Biology subjects

Sreenivasan, K. K.

Publications and source records attributed to Sreenivasan, K. K..

8 recordsLinked to original sources

bioRxiv2025

Probabilistic working memory representations in human cortex guide behavior

Models of working memory make fundamentally different commitments to the architecture of individual memories. Information-sparse models conceptualize individual memories single point estimates agnostic to meta-cognitive variables such a uncertainty. In contrast, information-rich models propose memories re encoded s probability distributions over feature space that embed memory uncertainty in the shape of the distribution To distinguish these accounts, constructed probability distributions of memory from participants iterative reports of motion direction each trial. Remarkably, the idiosyncratic shape of these distributions (e.g., asymmetry) single trials matched the shape of neural probability distributions decoded from fMRI patterns measured from occipital and parietal cortex. Consistent with information-rich models, the neural representation of n individual memory encodes mor than the memorized feature; its variance (i.e., width and asymmetry) encodes idiosyncrasies whose read-out predicts memory behavior.

neuroscience↗

bioRxiv2025

Neural Subspaces Encode Sequential Working Memory, but Neural Sequences Do Not

The neural mechanisms of multiple-item working memory are not well understood. In the current study, we address two competing hypotheses about the neural basis of sequential working memory: neural subspaces versus neural sequences. Using broadband MEG data from human participants, we applied dimensionality reduction and multivariate decoding techniques to test whether sequential items are maintained during the retention period through the reactivation of individual items in sequence (neural sequences), or by organizing them into distinct low-dimensional subspaces (neural subspaces). Our results revealed behaviorally relevant, low-dimensional neural subspaces that organized memory representations during the retention period but not during stimulus encoding, supporting the neural subspaces hypothesis. In contrast, we found no evidence of sequential neural replay during the delay period, contrary to predictions from the neural sequences hypothesis. Together, our findings suggest that sequential working memory is maintained through structured geometric organization in low-dimensional representational space, rather than through the sequential reactivation of individual items.

neuroscience↗

bioRxiv2024

Alpha phase-coding supports feature binding during working memory maintenance

The ability to successfully retain and manipulate information in working memory (WM) requires that objects individual features are bound into cohesive representations; yet, the mechanisms supporting feature binding remain unclear. Binding (or swap) errors, where memorized features are erroneously associated with the wrong object, can provide a window into the intrinsic limits in capacity of WM that represent a key bottleneck in our cognitive ability. We tested the hypothesis that binding in WM is accomplished via neural phase synchrony and that swap errors result from perturbations in this synchrony. Using magnetoencephalography data collected from human subjects in a task designed to induce swap errors, we showed that swaps are characterized by reduced phase-locked oscillatory activity during memory retention, as predicted by an attractor model of spiking neural networks. Further, we found that this reduction arises from increased phase coding variability in the alpha-band over a distributed network of sensorimotor areas. Our findings demonstrate that feature binding in WM is accomplished through phase coding dynamics that emerge from the competition between different memories. SignificanceWe investigate the neural basis of working memory, focusing on how feature binding is accomplished and how binding or swap errors arise. Using magnetoencephalography, we found that stable phase-locking of alpha oscillations supports correct feature binding, while swap errors correlate with reduced alpha phase preservation, localized to specific brain areas. These findings align with a biologically-plausible computational model predicting that temporal synchrony in neuronal firing underpins feature binding. This work advances our understanding of the neural mechanisms of working memory, providing empirical support for theories of time-based binding and demonstrating the utility of biophysically-realistic models in human neuroimaging studies.

