Neural Quadratic Discriminant Analysis: Nonlinear Decoding with V1-Like Computation.

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.

journal_name

Neural Comput

journal_title

Neural computation

authors

Pagan M,Simoncelli EP,Rust NC

doi

10.1162/NECO_a_00890

subject

Has Abstract

pub_date

2016-11-01 00:00:00

pages

2291-2319

issue

11

eissn

0899-7667

issn

1530-888X

journal_volume

28

pub_type

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