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.

通过应用局部、始终开启的可塑性规则，我们能够在由两个群体组成的递归网络中学习复杂序列，我们的模型具有资源高效性，仅需少量神经元即可学习复杂序列，我们通过鸟鸣学习的模拟中展示了这些特点，在该模拟中，我们的网络首先学习了一个长的非马尔可夫序列，尽管存在外部干扰，它们能够稳健地复现此序列。