ReviewNeuron2021
Improving scalability in systems neuroscience.
Review in Neuron, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- From microfluidics to nanodelivery: artificial intelligence reshapes neuropharmacology research strategies.Frontiers in pharmacology · 2026Review
- A software platform for real-time and adaptive neuroscience experiments.Nature communications · 2025Article
- Harnessing electroencephalography connectomes for cognitive and clinical neuroscience.Nature biomedical engineering · 2025Review
- Large-scale foundation models and generative AI for BigData neuroscience.Neuroscience research · 2025Review
- Identifying behavioral links to neural dynamics of multifiber photometry recordings in a mouse social behavior network.Journal of neural engineering · 2024Article
- Real-time analysis of large-scale neuronal imaging enables closed-loop investigation of neural dynamics.Nature neuroscience · 2024Article
- Wireless closed-loop deep brain stimulation using microelectrode array probes.Journal of Zhejiang University. Science. B · 2024Review
- Identifying behavioral links to neural dynamics of multifiber photometry recordings in a mouse social behavior network.bioRxiv : the preprint server for biology · 2024Article
- Large-Area Field Potential Imaging Having Single Neuron Resolution Using 236 880 Electrodes CMOS-MEA Technology.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- How our understanding of memory replay evolves.Journal of neurophysiology · 2023Review
- Modern views of machine learning for precision psychiatry.Patterns (New York, N.Y.) · 2022Review
- Uncovering spatial representations from spatiotemporal patterns of rodent hippocampal field potentials.Cell reports methods · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Emerging technologies to acquire data at increasingly greater scales promise to transform discovery in systems neuroscience. However, current exponential growth in the scale of data acquisition is a double-edged sword. Scaling up data acquisition can speed up the cycle of discovery but can also misinterpret the results or possibly slow down the cycle because of challenges presented by the curse of high-dimensional data. Active, adaptive, closed-loop experimental paradigms use hardware and algorithms optimized to enable time-critical computation to provide feedback that interprets the observations and tests hypotheses to actively update the stimulus or stimulation parameters. In this perspective, we review important concepts of active and adaptive experiments and discuss how selectively constraining the dimensionality and optimizing strategies at different stages of discovery loop can help mitigate the curse of high-dimensional data. Active and adaptive closed-loop experimental paradigms can speed up discovery despite an exponentially increasing data scale, offering a road map to timely and iterative hypothesis revision and discovery in an era of exponential growth in neuroscience.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.