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Rößler N, Jungenitz T, Sigler A, Bird A, Mittag M, Rhee JS, Deller T, Cuntz H, Brose N, Schwarzacher SW, Jedlicka P. Skewed distribution of spines is independent of presynaptic transmitter release and synaptic plasticity, and emerges early during adult neurogenesis. Open Biol 2023; 13:230063. [PMID: 37528732 PMCID: PMC10394416 DOI: 10.1098/rsob.230063] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/03/2023] Open
Abstract
Dendritic spines are crucial for excitatory synaptic transmission as the size of a spine head correlates with the strength of its synapse. The distribution of spine head sizes follows a lognormal-like distribution with more small spines than large ones. We analysed the impact of synaptic activity and plasticity on the spine size distribution in adult-born hippocampal granule cells from rats with induced homo- and heterosynaptic long-term plasticity in vivo and CA1 pyramidal cells from Munc13-1/Munc13-2 knockout mice with completely blocked synaptic transmission. Neither the induction of extrinsic synaptic plasticity nor the blockage of presynaptic activity degrades the lognormal-like distribution but changes its mean, variance and skewness. The skewed distribution develops early in the life of the neuron. Our findings and their computational modelling support the idea that intrinsic synaptic plasticity is sufficient for the generation, while a combination of intrinsic and extrinsic synaptic plasticity maintains lognormal-like distribution of spines.
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Affiliation(s)
- Nina Rößler
- Faculty of Medicine, ICAR3R-Interdisciplinary Centre for 3Rs in Animal Research, Justus Liebig University Giessen, Rudolf-Buchheim-Straße 6, Giessen, D-35392, Germany
- Neuroscience Center, Institute of Clinical Neuroanatomy, Goethe University Frankfurt, Frankfurt am Main, 60528, Germany
| | - Tassilo Jungenitz
- Neuroscience Center, Institute of Clinical Neuroanatomy, Goethe University Frankfurt, Frankfurt am Main, 60528, Germany
| | - Albrecht Sigler
- Department of Molecular Neurobiology, Max Planck Institute of Multidisciplinary Sciences, Göttingen, 37077, Germany
| | - Alexander Bird
- Faculty of Medicine, ICAR3R-Interdisciplinary Centre for 3Rs in Animal Research, Justus Liebig University Giessen, Rudolf-Buchheim-Straße 6, Giessen, D-35392, Germany
- Frankfurt Institute for Advanced Studies, Frankfurt am Main, 60438, Germany
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Frankfurt am Main, 60528, Germany
| | - Martin Mittag
- Faculty of Medicine, ICAR3R-Interdisciplinary Centre for 3Rs in Animal Research, Justus Liebig University Giessen, Rudolf-Buchheim-Straße 6, Giessen, D-35392, Germany
- Neuroscience Center, Institute of Clinical Neuroanatomy, Goethe University Frankfurt, Frankfurt am Main, 60528, Germany
| | - Jeong Seop Rhee
- Department of Molecular Neurobiology, Max Planck Institute of Multidisciplinary Sciences, Göttingen, 37077, Germany
| | - Thomas Deller
- Neuroscience Center, Institute of Clinical Neuroanatomy, Goethe University Frankfurt, Frankfurt am Main, 60528, Germany
| | - Hermann Cuntz
- Faculty of Medicine, ICAR3R-Interdisciplinary Centre for 3Rs in Animal Research, Justus Liebig University Giessen, Rudolf-Buchheim-Straße 6, Giessen, D-35392, Germany
- Frankfurt Institute for Advanced Studies, Frankfurt am Main, 60438, Germany
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Frankfurt am Main, 60528, Germany
| | - Nils Brose
- Department of Molecular Neurobiology, Max Planck Institute of Multidisciplinary Sciences, Göttingen, 37077, Germany
| | - Stephan W Schwarzacher
- Neuroscience Center, Institute of Clinical Neuroanatomy, Goethe University Frankfurt, Frankfurt am Main, 60528, Germany
| | - Peter Jedlicka
- Faculty of Medicine, ICAR3R-Interdisciplinary Centre for 3Rs in Animal Research, Justus Liebig University Giessen, Rudolf-Buchheim-Straße 6, Giessen, D-35392, Germany
- Neuroscience Center, Institute of Clinical Neuroanatomy, Goethe University Frankfurt, Frankfurt am Main, 60528, Germany
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Zhao Y, Lin X, Zhang Z, Wang X, He X, Yang L. STDP-based adaptive graph convolutional networks for automatic sleep staging. Front Neurosci 2023; 17:1158246. [PMID: 37152593 PMCID: PMC10157055 DOI: 10.3389/fnins.2023.1158246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 04/03/2023] [Indexed: 05/09/2023] Open
