Models Research Journal About GitHub
Neuro-AI Research Platform

Intelligence at the
edge of biology.

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.

Three models. One thesis.

Each model sits at the intersection of AI capability and biological insight — deployed, demo-ready, and built to withstand technical scrutiny.

MODEL 01
LIVE

Brain Tumor MRI Classifier

Medical Computer Vision

4-class classification on 7,023 MRI scans. 96.2% test accuracy. A deep CNN deployed with FastAPI, containerized with Docker, and running on Azure.

CNN FastAPI Docker TensorFlow
Try live demo →
MODEL 02
PLANNED

Virtual Lesions Visualizer

Neuro-AI / Interpretability

Ablate specific layers and channels in VGG-16 or ResNet-50. Measure whether prediction deficits mirror biological visual deficits — prosopagnosia, akinetopsia, achromatopsia.

PyTorch Grad-CAM Saliency Maps Interactive
MODEL 03
PLANNED

Medical Literature Q&A

NLP / Retrieval-Augmented Generation

Ingest PubMed abstracts, build a retrieval pipeline, generate domain-specific answers with source citations. Medical RAG with cited sources — not another generic chatbot.

LangChain ChromaDB Transformers FastAPI

Neuro-AI Research Track

An 8-week independent study mapping biological neural systems to deep learning architectures. Every lab produces a deployable artifact.

Phase 1

Bio-Electric Foundation

Weeks 1–2

Neuroanatomy, Hodgkin–Huxley neuron models, Leaky Integrate-and-Fire simulations, and mapping the human visual stream against CNN feature hierarchies.

Phase 2

Systems & Computational Principles

Weeks 3–5

Sparse coding, synaptic plasticity, STDP learning rules, Hopfield networks, place cell representations, and RL agents with biologically-inspired state encodings.

Phase 3

High-Level Cognition & Synthesis

Weeks 6–8

Language processing parallels, mechanistic interpretability, circuit analysis, and a capstone BCI neural signal decoder using EEG motor imagery data.

Key Papers

The theoretical backbone — primary literature driving each phase of the study.

  • Felleman & Van Essen (1991)
    Distributed Hierarchical Processing in Primate Cortex
  • Hodgkin & Huxley (1952)
    Quantitative Description of Membrane Current
  • Olshausen & Field (1996)
    Emergence of Simple-Cell Receptive Fields
  • Bi & Poo (1998)
    Synaptic Modifications in Hippocampal Neurons
  • Moser, Kropff & Moser (2008)
    Place Cells, Grid Cells & Spatial Representation
  • Hassabis et al. (2017)
    Neuroscience-Inspired Artificial Intelligence

Notes from the lab.

Thoughts on neuroscience, deep learning, and the space between — written as I build.

MAR 5, 2026

AI Weather Forecasting's Critical Blind Spot

Google's GraphCast can generate a 10-day forecast in under a minute. But when researchers tested AI models on Category 5 hurricanes, they failed catastrophically. What this means for AI in safety-critical domains.

AI Safety Weather Distribution Shift
APR 18, 2026

Day Zero: Building Arxelos from Scratch

From an empty repo to a deployed brain tumor classifier in one session. FastAPI backend, Docker containerization, Azure deployment — and the architecture decisions behind a platform designed to grow.

MLOps FastAPI Azure Docker
COMING SOON

Why Virtual Lesions Matter

What happens when you break a neural network the same way a stroke breaks a brain? Exploring the parallels between CNN ablation studies and biological visual deficits.

Neuro-AI Interpretability

Built by Aryan Patel

MS in Artificial Intelligence at Northeastern University. Background in deep learning, computer vision, and NLP. Building toward the intersection of neuroscience and machine intelligence.