Arxelos deploys research-grade AI models at the intersection of neuroscience and deep learning. Each model is a working artifact — built to demonstrate, not just describe.
Each model sits at the intersection of AI capability and biological insight — deployed, demo-ready, and built to withstand technical scrutiny.
4-class classification on 7,023 MRI scans. 96.2% test accuracy. The fast win — already built, now being productionized with a clean upload UI and cloud deployment.
Ablate specific layers and channels in VGG-16 or ResNet-50. Measure whether prediction deficits mirror biological visual deficits — prosopagnosia, akinetopsia, achromatopsia.
Ingest PubMed abstracts, build a retrieval pipeline, generate domain-specific answers with source citations. Medical RAG with cited sources — not another generic chatbot.
An 8-week independent study mapping biological neural systems to deep learning architectures. Every lab produces a deployable artifact.
Neuroanatomy, Hodgkin–Huxley neuron models, Leaky Integrate-and-Fire simulations, and mapping the human visual stream against CNN feature hierarchies.
Sparse coding, synaptic plasticity, STDP learning rules, Hopfield networks, place cell representations, and RL agents with biologically-inspired state encodings.
Language processing parallels, mechanistic interpretability, circuit analysis, and a capstone BCI neural signal decoder using EEG motor imagery data.
The theoretical backbone — primary literature driving each phase of the study.