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James Johansen, PhD

Engineering and Computational Sciences

School of Engineering

Liberty University

1971 University Blvd.

Lynchburg, VA 24515

Author's Biography

James D. Johansen has an interdisciplinary Ph.D. from Liberty University, Lynchburg, VA, USA, in 2019, two master’s degrees in science and religion, and Christian apologetics, from Biola University, La Mirada, CA, USA, in 2015 and 2012, and electrical engineering master’s and bachelor’s degrees from the University of Southern California, Los Angeles, CA, USA, in 1985 and 1982. He is an adjunct professor at Liberty University in their engineering and computational science department, an adjunct professor at the Master’s University, Placentia, CA, USA, a researcher in theoretical biology, and an adjunct professor at Biola University, La Mirada, CA, USA, in their chemistry, physics, and engineering department, a part-time assistant professor at Azusa Pacific University in their engineering and computer science department, and an adjunct professor at Regent University in their graduate school. He has over two decades of experience in systems engineering at two federally funded research and development companies supporting the aerospace industry. He has over 20 conference papers and journal articles, plus a book chapter. He is a member of INCOSE, IEEE, MORS, ETS, CBS, and EPS professional societies.

Presentation Type

Full Paper Presentation


The human brain functions at a level beyond any other brain in all of creation. With mankind being made in the image of God, there must be signatures of this fact in its design. Modeling the human brain that develops an architectural framework offers the potential to unpack this reality. This premise offers a rich area to explore that expands the creation model to capture the engineering framework God used in creation. This paper will focus on brain neurons and neural networks using systems engineering modeling tools.

The systems modeling language (SysML) will be used to capture a model of neuron systems and neural network architectures. These topics can get highly complex. The goal is to generate valuable systems engineering models that can shed light on the state-of-the-art understanding of neurons and neural networks and offer perspectives on what should be focused on in future biomimicry endeavors. Two major themes will be explored in this paper, (1) analyzing the biological neuron and developing a functional model at several levels of detail, and (2) exploring neuromorphic computing and characterizing how it is done in biological systems versus human architected systems.

The human brain is the gold standard that engineers seek to emulate on many levels. What areas in biomimicry for brain neuromorphic processing can be improved and why? Answering this question will require exploring and capturing the system biology models and architecture patterns. Unfortunately, this can quickly get very complex. Using systems engineering methods, the goal is to capture the significant drivers of the architectural approach to the level that is required to provide improved guidance for future work. This will be accomplished by utilizing engineering methods and processes toward the biological systems of neurons and brain neural networks.

There is plenty of interest in modeling hardware and computer architectures after brain computing paradigms. Even with all the advancements in the computer, software, artificial intelligence, and machine learning industries, they fall short of what can be done by cognitive human brain activity. Given extensive data center class computing resources, some functionality can be replicated but at a considerable energy impact. To move towards the next level of effective biomimicry of brain computing, a worthwhile endeavor is to look closer at the biological system architecture at all levels to ensure a proper understanding of its function is well documented. The brain is highly complex and, for the most part, inaccessible in its protective cranial enclosure. While it is operational, direct access is impossible except when invasive brain surgery techniques are used.


Engineering | Physics


Neuroscience, neuromorphic computing, neuron modeling, spiking neural networks, design patterns, model-based systems engineering (MBSE), systems modeling language (SysML), architectural modeling, biomimicry, creation model




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