Independent researcher in neural computation.
CLICK TO STARTCuriosity and personal experience have been a stepping stone for me in many pursuits, two of which today are medicine and research in neural computation. I have been a person who is driven by the seeking of knowledge in system understanding and that lead me to studying how complex systems work such as the human body and use that understanding to help people. Across both pursuits, my aim is to turn what I learn into something useful for humanity whether through scientific research, technology development, or the practice of medicine.
My interest in systems first developed through practical exploration of mechanical and electronic systems from things that was sitting in my house. Those experiences of taking things apart and trying to understand from first principles why and how things work helped me understand system level relations that determine the behavior of that whole system. This perspective framework has become the foundation for how I approach other subjects, including biology.
My desire to help people led me toward medicine, while my interest in systems shaped how I approached the science behind it. At Florida Atlantic University, I studied Medical Biology and completed my degree in preparation for a medical career, and in that process I was exposed to anatomy, physiology, biomechanics, cell biology, physics, chemistry, etc... and that expanded the perspective I originally developed through engineering. Studying the human body changed how I understood the relationship between structure, interaction, and function, and how I thought about designing technology.
During those undergraduate studies, I developed a machine-learning model for disease classification. That project brought computation into the biological questions I was studying. Building it also drew my attention to how learning worked in that particular system of how training changed the model, but those changes did not continue in the same way when the model was running. I began asking whether a computational system could learn through its ongoing operation and retain those changes for later use.
I began researching & developing a model architecture originally using the conventional systems and equations of today's AI models but I kept realizing something within the fundamental equations of the AI models today, they don't actually allow for continuous learning if used as is with small manipulations. So that brought up another question, what would classify as being an equation and learning algorithm that can learn continuously, how would it work if we don't take anything from todays models and most importantly will it actually be able to learn and retain information on the fly.
That is what made me realize I needed to start from the ground up, from scratch, and research & develop a new way of AI neural computation. That's when LCNA was born.
I formulated LCNA's mathematical foundation and developed its initial prototype independently. My background in medical biology informs this work, particularly my interest in the relationship between learning, memory, and computation.
The first experiments focused on a reduced, supervised prototype. I examined whether the effects of a learning experience persisted and whether they mattered to later responses.
The initial work produced limited retention findings and a basis for further investigation.
Changes following a controlled learning experience remained observable in later tests within the experimental setting.
Controlled comparisons supported a relationship between the retained effects and later responses.
The retention pattern appeared in three development runs. These were internal research results, not independent replications.
I am now examining the neuroscience of learning and memory to guide LCNA's further development.
The reduced learning experiment did not meet its full validation criteria. Its retention findings are limited to the tested conditions.
The current evidence does not establish continual learning, general reasoning, biological fidelity, or a performance advantage.
I am seeking research support and grant funding to advance LCNA's scientific foundations and experimental validation.
I welcome discussions about the work and ways to support it.