This has two consequences for brain-inspired computing. First, truly neuromorphic computing will be fundamentally different from the familiar Turing machines, where a range of programs can run on a single machine. With the algorithm physically implemented in the network structure of a neuromorphic computer, sequential programming ideas simply do not apply. Although this means we will have to compute in a new and different paradigm, there are clear upsides: brain-inspired computing may well open up avenues of information processing that are impossible with traditional machines.¹²

Second, architecture (hardware) choices affect the range of algorithms that can be run on each implementation. At the most basic level, the closer to normal silicon computing, the more flexible and reprogrammable the machine will be; the more analogue and physical, the more the algorithms are fixed by the architecture choice. The hardware-specificity of neuromorphic computing has limiting effects on both innovation and progress, and there is a need for standardisation in the way algorithms can be implemented. There is progress here: in October 2020, for instance, researchers laid out a conceptual foundation for designing algorithms and hardware separately.¹³