Abstract
Automatic sleep staging is important for improving diagnosis and treatment, and machine learning with neuroscience explainability of sleep staging is shown to be a suitable method to solve this problem. In this paper, an explainable model for automatic sleep staging is proposed. Inspired by the Spike-Timing-Dependent Plasticity (STDP), an adaptive Graph Convolutional Network (GCN) is established to extract features from the Polysomnography (PSG) signal, named STDP-GCN. In detail, the channel of the PSG signal can be regarded as a neuron, the synapse strength between neurons can be constructed by the STDP mechanism, and the connection between different channels of the PSG signal constitutes a graph structure. After utilizing GCN to extract spatial features, temporal convolution is used to extract transition rules between sleep stages, and a fully connected neural network is used for classification. To enhance the strength of the model and minimize the effect of individual physiological signal discrepancies on classification accuracy, STDP-GCN utilizes domain adversarial training. Experiments demonstrate that the performance of STDP-GCN is comparable to the current state-of-the-art models.
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Khaledi-Nasab A, Kromer JA, Tass PA. Long-Lasting Desynchronization Effects of Coordinated Reset Stimulation Improved by Random Jitters. Front Physiol 2021; 12:719680. [PMID: 34630142 PMCID: PMC8497886 DOI: 10.3389/fphys.2021.719680] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 08/12/2021] [Indexed: 12/30/2022] Open
Abstract
Abnormally strong synchronized activity is related to several neurological disorders, including essential tremor, epilepsy, and Parkinson's disease. Chronic high-frequency deep brain stimulation (HF DBS) is an established treatment for advanced Parkinson's disease. To reduce the delivered integral electrical current, novel theory-based stimulation techniques such as coordinated reset (CR) stimulation directly counteract the abnormal synchronous firing by delivering phase-shifted stimuli through multiple stimulation sites. In computational studies in neuronal networks with spike-timing-dependent plasticity (STDP), it was shown that CR stimulation down-regulates synaptic weights and drives the network into an attractor of a stable desynchronized state. This led to desynchronization effects that outlasted the stimulation. Corresponding long-lasting therapeutic effects were observed in preclinical and clinical studies. Computational studies suggest that long-lasting effects of CR stimulation depend on the adjustment of the stimulation frequency to the dominant synchronous rhythm. This may limit clinical applicability as different pathological rhythms may coexist. To increase the robustness of the long-lasting effects, we study randomized versions of CR stimulation in networks of leaky integrate-and-fire neurons with STDP. Randomization is obtained by adding random jitters to the stimulation times and by shuffling the sequence of stimulation site activations. We study the corresponding long-lasting effects using analytical calculations and computer simulations. We show that random jitters increase the robustness of long-lasting effects with respect to changes of the number of stimulation sites and the stimulation frequency. In contrast, shuffling does not increase parameter robustness of long-lasting effects. Studying the relation between acute, acute after-, and long-lasting effects of stimulation, we find that both acute after- and long-lasting effects are strongly determined by the stimulation-induced synaptic reshaping, whereas acute effects solely depend on the statistics of administered stimuli. We find that the stimulation duration is another important parameter, as effective stimulation only entails long-lasting effects after a sufficient stimulation duration. Our results show that long-lasting therapeutic effects of CR stimulation with random jitters are more robust than those of regular CR stimulation. This might reduce the parameter adjustment time in future clinical trials and make CR with random jitters more suitable for treating brain disorders with abnormal synchronization in multiple frequency bands.
