  The brain can learn to solve a wide range of tasks with high temporal and
energetic efficiency. However, most biological models are composed of simple
single compartment neurons and cannot achieve the state-of-art performances of
artificial intelligence. We propose a multi-compartment model of pyramidal
neuron, in which bursts and dendritic input segregation give the possibility to
plausibly support a biological target-based learning. In target-based learning,
the internal solution of a problem (a spatio temporal pattern of bursts in our
case) is suggested to the network, bypassing the problems of error
backpropagation and credit assignment. Finally, we show that this neuronal
architecture naturally support the orchestration of hierarchical imitation
learning, enabling the decomposition of challenging long-horizon
decision-making tasks into simpler subtasks.
