Behavioral decomposition reveals rich encoding structure employed across neocortex

Mimica, Bartul; Tombaz, Tuçe; Battistin, Claudia; Fuglstad, Jingyi Guo; Dunn, Benjamin; Whitlock, Jonathan R.

doi:10.1101/2022.02.08.479515

Cited by 10 publications

(10 citation statements)

References 91 publications

(130 reference statements)

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“…This advantage for early areas may be due in part to the higher signal-tonoise ratio (SNR) of decoding accuracy in V1-V3, but it may also suggest that representations in these areas are particularly important for performance of our decision task. The findings of strong task-dependent effects in early retinotopic areas align with recent rodent studies, which show that representations within sensory areas contain information pertinent to task goals, motor outcomes, and prior knowledge about sensory environments (Ebrahimi et al, 2022;Findling et al, 2023;Mimica et al, 2023;Niell & Stryker, 2010;Stringer et al, 2019). Extending these findings, our study demonstrates that human visual areas are more actively involved with decision-related computation than previously thought.…”

Section: Discussionsupporting

confidence: 89%

Dynamic categorization rules alter representations in human visual cortex

Henderson,

Serences,

Rungratsameetaweemana

2023

Preprint

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Everyday visual search tasks require objects to be categorized according to behavioral goals. For example, when searching for an apple at the supermarket, one might first find the Granny Smith apples by separating all visible apples into the categories “green” and “non-green”. However, suddenly remembering that your family actually likes Fuji apples would necessitate reconfiguring the boundary to separate “red” from “red-yellow” objects. Despite this need for flexibility, prior research on categorization has largely focused on understanding neural changes related to overlearning a single category boundary that bifurcates an object space. At the same time, studies of feature-based attention have provided some insight into flexible selection of features, but have mainly focused on selection of a single, usually low-level, feature, which is rarely sufficient to capture the complexity of categorizing higher-dimensional object sets. Here we addressed these gaps by asking human participants to categorize novel shape stimuli according to different linear and non-linear boundaries, a task that requires dynamically reconfiguring selective attention to emphasize different sets of abstract features. Using fMRI and multivariate analyses of retinotopically-defined visual areas, we found that shape representations in visual cortex became more distinct across relevant category boundaries in a context-dependent manner, with the largest changes in discriminability observed for stimuli near the category boundary. Importantly, these attention-induced modulations were linked to categorization performance. Together, these findings demonstrate that adaptive attentional modulations can alter representations of abstract feature dimensions in visual cortex to optimize object separability based on currently relevant category boundaries.

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Section: Discussionsupporting

confidence: 89%

Dynamic categorization rules alter representations in human visual cortex

Henderson,

Serences,

Rungratsameetaweemana

2023

Preprint

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“…6). This suggests that encoding of behavioral state across cortex may occur at multiple levels of the behavioral hierarchy, in addition to that of “low-level” behavioral arousal/movement primitives such as pupil diameter, whisker motion energy, and walking speed (see Mimica et al, 2023).…”

Section: Discussionmentioning

confidence: 99%

“…We expect that, in general, the predictive contribution of high level behavioral motifs relative to movement and arousal primitives will be greater both in lateral areas, such as A1, in non-primary CCF areas further up the cortical hierarchy, such as secondary sensory cortices and M2 (Wang et al, 2023; bioRxiv), and over intermediate and longer timescales of activity aligned with purposeful and/or goal oriented movements organized at higher levels of the behavioral hierarchy (Mimica et al, 2023).…”

Section: Modulation Of Neural Activity By Spontaneous Movements and A...mentioning

confidence: 99%

Pan-cortical 2-photon mesoscopic imaging and neurobehavioral alignment in awake, behaving mice

Vickers,

McCormick

2023

Preprint

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The flow of neural activity across the neocortex during active sensory discrimination is constrained by task-specific cognitive demands, movements, and internal states. During behavior, the brain appears to sample from a broad repertoire of activation motifs. Understanding how these patterns of local and global activity are selected in relation to both spontaneous and task-dependent behavior requires in-depth study of densely sampled activity at single neuron resolution across large regions of cortex. In a significant advance toward this goal, we developed procedures to record mesoscale 2-photon Ca2+ imaging data from two novel in vivo preparations that, between them, allow simultaneous access to nearly all of the mouse dorsal and lateral neocortex. As a proof of principle, we aligned neural activity with both behavioral primitives and high-level motifs to reveal the existence of large populations of neurons that coordinated their activity across cortical areas with spontaneous changes in movement and/or arousal. The methods we detail here facilitate the identification and exploration of widespread, spatially heterogeneous neural ensembles whose activity is related to diverse aspects of behavior.

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“…Estimation of the eigenposes relies on an accurate estimation of the covariance matrix C Eq. (2). We first developed an algorithm based on K-nearest neighbours to fill up missing values in the dataset (see "Methods").…”

Section: Robust Estimation Of the Mean Posementioning

confidence: 99%

Three-dimensional unsupervised probabilistic pose reconstruction (3D-UPPER) for freely moving animals

Ebrahimi

Orlowska‐Feuer

Huang

et al. 2023

Sci Rep

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A key step in understanding animal behaviour relies in the ability to quantify poses and movements. Methods to track body landmarks in 2D have made great progress over the last few years but accurate 3D reconstruction of freely moving animals still represents a challenge. To address this challenge here we develop the 3D-UPPER algorithm, which is fully automated, requires no a priori knowledge of the properties of the body and can also be applied to 2D data. We find that 3D-UPPER reduces by $$>10$$ > 10 fold the error in 3D reconstruction of mouse body during freely moving behaviour compared with the traditional triangulation of 2D data. To achieve that, 3D-UPPER performs an unsupervised estimation of a Statistical Shape Model (SSM) and uses this model to constrain the viable 3D coordinates. We show, by using simulated data, that our SSM estimator is robust even in datasets containing up to 50% of poses with outliers and/or missing data. In simulated and real data SSM estimation converges rapidly, capturing behaviourally relevant changes in body shape associated with exploratory behaviours (e.g. with rearing and changes in body orientation). Altogether 3D-UPPER represents a simple tool to minimise errors in 3D reconstruction while capturing meaningful behavioural parameters.

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Behavioral decomposition reveals rich encoding structure employed across neocortex

Cited by 10 publications

References 91 publications

Dynamic categorization rules alter representations in human visual cortex

Dynamic categorization rules alter representations in human visual cortex

Pan-cortical 2-photon mesoscopic imaging and neurobehavioral alignment in awake, behaving mice

Three-dimensional unsupervised probabilistic pose reconstruction (3D-UPPER) for freely moving animals

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