Article
Engineering, Electrical & Electronic
Mahmudul Alam Shakib, Zhaolin Gao, Caterina Lamuta
Summary: This study investigates different types of synaptic plasticity properties of geopolymer memristors, demonstrating short-term and long-term memory, Hebbian learning inspired spike-timing-dependent plasticity, spike-rate-dependent plasticity, history-dependent plasticity, paired-pulse facilitation, paired-pulse depression, and post-tetanic potentiation. These properties are attributed to the movement of ions induced by electro-osmosis in the capillaries and pores of the geopolymer memristors. They show great potential for the use of geopolymers in neuromorphic computing applications.
ACS APPLIED ELECTRONIC MATERIALS
(2023)
Article
Materials Science, Ceramics
Anhe Bamao, Yaxin Xia, Guokun Ma, Xiaoxu Yuan, Zisheng Yang, Ao Chen, Chun-Chu Lin, Ting-Chang Chang, Hao Wang
Summary: A new nonvolatile memory device Pt/LiNbOx/TiN was fabricated by doping lithium metal atoms in solid electrolyte NbOx, which exhibited typical bipolar resistive storage characteristics. Continuous conductance states were obtained in both DC and pulsed modes, and the paired-pulse facilitation (PPF) and spike-timing-dependent plasticity learning (STDP) behavior were successfully simulated. Furthermore, the LiNbOx memristor-based convolutional neural network (CNN) achieved high accuracy in MNIST dataset, indicating its potential for neuromorphic computations.
CERAMICS INTERNATIONAL
(2023)
Review
Neurosciences
Yanis Inglebert, Dominique Debanne
Summary: The importance of considering physiological levels of extracellular calcium concentration in studying functional plasticity is discussed in this study.
FRONTIERS IN CELLULAR NEUROSCIENCE
(2021)
Article
Chemistry, Physical
Dongyeol Ju, Jang Hyun Kim, Sungjun Kim
Summary: This study focused on the uniformity and potential applications of Ti/TaOx/ITO devices in neuromorphic systems. The devices' thickness and chemical composition were verified using transmission electron microscopy (TEM). The I-V curves of the devices were controlled by compliance current and reset voltage, and multilevel characteristics were demonstrated for high-density memory and neuromorphic systems. Improvement was made to enhance the linearity of potentiation and depression for better pattern recognition accuracy in a neural network. Spike-timing-dependent plasticity (STDP) was performed to mimic Hebbian learning. ©2023 Elsevier B.V. All rights reserved.
JOURNAL OF ALLOYS AND COMPOUNDS
(2023)
Article
Chemistry, Multidisciplinary
Dongyeol Ju, Sunghun Kim, Sungjun Kim
Summary: In this paper, an ITO/SiN/TaN memristor device is fabricated and its electrical characteristics for a neuromorphic system are analyzed. The device structure and chemical properties are investigated using transmission electron microscopy and X-ray photoelectron spectroscopy. Uniform bipolar switching is achieved through DC sweep under a compliance current of 5 mA. The analog reset phenomenon is observed by modulating the reset voltage for long-term memory, while short-term memory characteristics are obtained by controlling the strength of the pulse response. Finally, bio-inspired synaptic characteristics are emulated using Hebbian learning rules. The coexistence of short-term and long-term memories in the ITO/SiN/TaN device is believed to provide flexibility in device design for future neuromorphic applications.
Review
Computer Science, Artificial Intelligence
Anthony Triche, Anthony S. Maida, Ashok Kumar
Summary: Recent works have connected Hebbian plasticity with reinforcement learning, resulting in a class of trial-and-error learning called neo-Hebbian plasticity. Inspired by the role of dopamine in synaptic modification, neo-Hebbian RL methods selectively reinforce associations to enable learning exploitative behaviors. This review focuses on the exploration-exploitation balance under the neo-Hebbian RL framework and suggests potential improvements through stronger incorporation of intrinsic motivators.
Article
Engineering, Electrical & Electronic
Yunus Babacan, Abdullah Yesil, Omer Faruk Tozlu, Firat Kacar
Summary: This paper investigated the STDP property of some basic memristor circuits and demonstrated through mathematical and simulation results that none of them could support the STDP learning rule.
