In this review, we compare methods for temporal sequence learning (TSL) across the
disciplines machine-control, classical conditioning, neuronal models for TSL as well as …

The last 50 years has witnessed considerable research in the area of neural networks
resulting in a range of architectures, learning algorithms and demonstrative applications. A …

A spiking neural network model is used to identify characters in a character set. The network
is a two layered structure consisting of integrate-and-fire and active dendrite neurons. There …

Spike-timing-dependent plasticity (STDP) is described by long-term potentiation (LTP), when
a presynaptic event precedes a postsynaptic event, and by long-term depression (LTD) …

Although artificial neural networks have taken their inspiration from natural neurological
systems, they have largely ignored the genetic basis of neural functions. Indeed …

Gul Muhammad Khan In Search of Learning Genes Page 1 Studies in Computational
Intelligence 725 Gul Muhammad Khan Evolution of Artificial Neural Development In Search of …

We present a neurobiologically motivated model of a neuron with active dendrites and
dynamic synapses, and a training algorithm which builds upon single spike-timing …

A coevolutionary competitive learning environment for two antagonistic agents is presented.
The agents are controlled by a new kind of computational network based on a …

We present an approach for recognition and clustering of spatio temporal patterns based on
networks of spiking neurons with active dendrites and dynamic synapses. We introduce a …

A neuro-inspired multi-chromosomal genotype for a single developmental neuron capable
of learning and develo** memory is proposed. This genotype is evolved so that the …