that receive inputs from pyramidal cells. We show that the astrocytic regulation of signal transmission between neurons improves the firing synchrony and extends the region of coherent oscillations in the biologically relevant values of synaptic conductance. Astrocyte-mediated potentiation of inhibitory synaptic transmission markedly enhances the coherence of network oscillations over a broad range of model parameters. Astrocytic regulation of excitatory synaptic input improves the robustness of interneuron network gamma oscillations induced by physiologically relevant excitatory model drive. These findings suggest a mechanism, by which the astrocytes become involved in cognitive function and information processing through modulating fast neural network dynamics.
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7
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Bistability and Chaos Emergence in Spontaneous Dynamics of Astrocytic Calcium Concentration. MATHEMATICS 2022. [DOI: 10.3390/math10081337] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
In this work, we consider a mathematical model describing spontaneous calcium signaling in astrocytes. Based on biologically relevant principles, this model simulates experimentally observed calcium oscillations and can predict the emergence of complicated dynamics. Using analytical and numerical analysis, various attracting sets were found and investigated. Employing bifurcation theory analysis, we examined steady state solutions, bistability, simple and complicated periodic limit cycles and also chaotic attractors. We found that astrocytes possess a variety of complex dynamical modes, including chaos and multistability, that can further provide different modulations of neuronal circuits, enhancing their plasticity and flexibility.
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8
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Astrocytes mediate analogous memory in a multi-layer neuron–astrocyte network. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-06936-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
AbstractModeling the neuronal processes underlying short-term working memory remains the focus of many theoretical studies in neuroscience. In this paper, we propose a mathematical model of a spiking neural network (SNN) which simulates the way a fragment of information is maintained as a robust activity pattern for several seconds and the way it completely disappears if no other stimuli are fed to the system. Such short-term memory traces are preserved due to the activation of astrocytes accompanying the SNN. The astrocytes exhibit calcium transients at a time scale of seconds. These transients further modulate the efficiency of synaptic transmission and, hence, the firing rate of neighboring neurons at diverse timescales through gliotransmitter release. We demonstrate how such transients continuously encode frequencies of neuronal discharges and provide robust short-term storage of analogous information. This kind of short-term memory can store relevant information for seconds and then completely forget it to avoid overlapping with forthcoming patterns. The SNN is inter-connected with the astrocytic layer by local inter-cellular diffusive connections. The astrocytes are activated only when the neighboring neurons fire synchronously, e.g., when an information pattern is loaded. For illustration, we took grayscale photographs of people’s faces where the shades of gray correspond to the level of applied current which stimulates the neurons. The astrocyte feedback modulates (facilitates) synaptic transmission by varying the frequency of neuronal firing. We show how arbitrary patterns can be loaded, then stored for a certain interval of time, and retrieved if the appropriate clue pattern is applied to the input.
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9
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Gordleeva SY, Tsybina YA, Krivonosov MI, Ivanchenko MV, Zaikin AA, Kazantsev VB, Gorban AN. Modeling Working Memory in a Spiking Neuron Network Accompanied by Astrocytes. Front Cell Neurosci 2021; 15:631485. [PMID: 33867939 PMCID: PMC8044545 DOI: 10.3389/fncel.2021.631485] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Accepted: 03/04/2021] [Indexed: 01/07/2023] Open
Abstract
We propose a novel biologically plausible computational model of working memory (WM) implemented by a spiking neuron network (SNN) interacting with a network of astrocytes. The SNN is modeled by synaptically coupled Izhikevich neurons with a non-specific architecture connection topology. Astrocytes generating calcium signals are connected by local gap junction diffusive couplings and interact with neurons via chemicals diffused in the extracellular space. Calcium elevations occur in response to the increased concentration of the neurotransmitter released by spiking neurons when a group of them fire coherently. In turn, gliotransmitters are released by activated astrocytes modulating the strength of the synaptic connections in the corresponding neuronal group. Input information is encoded as two-dimensional patterns of short applied current pulses stimulating neurons. The output is taken from frequencies of transient discharges of corresponding neurons. We show how a set of information patterns with quite significant overlapping areas can be uploaded into the neuron-astrocyte network and stored for several seconds. Information retrieval is organized by the application of a cue pattern representing one from the memory set distorted by noise. We found that successful retrieval with the level of the correlation between the recalled pattern and ideal pattern exceeding 90% is possible for the multi-item WM task. Having analyzed the dynamical mechanism of WM formation, we discovered that astrocytes operating at a time scale of a dozen of seconds can successfully store traces of neuronal activations corresponding to information patterns. In the retrieval stage, the astrocytic network selectively modulates synaptic connections in the SNN leading to successful recall. Information and dynamical characteristics of the proposed WM model agrees with classical concepts and other WM models.
