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:仿生计算一直以来都关注神经元和突触的研究,但是突触之间的联系也同样重要,这些联系就是树突。在本文中,我们探讨了复制树突计算的动机,并提出了一个指导未来构建的框架。该框架确定了树突的关键特性,并在声音定位任务中展示了树突计算的示例。我们评估了树突对BiLSTM神经网络性能的影响,发现树突预处理可以减小达到性能阈值所需的网络规模。