## 4 Perspective on Verification, Validation, and Risk Assessment for Brain-Inspired Computing  
Verification and Validation (V&V) processes are independent procedures that are used together for ensuring that a product, service, or system meets requirements and specifications, and that it fulfills its intended purpose [14; 15]. It is important to define under what conditions the technology (or combined technologies) will be used--the Context of Use (COU). In medicine, the COU is critical for quantifying safety, efficacy, and risk to the patient. This text is not meant to guide the development of V&V programs, but evaluate how this process may be translated for use specifically in brain-inspired computing strategies. Industrial companies have an internal V&V process to ensure the safety and efficacy of their product and define failure modes and risk. Many of the guiding technical documents are online, and there are end-to-end examples available [9]. There are many international committees that work together to guide global regulatory standards. A few examples include the American Society of Mechanical Engineering (ASME), which is represented as V&V committees, VV10-70, ranging from solid mechanics to Artificial Intelligence. There has also been recent updated guidance from the Food and Drug Administration (FDA) on the use of Software as a Medical Device (SaMD), in addition to the European Economic Area (EEA) and the International Medical Device Regulators Forum (IMDRF) [16]. Consideration for Computational Modeling and Simulation Relative to Brain-Inspired Computing Architectures  
_Computational Simulation:_ Computational modeling can be used as a tool to evaluate multiple physical elements that affect human physiology and anatomy. For example, computational simulation may be used to predict the vorticity or wall shear stress (WSS), which is a predictive surrogate marker for the development of a thrombus. Computational modeling can be used in combination with clinical outcomes and is the subject material of V&V40: Assessing Credibility of Computational Modeling through Verification and Validation: applications to Medical Devices. These working groups utilize a framework for investigators to establish the credibility of the computational model for a specific COU and build on documented evidence from evaluating the model risk and identifying the credibility goals in order to create a plan for assessing credibility. In the context of brain-inspired computing, there are numerous applications that will overlay this process with the validation of the framework itself For example, a model of cardiac function where there is an in silico trial and/or patient-specific prediction of an adaptive pacing strategy to predict long-term outcomes, or many other paradigms where there is a model in a model. It is important to consider how the models behave and how they reflect reality. _Software as a Medical Device (SaMD):_ There have been many recent updates to the language and definitions of SaMD--the current, non-binding recommendations for SaMD can be found here: Available online: [[https://www.fda.gov/media/154985/download](https://www.fda.gov/media/154985/download)]([https://www.fda.gov/media/154985/download](https://www.fda.gov/media/154985/download)) (accessed on 28 February 2022), which describes SaMD as a medical device intended to be used for one or more medical purposes without being part of the hardware medical device. However, it also needs to be taken into consideration that SaMD can be in SiMD (software in a Medical Device). Within computational modeling and simulation, there is also an outline for in silico trial and Computational Modeling & Simulation (CM&S)-qualified tools for developing or validating a medical device. _Risk:_ There are multiple different conversations regarding \"risk.\" There is a risk associated with consequence, or decision consequence; for example, if a prediction is wrong, what is the risk to the patient? Secondly, how much is the model informing the clinical decision?