  Behavior can be described as a temporal sequence of actions driven by neural
activity. To learn complex sequential patterns in neural networks, memories of
past activities need to persist on significantly longer timescales than
relaxation times of single-neuron activity. While recurrent networks can
produce such long transients, training these networks in a biologically
plausible way is challenging. One approach has been reservoir computing, where
only weights from a recurrent network to a readout are learned. Other models
achieve learning of recurrent synaptic weights using propagated errors.
However, their biological plausibility typically suffers from issues with
locality, resource allocation or parameter scales and tuning. We suggest that
many of these issues can be alleviated by considering dendritic information
storage and computation. By applying a fully local, always-on plasticity rule
we are able to learn complex sequences in a recurrent network comprised of two
populations. Importantly, our model is resource-efficient, enabling the
learning of complex sequences using only a small number of neurons. We
demonstrate these features in a mock-up of birdsong learning, in which our
networks first learn a long, non-Markovian sequence that they can then
reproduce robustly despite external disturbances.
