Demo workflows

Three suggested walkthroughs for testing retrieval, review, evaluation, audit, export, and feedback.

claustrum BDNF sleep memory consolidation

Research Idea

claustrum BDNF sleep memory consolidation

Expected scan strategy

Neuroscience query tiers emphasizing claustrum, BDNF, sleep, memory consolidation, rodent models, circuit connectivity, and behavior readouts.

Expected evidence categories

Highly similar, same gene/protein, same brain region, same behavior paradigm, review/background.

How to interpret results

Look carefully for papers combining claustrum, BDNF signaling, sleep state, and memory consolidation. A near-prior defines novelty risk and the evidence needed for a careful claim.

brain foundation model Neuropixels neural decoding

Research Idea

brain foundation model Neuropixels neural decoding

Expected scan strategy

AI-bio query tiers emphasizing neural data modality, foundation model type, decoding task, benchmark, validation cohort, and biological interpretability.

Expected evidence categories

Same model/task, same data modality, same prediction target, same benchmark, methodologically similar.

How to interpret results

Look carefully for closest prior models and pipelines. Novelty risk is high when the only change is model architecture or dataset without external validation or biological interpretation.

BDNF exon 6 claustrum sleep anesthesia

Research Idea

BDNF exon 6 claustrum sleep anesthesia

Expected scan strategy

Biomedical-oriented query tiers emphasizing BDNF exon 6, claustrum, sleep state, anesthesia, and gene-expression context.

Expected evidence categories

Same application different method, methodologically similar, review/background, low relevance.

How to interpret results

Expect PubMed coverage to matter. Audit should flag PubMed issues if biomedical records are missing or source status is failed.

rodent EEG EMG automatic sleep staging

Research Idea

rodent EEG EMG automatic sleep staging

Expected scan strategy

AI/method queries around rodent EEG, EMG, sleep staging, automatic classification, machine learning, and validation datasets.

Expected evidence categories

Highly similar, methodologically similar, review/background, low relevance.

How to interpret results

High similarity is likely because sleep staging is mature. Novelty may come from dataset domain, model robustness, annotation burden, or deployment workflow.