ArticlePLoS computational biology2013
Inferring nonlinear neuronal computation based on physiologically plausible inputs.
Article in PLoS computational biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 85 papers.
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85 citing papers in PubMed.
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- Animal behavioral analysis and neural encoding with transformer-based self-supervised pretraining.ArXiv · 2026Article
- Laminar Distribution of Spectrotemporal Receptive Field Subtypes in the Primary Auditory Cortex of Echolocating Bats.Annals of the New York Academy of Sciences · 2025Article
- A Neural Model for V1 That Incorporates Dendritic Nonlinearities and Backpropagating Action Potentials.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Improved inference of latent neural states from calcium imaging data.bioRxiv : the preprint server for biology · 2025Article
- Filter-based models of suppression in retinal ganglion cells: Comparison and generalization across species and stimuli.PLoS computational biology · 2025Article
- Optimal Estimation of Local Motion-in-Depth with Naturalistic Stimuli.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Feature selectivity and invariance in marsupial primary visual cortex.The Journal of physiology · 2025Article
- Feature-specific divisive normalization improves natural image encoding for depth perception.bioRxiv : the preprint server for biology · 2024Article
- Applying Super-Resolution and Tomography Concepts to Identify Receptive Field Subunits in the Retina.PLoS computational biology · 2024Article
- Estimating receptive fields of simple and complex cells in early visual cortex: A convolutional neural network model with parameterized rectification.PLoS computational biology · 2024Article
- Time-varying generalized linear models: characterizing and decoding neuronal dynamics in higher visual areas.Frontiers in computational neuroscience · 2024Review
- Large-scale electrophysiology and deep learning reveal distorted neural signal dynamics after hearing loss.eLife · 2023Article
- A convolutional neural network provides a generalizable model of natural sound coding by neural populations in auditory cortex.PLoS computational biology · 2023Article
- Characterization of extracellular spike waveforms recorded in wallaby primary visual cortex.Frontiers in neuroscience · 2023Review
- Orientation pinwheels in primary visual cortex of a highly visual marsupial.Science advances · 2022Article
- Model-based characterization of the selectivity of neurons in primary visual cortex.Journal of neurophysiology · 2022Article
- How Stimulus Statistics Affect the Receptive Fields of Cells in Primary Visual Cortex.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2022Article
25 more citing papers are in PubMed but not listed here.
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3 authors.
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Abstract
The computation represented by a sensory neuron's response to stimuli is constructed from an array of physiological processes both belonging to that neuron and inherited from its inputs. Although many of these physiological processes are known to be nonlinear, linear approximations are commonly used to describe the stimulus selectivity of sensory neurons (i.e., linear receptive fields). Here we present an approach for modeling sensory processing, termed the Nonlinear Input Model (NIM), which is based on the hypothesis that the dominant nonlinearities imposed by physiological mechanisms arise from rectification of a neuron's inputs. Incorporating such 'upstream nonlinearities' within the standard linear-nonlinear (LN) cascade modeling structure implicitly allows for the identification of multiple stimulus features driving a neuron's response, which become directly interpretable as either excitatory or inhibitory. Because its form is analogous to an integrate-and-fire neuron receiving excitatory and inhibitory inputs, model fitting can be guided by prior knowledge about the inputs to a given neuron, and elements of the resulting model can often result in specific physiological predictions. Furthermore, by providing an explicit probabilistic model with a relatively simple nonlinear structure, its parameters can be efficiently optimized and appropriately regularized. Parameter estimation is robust and efficient even with large numbers of model components and in the context of high-dimensional stimuli with complex statistical structure (e.g. natural stimuli). We describe detailed methods for estimating the model parameters, and illustrate the advantages of the NIM using a range of example sensory neurons in the visual and auditory systems. We thus present a modeling framework that can capture a broad range of nonlinear response functions while providing physiologically interpretable descriptions of neural computation.
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