Self-configuring feedback loops for sensorimotor control.

How dynamic interactions between nervous system regions in mammals performs online motor control remains an unsolved problem. In this paper, we show that feedback control is a simple, yet powerful way to understand the neural dynamics of sensorimotor control. We make our case using a minimal model comprising spinal cord, sensory and motor cortex, coupled by long connections that are plastic.

How the credit assignment problems in motor control could be solved after the cerebellum predicts increases in error.

We present a cerebellar architecture with two main characteristics. The first one is that complex spikes respond to increases in sensory errors. The second one is that cerebellar modules associate particular contexts where errors have increased in the past with corrective commands that stop the increase in error.

A model for complex sequence learning and reproduction in neural populations.

Temporal patterns of activity which repeat above chance level in the brains of vertebrates and in the mammalian neocortex have been reported experimentally. This temporal structure is thought to subserve functions such as movement, speech, and generation of rhythms. Several studies aim to explain how particular sequences of activity are learned, stored, and reproduced.