Movement generation with circuits of spiking neurons
How can complex movements that take hundreds of milliseconds be generated by stereotypical neural microcircuits consisting of spiking neurons with a much faster dynamics? We show that linear readouts from generic neural microcircuit models can be trained to generate basic arm movements. Such movement generation is independent of the arm-model used and the type of feedbacks that the circuit receives, and generalizes to new targets for reaching movements. We demonstrate this by considering two different models of a two-jointed arm, a standard model from robotics and a standard model from biology, that each generate different kinds of feedback. Feedbacks that arrive with biologically realistic delays of 50 - 280 ms turn out to give rise to the best performance. If a feedback with such desirable delay is not available, the neural microcircuit model also achieves good performance if it uses internally generated estimates of such feedback. Existing methods for movement generation in robotics that take the particular dynamics of sensors and actuators into account ("embodiment of motor systems") are taken one step further with this approach, which provides methods for also using the "embodiment of neural computation", i.e., the inherent dynamics and spatial structure of neural circuits, for the generation of movements.