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Affiliation(s)
- Ali Khaledi-Nasab
- Department of Neurosurgery, Stanford University, Stanford, CA, United States
| | - Justus A Kromer
- Department of Neurosurgery, Stanford University, Stanford, CA, United States
| | - Peter A Tass
- Department of Neurosurgery, Stanford University, Stanford, CA, United States
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Lin CY, Chen J, Chen PH, Chang TC, Wu Y, Eshraghian JK, Moon J, Yoo S, Wang YH, Chen WC, Wang ZY, Huang HC, Li Y, Miao X, Lu WD, Sze SM. Adaptive Synaptic Memory via Lithium Ion Modulation in RRAM Devices. Small 2020; 16:e2003964. [PMID: 32996256 DOI: 10.1002/smll.202003964] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 08/12/2020] [Indexed: 06/11/2023]
Abstract
Biologically plausible computing systems require fine-grain tuning of analog synaptic characteristics. In this study, lithium-doped silicate resistive random access memory with a titanium nitride (TiN) electrode mimicking biological synapses is demonstrated. Biological plausibility of this RRAM device is thought to occur due to the low ionization energy of lithium ions, which enables controllable forming and filamentary retraction spontaneously or under an applied voltage. The TiN electrode can effectively store lithium ions, a principle widely adopted from battery construction, and allows state-dependent decay to be reliably achieved. As a result, this device offers multi-bit functionality and synaptic plasticity for simulating various strengths in neuronal connections. Both short-term memory and long-term memory are emulated across dynamical timescales. Spike-timing-dependent plasticity and paired-pulse facilitation are also demonstrated. These mechanisms are capable of self-pruning to generate efficient neural networks. Time-dependent resistance decay is observed for different conductance values, which mimics both biological and artificial memory pruning and conforms to the trend of the biological brain that prunes weak synaptic connections. By faithfully emulating learning rules that exist in human's higher cortical areas from STDP to synaptic pruning, the device has the capacity to drive forward the development of highly efficient neuromorphic computing systems.
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Affiliation(s)
- Chih-Yang Lin
- Department of Physics, National Sun Yat-sen University, No.70 Lien-hai Road, Kaohsiung, 80424, Taiwan
| | - Jia Chen
- Wuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information, Huazhong University of Science and Technology, No.1037 Luoyu Road, Wuhan, 430074, China
| | - Po-Hsun Chen
- Department of Applied Science, R.O.C. Naval Academy, No.669 Junxiao Road, Kaohsiung, 81345, Taiwan
- Center for Nanoscience and Nanotechnology, National Sun Yat-sen University, No.70 Lien-hai Road, Kaohsiung, 80424, Taiwan
| | - Ting-Chang Chang
- Department of Physics, The Center of Crystal Research, National Sun Yat-sen University, No.70 Lien-hai Road, Kaohsiung, 80424, Taiwan
| | - Yuting Wu
- Electrical Engineering and Computer Science, University of Michigan, No.1301 Beal Avenue, Ann Arbor, Michigan, 48109-2122, USA
| | - Jason K Eshraghian
- Electrical Engineering and Computer Science, University of Michigan, No.1301 Beal Avenue, Ann Arbor, Michigan, 48109-2122, USA
| | - John Moon
- Electrical Engineering and Computer Science, University of Michigan, No.1301 Beal Avenue, Ann Arbor, Michigan, 48109-2122, USA
| | - Sangmin Yoo
- Electrical Engineering and Computer Science, University of Michigan, No.1301 Beal Avenue, Ann Arbor, Michigan, 48109-2122, USA
| | - Yu-Hsun Wang
- Department of Electronics Engineering and Institute of Electronics, National Chiao Tung University, No.1001 University Road, Hsinchu, 30010, Taiwan
| | - Wen-Chung Chen
- Department of Materials and Optoelectronic Science, National Sun Yat-sen University, No.70 Lien-hai Road, Kaohsiung, 80424, Taiwan
| | - Zhi-Yang Wang
- Department of Materials and Optoelectronic Science, National Sun Yat-sen University, No.70 Lien-hai Road, Kaohsiung, 80424, Taiwan
| | - Hui-Chun Huang
- Department of Materials and Optoelectronic Science, National Sun Yat-sen University, No.70 Lien-hai Road, Kaohsiung, 80424, Taiwan
| | - Yi Li
- Wuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information, Huazhong University of Science and Technology, No.1037 Luoyu Road, Wuhan, 430074, China
| | - Xiangshui Miao
- Wuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information, Huazhong University of Science and Technology, No.1037 Luoyu Road, Wuhan, 430074, China
| | - Wei D Lu
- Electrical Engineering and Computer Science, University of Michigan, No.1301 Beal Avenue, Ann Arbor, Michigan, 48109-2122, USA
| | - Simon M Sze
- Department of Electronics Engineering and Institute of Electronics, National Chiao Tung University, No.1001 University Road, Hsinchu, 30010, Taiwan