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Affiliation(s)
- Susanna Yu Gordleeva
- Scientific and Educational Mathematical Center "Mathematics of Future Technology," 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
| | - Yuliya A Tsybina
- Scientific and Educational Mathematical Center "Mathematics of Future Technology," Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Mikhail I Krivonosov
- Scientific and Educational Mathematical Center "Mathematics of Future Technology," Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Mikhail V Ivanchenko
- Scientific and Educational Mathematical Center "Mathematics of Future Technology," Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Alexey A Zaikin
- Scientific and Educational Mathematical Center "Mathematics of Future Technology," Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia.,Center for Analysis of Complex Systems, Sechenov First Moscow State Medical University, Sechenov University, Moscow, Russia.,Institute for Women's Health and Department of Mathematics, University College London, London, United Kingdom
| | - Victor B Kazantsev
- Scientific and Educational Mathematical Center "Mathematics of Future Technology," 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.,Neuroscience Research Institute, Samara State Medical University, Samara, Russia
| | - Alexander N Gorban
- Scientific and Educational Mathematical Center "Mathematics of Future Technology," Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia.,Department of Mathematics, University of Leicester, Leicester, United Kingdom
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10
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Rastogi M, Lu S, Islam N, Sengupta A. On the Self-Repair Role of Astrocytes in STDP Enabled Unsupervised SNNs. Front Neurosci 2021; 14:603796. [PMID: 33519358 PMCID: PMC7841294 DOI: 10.3389/fnins.2020.603796] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Accepted: 11/27/2020] [Indexed: 11/29/2022] Open
Abstract
Neuromorphic computing is emerging to be a disruptive computational paradigm that attempts to emulate various facets of the underlying structure and functionalities of the brain in the algorithm and hardware design of next-generation machine learning platforms. This work goes beyond the focus of current neuromorphic computing architectures on computational models for neuron and synapse to examine other computational units of the biological brain that might contribute to cognition and especially self-repair. We draw inspiration and insights from computational neuroscience regarding functionalities of glial cells and explore their role in the fault-tolerant capacity of Spiking Neural Networks (SNNs) trained in an unsupervised fashion using Spike-Timing Dependent Plasticity (STDP). We characterize the degree of self-repair that can be enabled in such networks with varying degree of faults ranging from 50 to 90% and evaluate our proposal on the MNIST and Fashion-MNIST datasets.
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Affiliation(s)
- Mehul Rastogi
- School of Electrical Engineering and Computer Science, Pennsylvania State University (PSU), University Park, PA, United States
- Department of Computer Science and Information Systems, Birla Institute of Technology and Science Pilani, Goa Campus, India
| | - Sen Lu
- School of Electrical Engineering and Computer Science, Pennsylvania State University (PSU), University Park, PA, United States
| | - Nafiul Islam
- School of Electrical Engineering and Computer Science, Pennsylvania State University (PSU), University Park, PA, United States
| | - Abhronil Sengupta
- School of Electrical Engineering and Computer Science, Pennsylvania State University (PSU), University Park, PA, United States
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11
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Pang L, Liu J, Harkin J, Martin G, McElholm M, Javed A, McDaid L. Case Study-Spiking Neural Network Hardware System for Structural Health Monitoring. SENSORS (BASEL, SWITZERLAND) 2020; 20:s20185126. [PMID: 32911869 PMCID: PMC7570929 DOI: 10.3390/s20185126] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Revised: 09/01/2020] [Accepted: 09/03/2020] [Indexed: 05/14/2023]
Abstract
This case study provides feasibility analysis of adapting Spiking Neural Networks (SNN) based Structural Health Monitoring (SHM) system to explore low-cost solution for inspection of structural health of damaged buildings which survived after natural disaster that is, earthquakes or similar activities. Various techniques are used to detect the structural health status of a building for performance benchmarking, including different feature extraction methods and classification techniques (e.g., SNN, K-means and artificial neural network etc.). The SNN is utilized to process the sensory data generated from full-scale seven-story reinforced concrete building to verify the classification performances. Results show that the proposed SNN hardware has high classification accuracy, reliability, longevity and low hardware area overhead.
