Technical perspective: Race logic presents a novel form of encoding

Perhaps even closer to my heart is the more abstract principles on which race logic rests. At its core, race logic is inspired by several aspects of how neuroscientists believe that the brain computes. These include, for example, the notion that time encodes computation, the concept of radial basis functions where larger signals trigger neurons more rapidly, and the inclusion of race logic primitives that are inspired by inhibitory post-synaptic potentials in the neo-cortex. Computer scientists have long been fascinated by the idea of drawing lessons from biology and nature to build better abstractions and methods for computing, spurring research on neuromorphic systems, natural algorithms, the emergence of intelligence, and more. These endeavors are often faced with the following question: To what degree is it useful for concepts from biology/nature to be replicated in systems/algorithms? Does, for example, the fact that computer systems rely on silicon and digital technologies, which differ from the elements and proteins used to realize life, mean that more abstract principles from natural computing need to be considered instead? And if so, what are the abstractions from nature appropriate for mimicry in computer systems? Race logic offers perspective on this debate by lifting underlying principles of computation in the brain and abstracting them so that they may be suitable for deployment using silicon technologies. I believe this is what enables race logic to achieve efficiency across all three of sensor layer, learning algorithm, and architecture layer. As the authors point out, achieving all three is a rarity and, I believe, a testament to the educational value of this paper. I hope you enjoy reading about race logic as much as I have.