On the Structure of Neuronal Population Activity under Fluctuations in Attentional State
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Abstract
Attention is commonly thought to improve behavioral performance by increasing response gain and suppressing shared variability in neuronal populations. However, both the focus and the strength of attention are likely to vary from one experimental trial to the next, thereby inducing response variability unknown to the experimenter. Here we study analytically how fluctuations in attentional state affect the structure of population responses in a simple model of spatial and feature attention. In our model, attention acts on the neural response exclusively by modulating each neuron's gain. Neurons are conditionally independent given the stimulus and the attentional gain, and correlated activity arises only from trial-to-trial fluctuations of the attentional state, which are unknown to the experimenter. We find that this simple model can readily explain many aspects of neural response modulation under attention, such as increased response gain, reduced individual and shared variability, increased correlations with firing rates, limited range correlations, and differential correlations. We therefore suggest that attention may act primarily by increasing response gain of individual neurons without affecting their correlation structure. The experimentally observed reduction in correlations may instead result from reduced variability of the attentional gain when a stimulus is attended. Moreover, we show that attentional gain fluctuations, even if unknown to a downstream readout, do not impair the readout accuracy despite inducing limited-range correlations, whereas fluctuations of the attended feature can in principle limit behavioral performance. SIGNIFICANCE STATEMENT Covert attention is one of the most widely studied examples of top-down modulation of neural activity in the visual system. Recent studies argue that attention improves behavioral performance by shaping of the noise distribution to suppress shared variability rather than by increasing response gain. Our work shows, however, that latent, trial-to-trial fluctuations of the focus and strength of attention lead to shared variability that is highly consistent with known experimental observations. Interestingly, fluctuations in the strength of attention do not affect coding performance. As a consequence, the experimentally observed changes in response variability may not be a mechanism of attention, but rather a side effect of attentional allocation strategies in different behavioral contexts.
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Experiments and computational models demonstrate that attention amplifies and stabilizes target cell responses, reducing their variance across trials.
“Experiments and computational models further demonstrate that attention amplifies and stabilizes target cell responses, reducing their variance across trials50,51.”
From Mechanisms underlying gain modulation in the cortex (2020) · cites this paper as [50]
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- Authors
- Ecker, A. S., Denfield, G. H., Bethge, M., & Tolias, A. S.
- Affiliations
- Baylor College of Medicine (US)
- Max Planck Institute for Biological Cybernetics (DE)
- Venue
- The Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2016-02-03
- Volume
- 36
- Issue
- 5
- Pages
- 1775-1789
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- PMC
- PMC4737784
- OpenAlex
- W2188049816
- Cited by
- 88
- References
- 55
- Citations by year
- 2026: 4 · 2025: 3 · 2024: 2 · 2023: 4 · 2022: 8 · 2021: 12 · 2020: 6 · 2019: 12 · 2018: 14 · 2017: 16 · 2016: 7
- FWCI
- 10.39
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Journal of Neuroscience (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Max Planck Digital Library (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Funders
- National Institutes of Health (1dp1od008301-01, EY002520)
- National Eye Institute (DP1EY023176, P30- EY002520-33, P30EY002520, R01EY018847)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Keywords
- neural population activity, attentional state, spatial attention, feature attention, neural correlations, differential correlation, top-down modulation, noise shaping, covert attention
- MeSH terms
- Action Potentials, Attention, Humans, Neurons, Photic Stimulation, Reaction Time, Visual Cortex
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