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. 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 motion generation circuitry'',
i.e., the inherent dynamics and spatial structure of neural circuits, for the
generation of movements.
Reference: P. Joshi and W. Maass.
Movement generation with circuits of spiking neurons.
Neural Computation, 17(8):1715-1738, 2005.