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Bianchi S, Muñoz-Martin I, Ielmini D. Bio-Inspired Techniques in a Fully Digital Approach for Lifelong Learning. Front Neurosci 2020; 14:379. [PMID: 32425749 PMCID: PMC7203347 DOI: 10.3389/fnins.2020.00379] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Accepted: 03/27/2020] [Indexed: 11/13/2022] Open
Abstract
Lifelong learning has deeply underpinned the resilience of biological organisms respect to a constantly changing environment. This flexibility has allowed the evolution of parallel-distributed systems able to merge past information with new stimulus for accurate and efficient brain-computation. Nowadays, there is a strong attempt to reproduce such intelligent systems in standard artificial neural networks (ANNs). However, despite some great results in specific tasks, ANNs still appear too rigid and static in real life respect to the biological systems. Thus, it is necessary to define a new neural paradigm capable of merging the lifelong resilience of biological organisms with the great accuracy of ANNs. Here, we present a digital implementation of a novel mixed supervised-unsupervised neural network capable of performing lifelong learning. The network uses a set of convolutional filters to extract features from the input images of the MNIST and the Fashion-MNIST training datasets. This information defines an original combination of responses of both trained classes and non-trained classes by transfer learning. The responses are then used in the subsequent unsupervised learning based on spike-timing dependent plasticity (STDP). This procedure allows the clustering of non-trained information thanks to bio-inspired algorithms such as neuronal redundancy and spike-frequency adaptation. We demonstrate the implementation of the neural network in a fully digital environment, such as the Xilinx Zynq-7000 System on Chip (SoC). We illustrate a user-friendly interface to test the network by choosing the number and the type of the non-trained classes, or drawing a custom pattern on a tablet. Finally, we propose a comparison of this work with networks based on memristive synaptic devices capable of continual learning, highlighting the main differences and capabilities respect to a fully digital approach.
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Affiliation(s)
| | | | - Daniele Ielmini
- Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB), Politecnico di Milano, Milan, Italy
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Cho Y, Lee JY, Yu E, Han JH, Baek MH, Cho S, Park BG. Design and Characterization of Semi-Floating-Gate Synaptic Transistor. Micromachines (Basel) 2019; 10:mi10010032. [PMID: 30621033 PMCID: PMC6357002 DOI: 10.3390/mi10010032] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2018] [Revised: 12/31/2018] [Accepted: 01/02/2019] [Indexed: 12/23/2022]
Abstract
In this work, a study on a semi-floating-gate synaptic transistor (SFGST) is performed to verify its feasibility in the more energy-efficient hardware-driven neuromorphic system. To realize short- and long-term potentiation (STP/LTP) in the SFGST, a poly-Si semi-floating gate (SFG) and a SiN charge-trap layer are utilized, respectively. When an adequate number of holes are accumulated in the SFG, they are injected into the nitride charge-trap layer by the Fowler–Nordheim tunneling mechanism. Moreover, since the SFG is charged by an embedded tunneling field-effect transistor existing between the channel and the drain junction when the post-synaptic spike occurs after the pre-synaptic spike, and vice versa, the SFG is discharged by the diode when the post-synaptic spike takes place before the pre-synaptic spike. This indicates that the SFGST can attain STP/LTP and spike-timing-dependent plasticity behaviors. These characteristics of the SFGST in the highly miniaturized transistor structure can contribute to the neuromorphic chip such that the total system may operate as fast as the human brain with low power consumption and high integration density.
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Affiliation(s)
- Yongbeom Cho
- Department of Electronics Engineering, Gachon University, Gyeonggi-do 13120, Korea.
| | - Jae Yoon Lee
- Department of Electronics Engineering, Gachon University, Gyeonggi-do 13120, Korea.
| | - Eunseon Yu
- Department of Electronics Engineering, Gachon University, Gyeonggi-do 13120, Korea.
| | - Jae-Hee Han
- Department of Energy IT, Gachon University, Gyeonggi-do 13120, Korea.
| | - Myung-Hyun Baek
- Department of Electrical and Computer Engineering, Seoul National University, Seoul 08826, Korea.
| | - Seongjae Cho
- Department of Electronics Engineering, Gachon University, Gyeonggi-do 13120, Korea.
| | - Byung-Gook Park
- Department of Electrical and Computer Engineering, Seoul National University, Seoul 08826, Korea.
