» Articles » PMID: 25568112

Cortical State Determines Global Variability and Correlations in Visual Cortex

Overview
Journal J Neurosci
Specialty Neurology
Date 2015 Jan 9
PMID 25568112
Citations 121
Authors
Affiliations
Soon will be listed here.
Abstract

The response of neurons in sensory cortex to repeated stimulus presentations is highly variable. To investigate the nature of this variability, we compared the spike activity of neurons in the primary visual cortex (V1) of cats with that of their afferents from lateral geniculate nucleus (LGN), in response to similar stimuli. We found variability to be much higher in V1 than in LGN. To investigate the sources of the additional variability, we measured the spiking activity of large V1 populations and found that much of the variability was shared across neurons: the variable portion of the responses of one neuron could be well predicted from the summed activity of the rest of the neurons. Variability thus mostly reflected global fluctuations affecting all neurons. The size and prevalence of these fluctuations, both in responses to stimuli and in ongoing activity, depended on cortical state, being larger in synchronized states than in more desynchronized states. Contrary to previous reports, these fluctuations invested the overall population, regardless of preferred orientation. The global fluctuations substantially increased variability in single neurons and correlations among pairs of neurons. Once this effect was removed, pairwise correlations were reduced and were similar regardless of cortical state. These results highlight the importance of cortical state in controlling cortical operation and can help reconcile previous studies, which differed widely in their estimate of neuronal variability and pairwise correlations.

Citing Articles

Deciphering neuronal variability across states reveals dynamic sensory encoding.

Akella S, Ledochowitsch P, Siegle J, Belski H, Denman D, Buice M Nat Commun. 2025; 16(1):1768.

PMID: 39971911 PMC: 11839951. DOI: 10.1038/s41467-025-56733-w.


Between-area communication through the lens of within-area neuronal dynamics.

Gozel O, Doiron B Sci Adv. 2024; 10(42):eadl6120.

PMID: 39413191 PMC: 11482330. DOI: 10.1126/sciadv.adl6120.


Mapping brain state-dependent sensory responses across the mouse cortex.

Montagni E, Resta F, Tort-Colet N, Scaglione A, Mazzamuto G, Destexhe A iScience. 2024; 27(5):109692.

PMID: 38689637 PMC: 11059133. DOI: 10.1016/j.isci.2024.109692.


What does the mean mean? A simple test for neuroscience.

Tlaie A, Shapcott K, van der Plas T, Rowland J, Lees R, Keeling J PLoS Comput Biol. 2024; 20(4):e1012000.

PMID: 38640119 PMC: 11062559. DOI: 10.1371/journal.pcbi.1012000.


Trial-by-trial variability in cortical responses exhibits scaling of spatial correlations predicted from critical dynamics.

Ribeiro T, Jendrichovsky P, Yu S, Martin D, Kanold P, Chialvo D Cell Rep. 2024; 43(2):113762.

PMID: 38341856 PMC: 10956720. DOI: 10.1016/j.celrep.2024.113762.


References
1.
Heggelund P, Albus K . Response variability and orientation discrimination of single cells in striate cortex of cat. Exp Brain Res. 1978; 32(2):197-211. DOI: 10.1007/BF00239727. View

2.
Kohn A, Smith M . Stimulus dependence of neuronal correlation in primary visual cortex of the macaque. J Neurosci. 2005; 25(14):3661-73. PMC: 6725370. DOI: 10.1523/JNEUROSCI.5106-04.2005. View

3.
Tso D, Gilbert C, Wiesel T . Relationships between horizontal interactions and functional architecture in cat striate cortex as revealed by cross-correlation analysis. J Neurosci. 1986; 6(4):1160-70. PMC: 6568437. View

4.
Greenberg D, Houweling A, Kerr J . Population imaging of ongoing neuronal activity in the visual cortex of awake rats. Nat Neurosci. 2008; 11(7):749-51. DOI: 10.1038/nn.2140. View

5.
Goard M, Dan Y . Basal forebrain activation enhances cortical coding of natural scenes. Nat Neurosci. 2009; 12(11):1444-9. PMC: 3576925. DOI: 10.1038/nn.2402. View