  The brain can efficiently learn a wide range of tasks, motivating the search
for biologically inspired learning rules for improving current artificial
intelligence technology. Most biological models are composed of point neurons,
and cannot achieve the state-of-the-art performances in machine learning.
Recent works have proposed that segregation of dendritic input (neurons receive
sensory information and higher-order feedback in segregated compartments) and
generation of high-frequency bursts of spikes would support error
backpropagation in biological neurons. However, these approaches require
propagating errors with a fine spatio-temporal structure to the neurons, which
is unlikely to be feasible in a biological network.
  To relax this assumption, we suggest that bursts and dendritic input
segregation provide a natural support for biologically plausible target-based
learning, which does not require error propagation. We propose a pyramidal
neuron model composed of three separated compartments. A coincidence mechanism
between the basal and the apical compartments allows for generating
high-frequency bursts of spikes. This architecture allows for a burst-dependent
learning rule, based on the comparison between the target bursting activity
triggered by the teaching signal and the one caused by the recurrent
connections, providing the support for target-based learning. We show that this
framework can be used to efficiently solve spatio-temporal tasks, such as the
store and recall of 3D trajectories.
  Finally, we suggest that this neuronal architecture naturally allows for
orchestrating ``hierarchical imitation learning'', enabling the decomposition
of challenging long-horizon decision-making tasks into simpler subtasks. This
can be implemented in a two-level network, where the high-network acts as a
``manager'' and produces the contextual signal for the low-network, the
``worker''.

Neurons and Cognition