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Masquelier T. STDP Allows Close-to-Optimal Spatiotemporal Spike Pattern Detection by Single Coincidence Detector Neurons. Neuroscience 2018; 389:133-140. [PMID: 28668487 PMCID: PMC6372004 DOI: 10.1016/j.neuroscience.2017.06.032] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2017] [Accepted: 06/19/2017] [Indexed: 11/24/2022]
Abstract
Repeating spike patterns exist and are informative. Can a single cell do the readout? We show how a leaky integrate-and-fire (LIF) can do this readout optimally. The optimal membrane time constant is short, possibly much shorter than the pattern. Spike-timing-dependent plasticity (STDP) can turn a neuron into an optimal detector. These results may explain how humans can learn repeating visual or auditory sequences.
Repeating spatiotemporal spike patterns exist and carry information. How this information is extracted by downstream neurons is unclear. Here we theoretically investigate to what extent a single cell could detect a given spike pattern and what the optimal parameters to do so are, in particular the membrane time constant τ. Using a leaky integrate-and-fire (LIF) neuron with homogeneous Poisson input, we computed this optimum analytically. We found that a relatively small τ (at most a few tens of ms) is usually optimal, even when the pattern is much longer. This is somewhat counter-intuitive as the resulting detector ignores most of the pattern, due to its fast memory decay. Next, we wondered if spike-timing-dependent plasticity (STDP) could enable a neuron to reach the theoretical optimum. We simulated a LIF equipped with additive STDP, and repeatedly exposed it to a given input spike pattern. As in previous studies, the LIF progressively became selective to the repeating pattern with no supervision, even when the pattern was embedded in Poisson activity. Here we show that, using certain STDP parameters, the resulting pattern detector is optimal. These mechanisms may explain how humans learn repeating sensory sequences. Long sequences could be recognized thanks to coincidence detectors working at a much shorter timescale. This is consistent with the fact that recognition is still possible if a sound sequence is compressed, played backward, or scrambled using 10-ms bins. Coincidence detection is a simple yet powerful mechanism, which could be the main function of neurons in the brain.
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Masquelier T, Kheradpisheh SR. Optimal Localist and Distributed Coding of Spatiotemporal Spike Patterns Through STDP and Coincidence Detection. Front Comput Neurosci 2018; 12:74. [PMID: 30279653 PMCID: PMC6153331 DOI: 10.3389/fncom.2018.00074] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2018] [Accepted: 08/17/2018] [Indexed: 11/13/2022] Open
Abstract
Repeating spatiotemporal spike patterns exist and carry information. Here we investigated how a single spiking neuron can optimally respond to one given pattern (localist coding), or to either one of several patterns (distributed coding, i.e., the neuron's response is ambiguous but the identity of the pattern could be inferred from the response of multiple neurons), but not to random inputs. To do so, we extended a theory developed in a previous paper (Masquelier, 2017), which was limited to localist coding. More specifically, we computed analytically the signal-to-noise ratio (SNR) of a multi-pattern-detector neuron, using a threshold-free leaky integrate-and-fire (LIF) neuron model with non-plastic unitary synapses and homogeneous Poisson inputs. Surprisingly, when increasing the number of patterns, the SNR decreases slowly, and remains acceptable for several tens of independent patterns. In addition, we investigated whether spike-timing-dependent plasticity (STDP) could enable a neuron to reach the theoretical optimal SNR. To this aim, we simulated a LIF equipped with STDP, and repeatedly exposed it to multiple input spike patterns, embedded in equally dense Poisson spike trains. The LIF progressively became selective to every repeating pattern with no supervision, and stopped discharging during the Poisson spike trains. Furthermore, tuning certain STDP parameters, the resulting pattern detectors were optimal. Tens of independent patterns could be learned by a single neuron using a low adaptive threshold, in contrast with previous studies, in which higher thresholds led to localist coding only. Taken together these results suggest that coincidence detection and STDP are powerful mechanisms, fully compatible with distributed coding. Yet we acknowledge that our theory is limited to single neurons, and thus also applies to feed-forward networks, but not to recurrent ones.
