PubMed Health⌕ Search

Biomedical subjects

A Prügel-Bennett

Publications and source records attributed to A Prügel-Bennett.

4 recordsLinked to original sources
PubMed2000

Evolving populations with overlapping generations.

In this paper, we extend a previously published model of an evolving finite population of multi-locus organisms, where the dynamics of the evolving system are described by the cumulants of the population efficacy distribution. We consider the case of overlapping generations and compare it to the previously studied case where generations are discrete. In the weak selection limit, we can solve the dynamics analytically and show that the changes in population genetic variance due to stochastic effects-genetic drift-is twice as great when generations overlap. The comparison of the dynamics of the two models shows many of the features seen in the comparison, performed by Moran, of simple one-locus genetic models with overlapping and non-overlapping generations. Studying the dynamics of the two models gives some insights into these comparisons.

Animals↗
PubMed1997

Modelling evolving populations.

A formalism is presented for modelling the evolutionary dynamics of a population of gene sequences. The formalism was originally developed for describing genetic algorithms. In this paper the formalism is elaborated by considering the evolution of an ensemble of populations. This allows the evolution to be modelled more accurately. To illustrate the formalism the problem of a population of gene sequences evolving in a multiplicative fitness landspace is considered. A comparison with simulations is made and shows very good agreement. More complicated problems have already been investigated including sexual recombination and evolution in a multi-valleyed fitness landscape. These results will be briefly reviewed.

Animals↗
PubMed1996

Learning synfire chains: turning noise into signal.

We develop a model of cortical coding of stimuli by the sequences of activation patterns that they ignite in an initially random network. Hebbian learning then stabilizes these sequences, making them attractors of the dynamics. There is a competition between the capacity of the network and the stability of the sequences; for small stability parameter epsilon (the strength of the mean stabilizing PSP in the neurons in a learned sequence) the capacity is proportional to 1/epsilon 2. For epsilon of the order of or less than the PSPs of the untrained network, the capacity exceeds that for sequences learned from tabula rasa.

Algorithms↗