invertebrate sensori-motor circuits

[Fu-Sheng Tsung]

I will be presenting our work on modeling the lobster's gastric
circuit, which is a central pattern generator consisting of 11
neurons.

We use Williams-Zipser's recurrent learning algorithm; the model
network has one unit for each neuron and the connectivity is
constrained to be the same as the gastric circuit.

The main result is that such a simple network of sigmoidal units
can reproduce a good approximation of the oscillation generated
by the in-vitro gastric circuit (w.r.t. phase and amplitude).  
Note that none of the units/neurons are oscillatory by themselves.  The 
learned oscillation is very stable as is the real circuit.  Experimentation
with the model suggests that the network topology is intimately
related to the phase relationships of the oscillations a network
can (stably) generate.

This is NOT a detailed model of the gastric neurons, as it
models only the input/output function and the connectivity
of the circuit.

Reference: Fu-Sheng Tsung, Gary Cottrell, Allen Selverston,
"Some Experiments On Learning Stable Network Oscillations."
(to appear in IJCNN90, June, San Diego).

R. Williams & D. Zipser, "A learning algorithm for continually
running, fully recurrent neural networks."  Neural Computation,
1, 270-280 (1989).

Fu-Sheng Tsung
UCSD, tsung at cs.ucsd.edu