Spike timing dependent plasticity (STDP) is a learning rule that modifies synaptic strength as
a function of the relative timing of pre-and postsynaptic spikes. When a neuron is repeatedly …

Recent findings of spike timing-dependent plasticity (STDP) have stimulated much interest
among experimentalists and theorists. Beyond the traditional correlation-based Hebbian …

Spike timing-dependent plasticity (STDP) is a learning rule that modifies the strength of a
neuron's synapses as a function of the precise temporal relations between input and output …

The autoencoder algorithm is a simple but powerful unsupervised method for training neural
networks. Autoencoder networks can learn sparse distributed codes similar to those seen in …

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 …

A biologically inspired approach to learning temporally correlated patterns from a spiking
silicon retina is presented. Spikes are generated from the retina in response to relative …

Understanding how the human brain is able to efficiently perceive and understand a visual
scene is still a field of ongoing research. Although many studies have focused on the design …

Activity-dependent modification of synaptic strengths due to spike-timing-dependent
plasticity (STDP) is sensitive to correlations between pre-and postsynaptic firing over …

A new learning rule (Precise-Spike-Driven (PSD) Synaptic Plasticity) is proposed for
processing and memorizing spatiotemporal patterns. PSD is a supervised learning rule that …

Spike timing–dependent plasticity (STDP) as a Hebbian synaptic learning rule has been
demonstrated in various neural circuits over a wide spectrum of species, from insects to …