Increasing sensor flexibility through neuromodulation

Both biological and man-made motor control networks require input from sens ors to allow for modification of the motor program. Real sensory neurons ar e more flexible than typical robotic sensors because they are dynamic rathe r than static. The membrane properties of neurons and hence their excitabil ity can be modified by the presence of neuromodulatory substances. In the c ase of a sensory neuron, this can change, in a functionally significant way , the code used to describe a stimulus. For instance, extension of the neur on's dynamic range or modification of its filtering characteristics can res ult. This flexibility has an apparent cost. The code used may be situation- dependent and hence difficult to interpret. To address this issue and to un derstand how neuromodulation is used effectively in a motor control network , I am studying the GPR2 stretch receptor in the crustacean stomatogastric nervous system. Several different neuromodulatory substances can modify its encoding properties. Comparisons of physiological and anatomical evidence suggest that neuromodulation can be effected both by GPR2 itself and by oth er neurons in the network. These results suggest that the analog of neuromo dulation might be useful for improving sensor performance in an artificial motor control system.