Attentional filtering of visual information by neuronal ensembles in the primate lateral prefrontal cortex.

Authors

Sébastien Tremblay, Cognitive Neurophysiology Laboratory, Department of Physiology, McGill University, Montreal, QC H3G 1Y6, Canada & Integrated Program in Neuroscience, Montreal Neurological Institute, McGill University, Montreal, QC H3A 2B4, Canada
Florian Pieper, Institute for Neuro- & Pathophysiology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
Adam Sachs, Division of Neurosurgery, Department of Surgery, The Ottawa Hospital Research Institute, University of Ottawa, Ottawa, ON K1Y 4E9, Canada
Julio Martinez-Trujillo, Cognitive Neurophysiology Laboratory, Department of Physiology, McGill University, Montreal, QC H3G 1Y6, Canada & Robarts Research Institute, Departments of Psychiatry, Physiology and Pharmacology, Western University, London, ON N6A 5C1, CanadaFollow

Document Type

Article

Publication Date

1-7-2015

Journal

Neuron

Volume

85

Issue

1

First Page

202

Last Page

215

URL with Digital Object Identifier

10.1016/j.neuron.2014.11.021

Abstract

The activity of neurons in the primate lateral prefrontal cortex (LPFC) is strongly modulated by visual attention. Such a modulation has mostly been documented by averaging the activity of independently recorded neurons over repeated experimental trials. However, in realistic settings, ensembles of simultaneously active LPFC neurons must generate attentional signals on a single-trial basis, despite the individual and correlated variability of neuronal responses. Whether, under these circumstances, the LPFC can reliably generate attentional signals is unclear. Here, we show that the simultaneous activity of neuronal ensembles in the primate LPFC can be reliably decoded to predict the allocation of attention on a single-trial basis. Decoding was sensitive to the noise correlation structure of the ensembles. Additionally, it was resilient to distractors, predictive of behavior, and stable over weeks. Thus, LPFC neuronal ensemble activity can reliably encode attention within behavioral time frames, despite the noisy and correlated nature of neuronal activity.