Spike-frequency adaptation (SFA) is widespread in the CNS, but its function remains
unclear. In neocortical pyramidal neurons, adaptation manifests itself by an increase in the …

We introduce a supervised learning algorithm for multilayer spiking neural networks. The
algorithm overcomes a limitation of existing learning algorithms: it can be applied to neurons …

Artificial Neural Networks (ANNs) are bio-inspired models of neural computation that have
proven highly effective. Still, ANNs lack a natural notion of time, and neural units in ANNs …

Over the last decade, various spiking neural network models have been proposed, along
with a similarly increasing interest in spiking models of computation in computational …

We develop a learning rule for networks of spiking neurons where signals are encoded
using fractionally predictive spike-coding. In this paradigm, neural output signals are …

Humans can quickly recognize objects in a dynamically changing world. This ability is
showcased by the fact that observers succeed at recognizing objects in rapidly changing …

This report provides a wide-angle survey on computational paradigms which have a
possible bearing on the development of unconventional computational substrates and …

Neural adaptation underlies the ability of neurons to maximize encoded information over a
wide dynamic range of input stimuli. While adaptation is an intrinsic feature of neuronal …

Spiking neural networks are characterised by the spiking neuron models they use and how
these spiking neurons process information communicated through spikes–the neural code …

Understanding how neurons are able to efficiently encode information is a topic with
applications ranging from more efficient neural network chips, to robot control, and also to …