Non-random connectivity can emerge without structured external input driven by activity-
dependent mechanisms of synaptic plasticity based on precise spiking patterns. Here we …

The neural representation of a stimulus is repeatedly transformed as it moves from the
sensory periphery to deeper layers of the nervous system. Sparsening transformations are …

We propose a model for synaptic plasticity based on a calcium signaling cascade. The
model simplifies the full signaling pathways from a calcium influx to the phosphorylation …

Latency reduction in postsynaptic spikes is a well-known effect of spiking time-dependent
plasticity. We expand this notion for long postsynaptic spike trains on single neurons …

Experiments have shown that the same stimulation pattern that causes Long-Term
Potentiation in proximal synapses, will induce Long-Term Depression in distal ones. In order …

Spiking neural networks (SNN) increasingly attract attention for their similarity to the
biological neural system. Hardware implementation of spiking neural networks, however …

Spiking neural networks (SNNs) can achieve lower latency and higher efficiency compared
with traditional neural networks if they are implemented in dedicated neuromorphic …

Deep learning believed to be a promising approach for solving specific problems in the field
of artificial intelligence whenever a large amount of data and computation is available …

Abstract (EN) At a first glance, artificial neural networks, with engineered learning algorithms
and carefully chosen nonlinearities, are nothing like the complicated self-organized spiking …

Structure, dynamics and self-organization in recurrent neural networks : from machine
learning to theoretical neuroscience :: MPG.PuRe Deutsch Hilfe Datenschutzhinweis …