Stable Hebbian Learning from Spike Timing-Dependent Plasticity [PDF]
Journal of Neuroscience, 2000 We explore a synaptic plasticity model that incorporates recent findings that potentiation and depression can be induced by precisely timed pairs of synaptic events and postsynaptic spikes.
Mark C. W. van Rossum +2 more
semanticscholar +5 more sources
Slowness: an objective for spike-timing-dependent plasticity? [PDF]
PLoS Computational Biology, 2007 Our nervous system can efficiently recognize objects in spite of changes in contextual variables such as perspective or lighting conditions. Several lines of research have proposed that this ability for invariant recognition is learned by exploiting the ...
Henning Sprekeler +2 more
doaj +15 more sources
Inhibitory Spike-Timing-Dependent Plasticity Can Account for Pathological Strengthening of Pallido-Subthalamic Synapses in Parkinson's Disease. [PDF]
Front Physiol, 2022 Parkinson’s disease (PD) is a neurodegenerative brain disorder associated with dysfunction of the basal ganglia (BG) circuitry. Dopamine (DA) depletion in experimental PD models leads to the pathological strengthening of pallido-subthalamic synaptic ...
Madadi Asl M +3 more
europepmc +2 more sources
Spike-timing dependent plasticity in inhibitory circuits [PDF]
Frontiers in Synaptic Neuroscience, 2010 Inhibitory circuits in the brain rely on GABA-releasing interneurons. For long, inhibitory circuits were considered weakly plastic in the face of patterns of neuronal activity that trigger long-term changes in the synapses between excitatory principal ...
Karri P Lamsa +2 more
doaj +2 more sources
SSTDP: Supervised Spike Timing Dependent Plasticity for Efficient Spiking Neural Network Training. [PDF]
Front Neurosci, 2021 Spiking Neural Networks (SNNs) are a pathway that could potentially empower low-power event-driven neuromorphic hardware due to their spatio-temporal information processing capability and high biological plausibility.
Liu F +5 more
europepmc +2 more sources
Characterization of Generalizability of Spike Timing Dependent Plasticity Trained Spiking Neural Networks. [PDF]
Front Neurosci, 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.
Chakraborty B, Mukhopadhyay S.
europepmc +3 more sources
A Computational Model of Working Memory Based on Spike-Timing-Dependent Plasticity. [PDF]
Front Comput Neurosci, 2021 Working memory is closely involved in various cognitive activities, but its neural mechanism is still under exploration. The mainstream view has long been that persistent activity is the neural basis of working memory, but recent experiments have ...
Huang QS, Wei H.
europepmc +2 more sources
Travelling spindles create necessary conditions for spike-timing-dependent plasticity in humans. [PDF]
Nat Commun, 2021 Sleep spindles facilitate memory consolidation in the cortex during mammalian non-rapid eye movement sleep. In rodents, phase-locked firing during spindles may facilitate spike-timing-dependent plasticity by grouping pre-then-post-synaptic cell firing ...
Dickey CW +5 more
europepmc +2 more sources
Concurrent Thalamostriatal and Corticostriatal Spike-Timing-Dependent Plasticity and Heterosynaptic Interactions Shape Striatal Plasticity Map [PDF]
Cerebral Cortex, 2020 The striatum integrates inputs from the cortex and thalamus, which display concomitant or sequential activity. The striatum assists in forming memory, with acquisition of the behavioral repertoire being associated with corticostriatal (CS) plasticity ...
Alexandre Mendes +5 more
openalex +2 more sources
Multiplexing rhythmic information by spike timing dependent plasticity. [PDF]
PLoS Computational Biology, 2020 Rhythmic activity has been associated with a wide range of cognitive processes including the encoding of sensory information, navigation, the transfer of information and others.
Nimrod Sherf, Maoz Shamir
doaj +2 more sources