Evolving Synaptic Plasticity with an Evolutionary Cellular Development Model

Since synaptic plasticity is regarded as a potential mechanism for memory formation and learning, there is growing interest in the study of its underlying mechanisms. Recently several evolutionary models of cellular development have been presented, but none have been shown to be able to evolve a range of biological synaptic plasticity regimes. In this paper we present a biologically plausible evolutionary cellular development model and test its ability to evolve different biological synaptic plasticity regimes. The core of the model is a genomic and proteomic regulation network which controls cells and their neurites in a 2D environment. The model has previously been shown to successfully evolve behaving organisms, enable gene related phenomena, and produce biological neural mechanisms such as temporal representations. Several experiments are described in which the model evolves different synaptic plasticity regimes using a direct fitness function. Other experiments examine the ability of the model to evolve simple plasticity regimes in a task -based fitness function environment. These results suggest that such evolutionary cellular development models have the potential to be used as a research tool for investigating the evolutionary aspects of synaptic plasticity and at the same time can serve as the basis for novel artificial computational systems

What makes a problem hard for a genetic algorithm? Some anomalous results and their explanation

What makes a problem easy or hard for a genetic algorithm (GA)? This question has become increasingly important as people have tried to apply the GA to ever more diverse types of problems. Much previous work on this question has studied the relationship between GA performance and the structure of a given fitness function when it is expressed as a Walsh polynomial . The work of Bethke, Goldberg, and others has produced certain theoretical results about this relationship. In this article we review these theoretical results, and then discuss a number of seemingly anomalous experimental results reported by Tanese concerning the performance of the GA on a subclass of Walsh polynomials, some members of which were expected to be easy for the GA to optimize. Tanese found that the GA was poor at optimizing all functions in this subclass, that a partitioning of a single large population into a number of smaller independent populations seemed to improve performance, and that hillelimbing outperformed both the original and partitioned forms of the GA on these functions. These results seemed to contradict several commonly held expectations about GAs.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46892/1/10994_2004_Article_BF00993046.pd

Self-selection of Input Stimuli for Improving Performance

A system which behaves in an environment can increase its performance level in two different ways. It can improve its ability to react efficiently to any stimulus that may come from the environment or it can acquire an ability to expose itself only to a sub-class of stimuli to which it knows how to respond efficiently. The possibility that a system can solve a task by selecting favourable stimuli is rarely considered in designing intelligent systems. In this paper we show that this type of ability can play a very powerful role in explaining a system's performance. Introduction Cognitive science and robotics have been dominated for years by a static conception of intelligence. More recently there has been a growing awareness of how important active motor behavior, for example in object manipulation, is for understanding natural intelligence and for developing really useful artificial intelligence. In this paper we analyze the capacity of a system to self-select with its behavior its o..

Some advantages and disadvantages of recombination

Abstract: Theory for the evolutionary properties of recombination is reviewed, both in terms of modi er genes and approaches that use group selection. Analytical and numerical methods are used to investigate the time to rst appearance of an advantageous two locus chromosome. On average, whether or not a double mutant chromosome is advantageous, it will appear for the rst time faster with recombination than without. A comparison is made with recent work on genetic algorithms. It is shown, using simple examples, that whether recombination speeds the production of a favorable multilocus chromosome depends on the shape of the tness function and the initial allele frequencies

Document Classification with Unsupervised Artificial Neural Networks

Text collections may be regarded as an almost perfect application arena for unsupervised neural networks. This is because many operations computers have to perform on text documents are classification tasks based on noisy patterns. In particular we rely on self-organizing maps which produce a map of the document space after their training process. From geography, however, it is known that maps are not always the best way to represent information spaces. For most applications it is better to provide a hierarchical view of the underlying data collection in form of an atlas where, starting from a map representing the complete data collection, different regions are shown at finer levels of granularity. Using an atlas, the user can easily "zoom" into regions of particular interest while still having general maps for overall orientation. We show that a similar display can be obtained by using hierarchical feature maps to represent the contents of a document archive. These neural networks have layerd architecture where each layer consists of a number of individual self-organizing maps. By this, the contents of the text archive may be represented at arbitrary detail while still having the general maps available for global orientation