neuroscience↗

bioRxiv2022

Common neural mechanisms control attention and working memory

Although previous studies point to qualitative similarities between working memory (WM) and attention, the degree to which these two constructs rely on shared neural mechanisms remains unknown. Focusing on one such potentially shared mechanism, we tested the hypothesis that selecting an item within WM utilizes similar neural mechanisms as selecting a visible item via a shift of attention. We used fMRI and machine learning to decode both the selection among items visually available and the selection among items stored in WM in human subjects (both sexes). Patterns of activity in visual, parietal, and to a lesser extent frontal cortex predicted the locations of the selected items. Critically, these patterns were strikingly interchangeable; classifiers trained on data during attentional selection predicted selection from WM, and classifiers trained on data during selection from memory predicted attentional selection. Using models of voxel receptive fields, we visualized topographic population activity that revealed gain enhancements at the locations of the externally and internally selected items. Our results suggest that selecting among perceived items and selecting among items in WM share a common mechanism. This common mechanism, analogous to a shift of spatial attention, controls the relative gains of neural populations that encode behaviorally relevant information. Significance statementHow we allocate our attention to external stimuli that we see and to internal representations of stimuli stored in memory might rely on a common mechanism. Supporting this hypothesis, we demonstrated that not only could patterns of human brain activity predict which items were selected during perception and memory, but that these patterns were interchangeable during external and internal selection. Additionally, this generalized selection mechanism operates by changes in the gains of the neural populations both encoding attended sensory representations and storing relevant memory representations.

neuroscience↗

bioRxiv2021

Across-area synchronization supports feature integration in working memory

Working memory function is severely limited. One key limitation that constrains the ability to maintain multiple items in working memory simultaneously is so-called swap errors. These errors occur when an inaccurate response is in fact accurate relative to a non-target stimulus, reflecting the failure to maintain the appropriate association or "binding" between the features that define one object (e.g., color and location). The mechanisms underlying feature binding in working memory remain unknown. Here, we tested the hypothesis that features are bound in memory through synchrony across feature-specific neural assemblies. We built a biophysical neural network model composed of two one-dimensional attractor networks - one for color and one for location - simulating feature storage in different cortical areas. Within each area, gamma oscillations were induced during bump attractor activity through the interplay of fast recurrent excitation and slower feedback inhibition. As a result, different memorized items were held at different phases of the networks oscillation. These two areas were then reciprocally connected via weak cortico-cortical excitation, accomplishing binding between color and location through the synchronization of pairs of bumps across the two areas. Encoding and decoding of color-location associations was accomplished through rate coding, overcoming a long-standing limitation of binding through synchrony. In some simulations, swap errors arose: "color bumps" abruptly changed their phase relationship with "location bumps". This model, which leverages the explanatory power of similar attractor models, specifies a plausible mechanism for feature binding and makes specific predictions about swap errors that are testable at behavioral and neurophysiological levels.

neuroscience↗

bioRxiv2021

Working Memory Representations in Visual Cortex Mediate the Effects of Distraction

Although the contents of working memory (WM) can be decoded from activity in visual cortex, these representations may play a limited role if they are not robust to distraction. Here, we used model-based fMRI to estimate the impact that a distracting visual task had on WM representations in several visual field maps in visual and frontoparietal association cortex. Distraction caused the fidelity of WM representations in all maps to briefly dip when both the memorandum and distractor were jointly encoded by the population activities. Moreover, distraction induced small biases in memory errors which were predicted by biases in neural decoding in early visual cortex, but not other regions. Although distraction briefly disrupts WM representations, the widespread redundancy with which WM information is encoded may protect against catastrophic loss. In early visual cortex, nonetheless, the neural representation of information in WM and behavioral performance were intertwined, solidifying its importance in memory.

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bioRxiv2020

Using a betting game to reveal the rich nature of visual working memories

When we ask people to hold a color in working memory, what do they store? Do they remember colors as point estimates (e.g. a particular shade of red) or are memory representations richer, such as uncertainty distributions over feature space? We developed a novel paradigm (a betting game) to measure the nature of working memory representations. Participants were shown a set of colored circles and, after a brief memory delay, asked about one of the objects. Rather than reporting a single color, participants placed multiple bets to create distributions in color space. The dispersion of bets was correlated with performance, indicating that participants internal uncertainty guided bet placement. Furthermore, relative to the first response, memory performance improved when averaging across multiple bets, showing that memories contain more information than can be conveyed in a single response. Finally, information about the item in memory was present in subsequent responses even when the first response would generally be classified as a guess or report of an incorrect item, suggesting that such failures are not all-or-none. Thus, memory representations are more than noisy point estimates; they are surprisingly rich and probabilistic.

neuroscience↗

bioRxiv2020

Spatially-specific working memory activity in the human superior colliculus

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 male and female human 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 StatementUsing 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.

neuroscience↗