The BCM rule allows a spinal cord model to learn rhythmic movements

Abstract

Animal locomotion is hypothesized to be controlled by a central pattern generator in the spinal cord. Experiments and models show that rhythm generating neurons and genetically determined network properties could sustain oscillatory output activity suitable for locomotion. However, current CPG models do not explain how a spinal cord circuitry, which has the same basic genetic plan across species, can adapt to control the different biomechanical properties and locomotion patterns existing in these species. Here we demonstrate that rhythmic and alternating movements in pendulum models can be learned by a monolayer spinal cord circuitry model using the BCM learning rule, which has been previously proposed to explain learning in the visual cortex. These results provide an alternative theory to CPG models, because rhythm generating neurons and genetically defined connectivity are not required in our model.Author summaryThe central pattern generator is the leading hypothesis of locomotor control in animals. There, rhythm generating neurons and genetically defined neural connectivity would form a circuit generating activity patterns suitable for locomotion. We provide a new hypothesis of locomotor control, where rhythmic patterns are learned by a Hebbian learning rule from a mechanical system that has an intrinsic tendency to oscillate.

Neuronal networks quantified as vector fields

“…The brain controls the activity of the neuron population through the landscape of the synaptic connectivity of its network. The trajectories through the synaptic connectivity-defined state space are the solutions, or behaviors, with which we can respond to sensory inputs, at the cortical level [2] as well as in the spinal cord circuitry [3, 4, 8]. Therefore, the plurality of solutions [6, 2] depends on the complexity of the network state space.…”