Introduction
Overview
Superconducting Optoelectronic Networks (SOENs) combine photonics and superconductors to instantiate computing systems that approach the fundamental limits of information processing in terms of speed and scalability. Overcoming the engineering challenges of integrating these technologies into one system has consistently been aided by using neuro-inspired architectures. SOENs are natively Spiking Neural Networks (SNNs) of loop neurons, which themselves are comprised of many subsequent dendrites organized into intricate morphologies.
sim_soens uses the forward euler method to simulate the dynamics of SOENs one time-step at a time (with the expection of a few speed-motivated tricks). The simulator has an easy-to-use python front end. A user defines a network of specified neurons and simulations instrucitons. sim_soens runs this network over these instructions with either a detail-rich python backend, or more constrained (but much faster) julia version. There are number of run-options described in greater detail in the system architecture section of this documentation.
The user may wish to define custom neurons with SuperNode class or generic neurons from neuron_library.py. Likewise, there is a custom network class SuperNet and a collection of pre-defined networks in network_library.py. All of these options come equipped with in-built plotting and analytic functions.