ReviewFrontiers in systems neuroscience2016
Models of Neuronal Stimulus-Response Functions: Elaboration, Estimation, and Evaluation.
Review in Frontiers in systems neuroscience, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- Contrastive learning to fine-tune feature extraction models for the visual cortex.PLoS computational biology · 2026Article
- Invariant correlation: a general and straightforward method to quantify invariance in time-varying neural signals.Proceedings of the ... Conference on Cognitive Computational Neuroscience · 2026Article
- Neurons in auditory cortex integrate information within a constrained and context-invariant temporal window.Current biology : CB · 2025Article
- Neural Response Reliability as a Marker of the Transition of Neural Codes along Auditory Pathways.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Temporal integration in human auditory cortex is predominantly yoked to absolute time.Nature neuroscience · 2025Article
- Neurons in auditory cortex integrate information within constrained temporal windows that are invariant to the stimulus context and information rate.bioRxiv : the preprint server for biology · 2025Article
- Neuromorphic algorithms for brain implants: a review.Frontiers in neuroscience · 2025Review
- Modelling neural coding in the auditory midbrain with high resolution and accuracy.Nature machine intelligence · 2025Article
- Mapping the computational similarity of individual neurons within large-scale ensemble recordings using the SIMNETS analysis framework.Frontiers in neuroscience · 2025Article
- Regularizing hyperparameters of interacting neural signals in the mouse cortex reflect states of arousal.PLoS computational biology · 2024Article
- Receptive-field nonlinearities in primary auditory cortex: a comparative perspective.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- Introducing the STReaC (Spike Train Response Classification) toolbox.Journal of neuroscience methods · 2024Article
- Time-varying generalized linear models: characterizing and decoding neuronal dynamics in higher visual areas.Frontiers in computational neuroscience · 2024Review
- Identification of interacting neural populations: methods and statistical considerations.Journal of neurophysiology · 2023Review
- A convolutional neural network provides a generalizable model of natural sound coding by neural populations in auditory cortex.PLoS computational biology · 2023Article
- Delta-band neural tracking primarily reflects rule-based chunking instead of semantic relatedness between words.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- Exploiting colour space geometry for visual stimulus design across animals.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2022Article
- Fitting of TC model according to key parameters affecting Parkinson's state based on improved particle swarm optimization algorithm.Scientific reports · 2022Article
- Multiscale temporal integration organizes hierarchical computation in human auditory cortex.Nature human behaviour · 2022Article
- Plasticity of Multidimensional Receptive Fields in Core Rat Auditory Cortex Directed by Sound Statistics.Neuroscience · 2021Article
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Authors and funding
4 authors.
Funding
Abstract
Rich, dynamic, and dense sensory stimuli are encoded within the nervous system by the time-varying activity of many individual neurons. A fundamental approach to understanding the nature of the encoded representation is to characterize the function that relates the moment-by-moment firing of a neuron to the recent history of a complex sensory input. This review provides a unifying and critical survey of the techniques that have been brought to bear on this effort thus far-ranging from the classical linear receptive field model to modern approaches incorporating normalization and other nonlinearities. We address separately the structure of the models; the criteria and algorithms used to identify the model parameters; and the role of regularizing terms or "priors." In each case we consider benefits or drawbacks of various proposals, providing examples for when these methods work and when they may fail. Emphasis is placed on key concepts rather than mathematical details, so as to make the discussion accessible to readers from outside the field. Finally, we review ways in which the agreement between an assumed model and the neuron's response may be quantified. Re-implemented and unified code for many of the methods are made freely available.
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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.