ReviewFrontiers in neural circuits2019
Probabilistic Encoding Models for Multivariate Neural Data.
Review in Frontiers in neural circuits, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Decodanda: a Python toolbox for best-practice decoding and geometric analysis of neural representations.bioRxiv : the preprint server for biology · 2026Article
- Neuron synchronization analyzed through spatial-temporal attention.Frontiers in computational neuroscience · 2025Article
- Regularizing hyperparameters of interacting neural signals in the mouse cortex reflect states of arousal.PLoS computational biology · 2024Article
- Predicting the temporal-dynamic trajectories of cortical neuronal responses in non-human primates based on deep spiking neural network.Cognitive neurodynamics · 2024Article
- Modeling the diverse effects of divisive normalization on noise correlations.PLoS computational biology · 2023Article
- A Poisson generalized linear model application to disentangle the effects of various parameters on neurophysiological discharges.STAR protocols · 2021Article
- Context-Dependent Multiplexing by Individual VTA Dopamine Neurons.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2020Article
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
No grant is acknowledged in the PubMed record.
Abstract
A key problem in systems neuroscience is to characterize how populations of neurons encode information in their patterns of activity. An understanding of the encoding process is essential both for gaining insight into the origins of perception and for the development of brain-computer interfaces. However, this characterization is complicated by the highly variable nature of neural responses, and thus usually requires probabilistic methods for analysis. Drawing on techniques from statistical modeling and machine learning, we review recent methods for extracting important variables that quantitatively describe how sensory information is encoded in neural activity. In particular, we discuss methods for estimating receptive fields, modeling neural population dynamics, and inferring low dimensional latent structure from a population of neurons, in the context of both electrophysiology and calcium imaging data.
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.