ArticlePLoS computational biology2018
Inferring hidden structure in multilayered neural circuits.
Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 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
33 citing papers in PubMed, 74 citations in OpenAlex.
- Modeling spatial contrast sensitivity in responses of primate retinal ganglion cells to natural movies.PLoS computational biology · 2026Article
- Information transfer and recovery for the sense of touch.Cerebral cortex (New York, N.Y. : 1991) · 2025Article
- FOUNDATIONS OF VISUAL FORM SELECTIVITY IN MACAQUE AREAS V1 AND V2.bioRxiv : the preprint server for biology · 2025Article
- Article
- Applying Super-Resolution and Tomography Concepts to Identify Receptive Field Subunits in the Retina.PLoS computational biology · 2024Article
- Article
- Scalable Gaussian process inference of neural responses to natural images.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Efficient coding of natural scenes improves neural system identification.PLoS computational biology · 2023Article
- Grid cells, border cells, and discrete complex analysis.Frontiers in computational neuroscience · 2023Article
- Systematic reduction of the dimensionality of natural scenes allows accurate predictions of retinal ganglion cell spike outputs.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Electrophysiological dataset from macaque visual cortical area MST in response to a novel motion stimulus.Scientific data · 2022Article
- Simple model for encoding natural images by retinal ganglion cells with nonlinear spatial integration.PLoS computational biology · 2022Article
- Machine Learning in Neuro-Oncology, Epilepsy, Alzheimer's Disease, and Schizophrenia.Acta neurochirurgica. Supplement · 2022Review
- Article
- Scene statistics and noise determine the relative arrangement of receptive field mosaics.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- Nonlinear Spatial Integration Underlies the Diversity of Retinal Ganglion Cell Responses to Natural Images.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2021Article
- Computational challenges and opportunities for a bi-directional artificial retina.Journal of neural engineering · 2020Article
- What the salamander eye has been telling the vision scientist's brain.Seminars in cell & developmental biology · 2020Review
- Article
- Discovering Precise Temporal Patterns in Large-Scale Neural Recordings through Robust and Interpretable Time Warping.Neuron · 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
4 authors at 1 institution in 1 country.
Funding
Abstract
A central challenge in sensory neuroscience involves understanding how neural circuits shape computations across cascaded cell layers. Here we attempt to reconstruct the response properties of experimentally unobserved neurons in the interior of a multilayered neural circuit, using cascaded linear-nonlinear (LN-LN) models. We combine non-smooth regularization with proximal consensus algorithms to overcome difficulties in fitting such models that arise from the high dimensionality of their parameter space. We apply this framework to retinal ganglion cell processing, learning LN-LN models of retinal circuitry consisting of thousands of parameters, using 40 minutes of responses to white noise. Our models demonstrate a 53% improvement in predicting ganglion cell spikes over classical linear-nonlinear (LN) models. Internal nonlinear subunits of the model match properties of retinal bipolar cells in both receptive field structure and number. Subunits have consistently high thresholds, supressing all but a small fraction of inputs, leading to sparse activity patterns in which only one subunit drives ganglion cell spiking at any time. From the model's parameters, we predict that the removal of visual redundancies through stimulus decorrelation across space, a central tenet of efficient coding theory, originates primarily from bipolar cell synapses. Furthermore, the composite nonlinear computation performed by retinal circuitry corresponds to a boolean OR function applied to bipolar cell feature detectors. Our methods are statistically and computationally efficient, enabling us to rapidly learn hierarchical non-linear models as well as efficiently compute widely used descriptive statistics such as the spike triggered average (STA) and covariance (STC) for high dimensional stimuli. This general computational framework may aid in extracting principles of nonlinear hierarchical sensory processing across diverse modalities from limited 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.