Results 31 to 40 of about 16,068 (283)
Competitive Learning in a Spiking Neural Network: Towards an Intelligent Pattern Classifier
Sensors, 2020 One of the modern trends in the design of human−machine interfaces (HMI) is to involve the so called spiking neuron networks (SNNs) in signal processing. The SNNs can be trained by simple and efficient biologically inspired algorithms.
Sergey A. Lobov +4 more
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Shaping corticostriatal connectivity with STDP [PDF]
BMC Neuroscience, 2011 The striatum, the main input nucleus of the basal ganglia, is one of the major sites for learning and decision making. The striatum receives massive convergent inputs from the cortex. Task related cortical activity show weak pairwise correlations, thus, individual medium-sized spiny neurons (MSNs) in the striatum are very likely to receive correlated ...
Man Yi Yim +4 more
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STDP in recurrent neuronal networks [PDF]
Frontiers in Computational Neuroscience, 2010 Recent results about spike-timing-dependent plasticity (STDP) in recurrently connected neurons are reviewed, with a focus on the relationship between the weight dynamics and the emergence of network structure. In particular, the evolution of synaptic weights in the two cases of incoming connections for a single neuron and recurrent connections are ...
Matthieu Gilson +6 more
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An Adaptive STDP Learning Rule for Neuromorphic Systems [PDF]
Frontiers in Neuroscience, 2021 The promise of neuromorphic computing to develop ultra-low-power intelligent devices lies in its ability to localize information processing and memory storage in synaptic circuits much like the synapses in the brain. Spiking neural networks modeled using high-resolution synapses and armed with local unsupervised learning rules like spike time-dependent
Ashish Gautam, Takashi Kohno
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Intrinsic stability of temporally shifted spike-timing dependent plasticity. [PDF]
PLoS Computational Biology, 2010 Spike-timing dependent plasticity (STDP), a widespread synaptic modification mechanism, is sensitive to correlations between presynaptic spike trains and it generates competition among synapses.
Baktash Babadi, L F Abbott
doaj +1 more source
Bio-plausible digital implementation of a reward modulated STDP synapse
Neural computing & applications (Print), 2022 Reward-modulated Spike-Timing-Dependent Plasticity (R-STDP) is a learning method for Spiking Neural Network (SNN) that makes use of an external learning signal to modulate the synaptic plasticity produced by Spike-Timing-Dependent Plasticity (STDP ...
Fernando M. Quintana +2 more
semanticscholar +1 more source
Efficacy of Shexiang Tongxin Dropping Pills in a Swine Model of Coronary Slow Flow
Frontiers in Physiology, 2022 Objective: Preliminary clinical studies have confirmed that Shexiang Tongxin dropping pills (STDPs) could improve angina pectoris and attenuate vascular endothelial dysfunction in patients with slow coronary flow, but the underlying mechanism is not ...
Yupeng Bai +6 more
doaj +1 more source
CMOS Circuit Implementation of Spiking Neural Network for Pattern Recognition Using On-chip Unsupervised STDP Learning [PDF]
arXiv.org, 2022 Computation on a large volume of data at high speed and low power requires energy-efficient computing architectures. Spiking neural network (SNN) with bio-inspired spike-timing-dependent plasticity learning (STDP) is a promising solution for energy ...
Sahibia Kaur Vohra +3 more
semanticscholar +1 more source
Robustness of STDP to spike timing jitter [PDF]
Scientific Reports, 2018 AbstractIn Hebbian plasticity, neural circuits adjust their synaptic weights depending on patterned firing. Spike-timing-dependent plasticity (STDP), a synaptic Hebbian learning rule, relies on the order and timing of the paired activities in pre- and postsynaptic neurons.
Yihui Cui +4 more
openaire +6 more sources
Frontiers in Neuroscience, 2021 A Spiking Neural Network (SNN) is trained with Spike Timing Dependent Plasticity (STDP), which is a neuro-inspired unsupervised learning method for various machine learning applications.
Biswadeep Chakraborty +1 more
doaj +1 more source