Talk at: Neuroplasticity: From Bench to Machine Learning (13 July 2018 - 14 July 2018 , University of Surrey, Guildford, UK) Abstract: Hebbian spike timing dependant plasticity strengthens the synapse from one neuron to another if the spikes from the first neuron tend to precede the spikes from the other. However, this doesn't always lead to Hebbian learning; here two examples will be presented of anti-Hebbian learning from spike timing dependent plasticity
According to Hebbian theory, neural networks refine their connectivity by patterned firing of action...
Hebbian plasticity, a synaptic mechanism which detects and amplifies co-activity between neurons, is...
Cortical and subcortical microcircuits are continuously modified throughout life. Despite ongoing ch...
<p>Talk at:</p> <p>Neuroplasticity: From Bench to Machine Learning (13 July 2018 - 14 July 2018 , U...
Abstract. Spike-timing-dependent plasticity (STDP) strengthens synapses that are activated immediate...
We study the capacity of Hodgkin-Huxley neuron in a network to change temporarily or permanently the...
Copyright © 2012 S. Fernando and K. Yamada. This is an open access article distributed under the Cre...
Abstract:- Among a lot of models for learning in neural networks, Hebbian and anti-Hebbian learnings...
This study was possible by partial financial support from the following Brazilian government agencie...
We analyze the conditions under which synaptic learning rules based on action potential timing can ...
International audienceIn Hebbian plasticity, neural circuits adjust their synaptic weights depending...
Spike Timing-Dependent Plasticity has been found to assume many different forms. The classic STDP cu...
Synaptic plasticity is thought to be the neuronal correlate of learning. Moreover, modification of s...
Poster presentation at Eighteenth Annual Computational Neuroscience Meeting: CNS'2009This work was f...
The fundamental paradigm of Hebbian learning has recently re-ceived a novel interpretation with the ...
According to Hebbian theory, neural networks refine their connectivity by patterned firing of action...
Hebbian plasticity, a synaptic mechanism which detects and amplifies co-activity between neurons, is...
Cortical and subcortical microcircuits are continuously modified throughout life. Despite ongoing ch...
<p>Talk at:</p> <p>Neuroplasticity: From Bench to Machine Learning (13 July 2018 - 14 July 2018 , U...
Abstract. Spike-timing-dependent plasticity (STDP) strengthens synapses that are activated immediate...
We study the capacity of Hodgkin-Huxley neuron in a network to change temporarily or permanently the...
Copyright © 2012 S. Fernando and K. Yamada. This is an open access article distributed under the Cre...
Abstract:- Among a lot of models for learning in neural networks, Hebbian and anti-Hebbian learnings...
This study was possible by partial financial support from the following Brazilian government agencie...
We analyze the conditions under which synaptic learning rules based on action potential timing can ...
International audienceIn Hebbian plasticity, neural circuits adjust their synaptic weights depending...
Spike Timing-Dependent Plasticity has been found to assume many different forms. The classic STDP cu...
Synaptic plasticity is thought to be the neuronal correlate of learning. Moreover, modification of s...
Poster presentation at Eighteenth Annual Computational Neuroscience Meeting: CNS'2009This work was f...
The fundamental paradigm of Hebbian learning has recently re-ceived a novel interpretation with the ...
According to Hebbian theory, neural networks refine their connectivity by patterned firing of action...
Hebbian plasticity, a synaptic mechanism which detects and amplifies co-activity between neurons, is...
Cortical and subcortical microcircuits are continuously modified throughout life. Despite ongoing ch...