Computation can be seen as symbol manipulation Indeed, a computation is a sequence of simple, well-defined steps that can lead to the solution of a problem. The problem itself must be defined exactly and unambiguously, and each step in the computation that solves the problem must be described in very specific terms.

Although anatomical connectivity of the nematode Caenorhabditis elegans has been almost completely described, determination of the neurophysiological basis of behavior is just beginning. Here, we performed a stochastic search to determine neural network parameters sufficient for a model worm to exhibit chemotaxis, a form of spatial orientation behavior in which turning probability is modulated by the rate of change of chemical concentration. To better comprehend network solutions, we developed a novel method (Neural Dynamic Clustering) to identify neural dynamic motifs. We identified two types of motifs, one of which had been previously identified, and validated the behavior generated by the motifs against experimental chemotaxis.

In Caenorhabditis elegans, spatial orientation behavior in a chemical gradient (chemotaxis) involves bouts of turning (pirouettes) modulated by the change in concentration of attractant. Ablation of identified neurons has delineated a candidate neural network for chemotaxis in C. elegans. The aim of our research is to generate testable models of how the network computes behavioral state and consequently, turning frequency, in response to changes in concentration. We were able to train neural networks to exhibit known chemotaxis rules using experimental data from chemotaxing C. elegans. The resultant network solutions involved three to five dynamically contributing neurons. Here we have analyzed the three neuron solutions and found three distinguishing features: a fast excitatory and delayed inhibitory connection, which acts as a differentiator; self-connections, which act to regulate neural response speed similar to synaptic time-constants; and recurrent inhibitory connections, which regulate second order network response characteristics. We plan to use this model to predict and interpret the results of laser ablations of neurons and genetic mutation in the C. elegans chemotaxis network.

SUMMARY: The Genetic Simulation Library (GSL) is a set of C++ programs and classes that can be used in individual-based simulation models of plant and animal populations. The classes in this library build representations of genes for a wide variety of different types of organisms, from asexually reproducing single-cell organisms to chromosome-structured sexually reproducing species. AVAILABILITY: http://www.csi.uoregon.edu/projects/genetics/GSL . Free for non-commercial applications; others should contact the author.