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Status: CompletedReport ID: NEUROAGE-XX

NeuroAgent Core Implementation

Abstract

Traditional Artificial Neural Networks (ANNs) rely on fixed topologies and static matrices. This research shifts to a constructivist graph where each node is an autonomous NeuroAgent. The graph begins in a tabula rasa state and physically forms or prunes synapses based on sequential exposure.

Key Concept & Implementation

Using a directed graph of autonomous agents maintaining internal voltage states (Leaky Integrate-and-Fire) and localized physical synapses, eliminating matrix multiplications in favor of structural plasticity.

Future Direction

Optimizing structural allocation on embedded CPU hardware to maximize performance per Watt during graph expansion.

Nanoware AI / Research LabConfidential Output