Thus, a neuron divides computing elements at a lower level than in electronic computing. Plus, a network of neurons forms a grid of computers, making a computer cluster commonly called a neural network. Although the Von Neuman architecture does not completely characterize the features of biological neurons and neural networks (Zhang 2020), it is still a useful and familiar benchmark to frame some aspects of the nature of the neuronal computing landscape. Perhaps a biological neural network can be viewed as exemplifying an implementation of a Von Neumann architecture like an artificial neural network (Christensen 2022). While the aggregate neural network forms a more comprehensive central processing unit, each neuron contains a more focused processing capability which has yet to be comprehensively characterized. Although there is a standard classification of neurons, there are variations in the type of neurons throughout the human body. Neurons in the brain are focused on commutating. The central nervous system neurons are often more focused on connections and signaling. Neurons connected to sensory organs focus on receiving signals and translating information into spiking pulses that can be passed on to the brain for processing and resulting decisions from the inputs. G. Neural network computing Once neurons connect, they form neural networks. When these connections are established, they work together to determine a solution. How the neurons work together involves a variety of factors. Several neurons can pass a signal to the next-level neuron, which weighs the inputs from the various inputs and produces a result. This forms the basis for creating a neural network. Neural networks are a widely used and essential architectural feature in artificial intelligence and machine learning. In the brain, localized areas focus on different types of functions. A degree of human brain mapping has been done by direct brain stimulation during some cranial operations where it required having the brain exposed (Kim 2018; Huang 2017; Böhm 2016; Wanner 2018; Ryu 2018; Huckleberry 2018; Faraut 2018). Thus, there are categories of neural networks localized to specific parts of every human brain. II. METHODOLOGY A. Engineering perspective toward biological systems In terms of method orientation, this paper utilizes an engineering perspective when analyzing neuron-related biological data and how to consider architecture and function. This contrasts with the excellent and extensive body of neuroscience and neurology research work. This paper aims to provide a new context that can uncover additional insight. Systems engineering is a mature discipline with a well-defined architectural and design methodology. One can conceive of a needed capability, define the requirements, and progress to a functional layout. A design is selected and developed after making design trades and analyzing alternatives. Manufacturing and testing then take place. Another methodological consideration throughout this paper is a parallel discussion of biological and artificial neuronal systems. Exploring biological systems can aid in artificial designs that aid neuromorphic computing, neural networks, and machine learning applications. The exploration of artificial systems provides insights into detailed system developments that can be compared with a biological counterpart to see what is the same and what differs. Thus, each neuronal system evaluation can benefit from the other. This project assumes that an architecture is already in place when looking at biological systems. Therefore, reverse engineering can be done to uncover the approach and the design choices taken. Engineering tools can help in this process, and they are discussed next. B. Use of systems engineering tools System design and development have matured over several centuries, if not for millennia. As this process has matured, the development and application of engineering tools have become more standardized, and their utility has been demonstrated to provide a significant return on investment in product development. Applying these tools to biological systems is just starting to show its utility. This paper will not focus on a historical survey of this progress but instead jump in and use these tools. Systems engineering methods can help product development over all the phases of a system being evaluated, designed, tested, and deployed. This is readily applied in neuromorphic computing and the creation of artificial neural networks (Christensen 2022; Zheng 2019; Schuman 2022; Shrestha 2022). An orderly way of exploring neurons and neuromorphic computing is to group the topics into architectural levels. The layers of the compute stack are used to frame the neuronal capability. This process is developed in Section 4. Each of these levels is defined, along with research questions identified with each of them. C. Utilization of a Creation Model This paper will include a creation week model. Using engineering tools, it will point out the created works mentioned each day and the order by which things were created. It will show how creating human beings last is significant. III. RESULTS This section will discuss the Creation Model, the architecture framework that utilizes the full stack compute model, neuron models, and neural circuit and neural network models. In terms of the paper’s research questions, each of these topics will help answer them. The neuron and neural network architectural models provide a larger context for their operations. The Creation Model converts the creation account into engineering and architectural terms to compare it with the neural models and the full compute stack framework. Since the full compute stack model cannot adequately address the mental facilities of human beings, a use case that includes lower-level brain function captured in the full compute stack, higher-level abstracting thinking that goes beyond generative artificial intelligence, and the engagements with the Holy Spirit. Note that the architectural model diagrams may seem straightforward or simplistic. There is an engineering rationale behind this. Systems engineering thinking tries to see the big picture. To do this requires thinking about “black boxes” and their relationships, where the details within individual architecture components, or black boxes, may not be considered at this moment of high-level exploration to see the big picture. This is part of the methodology being addressed JOHANSEN Human brain function and the creation model 2023 ICC 290
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