ArticleeLife2019
Inferring synaptic inputs from spikes with a conductance-based neural encoding model.
Article in eLife, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 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
20 citing papers in PubMed, 35 citations in OpenAlex.
- Rapid, interpretable data-driven models of neural dynamics using recurrent mechanistic models.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Filter-based models of suppression in retinal ganglion cells: Comparison and generalization across species and stimuli.PLoS computational biology · 2025Article
- Quantitative prediction of intracellular dynamics and synaptic currents in a small neural circuit.Frontiers in computational neuroscience · 2025Article
- Neuromorphic algorithms for brain implants: a review.Frontiers in neuroscience · 2025Review
- Disparate nonlinear neural dynamics measured with different techniques in macaque and human V1.Scientific reports · 2024Article
- Dynamic Nonlinear Spatial Integrations on Encoding Contrasting Stimuli of Tectal Neurons.Animals : an open access journal from MDPI · 2024Article
- Time-varying generalized linear models: characterizing and decoding neuronal dynamics in higher visual areas.Frontiers in computational neuroscience · 2024Review
- Short-term postsynaptic plasticity facilitates predictive tracking in continuous attractors.Frontiers in computational neuroscience · 2023Article
- Grid cells, border cells, and discrete complex analysis.Frontiers in computational neuroscience · 2023Article
- Fitting of TC model according to key parameters affecting Parkinson's state based on improved particle swarm optimization algorithm.Scientific reports · 2022Article
- An in silico model for determining the influence of neuronal co-activity on rodent spatial behavior.Journal of neuroscience methods · 2022Article
- Dissecting cascade computational components in spiking neural networks.PLoS computational biology · 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
- Efficient spline regression for neural spiking data.PloS one · 2021Article
- Poisson balanced spiking networks.PLoS computational biology · 2020Article
- Accurate detection of spontaneous seizures using a generalized linear model with external validation.Epilepsia · 2020Article
- Modeling the Short-Term Dynamics ofThe Journal of neuroscience : the official journal of the Society for Neuroscience · 2020Article
- Capturing Multiple Timescales of Adaptation to Second-Order Statistics With Generalized Linear Models: Gain Scaling and Fractional Differentiation.Frontiers in systems neuroscience · 2020Article
- Model-Based Inference of Synaptic Transmission.Frontiers in synaptic neuroscience · 2019Review
- Probabilistic Encoding Models for Multivariate Neural Data.Frontiers in neural circuits · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Descriptive statistical models of neural responses generally aim to characterize the mapping from stimuli to spike responses while ignoring biophysical details of the encoding process. Here, we introduce an alternative approach, the conductance-based encoding model (CBEM), which describes a mapping from stimuli to excitatory and inhibitory synaptic conductances governing the dynamics of sub-threshold membrane potential. Remarkably, we show that the CBEM can be fit to extracellular spike train data and then used to predict excitatory and inhibitory synaptic currents. We validate these predictions with intracellular recordings from macaque retinal ganglion cells. Moreover, we offer a novel quasi-biophysical interpretation of the Poisson generalized linear model (GLM) as a special case of the CBEM in which excitation and inhibition are perfectly balanced. This work forges a new link between statistical and biophysical models of neural encoding and sheds new light on the biophysical variables that underlie spiking in the early visual pathway.
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.