The value of neuromorphic computers depends crucially on our ability to program them for relevant tasks. Currently, neuromorphic computers are mostly limited to machine learning methods adapted from deep learning. However, neuromorphic computers have potential far beyond deep learning if we can only make use of their computational properties to harness their full power. Neuromorphic programming will necessarily be different from conventional programming, requiring a paradigm shift in how we think about programming in general. The contributions of this paper are 1) a conceptual analysis of what "programming" means in the context of neuromorphic computers and 2) an exploration of existing programming paradigms that are promising yet overlooked in neuromorphic computing. The goal is to expand the horizon of neuromorphic programming methods, thereby allowing researchers to move beyond the shackles of current methods and explore novel directions.

翻译：神经形态计算机的价值在很大程度上取决于我们能否为其编程以执行相关任务。目前，神经形态计算机主要局限于从深度学习改编而来的机器学习方法。然而，只要我们能够利用其计算特性来发挥全部潜力，神经形态计算机的潜能远不止深度学习。神经形态编程必然不同于传统编程，需要在编程思维上进行范式转变。本文的贡献在于：1）对神经形态计算机背景下"编程"含义的概念性分析；2）探索神经形态计算中尚被忽视却具有前景的现有编程范式。旨在拓展神经形态编程方法的视野，从而使研究者能够突破现有方法的束缚，探索全新方向。