Abstract [eng] Artificial neural networks developed in the scientific field of machine learning
are used in practical applications, such as data recognition, prediction of processes and etc …

It has been demonstrated, that spike-timing-dependent plasticity (STDP) learning rule can
be applied to train neuron to become selective to a spatiotemporal spike pattern. In this …

Spike‐timing‐dependent plasticity (STDP) is a set of Hebbian learning rules which are
based firmly on biological evidence. STDP learning is capable of detecting spatiotemporal …

Spike-timing-dependent plasticity (STDP) is a set of Hebbian learning rules which are firmly
based on biological evidence. It has been demonstrated that one of the STDP learning rules …

Spike-timing-dependent plasticity (STDP) is a set of Hebbian learning rules firmly based on
biological evidence. It has been demonstrated that one of the STDP learning rules is suited …

Temporal relationships between neuronal firing and plasticity have received significant
attention in recent decades. Neurophysiological studies have shown the phenomenon of …

This paper presents an extension of previous work whereby the Spike Timing Dependant
Plasticity (STDP) rule was used to train a two layer Spiking Neural Network (SNN). In that …

Recent neurophysiological research has focused on the temporal relationships between
neuronal firing and plasticity, and has shown the phenomenon of spike-timing-dependent …

Recognition tasks performed by humans and animals require the learning and storage of
representations by neuronal networks of external sensory stimuli. Recent studies have …

Fig. 1 Recurrent Synchronized Bursts (rSB) and the change in the precision of arrival times
of the first rSB following∆ t training.(a) Rasterplot showing rSB following a probing pulse (20 …