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：生物启发计算聚焦于神经元和突触并取得了巨大成功。然而，连接这些结构的树突同样扮演着重要角色。本文研究了复制树突计算的动机，并提出一个框架以指导未来树突构建的尝试。该框架识别了树突的关键特性，并在声源定位任务中给出了树突计算的实例。我们评估了树突对BiLSTM神经网络性能的影响，发现树突预处理降低了达到阈值性能所需的网络规模。