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Affiliation(s)
- Lili Pang
- Industrial Center/School of Innovation and Entrepreneurship, Nanjing Institute of Technology, Nanjing 211167, China
- Correspondence: (L.P.); (J.L.)
| | - Junxiu Liu
- School of Computing, Engineering and Intelligent Systems, Ulster University, Derry BT48 7JL, UK; (J.H.); (G.M.); (M.M.); (A.J.); (L.M.)
- Correspondence: (L.P.); (J.L.)
| | - Jim Harkin
- School of Computing, Engineering and Intelligent Systems, Ulster University, Derry BT48 7JL, UK; (J.H.); (G.M.); (M.M.); (A.J.); (L.M.)
| | - George Martin
- School of Computing, Engineering and Intelligent Systems, Ulster University, Derry BT48 7JL, UK; (J.H.); (G.M.); (M.M.); (A.J.); (L.M.)
| | - Malachy McElholm
- School of Computing, Engineering and Intelligent Systems, Ulster University, Derry BT48 7JL, UK; (J.H.); (G.M.); (M.M.); (A.J.); (L.M.)
| | - Aqib Javed
- School of Computing, Engineering and Intelligent Systems, Ulster University, Derry BT48 7JL, UK; (J.H.); (G.M.); (M.M.); (A.J.); (L.M.)
| | - Liam McDaid
- School of Computing, Engineering and Intelligent Systems, Ulster University, Derry BT48 7JL, UK; (J.H.); (G.M.); (M.M.); (A.J.); (L.M.)
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12
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Liu J, Wu G, Luo Y, Qiu S, Yang S, Li W, Bi Y. EEG-Based Emotion Classification Using a Deep Neural Network and Sparse Autoencoder. Front Syst Neurosci 2020; 14:43. [PMID: 32982703 PMCID: PMC7492909 DOI: 10.3389/fnsys.2020.00043] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Accepted: 06/12/2020] [Indexed: 11/13/2022] Open
Abstract
Emotion classification based on brain-computer interface (BCI) systems is an appealing research topic. Recently, deep learning has been employed for the emotion classifications of BCI systems and compared to traditional classification methods improved results have been obtained. In this paper, a novel deep neural network is proposed for emotion classification using EEG systems, which combines the Convolutional Neural Network (CNN), Sparse Autoencoder (SAE), and Deep Neural Network (DNN) together. In the proposed network, the features extracted by the CNN are first sent to SAE for encoding and decoding. Then the data with reduced redundancy are used as the input features of a DNN for classification task. The public datasets of DEAP and SEED are used for testing. Experimental results show that the proposed network is more effective than conventional CNN methods on the emotion recognitions. For the DEAP dataset, the highest recognition accuracies of 89.49% and 92.86% are achieved for valence and arousal, respectively. For the SEED dataset, however, the best recognition accuracy reaches 96.77%. By combining the CNN, SAE, and DNN and training them separately, the proposed network is shown as an efficient method with a faster convergence than the conventional CNN.
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Affiliation(s)
- Junxiu Liu
- School of Electronic Engineering, Guangxi Normal University, Guilin, China
- Guangxi Key Lab of Multi-Source Information Mining & Security, Guangxi Normal University, Guilin, China
| | - Guopei Wu
- School of Electronic Engineering, Guangxi Normal University, Guilin, China
- Guangxi Key Lab of Multi-Source Information Mining & Security, Guangxi Normal University, Guilin, China
| | - Yuling Luo
- School of Electronic Engineering, Guangxi Normal University, Guilin, China
- Guangxi Key Lab of Multi-Source Information Mining & Security, Guangxi Normal University, Guilin, China
| | - Senhui Qiu
- School of Electronic Engineering, Guangxi Normal University, Guilin, China
- Guangxi Key Lab of Multi-Source Information Mining & Security, Guangxi Normal University, Guilin, China
- Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing, Guilin, China
| | - Su Yang
- Department of Computer Science and Software Engineering, Xi'an Jiaotong-Liverpool University, Suzhou, China
| | - Wei Li
- Academy for Engineering & Technology, Fudan University, Shanghai, China
- Department of Electronic Engineering, The University of York, York, United Kingdom
| | - Yifei Bi
- College of Foreign Languages, University of Shanghai for Science and Technology, Shanghai, China
- Department of Psychology, The University of York, York, United Kingdom
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13
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Hardware-Intrinsic Multi-Layer Security: A New Frontier for 5G Enabled IIoT. SENSORS 2020; 20:s20071963. [PMID: 32244458 PMCID: PMC7180754 DOI: 10.3390/s20071963] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Revised: 03/24/2020] [Accepted: 03/28/2020] [Indexed: 11/17/2022]
Abstract
The introduction of 5G communication capabilities presents additional challenges for the development of products and services that can fully exploit the opportunities offered by high bandwidth, low latency networking. This is particularly relevant to an emerging interest in the Industrial Internet of Things (IIoT), which is a foundation stone of recent technological revolutions such as Digital Manufacturing. A crucial aspect of this is to securely authenticate complex transactions between IIoT devices, whilst marshalling adversarial requests for system authorisation, without the need for a centralised authentication mechanism which cannot scale to the size needed. In this article we combine Physically Unclonable Function (PUF) hardware (using Field Programmable Gate Arrays—FPGAs), together with a multi-layer approach to cloud computing from the National Institute of Standards and Technology (NIST). Through this, we demonstrate an approach to facilitate the development of improved multi-layer authentication mechanisms. We extend prior work to utilise hardware security primitives for adversarial trojan detection, which is inspired by a biological approach to parameter analysis. This approach is an effective demonstration of attack prevention, both from internal and external adversaries. The security is further hardened through observation of the device parameters of connected IIoT equipment. We demonstrate that the proposed architecture can service a significantly high load of device authentication requests using a multi-layer architecture in an arbitrarily acceptable time of less than 1 second.
