Results 51 to 60 of about 13,938 (239)
A spike-timing-dependent plasticity rule for dendritic spines
Nature Communications, 2020 The structural organization of excitatory inputs supporting spike-timing-dependent plasticity (STDP) in dendritic spines remains unknown. Using a spine STDP protocol, the authors uncover the STDP rules for single, clustered and distributed dendritic ...
Sabrina Tazerart +3 more
doaj +1 more source
Forward Table-Based Presynaptic Event-Triggered Spike-Timing-Dependent Plasticity
, 2016 Spike-timing-dependent plasticity (STDP) incurs both causal and acausal synaptic weight updates, for negative and positive time differences between pre-synaptic and post-synaptic spike events.
Augustine, Charles +8 more
core +1 more source
Neuronal avalanches of a self-organized neural network with active-neuron-dominant structure [PDF]
, 2012 Neuronal avalanche is a spontaneous neuronal activity which obeys a power-law distribution of population event sizes with an exponent of -3/2. It has been observed in the superficial layers of cortex both \emph{in vivo} and \emph{in vitro}. In this paper
Beggs J. +3 more
core +2 more sources
Calcium control of triphasic hippocampal STDP
Journal of Computational Neuroscience, 2012 Synaptic plasticity is believed to represent the neural correlate of mammalian learning and memory function. It has been demonstrated that changes in synaptic conductance can be induced by approximately synchronous pairings of pre- and post- synaptic action potentials delivered at low frequencies.
Bush, Daniel, Jin, Yaochu
openaire +5 more sources
Robustness of STDP to spike timing jitter [PDF]
Scientific Reports, 2018 Abstract In 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.
Cui, Yihui +4 more
openaire +2 more sources
Paired competing neurons improving STDP supervised local learning in spiking neural networks
Frontiers in NeuroscienceDirect training of Spiking Neural Networks (SNNs) on neuromorphic hardware has the potential to significantly reduce the energy consumption of artificial neural network training.
Gaspard Goupy +2 more
doaj +1 more source
Advanced Functional Materials, EarlyView.An ultra‐robust memristor based on SrTiO3‐CeO2 (S‐C) vertically aligned nanocomposite (VAN) achieving exceptional endurance of 1012 switching cycles via interface engineering. Artificial neural networks (ANNs) integrated with S‐C VAN memristors exhibit high training accuracy across multiple datasets.
Zedong Hu +12 more
wiley +1 more source
Emulating long-term synaptic dynamics with memristive devices [PDF]
, 2016 The potential of memristive devices is often seeing in implementing neuromorphic architectures for achieving brain-like computation. However, the designing procedures do not allow for extended manipulation of the material, unlike CMOS technology, the ...
Berdan, Radu +5 more
core +2 more sources
Advanced Functional Materials, EarlyView.A W/NbOx/Pt memristor demonstrates the coexistence of volatile, non‐volatile, and threshold switching characteristics. Volatile switching serves as a reservoir computing layer, providing dynamic short‐term processing. Non‐volatile switching, stabilized through ISPVA, improves reliable long‐term readout. Threshold switching operates as a leaky integrate
Ungbin Byun, Hyesung Na, Sungjun Kim
wiley +1 more source
STDP-based adaptive graph convolutional networks for automatic sleep staging
Frontiers in Neuroscience, 2023 Automatic sleep staging is important for improving diagnosis and treatment, and machine learning with neuroscience explainability of sleep staging is shown to be a suitable method to solve this problem.
Yuan Zhao +5 more
doaj +1 more source