Results 71 to 80 of about 27,240 (263)

Enhancing Synaptic Plasticity and Multistate Retention of Organic Neuromorphic Devices Using Anion‐Excessive Gel Electrolyte

Advanced Functional Materials, EarlyView.
Anion‐excessive gel‐based organic synaptic transistors (AEG‐OSTs) that can maintain electrical neutrality are developed to enhance synaptic plasticity and multistate retention. Key improvement is attributed to the maintenance of electrical neutrality in the electrolyte even after electrochemical doping, which reduces the Coulombic force acting on ...
Yousang Won, Jeongwoo Lee, Hyunwook Kim, Joon Hak Oh   +3 more
wiley   +1 more source

Hopf Bifurcation in Mean Field Explains Critical Avalanches in Excitation-Inhibition Balanced Neuronal Networks: A Mechanism for Multiscale Variability

Frontiers in Systems Neuroscience, 2020
Cortical neural circuits display highly irregular spiking in individual neurons but variably sized collective firing, oscillations and critical avalanches at the population level, all of which have functional importance for information processing ...
Junhao Liang, Junhao Liang, Tianshou Zhou, Changsong Zhou, Changsong Zhou   +4 more
doaj   +1 more source

Conductance‐Dependent Photoresponse in a Dynamic SrTiO3 Memristor for Biorealistic Computing

Advanced Functional Materials, EarlyView.
A nanoscale SrTiO3 memristor is shown to exhibit dynamic synaptic behavior through the interaction of local electrical and global optical signals. Its photoresponse depends quantitatively on the conductance state, which evolves and decays over tunable timescales, enabling ultralow‐power, biorealistic learning mechanisms for advanced in‐memory and ...
Christoph Weilenmann, Hanglin He, Marko Mladenović, Till Zellweger, Kevin Portner, Klemens Bauer, Guillaume Bellec, Mathieu Luisier, Alexandros Emboras   +8 more
wiley   +1 more source

Implementing Signature Neural Networks with Spiking Neurons

Frontiers in Computational Neuroscience, 2016
Spiking Neural Networks constitute the most promising approach to develop realistic ArtificialNeural Networks (ANNs). Unlike traditional firing rate-based paradigms, information coding inspiking models is based on the precise timing of individual spikes.
José Luis Carrillo-Medina, Roberto Latorre   +1 more
doaj   +1 more source

Neural encoding with unsupervised spiking convolutional neural network

Communications Biology, 2023
Accurately predicting the brain responses to various stimuli poses a significant challenge in neuroscience. Despite recent breakthroughs in neural encoding using convolutional neural networks (CNNs) in fMRI studies, there remain critical gaps between the
Chong Wang, Hongmei Yan, Wei Huang, Wei Sheng, Yuting Wang, Yun-Shuang Fan, Tao Liu, Ting Zou, Rong Li, Huafu Chen   +9 more
doaj   +1 more source

Gourd‐Inspired Design of Unit Cell with Multiple Gradients for Physiological‐Range Pressure Sensing

Advanced Functional Materials, EarlyView.
Gourd‐shaped micro‐dome arrays with coordinated modulus, conductivity, and geometric gradients co‐optimize sensitivity and linearity in piezoresistive tactile sensors. Under pressure, a solid upper dome embeds into a porous lower dome, triggering rapid contact‐area growth and series‐to‐parallel conduction, enabling unsaturated, intensity‐resolved ...
Jiayi Xu, Ming Lei, Yuanzhe Liang, Biao Qi, Ziyi Dai, Yong Zhao, Bingpu Zhou   +6 more
wiley   +1 more source

Optoelectronic Synaptic Devices Using Molecular Telluride Phase‐Change Inks for Three‐Factor Learning

Advanced Functional Materials, EarlyView.
Optoelectronic synaptic devices based on solution‐processed molecular telluride GST‐225 phase‐change inks are demonstrated for three‐factor learning. A global optical signal broadcast through a silicon waveguide induces non‐volatile conductance updates exclusively in locally electrically flagged memristors.
Kevin Portner, Dhananjeya Kumaar, Giacomo Torti, Killian Keller, Till Zellweger, Florian M. Schenk, Christoph Weilenmann, Daniel Chelladurai, Vara Prasad Jonnalagadda, Ghazi Sarwat Syed, Vanessa Wood, Jürg Leuthold, Alexandros Emboras, Maksym Yarema, Mathieu Luisier   +14 more
wiley   +1 more source

Pattern Recognition of Spiking Neural Networks Based on Visual Mechanism and Supervised Synaptic Learning

Neural Plasticity, 2020
Electrophysiological studies have shown that mammalian primary visual cortex are selective for the orientations of visual stimuli. Inspired by this mechanism, we propose a hierarchical spiking neural network (SNN) for image classification.
Xiumin Li, Hao Yi, Shengyuan Luo
doaj   +1 more source

SNNAX - Spiking Neural Networks in JAX

2024 International Conference on Neuromorphic Systems (ICONS)
Spiking Neural Networks (SNNs) simulators are essential tools to prototype biologically inspired models and neuromorphic hardware architectures and predict their performance. For such a tool, ease of use and flexibility are critical, but so is simulation speed especially given the complexity inherent to simulating SNN.
Lohoff, Jamie, Finkbeiner, Jan, Neftci, Emre   +2 more
openaire   +3 more sources

Quantum Spike Neural Network

, 2020
Utilizing quantum computers to deploy artificial neural networks (ANNs) will bring the potential of significant advancements in both speed and scale. In this paper, we propose a kind of quantum spike neural networks (SNNs) as well as comprehensively evaluate and give a detailed mathematical proof for the quantum SNNs, including its successful ...
Chen, Yanhu, Guo, Hongxiang, Wang, Cen, Gao, Xiong, Wu, Jian   +4 more
openaire   +2 more sources