Results 41 to 50 of about 36,106 (159)

Prospective Coding by Spiking Neurons

PLOS Computational Biology, 2016
Animals learn to make predictions, such as associating the sound of a bell with upcoming feeding or predicting a movement that a motor command is eliciting. How predictions are realized on the neuronal level and what plasticity rule underlies their learning is not well understood.
Johanni Brea, Alexisz Tamás Gaál, Robert Urbanczik, Walter Senn   +3 more
openaire   +5 more sources

A Transient Photoelectric Spiking Neuron Based on a Highly Robust MgO Composite Threshold Switching Memristor for Selective UV Perception

Advanced Electronic Materials
The biological photoreceptors in the retina convert light information into spikes, inspiring the emergence of artificial photoelectric spiking neurons. However, due to the lack of biocompatible and biodegradable characteristics, artificial photoelectric ...
Yaxiong Cao, Rui Wang, Saisai Wang, Tonglong Zeng, Wanlin Zhang, Jing Sun, Xiaohua Ma, Hong Wang, Yue Hao   +8 more
doaj   +1 more source

Gradient Descent Learning Algorithm for Spiking Neuron with Delay Adjustment [PDF]

Jisuanji gongcheng, 2019
The spiking neuron supervised learning algorithm adjusts the synaptic weight of the neuron by gradient descent method,but the accuracy gets low and the learning period gets long as the length of the target learning sequence increases.Therefore,a gradient
YANG Jing, XU Yan, ZHAO Xin
doaj   +1 more source

Belief Propagation in Networks of Spiking Neurons [PDF]

Neural Computation, 2009
From a theoretical point of view, statistical inference is an attractive model of brain operation. However, it is unclear how to implement these inferential processes in neuronal networks. We offer a solution to this problem by showing in detailed simulations how the belief propagation algorithm on a factor graph can be embedded in a network of ...
Andreas Steimer, Wolfgang Maass 0001, Rodney J. Douglas   +2 more
openaire   +4 more sources

Spike‐Based Spatiotemporal Processing Enabled by Oscillation Neuron for Energy‐Efficient Artificial Sensory Systems

Advanced Intelligent Systems, 2022
Developing an energy‐efficient artificial sensory system is of great significance for neuroprosthesis, neurorobotics, and intelligent human–machine interfaces.
Shuai Zhong, Yishu Zhang, Hao Zheng, Fangwen Yu, Rong Zhao   +4 more
doaj   +1 more source

Stationary Bumps in Networks of Spiking Neurons [PDF]

Neural Computation, 2001
We examine the existence and stability of spatially localized “bumps” of neuronal activity in a network of spiking neurons. Bumps have been proposed in mechanisms of visual orientation tuning, the rat head direction system, and working memory. We show that a bump solution can exist in a spiking network provided the neurons fire asynchronously within ...
Carlo R. Laing, Carson C. Chow
openaire   +2 more sources

WiN-GUI: A graphical tool for neuron-based encoding

SoftwareX
Neuromorphic computing relies on event-based, energy-efficient communication, inherently implying the need for conversion between real-valued (sensory) data and binary, sparse spiking representation.
Simon F. Müller-Cleve, Fernando M. Quintana, Vittorio Fra, Pedro L. Galindo, Fernando Perez-Peña, Gianvito Urgese, Chiara Bartolozzi   +6 more
doaj   +1 more source

Engineering Spiking Neurons Using Threshold Switching Devices for High-Efficient Neuromorphic Computing

Frontiers in Neuroscience, 2022
Inspired by the human brain, the spike-based neuromorphic system has attracted strong research enthusiasm because of the high energy efficiency and powerful computational capability, in which the spiking neurons and plastic synapses are two fundamental ...
Yanting Ding, Yanting Ding, Yajun Zhang, Xumeng Zhang, Xumeng Zhang, Xumeng Zhang, Pei Chen, Pei Chen, Zefeng Zhang, Yue Yang, Yue Yang, Lingli Cheng, Lingli Cheng, Chen Mu, Chen Mu, Ming Wang, Ming Wang, Du Xiang, Du Xiang, Guangjian Wu, Guangjian Wu, Guangjian Wu, Keji Zhou, Keji Zhou, Keji Zhou, Zhe Yuan, Qi Liu, Qi Liu, Qi Liu   +28 more
doaj   +1 more source

Automatic fitting of spiking neuron models to electrophysiological recordings

Frontiers in Neuroinformatics, 2010
Spiking models can accurately predict the spike trains produced by cortical neurons in response to somatically injected currents. Since the specific characteristics of the model depend on the neuron, a computational method is required to fit models to ...
Cyrille Rossant, Dan F M Goodman, Jonathan Platkiewicz, Romain Brette   +3 more
doaj   +1 more source

Glutamate Gated Spiking Neuron Model [PDF]

Annals of Neurosciences, 2014
Biological neuron models mainly analyze the behavior of neural networks. Neurons are described in terms of firing rates viz an analog signal.The Izhikevich neuron model is an efficient, powerful model of spiking neuron. This model is a reduction of Hodgkin-Huxley model to a two variable system and is capable of producing rich firing patterns for many ...
Deka, Krisha M, Roy, Soumik
openaire   +2 more sources