  Recent advancements in artificial intelligence have propelled the
capabilities of Large Language Models, yet their ability to mimic nuanced human
reasoning remains limited. This paper introduces a novel conceptual enhancement
to LLMs, termed the Artificial Neuron, designed to significantly bolster
cognitive processing by integrating external memory systems. This enhancement
mimics neurobiological processes, facilitating advanced reasoning and learning
through a dynamic feedback loop mechanism. We propose a unique framework
wherein each LLM interaction specifically in solving complex math word problems
and common sense reasoning tasks is recorded and analyzed. Incorrect responses
are refined using a higher capacity LLM or human in the loop corrections, and
both the query and the enhanced response are stored in a vector database,
structured much like neuronal synaptic connections. This Artificial Neuron thus
serves as an external memory aid, allowing the LLM to reference past
interactions and apply learned reasoning strategies to new problems. Our
experimental setup involves training with the GSM8K dataset for initial model
response generation, followed by systematic refinements through feedback loops.
Subsequent testing demonstrated a significant improvement in accuracy and
efficiency, underscoring the potential of external memory systems to advance
LLMs beyond current limitations. This approach not only enhances the LLM's
problem solving precision but also reduces computational redundancy, paving the
way for more sophisticated applications of artificial intelligence in cognitive
tasks. This paper details the methodology, implementation, and implications of
the Artificial Neuron model, offering a transformative perspective on enhancing
machine intelligence.