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Affiliation(s)
- Timothée Masquelier
- Centre de Recherche Cerveau et Cognition, UMR5549 CNRS-Université Toulouse 3, Toulouse, France.,Instituto de Microelectrónica de Sevilla (IMSE-CNM), CSIC, Universidad de Sevilla, Sevilla, Spain
| | - Saeed R Kheradpisheh
- Department of Computer Science, Faculty of Mathematical Sciences and Computer, Kharazmi University, Tehran, Iran
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Yu F, Zhu LQ, Gao WT, Fu YM, Xiao H, Tao J, Zhou JM. Chitosan-Based Polysaccharide-Gated Flexible Indium Tin Oxide Synaptic Transistor with Learning Abilities. ACS Appl Mater Interfaces 2018; 10:16881-16886. [PMID: 29687712 DOI: 10.1021/acsami.8b03274] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Recently, environment-friendly electronic devices are attracting increasing interest. "Green" artificial synapses with learning abilities are also interesting for neuromorphic platforms. Here, solution-processed chitosan-based polysaccharide electrolyte-gated indium tin oxide (ITO) synaptic transistors are fabricated on polyethylene terephthalate substrate. Good transistor performances against mechanical stress are observed. Short-term synaptic plasticities are mimicked on the proposed ITO synaptic transistor. When applying presynaptic and postsynaptic spikes on gate electrode and drain electrode respectively, spike-timing-dependent plasticity function is mimicked on the synaptic transistor. Transitions from sensory memory to short-term memory (STM) and from STM to long-term memory are also mimicked, demonstrating a "multistore model" brain memory. Furthermore, the flexible ITO synaptic transistor can be dissolved in deionized water easily, indicating potential green neuromorphic platform applications.
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Affiliation(s)
- Fei Yu
- Key Laboratory of Graphene Technologies and Applications of Zhejiang Province, Ningbo Institute of Materials Technology and Engineering , Chinese Academy of Sciences , Ningbo 315201 , Zhejiang , People's Republic of China
- Nano Science and Technology Institute , University of Science and Technology of China , Suzhou 215123 , People's Republic of China
- University of Chinese Academy of Sciences , Beijing 100049 , People's Republic of China
| | - Li Qiang Zhu
- Key Laboratory of Graphene Technologies and Applications of Zhejiang Province, Ningbo Institute of Materials Technology and Engineering , Chinese Academy of Sciences , Ningbo 315201 , Zhejiang , People's Republic of China
- University of Chinese Academy of Sciences , Beijing 100049 , People's Republic of China
| | - Wan Tian Gao
- Key Laboratory of Graphene Technologies and Applications of Zhejiang Province, Ningbo Institute of Materials Technology and Engineering , Chinese Academy of Sciences , Ningbo 315201 , Zhejiang , People's Republic of China
- School of Material Science and Engineering , Shanghai University , Shanghai 200444 , People's Republic of China
- University of Chinese Academy of Sciences , Beijing 100049 , People's Republic of China
| | - Yang Ming Fu
- Key Laboratory of Graphene Technologies and Applications of Zhejiang Province, Ningbo Institute of Materials Technology and Engineering , Chinese Academy of Sciences , Ningbo 315201 , Zhejiang , People's Republic of China
- University of Chinese Academy of Sciences , Beijing 100049 , People's Republic of China
| | - Hui Xiao
- Key Laboratory of Graphene Technologies and Applications of Zhejiang Province, Ningbo Institute of Materials Technology and Engineering , Chinese Academy of Sciences , Ningbo 315201 , Zhejiang , People's Republic of China
- University of Chinese Academy of Sciences , Beijing 100049 , People's Republic of China
| | - Jian Tao
- Key Laboratory of Graphene Technologies and Applications of Zhejiang Province, Ningbo Institute of Materials Technology and Engineering , Chinese Academy of Sciences , Ningbo 315201 , Zhejiang , People's Republic of China
- University of Chinese Academy of Sciences , Beijing 100049 , People's Republic of China
| | - Ju Mei Zhou
- Faculty of Maritime and Transportation , Ningbo University , Ningbo 315211 , Zhejiang , People's Republic of China
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