Attention improves information flow between neuronal populations without changing the communication subspace

Srinath, Ramanujan; Ruff, Douglas A.; Cohen, Marlene R.

doi:10.1016/j.cub.2021.09.076

Cited by 29 publications

(27 citation statements)

References 136 publications

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“…These findings are consistent with recent work suggesting that trial-by-trial covariability is primarily orthogonal to sensory coding dimensions and reflects non-sensory motor or cognitive variables, such as whisking, running, or arousal (Musall et al, 2019;Stringer et al, 2019b). Our results contribute to a growing body of evidence that covariability does not usually reflect information limiting noise, but instead reflects important cognitive processes active in different brain regions during sensory decision making (Srinath et al, 2021).…”

Section: Implications For the Role Of Correlated Actisuvity In Sensor...supporting

confidence: 91%

Task-specific invariant representation in auditory cortex

Heller¹,

Hamersky²,

David

2023

Preprint

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Categorical sensory representations are critical for many behaviors, including speech perception. In the auditory system, categorical information is thought to arise hierarchically, becoming increasingly prominent in higher order cortical regions. The neural mechanisms that support this robust and flexible computation remain poorly understood. Here, we studied sound representations in primary and non-primary auditory cortex while animals engaged in a challenging sound discrimination task. Population-level decoding of simultaneously recorded single neurons revealed that task engagement caused categorical sound representations to emerge in non-primary auditory cortex. In primary auditory cortex, task engagement caused a general enhancement of sound decoding that was not specific to task-relevant categories. These findings are consistent with mixed selectivity models of neural disentanglement, in which early sensory regions build an overcomplete representation of the world and allow neurons in downstream brain regions to flexibly and selectively read out behaviorally relevant, categorical information.

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Section: Implications For the Role Of Correlated Actisuvity In Sensor...supporting

confidence: 91%

Task-specific invariant representation in auditory cortex

Heller¹,

Hamersky²,

David

2023

Preprint

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“…The details of this analysis can be found in (46,86,87). We followed the same steps as previously published work (41,43) to estimate the dimensionality: We first determined the number of dimensions 𝑚 "-%. that maximized the cross-validated log-likelihood of the observed residuals.…”

Section: Factor Analysismentioning

confidence: 99%

Spatial context non-uniformly modulates inter-laminar communication in the primary visual cortex

Xu,

Morton,

Hudson

et al. 2024

Preprint

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Our visual experience is a result of the concerted activity of neuronal ensembles in the sensory hierarchy. Yet how the spatial organization of objects influences neural activity in this hierarchy remains poorly understood. We investigate how the inter-laminar interactions in the primary visual cortex (V1) are affected by visual stimuli in isolation or with flanking stimuli at various spatial configurations that are known to cause non-uniform degradation of perception. By employing dimensionality reduction approaches to simultaneous layer-specific population recordings, we establish that cortical layers interact through a structurally stable communication subspace. Spatial configuration of contextual stimuli differentially modulates inter-laminar communication efficacy, the balance between feedforward and feedback signaling, and contextual signaling in the superficial layers. Remarkably, these modulations mirror the spatially non-uniform aspects of perceptual degradation. Our results suggest a model of retinotopically non-uniform cortical connectivity in the output layers of V1 that influences communication in the sensory hierarchy.

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“…The models omit the computational stages by which brains integrate the response to the cue with the responses to the target, nor do they predict the distribution of cell response properties that one might expect in the brain of biological organisms that manifest behavioral cue effects and covert attention. Even with the progress in multi-neuronal and multi-size recordings, physiological studies probe a small subset of neurons and areas, typically less than 1000 neurons [21][22][23] . How to map these measurements into computational stages is not straightforward [24][25][26] .…”

Section: When Appearing At the Uncued Location (Invalid Cue Trials)mentioning

confidence: 99%

Bridging Neurons and Behavior in a Convolutional Neural Network with Emergent Human-like Covert Attention

Srivastava,

Wang,

Eckstein

2023

Preprint

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The cueing task is one of the most prominent paradigms to study covert attention. Observers are quicker and more accurate in detecting a target when it appears with a cue (valid trials) than when it appears opposite to the cue (invalid trials). How neuronal populations of cells across the visual hierarchy progressively represent and integrate visual information across the target, cues, and locations to give rise to the behavioral cueing effect is not well understood. To gain a theoretical understanding of the plausible system-wide neuronal population statistics, computations, and mechanisms mediating the cueing effect, we analyze the response properties of 180k neurons per network across layers of ten feedforward Convolutional Neural Networks (CNN). The CNNs are trained from images with luminance noise, without any explicit incorporation of an attention mechanism, and show human-like beneficial influences of cues on detection accuracy. Early visual hierarchy layers show retinotopic neurons separately tuned to target or cue with excitatory or inhibitory responses. Later layers show neurons that are jointly tuned to both target and cue and integrate information across locations. Consistent with physiological findings, we find an increased influence of the cue on target responses in higher layers (areas) in the network and computational stages similar to those of a Bayesian ideal observer, but with more gradual transitions. The cue influences the mean neuronal response to the target and distractor, and changes target sensitivity with two mechanisms: integration of information across locations at the dense layer with neurons more driven by the cued location, and an interaction with the thresholding Rectified Linear Activation Unit (ReLU) in the last convolution layer. We find novel neuronal properties not yet reported in physiological studies: the presence of cue-inhibitory neurons, inhibitory cue influences on target neurons, location-opponent cells, and higher sensitivity to the cue in intermediate layers than later layers. Together, our analyses illustrate a system-wide analysis of the brain computations that might give rise to behavioral cueing effects and provide a theoretical framework to inform neurophysiological studies.

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Attention improves information flow between neuronal populations without changing the communication subspace

Cited by 29 publications

References 136 publications

Task-specific invariant representation in auditory cortex

Task-specific invariant representation in auditory cortex

Spatial context non-uniformly modulates inter-laminar communication in the primary visual cortex

Bridging Neurons and Behavior in a Convolutional Neural Network with Emergent Human-like Covert Attention

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