Bio-inspired computing has focused on neuron and synapses with great success. However, the connections between these, the dendrites, also play an important role. In this paper, we investigate the motivation for replicating dendritic computation and present a framework to guide future attempts in their construction. The framework identifies key properties of the dendrites and presents and example of dendritic computation in the task of sound localisation. We evaluate the impact of dendrites on an BiLSTM neural network's performance, finding that dendrite pre-processing reduce the size of network required for a threshold performance.

翻译：生物启发的计算方法已经成功地关注了神经元和突触的特性。然而连接这些特性的树突也发挥了重要的作用。在本文中，我们研究了复制树突计算模型的动机，并提出了一个指导未来构建树突计算模型的框架。该框架识别了树突的关键特点，并在声音定位任务中给出了树突计算的示例。我们评估了树突对 BiLSTM 神经网络表现的影响，发现树突预处理可以减小网络所需大小，同时实现阈值以上的性能。