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：生物启发计算在神经元和突触方面取得了巨大成功。然而，连接这些结构的树突也起着重要作用。本文探讨了复制树突计算的动机，并提出了一个框架，以指导未来在构建树突计算方面的尝试。该框架确定了树突的关键特性，并以声音定位任务为例展示了树突计算。我们评估了树突对BiLSTM神经网络性能的影响，发现树突预处理可降低达到阈值性能所需的网络规模。