1997 Computational Neuroscience Meeting

Chemotaxis Control by Linear Recurrent Networks
T. C. Ferrée and S. R. Lockery
Institute of Neuroscience, University of Oregon, Eugene, OR 97403.

The nematode Caenorhabditis elegans provides an excellent opportunity to study biological computation. It has exactly 302 neurons, each with known morphology and connectivity, and electrophysiological experiments are now being performed on identified neurons. We study chemotaxis, the ability to orient up gradients of chemical attractants. To begin understanding the biological network, in this paper we study linear recurrent networks. By expanding the voltage response in time-derivatives of the chemical stimulus, we can identify what aspects of the stimulus are important. For the network studied here, we found that the stimulus and its first derivative alone are sufficient.

Supported by NIMH MH11373, NIMH MH51383, NSF IBN 9458102, ONR N00014-94-1-0642, the Sloan Foundation, and the Searle Scholars Program.