Title: Non-linear motor control by local learning in spiking neural networks,
Abstract: Learning weights in a spiking neural network with hidden neurons, using
local, stable and online rules, to control non-linear body dynamics is an open
problem. Here, we employ a supervised scheme, Feedback-based Online Local
Learning Of Weights (FOLLOW), to train a network of heterogeneous spiking
neurons with hidden layers, to control a two-link arm so as to reproduce a
desired state trajectory. The network first learns an inverse model of the
non-linear dynamics, i.e. from state trajectory as input to the network, it
learns to infer the continuous-time command that produced the trajectory.
Connection weights are adjusted via a local plasticity rule that involves
pre-synaptic firing and post-synaptic feedback of the error in the inferred
command. We choose a network architecture, termed differential feedforward,
that gives the lowest test error from different feedforward and recurrent
architectures. The learned inverse model is then used to generate a
continuous-time motor command to control the arm, given a desired trajectory.