Isolation-by-Distance and Outbreeding Depression Are Sufficient to Drive Parapatric Speciation in the Absence of Environmental Influences

A commonly held view in evolutionary biology is that speciation (the emergence of genetically distinct and reproductively incompatible subpopulations) is driven by external environmental constraints, such as localized barriers to dispersal or habitat-based variation in selection pressures. We have developed a spatially explicit model of a biological population to study the emergence of spatial and temporal patterns of genetic diversity in the absence of predetermined subpopulation boundaries. We propose a 2-D cellular automata model showing that an initially homogeneous population might spontaneously subdivide into reproductively incompatible species through sheer isolation-by-distance when the viability of offspring decreases as the genomes of parental gametes become increasingly different. This simple implementation of the Dobzhansky-Muller model provides the basis for assessing the process and completion of speciation, which is deemed to occur when there is complete postzygotic isolation between two subpopulations. The model shows an inherent tendency toward spatial self-organization, as has been the case with other spatially explicit models of evolution. A well-mixed version of the model exhibits a relatively stable and unimodal distribution of genetic differences as has been shown with previous models. A much more interesting pattern of temporal waves, however, emerges when the dispersal of individuals is limited to short distances. Each wave represents a subset of comparisons between members of emergent subpopulations diverging from one another, and a subset of these divergences proceeds to the point of speciation. The long-term persistence of diverging subpopulations is the essence of speciation in biological populations, so the rhythmic diversity waves that we have observed suggest an inherent disposition for a population experiencing isolation-by-distance to generate new species

An Evolutionary Autonomous Agent with Visual Cortex and Recurrent Spiking Columnar Neural Network

Abstract. Spiking neural networks are computationally more power-ful than conventional artificial neural networks [1]. Although this fact should make them especially desirable for use in evolutionary autono-mous agent research, several factors have limited their application. This work demonstrates an evolutionary agent with a sizeable recurrent spi-king neural network containing a biologically motivated columnar visual cortex. This model is instantiated in spiking neural network simulation software and challenged with a dynamic image recognition and memory task. We use a genetic algorithm to evolve generations of this brain mo-del that instinctively perform progressively better on the task. This early work builds a foundation for determining which features of biological neural networks are important for evolving capable dynamic cognitive agents.

A Novel Parallel Hardware and Software Solution for a Large-Scale Biologically Realistic Cortical Simulation ∗

This research addresses a major gap in our conceptual understanding of synaptic and brain-like network dynamics. Over the course of several years we have designed and implemented increasingly complex and powerful brain-like simulators which apply recent advances in computer and networking technology towards the goal of understanding brain function in terms of pulse-coded information networks. These simulations have been run on increasingly powerful clusters of computers. Currently we have a cluster of 208 processors with a total of 416 GB of RAM and more than a Terabyte of disk storage, interconnected with a Myrinet 2000 high-speed/low-latency interconnection network. On this cluster we are able to run simulations on the order of 3 million synapses per processor, with the capability of receiving stimulus input from remote devices