[Frontiers In Bioscience, Landmark, 23, 221-246, January 1, 2018]

Neural signatures of attention: insights from decoding population activity patterns

Panagiotis Sapountzis1, Georgia G. Gregoriou1,2

1Foundation 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


1. Abstract
2. Introduction
3. The attention network
4. Neural signatures of attention
4.1. Firing rate
4.2. Neural Synchrony
4.3. Response variability
4.4. Inter-neuronal correlations
5. Advantages of population analysis methods
6. Decoding of neural activity using pattern-classification algorithms
7. Insights into population coding of cognitive functions using decoding approaches
7.1. Population coding of attention
7.2. Mixed selectivity and adaptive coding
7.3. Temporal population dynamics
7.4. Sparse and distributed information coding
7.5. Temporal resolution of information code
7.6. The role of correlations
7.7. Contribution of the LFP signal
8. Comparison between different signal types
9. Implications for the development of brain-machine interfaces (BMIs)
10. Conclusion
11. Acknowledgement
12. References


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.


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Key Words: Visual Attention, Decoding, Machine-Learning Algorithm, Spikes, LFPs, Correlated Variability, Neuronal Synchronization, Review

Send correspondence to: Panagiotis Sapountzis, Foundation for Research and Technology Hellas, Institute of Applied and Computational Mathematics, N. Plastira 100, GR 70013, Heraklion Crete, Greece. Tel: 30-2810-394857, Fax: 30-2810-394840, E-mail: pasapoyn@iacm.forth.gr