Population Dynamics of Early Visual Cortex during Working Memory

Rahmati, Masih; Saber, Golbarg T.; Curtis, Clayton E.

doi:10.1162/jocn_a_01196

Cited by 40 publications

(48 citation statements)

References 59 publications

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“…In spatial working memory studies, sensory activity is thought to be maintained by active mechanisms from stimulus encoding through a seconds-long day. Many of these studies show that early visual areas have retinotopically specific signals throughout the delay (Sprague & Serences, 2013;Sprague et al, 2014;Rahmati et al, 2017), paralleling our findings. In imagery studies, eye-specific circuits presumed to be in V1 can be re-engaged if there is a delay of 5 minutes or less from when the subject viewed stimuli through the same eye, but not if there is a delay of 10 minutes (Ishai & Sagi, 1995).…”

Section: Relation To Other Forms Of Memory and Attentionsupporting

confidence: 89%

Perception and memory have distinct spatial tuning properties in human visual cortex

Favila

Kuhl

Winawer

2019

Preprint

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Neural activity evoked during perception is thought to be reactivated during later memory retrieval. While many studies have found evidence for memory reactivation in visual cortex, few have characterized differences between stimulus-driven and mnemonic activation. Here, we leveraged population receptive field (pRF) models to quantify spatial activity in the human visual system during perception and long-term memory retrieval. We found that mnemonic activity, like perceptual activity, was precisely retinotopically mapped. However, we also observed large, systematic differences between perceptual and mnemonic activity in measures of amplitude and precision. The magnitude of these differences varied according to position in the visual hierarchy, with the largest differences observed in early areas. These differences could not be accounted for by the fact that memory data had reduced signal-to-noise or the possibility that it contained a mixture of successful and failed retrieval trials. Finally, we explore predictions from several models, showing that a simple hierarchical model with reciprocal feedforward and feedback connections accounts for many of our observations. Our results reveal novel distinctions between perceptual and mnemonic activity in visual cortex and provide insight into the computational constraints governing memory reactivation. 1/21visual cortex during perception (Kay et al., 2013b). Using these models to quantify memory-triggered activity in the visual system offers the opportunity to precisely model reactivation in visual cortex and its relationship to visual perception. In particular, the fact that pRF models are stimulus-referred may aid in interpreting differences between perception and memory activation patterns by projecting these differences onto a small number of physical dimensions.Here, we used pRF models to characterize the properties of mnemonic activity in human visual cortex. We first trained human subjects to associate spatially localized stimuli with colored fixation cues. We then measured stimulus-triggered and memory-triggered activity in visual cortex using fMRI. Separately, we fit pRF models to independent fMRI data, which allowed us to estimate receptive field location and size within multiple visual field maps for each subject. Using pRF-based analyses, we quantified the location, amplitude, and precision of activity throughout these visual field maps during perception and memory retrieval. Finally, we explored what kind of cortical computations could account for our observations by simulating responses using a stimulus-referred pRF model and a simple hierarchical model of neocortex. Results BehaviorPrior to being scanned, subjects participated in a behavioral training session. During this session, subjects learned to associate four colored fixation dot cues with four stimuli. The four stimuli were unique radial frequency patterns presented at 45, 135, 225, or 315 degrees of polar angle and 2 degrees of eccentricity ( Fig. 1a,b). Subjects alternated between study and test ...

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Section: Relation To Other Forms Of Memory and Attentionsupporting

confidence: 89%

Perception and memory have distinct spatial tuning properties in human visual cortex

Favila

Kuhl

Winawer

2019

Preprint

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“…Therefore, motivated by the spatiotopic organization of the SC, we constructed a multivoxel model of how population activity in SC encodes spatial WM representations. Similar encoding models of fMRI data have been useful in testing hypotheses about how cortical areas store relevant features of WM representations ( Ester et al, 2015 ; Rahmati et al, 2018 ; Cai et al, 2019 ). Our results demonstrate that these models also work well in the subcortex, as we were able to model the population response in the SC that encoded spatial WM representations.…”

Section: Discussionmentioning

confidence: 99%

“…The success of these methods depends on both how encoding of features is distributed across a neural population (e.g., how visual space is distributed over V1) and how precisely aggregate voxelwise measures of neural activity are feature-tuned ( Brouwer and Heeger, 2009 ; Naselaris et al, 2011 ). It follows that WM representations of visual space are robustly encoded in areas with systematic retinotopic organization, such as early visual cortex ( Sprague et al, 2014 ; Rahmati et al, 2018 ), and to lesser extents in areas with coarse topography, such as frontoparietal cortex ( Jerde et al, 2012 ; Mackey et al, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

Spatially Specific Working Memory Activity in the Human Superior Colliculus

et al. 2020

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Theoretically, working memory (WM) representations are encoded by population activity of neurons with distributed tuning across the stored feature. Here, we leverage computational neuroimaging approaches to map the topographic organization of human superior colliculus (SC) and model how population activity in SC encodes WM representations. We first modeled receptive field properties of voxels in SC, deriving a detailed topographic organization resembling that of the primate SC. Neural activity within human (5 male and 1 female) SC persisted throughout a retention interval of several types of modified memory-guided saccade tasks. Assuming an underlying neural architecture of the SC based on its retinotopic organization, we used an encoding model to show that the pattern of activity in human SC represents locations stored in WM. Our tasks and models allowed us to dissociate the locations of visual targets and the motor metrics of memory-guided saccades from the spatial locations stored in WM, thus confirming that human SC represents true WM information. These data have several important implications. They add the SC to a growing number of cortical and subcortical brain areas that form distributed networks supporting WM functions. Moreover, they specify a clear neural mechanism by which topographically organized SC encodes WM representations. SIGNIFICANCE STATEMENT Using computational neuroimaging approaches, we mapped the topographic organization of human superior colliculus (SC) and modeled how population activity in SC encodes working memory (WM) representations, rather than simpler visual or motor properties that have been traditionally associated with the laminar maps in the primate SC. Together, these data both position the human SC into a distributed network of brain areas supporting WM and elucidate the neural mechanisms by which the SC supports WM.

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“…Moreover, few behaviors are guided by single neurons in isolation, and so assaying the joint activity of many neurons, and the resulting population-level representations, is necessary to gain insight into the neural underpinnings of cognition ( Jazayeri and Movshon, 2006 ; Ma et al, 2006 ; Graf et al, 2011 ). Indeed, IEMs have been used to assay the time course of covert attention ( Foster et al, 2017 ), understand the consequences of attentional manipulations within working memory ( Sprague et al, 2016 ; Rahmati et al, 2018 ), evaluate how allocation of attention impacts the representation of irrelevant visual stimuli across the visual field ( Sprague and Serences, 2013 ; Vo et al, 2017 ; Sprague et al, 2018 ), and probe the influence of top-down expectations on sensory stimulus representations ( Myers et al, 2015 ; Kok et al, 2017 ).…”

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confidence: 99%