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.

翻译：行为可被描述为由神经活动驱动的时间序列动作。要学习神经网络中的复杂序列模式，过去活动的记忆需在比单神经元活动弛豫时间显著更长的时间尺度上持续存在。尽管递归网络能产生此类长瞬态过程，但以生物学合理方式训练这些网络颇具挑战。一种方法是储层计算，仅学习从递归网络到读出层的权重；其他模型则通过传播误差信号实现递归突触权重的学习。然而，这些模型的生物合理性常受限于局部性、资源分配、参数规模或调谐等问题。我们提出，通过考虑树突的信息存储与计算可缓解诸多此类问题。应用完全局部且持续激活的可塑性规则，我们能在由两个神经元群体构成的递归网络中学习复杂序列。重要的是，本模型具有资源高效性，仅需少量神经元即可完成复杂序列学习。我们以鸣禽鸣曲学习的模拟系统验证了这些特性——网络首先习得一段长程非马尔可夫序列，随后即使在外部干扰下仍能稳健复现该序列。