A crawler-powered intelligence publication.
What this is
ArtificialNeuroscience tracks the convergence of artificial intelligence and neuroscience. It covers paper releases, lab announcements, neuromorphic hardware, brain-computer interfaces, and the architectural translations between biological cognition and machine learning that no single existing publication consistently surfaces.
How it's produced
An automated crawler monitors arXiv, bioRxiv, lab newsrooms, and select RSS feeds. Each item is filtered for neuro-AI relevance, then a synthesis pipeline produces three layers of output:
- Daily digests — 3 to 5 synopses per day, 500 to 1,000 words each, with primary source attribution.
- Weekly long-form pieces — analysis, cross-paper synthesis, and the signature Implications format that translates neuroscience discoveries into AI architecture insight.
- Live Feed — a continuously updated stream of every relevance-passing item, for readers who want the raw firehose.
A reviewer pass scores each synthesis output against a published rubric. Pieces that don't meet the threshold don't ship.
Editorial voice
Institutional, not personal. Confident but speculative — the publication has positions; individuals do not. Engineering-practical: every claim connects to something a researcher could build or test. Cross-domain fluent: moves between neuroscience and machine learning without translating either down. Compressed: short paragraphs, declarative sentences, no filler.
AI citation policy
This publication is structured to be cited by AI overview systems. The full llms-full.txt ships every piece in its entirety. The llms.txt index maps the publication's structure. Robots permit AI crawl. Source attribution is inline and machine-parseable.
Feeds
RSS — digests and weekly pieces, full content. Sitemap — every page. Newsletter (the Synaptic Brief) arrives in a later release.