A vast majority of computation in the brain is performed by spiking neural networks. Despite
the ubiquity of such spiking, we currently lack an understanding of how biological spiking …

The persistent modification of synaptic efficacy as a function of the relative timing of pre-and
postsynaptic spikes is a phenomenon known as spike-timing-dependent plasticity (STDP) …

Biological structures can be seen as collections of special devices coordinated by a matrix of
organization. Devices are dif? cult to evolve and are meticulously conserved through the …

In timing-based neural codes, neurons have to emit action potentials at precise moments in
time. We use a supervised learning paradigm to derive a synaptic update rule that optimizes …

Maximization of information transmission by a spiking-neuron model predicts changes of
synaptic connections that depend on timing of pre-and postsynaptic spikes and on the …

Certain aspects of the present disclosure support a technique for neural learning of natural
multi-spike trains in spiking neural networks. A synaptic weight can be adapted depending …

Certain aspects of the present disclosure Support a technique for neural temporal coding,
learning and recognition. A method of neural coding of large or long spatial-temporal …

Learning in biologically relevant neural-network models usually relies on Hebb learning
rules. The typical implementations of these rules change the synaptic strength on the basis …

In this paper, we describe a new Synaptic Plasticity Activity Rule (SAPR) developed for use
in networks of spiking neurons. Such networks can be used for simulations of physiological …

We studied the hypothesis that synaptic dynamics is controlled by three basic principles:(1)
synapses adapt their weights so that neurons can effectively transmit information,(2) …