IMR Press / FBL / Volume 23 / Issue 2 / DOI: 10.2741/4588

Frontiers in Bioscience-Landmark (FBL) is published by IMR Press from Volume 26 Issue 5 (2021). Previous articles were published by another publisher on a subscription basis, and they are hosted by IMR Press on as a courtesy and upon agreement with Frontiers in Bioscience.


Neural signatures of attention: insights from decoding population activity patterns

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1 Foundation for Research and Technology Hellas, Institute of Applied and Computational Mathematics, N. Plastira 100, GR70013 Heraklion, Crete Greece
2 University of Crete, Faculty of Medicine, P.O. Box 2208, GR71003, Heraklion, Crete, Greece
Front. Biosci. (Landmark Ed) 2018, 23(2), 221–246;
Published: 1 January 2018
(This article belongs to the Special Issue Electrophysiology from bench to bedside)

Understanding brain function and the computations that individual neurons and neuronal ensembles carry out during cognitive functions is one of the biggest challenges in neuroscientific research. To this end, invasive electrophysiological studies have provided important insights by recording the activity of single neurons in behaving animals. To average out noise, responses are typically averaged across repetitions and across neurons that are usually recorded on different days. However, the brain makes decisions on short time scales based on limited exposure to sensory stimulation by interpreting responses of populations of neurons on a moment to moment basis. Recent studies have employed machine-learning algorithms in attention and other cognitive tasks to decode the information content of distributed activity patterns across neuronal ensembles on a single trial basis. Here, we review results from studies that have used pattern-classification decoding approaches to explore the population representation of cognitive functions. These studies have offered significant insights into population coding mechanisms. Moreover, we discuss how such advances can aid the development of cognitive brain-computer interfaces.

Visual Attention
Machine-Learning Algorithm
Correlated Variability
Neuronal Synchronization
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