Decoding neural representational spaces using multivariate pattern analysis.

By James V. Haxby, Andrew C. Connolly, J. Swaroop Guntupalli. Annual Review of Neuroscience, 2014

724 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/7684d36482bde1691acc30a7c9a8f61b97db66ac

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Citations in the corpus

A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula (2016, methodology): “, 2001), often referred to as a decoding (Haxby et al., 2014; King and Dehaene, 2014; Quian Quiroga and Panzeri, 2009).”
How concepts are encoded in the human brain: A modality independent, category-based cortical organization of semantic knowledge (2016, background): “…level, the large-scale approach implies that during the functional inference of brain regions, only multivariate analyses were able to take into account the high-dimensional cortical activity during semantic processing and to compare neural contents with behavioral information (Haxby et al., 2014).”
The Brain Basis of Positive and Negative Affect: Evidence from a Meta-Analysis of the Human Neuroimaging Literature. (2016, background): “RSA reveals the structure of representations in terms of distances between response vectors within a given representational space (Haxby et al. 2014).”
A Primer on Pattern-Based Approaches to fMRI: Principles, Pitfalls, and Perspectives. (2015, methodology): “…specialized topics, such as cognitive neuroscience applications (Haynes and Rees, 2006; Norman et al., 2006; Tong and Pratte, 2012), neural coding (Haxby et al., 2014; Kriegeskorte, 2009; Kriegeskorte and Kievit, 2013; Naselaris et al., 2011; Serences and Saproo, 2012), and methods and algorithms…”
Cognitive computational neuroscience (2018, methodology): “Decoding has its roots in the neuronal-recording literature(77,40) and has become a popular tool for studying the content of representations in neuroimaging.(78,18,70,79,71,80) In the simplest case, decoding reveals which of two stimuli gave rise to a measured response pattern.”
Recurrence is required to capture the representational dynamics of the human visual system (2019, methodology): “To accomplish this, we here combine magnetoencephalography (MEG), source-based representational dynamics analysis (14, 15), and deep learning.”
Multisensory Causal Inference in the Brain (2015, methodology): “Luckily, methods of decoding analysis provide a means to establish such a link [20] and allowed the authors to associate each brain region of interest with the best-matching sensory estimate predicted by the inference model.”
Neural population coding: combining insights from microscopic and mass signals (2015, methodology): “Finally, advances in computational methods for single-trial analysis of multivariate data allow us to fully exploit the avenues opened by high-density brain measurements [11–14].”