Results 111 to 120 of about 68,442 (287)

Machine Learning‐Assisted Inverse Design of Soft and Multifunctional Hybrid Liquid Metal Composites

Advanced Functional Materials, EarlyView.
A machine learning framework is presented for inverse design of synthesizable multifunctional composites containing both liquid metal and solid inclusions. By integrating physics‐based modeling, data‐driven prediction, and Bayesian optimization, the approach enables intelligent design of experiments to identify optimal compositions and realize these ...
Lijun Zhou, Yunsik Ohm, Ren‐Mian Chin, Olivia Kerr, Krithika Manohar, Mohammad H. Malakooti   +5 more
wiley   +1 more source

Real Spike: Learning Real-Valued Spikes for Spiking Neural Networks

, 2022
Accepted by ...
Yufei Guo, Liwen Zhang 0001, Yuanpei Chen, Xinyi Tong 0001, Xiaode Liu, YingLei Wang, Xuhui Huang, Zhe Ma 0001   +7 more
openaire   +2 more sources

Integrated Field‐Free SOT Domain‐Wall Synapses and MTJ Stochastic Neurons for Hardware Boltzmann Machines

Advanced Functional Materials, EarlyView.
Field‐free spin‐orbit torque domain‐wall synapses integrated with stochastic MTJ neurons enable compact hardware Boltzmann machines. Leveraging intrinsic stochasticity and multi‐level conductance, the system achieves efficient probabilistic learning with high accuracy, demonstrating a scalable spintronic platform for energy‐efficient edge AI.
Aijaz H. Lone, Meng Tang, Camelia Florica, Bin He, Jingkai Xu, Selma Amara, Georgian Melinte, Xixiang Zhang, Gianluca Setti   +8 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

Self-Learning Fuzzy Spiking Neural Network as a Nonlinear Pulse-Position Threshold Detection Dynamic System Based on Second-Order Critically Damped Response Units [PDF]

, 2009
Architecture and learning algorithm of self-learning spiking neural network in fuzzy clustering task are outlined. Fuzzy receptive neurons for pulse-position transformation of input data are considered.
Bodyanskiy, Yevgeniy, Dolotov, Artem, Pliss, Iryna   +2 more
core  

Unsupervised Heart-rate Estimation in Wearables With Liquid States and A Probabilistic Readout

, 2017
Heart-rate estimation is a fundamental feature of modern wearable devices. In this paper we propose a machine intelligent approach for heart-rate estimation from electrocardiogram (ECG) data collected using wearable devices.
Adiraju, Prathyusha, Catthoor, Francky, Das, Anup, Dutt, Nikil, Groenendaal, Willemijn, Krichmar, Jeffrey L., Pradhapan, Paruthi, Rajan, Raj Thilak, Schaafsma, Siebren, Van Hoof, Chris   +9 more
core   +1 more source

Dual‐Inductive and Programmable Switching: A New Paradigm in Ionic Interface‐Controlled Perovskite Memory

Advanced Functional Materials, EarlyView.
A fully programmable, dual‐inductive switchable halide perovskite memristor is demonstrated through precise BDAI2‐mediated interface engineering. This ion‐modulating layer suppresses stochastic filamentary growth, enabling stable, non‐filamentary switching via dynamic barrier modulation.
So‐Yeon Kim, Juan Bisquert
wiley   +1 more source

Fast learning without synaptic plasticity in spiking neural networks

Scientific Reports
Spiking neural networks are of high current interest, both from the perspective of modelling neural networks of the brain and for porting their fast learning capability and energy efficiency into neuromorphic hardware. But so far we have not been able to
Anand Subramoney, Guillaume Bellec, Franz Scherr, Robert Legenstein, Wolfgang Maass   +4 more
doaj   +1 more source

A Markovian event-based framework for stochastic spiking neural networks

, 2011
In spiking neural networks, the information is conveyed by the spike times, that depend on the intrinsic dynamics of each neuron, the input they receive and on the connections between neurons. In this article we study the Markovian nature of the sequence
A Delorme, A Delorme, A Holden, A Lachal, A Lachal, A Roxin, A Tonnelier, Arbib, B Cessac, C Fricker, E Claverol, E Gobet, E Kandel, E Rolls, EM Izhikevich, H Gromoll, HC Tuckwell, HP McKean, I Karatzas, J Touboul, J Touboul, Jonathan D. Touboul, L Ricciardi, L Watts, M Cottrell, M Cottrell, M Davis, M Fabre-Thorpe, M Goldman, M Rudolph, MN Shadlen, N Brunel, Olivier D. Faugeras, P Kloeden, R Brette, R Brette, R Brette, S Asmussen, S Thorpe, T Makino, T Turova, TS Turova, W Gerstner, W Gerstner, WR Softky   +44 more
core   +3 more sources

Optoelectrical Devices for Neural Interfacing: Engineering Integration, Stability, and Multimodal Sensing

Advanced Healthcare Materials, EarlyView.
Implantable optoelectrical devices are an effective resource for the modulation and monitoring of neural activity with high spatiotemporal resolution. This review discusses current challenges faced by these devices and outlines future perspectives for the development of next‐generation neural interfaces targeting chronic, multisite, and multimodal ...
Stella Aslanoglou, Barbara Spagnolo, Antonio Balena, Massimo De Vittorio, Ferruccio Pisanello   +4 more
wiley   +1 more source