ArticleNeural computation2016
Neural Quadratic Discriminant Analysis: Nonlinear Decoding with V1-Like Computation.
Article in Neural computation, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Optimal Estimation of Local Motion-in-Depth with Naturalistic Stimuli.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- Unraveling Functional Diversity of Cortical Synaptic Architecture Through the Lens of Population Coding.Frontiers in synaptic neuroscience · 2022Article
- Revealing nonlinear neural decoding by analyzing choices.Nature communications · 2021Article
- The integration of visual and target signals in V4 and IT during visual object search.Journal of neurophysiology · 2019Article
- Stimulus- and goal-oriented frameworks for understanding natural vision.Nature neuroscience · 2019Review
- Rethinking assumptions about how trial and nuisance variability impact neural task performance in a fast-processing regime.Journal of neurophysiology · 2019Article
- Measurements of neuronal color tuning: Procedures, pitfalls, and alternatives.Vision research · 2018Article
- Article
- Linking normative models of natural tasks to descriptive models of neural response.Journal of vision · 2017Review
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Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Linear-nonlinear (LN) models and their extensions have proven successful in describing transformations from stimuli to spiking responses of neurons in early stages of sensory hierarchies. Neural responses at later stages are highly nonlinear and have generally been better characterized in terms of their decoding performance on prespecified tasks. Here we develop a biologically plausible decoding model for classification tasks, that we refer to as neural quadratic discriminant analysis (nQDA). Specifically, we reformulate an optimal quadratic classifier as an LN-LN computation, analogous to "subunit" encoding models that have been used to describe responses in retina and primary visual cortex. We propose a physiological mechanism by which the parameters of the nQDA classifier could be optimized, using a supervised variant of a Hebbian learning rule. As an example of its applicability, we show that nQDA provides a better account than many comparable alternatives for the transformation between neural representations in two high-level brain areas recorded as monkeys performed a visual delayed-match-to-sample task.
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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.