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.

Interneurons in leech ganglia receive multiple sensory inputs and make synaptic contacts with many motor neurons. These hidden units coordinate several different behaviors. The authors used physiological and anatomical constraints to construct a model of the local bending reflex. Dynamical networks were trained on experimentally derived input-output patterns using recurrent back propagation. Units in the model were modified to include electrical synapses and multiple synaptic time constants. The properties of the hidden units that emerged in the simulations matched those in the leech. The model and data support distributed rather than localist representations in the local bending reflex. The results also explain counterintuitive aspects of the local bending circuitry

Interneurons in leech ganglia receive multiple sensory inputs and make synaptic contacts with many motor neurons. These “hidden” units coordinate several different behaviors. We used physiological and anatomical constraints to construct a model of the local bending reflex. Dynamic networks were trained on experimentally derived input-output patterns using recurrent backpropagation. Units in the model were modified to include electrical synapses and multiple synaptic time constants. The properties of the hidden units that emerged in the simulations matched those in the leech. The model and data support distributed rather than localist representations in the local bending reflex. These results also explain counterintuitive aspects of the local bending circuitry.