Format

Send to

Choose Destination
Front Biosci (Landmark Ed). 2018 Jan 1;23:221-246.

Neural signatures of attention: insights from decoding population activity patterns.

Author information

1
Foundation for Research and Technology Hellas, Institute of Applied and Computational Mathematics, N. Plastira 100, GR70013 Heraklion, Crete Greece, pasapoyn@iacm.forth.gr.
2
Foundation for Research and Technology Hellas, Institute of Applied and Computational Mathematics, N. Plastira 100, GR70013 Heraklion, Crete Greece, University of Crete, Faculty of Medicine, P.O. Box 2208, GR71003, Heraklion, Crete, Greece.

Abstract

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.

PMID:
28930544
[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Frontiers in Bioscience
Loading ...
Support Center