(Newtonian) Space-Time Algebra
This work addresses the problem of modeling communication and computation in time-aware systems, such as spiking neural networks, but appears incremental as it builds on existing concepts like Allen's interval algebra.
The paper introduces space-time algebra as a mathematical model for encoding values as events in discretized linear time, ensuring consistency with temporal flow, and applies it to network design and temporal neural networks.
The space-time (s-t) algebra provides a mathematical model for communication and computation using values encoded as events in discretized linear (Newtonian) time. Consequently, the input-output behavior of s-t algebra and implemented functions are consistent with the flow of time. The s-t algebra and functions are formally defined. A network design framework for s-t functions is described, and the design of temporal neural networks, a form of spiking neural networks, is discussed as an extended case study. Finally, the relationship with Allen's interval algebra is briefly discussed.