erences refer to neuroscience and neuromorphic computing research insights that correlate with the processing portion of a neural computing architecture (Aljadeff 2016; Benjamin 2008; Bouvier 2019; Doberjeh 2016; Masqueler 2011; Rizzardi, 2019). Second, there is memory in a neuron. Each neuron must maintain awareness of what it must do at a given moment. Through learning activities, a neuron can respond to a stimulus in the same way repeatedly. Networks of neurons require memory to execute multi-stage activities. Each action requires memory, and various paper references explore aspects of neuroscience research concerning neuron and neural network memory (Huckleberry 2018; Faraut 2018). Third, neurons have capabilities for inputs and outputs, as demonstrated by studies examining and visualizing neural network structures. Computational load can be shared by having neurons work together in the appropriate input and output connections. The central nervous system connects neurons to pass sensor information and control and monitor motor functions. Neural circuits have been experimentally traced. Several paper references explore neuron and neural network interconnections (Boorboor 2016; Böhm 2016; Huang 2017; Wang 2021). Based on what is known about neurons to date, there are several characteristics that all neurons have. Neurons have dendrites, a cell body, and an axon, which according to this modeling approach, the synapse is included as a part that attaches to the axon. Neurons connect with other neurons through synapses, which capture the specific interneuron signaling approaches. Fig. 15 shows these functional blocks. 2. Neuron classification There are multiple ways one can categorize the classifications of neurons. One can consider the function and the structure. Fig. 16 shows the grouping of two classification categories that are used in this assessment. Since neurons are a key focus for this paper, this section provides useful characterization for this basic building block. Showcasing information via an architectural model perspective highlights taxonomy and implementation information in biological neurons and what approaches in biomimicry have been done in artificial neurons. Neural network models are shown in Fig. 22. Spiking neural networks are implemented in biological neurons. Artificial neural network models are simplifying attempts to implement some of the features in biological spiking neural networks within the current limitations of electronic chip part fabrication methods. Functional and structural classifications are highlighted below. Functional classifications capture the capabilities that are in the top-level biological neuron types by location in human beings. Structural classifications summarize the major biological neuron implementation types. a. functional classification Functional classification captures the significant types of neurons and their locations. The location of the neurons relates to their function. These functional locations are near the senses (sensory neurons), inside the brain (interneurons), inside the central nervous system (interneurons), and near muscles (motor neurons), as shown in Fig. 17. Neuroscience research seeks to refine this understanding, but from an architectural modeling point of view, these classifications are enough to provide a top-level understanding of sensing, motor function, computing, and learning. Neuron functional classifications highlight what neurons are intended to do and the architectural layout used to embrace its purpose. It does not fit directly one-to-one into a Von Neumann architecture of processor, memory, and input and output. Still, it does show all three of these Von Neumann features. There is not a clear distinction between hardware and software. Digital computing is not necessarily its base, but spikes are used to trigger action and pass information. A different architectural paradigm is utilized for this important connecting and computational building block. It is optimized to promote UC Imago Dei Layer Use Case Individual's Human Body Imago Dei Enterprise Layer Engage Body Engage Imago Dei Faculties Think Abstractly (as God Can) Direct Body <<stakeholder>> Holy Spirit Whole Individual Engage Body in Full Compute Stack Layers Engage Individual at Imago Dei Layer Figure 14. Imago Dei Enterprise Layer Above Full Compute Stack bdd BDD Neuron Model <<block>> <<system>> <<domain>> Neuron parts : Axon {unique} : Cell Body {unique} : Dendrite {unique} references : Applications {unique} : Algorithms {unique} : System Architecture {unique} : Microarchitecture {unique} : Circuits {unique} : Components and Devices {uni... : Materials {unique} <<block>> Axon parts : Synapse {unique} <<block>> Cell Body <<block>> Dendrite <<block>> Synapse references : Synapse Plasticity {unique} Figure 15. Neuron model showing major components. PKG Neuron Classification Categories Neuron Classification Categories <<block>> Neuron Structural Classifications parts : Unipolar and Pseudo Unipolar ... : Bipolar {unique} : Multipolar {unique} <<block>> <<system>> Neuron Functional Classifications parts : Motor Neuron {unique} : Interneuron - Brain {unique} : Sensory Neuron {unique} : Interneuron - CNS {unique} Neural Network Models <<block>> <<domain>> Neural Network Models parts : Spiking Neural Networks {unique} : Artificial Neural Networks {unique} <<block>> Artificial Neural Networks parts property conventio... : Hodgkins-Hurley... : Leaky Integrate=... : Izhikevich Model... <<block>> Spiking Neural Networks parts property data-... : Spike-Timin... Figure 16. Neuron classification categories. JOHANSEN Human brain function and the creation model 2023 ICC 301
RkJQdWJsaXNoZXIy MTM4ODY=