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14
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Lobov SA, Mikhaylov AN, Shamshin M, Makarov VA, Kazantsev VB. Spatial Properties of STDP in a Self-Learning Spiking Neural Network Enable Controlling a Mobile Robot. Front Neurosci 2020; 14:88. [PMID: 32174804 PMCID: PMC7054464 DOI: 10.3389/fnins.2020.00088] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Accepted: 01/22/2020] [Indexed: 11/13/2022] Open
Abstract
Development of spiking neural networks (SNNs) controlling mobile robots is one of the modern challenges in computational neuroscience and artificial intelligence. Such networks, being replicas of biological ones, are expected to have a higher computational potential than traditional artificial neural networks (ANNs). The critical problem is in the design of robust learning algorithms aimed at building a “living computer” based on SNNs. Here, we propose a simple SNN equipped with a Hebbian rule in the form of spike-timing-dependent plasticity (STDP). The SNN implements associative learning by exploiting the spatial properties of STDP. We show that a LEGO robot controlled by the SNN can exhibit classical and operant conditioning. Competition of spike-conducting pathways in the SNN plays a fundamental role in establishing associations of neural connections. It replaces the irrelevant associations by new ones in response to a change in stimuli. Thus, the robot gets the ability to relearn when the environment changes. The proposed SNN and the stimulation protocol can be further enhanced and tested in developing neuronal cultures, and also admit the use of memristive devices for hardware implementation.
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Affiliation(s)
- Sergey A Lobov
- Neurotechnology Department, 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
| | - Alexey N Mikhaylov
- Neurotechnology Department, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Maxim Shamshin
- Neurotechnology Department, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Valeri A Makarov
- Neurotechnology Department, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia.,Instituto de Matemática Interdisciplinar, Facultad de Ciencias Matemáticas, Universidad Complutense de Madrid, Madrid, Spain
| | - Victor B Kazantsev
- Neurotechnology Department, 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
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15
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Bio-Inspired Approaches to Safety and Security in IoT-Enabled Cyber-Physical Systems. SENSORS 2020; 20:s20030844. [PMID: 32033269 PMCID: PMC7038767 DOI: 10.3390/s20030844] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Revised: 02/02/2020] [Accepted: 02/02/2020] [Indexed: 11/17/2022]
Abstract
Internet of Things (IoT) and Cyber-Physical Systems (CPS) have profoundly influenced the way individuals and enterprises interact with the world. Although attacks on IoT devices are becoming more commonplace, security metrics often focus on software, network, and cloud security. For CPS systems employed in IoT applications, the implementation of hardware security is crucial. The identity of electronic circuits measured in terms of device parameters serves as a fingerprint. Estimating the parameters of this fingerprint assists the identification and prevention of Trojan attacks in a CPS. We demonstrate a bio-inspired approach for hardware Trojan detection using unsupervised learning methods. The bio-inspired principles of pattern identification use a Spiking Neural Network (SNN), and glial cells form the basis of this work. When hardware device parameters are in an acceptable range, the design produces a stable firing pattern. When unbalanced, the firing rate reduces to zero, indicating the presence of a Trojan. This network is tunable to accommodate natural variations in device parameters and to avoid false triggering of Trojan alerts. The tolerance is tuned using bio-inspired principles for various security requirements, such as forming high-alert systems for safety-critical missions. The Trojan detection circuit is resilient to a range of faults and attacks, both intentional and unintentional. Also, we devise a design-for-trust architecture by developing a bio-inspired device-locking mechanism. The proposed architecture is implemented on a Xilinx Artix-7 Field Programmable Gate Array (FPGA) device. Results demonstrate the suitability of the proposal for resource-constrained environments with minimal hardware and power dissipation profiles. The design is tested with a wide range of device parameters to demonstrate the effectiveness of Trojan detection. This work serves as a new approach to enable secure CPSs and to employ bio-inspired unsupervised machine intelligence.