AEU-INTERNATIONAL JOURNAL OF ELECTRONICS AND COMMUNICATIONS
(2022)
Article
Biochemistry & Molecular Biology
Maura Ferrer-Ferrer, Shaobo Jia, Rahul Kaushik, Jenny Schneeberg, Izabela Figiel, Stepan Aleshin, Andrey Mironov, Motahareh Safari, Renato Frischknecht, Jakub Wlodarczyk, Oleg Senkov, Alexander Dityatev
Summary: This study investigates the functional importance of neurotrypsin (NT) in synaptic plasticity, learning, and memory extinction. The results show that NT-deficient mice exhibit impaired long-term potentiation, contextual fear memory deficits, and sociability deficits. This highlights the crucial role of NT in synaptic formation and strengthening.
CELLULAR AND MOLECULAR LIFE SCIENCES
(2023)
Article
Multidisciplinary Sciences
Raphael Bergoin, Alessandro Torcini, Gustavo Deco, Mathias Quoy, Gorka Zamora-Lopez
Summary: Brain circuits display modular architecture at different scales. This study investigates the role of inhibition in structuring new neural assemblies driven by synchronization to various inputs. The presence of inhibitory neurons is crucial for the emergence and consolidation of modular structures in the neural network.
SCIENTIFIC REPORTS
(2023)
Article
Neurosciences
Nikhil Garg, Ismael Balafrej, Terrence C. Stewart, Jean-Michel Portal, Marc Bocquet, Damien Querlioz, Dominique Drouin, Jean Rouat, Yann Beilliard, Fabien Alibart
Summary: This study proposes a voltage-dependent synaptic plasticity learning rule for the implementation of Hebb's plasticity mechanism on neuromorphic hardware. The rule reduces the number of updates and does not require additional memory storage. The system-level performance is validated through a handwriting recognition task, and it does not require manual tuning of hyperparameters.
FRONTIERS IN NEUROSCIENCE
(2022)
Article
Computer Science, Artificial Intelligence
Marcin Bialas, Jacek Mandziuk
Summary: This article proposes an effective variant of STDP extended by an activation-dependent scale factor, serving as an efficient mechanism for the unsupervised development of RFs. The importance of synaptic scaling and lateral inhibition in the successful development of RFs is demonstrated, with the significance of maintaining high levels of synaptic scaling highlighted. Experimental results show that the proposed solution performs well in classification tasks on the MNIST dataset.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Dominique Chu, Huy Le Nguyen
Summary: In this study, the mathematical constraints on weights resulting from Hebbian and STDP learning rules applied to a spiking neuron with weight normalisation are analysed. The results show that normalised weights depend on promotion probabilities and learning rate correction terms. These relations are also useful for checking algorithm convergence and novelty detection.
Article
Biochemistry & Molecular Biology
Irene Martinez-Gallego, Antonio Rodriguez-Moreno, Yuniesky Andrade-Talavera
Summary: In this article, the involvement of group I mGluRs in STDP and their possible role as coincidence detectors are briefly reviewed.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2022)
Article
Biology
Xin Huang, Huika Xia, Qi Zhang, Colin Blakemore, Yan Nan, Wenyao Wang, Jie Gao, Spencer S. Ng, Jing Wen, Tiejun Huang, Xiaoqing Li, Mingliang Pu
Summary: Asynchronous stimulation can rebalance input to the visual cortex and improve amblyopia. Clinical studies have shown that asynchronous binocular training is a fast and effective treatment for anisometropic amblyopia, with long-lasting effects of at least 2 years.
SCIENCE CHINA-LIFE SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Anil Yaman, Giovanni Iacca, Decebal Constantin Mocanu, Matt Coler, George Fletcher, Mykola Pechenizkiy
Summary: The study aims to discover interpretable local Hebbian learning rules, optimize them using genetic algorithms to achieve autonomous global learning in two tasks, and eventually converge into a set of well-defined interpretable types.
EVOLUTIONARY COMPUTATION
(2021)