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Filippov VA, Bobylev AN, Busygin AN, Pisarev AD, Udovichenko SY. A biomorphic neuron model and principles of designing a neural network with memristor synapses for a biomorphic neuroprocessor. Neural Comput Appl 2019. [DOI: 10.1007/s00521-019-04383-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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Liu J, McDaid L, Araque A, Wade J, Harkin J, Karim S, Henshall DC, Connolly NMC, Johnson AP, Tyrrell AM, Timmis J, Millard AG, Hilder J, Halliday DM. GABA Regulation of Burst Firing in Hippocampal Astrocyte Neural Circuit: A Biophysical Model. Front Cell Neurosci 2019; 13:335. [PMID: 31396055 PMCID: PMC6664076 DOI: 10.3389/fncel.2019.00335] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2019] [Accepted: 07/08/2019] [Indexed: 12/30/2022] Open
Abstract
It is now widely accepted that glia cells and gamma-aminobutyric acidergic (GABA) interneurons dynamically regulate synaptic transmission and neuronal activity in time and space. This paper presents a biophysical model that captures the interaction between an astrocyte cell, a GABA interneuron and pre/postsynaptic neurons. Specifically, GABA released from a GABA interneuron triggers in astrocytes the release of calcium (Ca2+) from the endoplasmic reticulum via the inositol 1, 4, 5-trisphosphate (IP3) pathway. This results in gliotransmission which elevates the presynaptic transmission probability rate (PR) causing weight potentiation and a gradual increase in postsynaptic neuronal firing, that eventually stabilizes. However, by capturing the complex interactions between IP3, generated from both GABA and the 2-arachidonyl glycerol (2-AG) pathway, and PR, this paper shows that this interaction not only gives rise to an initial weight potentiation phase but also this phase is followed by postsynaptic bursting behavior. Moreover, the model will show that there is a presynaptic frequency range over which burst firing can occur. The proposed model offers a novel cellular level mechanism that may underpin both seizure-like activity and neuronal synchrony across different brain regions.
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Affiliation(s)
- Junxiu Liu
- School of Computing, Engineering and Intelligent Systems, Ulster University, Derry, United Kingdom
| | - Liam McDaid
- School of Computing, Engineering and Intelligent Systems, Ulster University, Derry, United Kingdom
| | - Alfonso Araque
- Department of Neuroscience, University of Minnesota, Minneapolis, MN, United States
| | - John Wade
- School of Computing, Engineering and Intelligent Systems, Ulster University, Derry, United Kingdom
| | - Jim Harkin
- School of Computing, Engineering and Intelligent Systems, Ulster University, Derry, United Kingdom
| | - Shvan Karim
- School of Computing, Engineering and Intelligent Systems, Ulster University, Derry, United Kingdom
| | - David C Henshall
- Department of Physiology and Medical Physics, Royal College of Surgeons in Ireland, Dublin, Ireland.,FutureNeuro Research Centre, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Niamh M C Connolly
- Department of Physiology and Medical Physics, Royal College of Surgeons in Ireland, Dublin, Ireland
| | - Anju P Johnson
- Department of Electronic Engineering, University of York, York, United Kingdom
| | - Andy M Tyrrell
- Department of Electronic Engineering, University of York, York, United Kingdom
| | - Jon Timmis
- Department of Electronic Engineering, University of York, York, United Kingdom
| | - Alan G Millard
- Department of Electronic Engineering, University of York, York, United Kingdom
| | - James Hilder
- Department of Electronic Engineering, University of York, York, United Kingdom
| | - David M Halliday
- Department of Electronic Engineering, University of York, York, United Kingdom
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