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Fattori, P.

Publications and source records attributed to Fattori, P..

3 recordsLinked to original sources

Integration of Motor Planning and Execution through Latent Structure Reorganization in the Posterior Parietal Cortex

The posterior parietal cortex (PPC) plays a central role in sensorimotor control, performing visuomotor transformations, supporting planning, and providing visual feedback. However, it is unknown how the neural populations in different PPC areas organize their activity during this process. It has been proposed that PPC activity reflects population-level dynamics rather than distinct subpopulations, raising the question of how the population flexibly reorganize between the two main phases of motor control, planning and execution. To address this question, we analyzed neural dynamics in three PPC areas (PE, PEc, V6A) in the context of a delayed reaching task, applying dimensionality reduction techniques. This approach allows identifying whether activity in each area is organized into independent or partially overlapping dynamics across task phases. We found evidence of area-specific population subspaces, distinct for movement planning and execution. Specifically, the analysis revealed that in PE, which is a predominantly somatomotor area, neural activity occupied nearly orthogonal subspaces between the two phases, suggesting independent dynamics for movement planning and execution. In contrast, in V6A and PEc, which are involved in visuomotor transformations, we identified both shared and exclusive subspaces, indicating a more flexible representation of motor information in these areas. Overall, our findings suggest that parietal circuits combine both separation and sharing of neural representation to support computations during the different movement stages, providing new insights into the role of the PPC in generating flexible motor behavior . Significance StatementThe posterior parietal cortex (PPC) plays a central role in sensorimotor control. How neural ensembles in the PPC transition from planning to executing goal-directed movements remains poorly understood. Using dimensionality reduction on macaque electrophysiological data from delayed reach tasks, we identify an anteroposterior gradient: posterior visuomotor areas exhibit both overlapping and segregated subspaces for planning and execution, whereas anterior somatomotor regions show predominantly orthogonal dynamics. This organizational gradient reflects persistent posterior encoding of action goals and posture, and more distinct anterior representations of body state across planning and execution. The topographical and functional shift from visuomotor to somatic coding aligns with hierarchical predictive coding accounts of sensorimotor control and advances our understanding of sensorimotor transformations in the PPC.

neuroscience↗

Dynamic Predictive Spatial Encoding of Motor Intentions In Area V6A of the Posterior Parietal Cortex

To compute motor plans or intentions, the nervous system must translate tar-get locations into body-centered coordinates. Visual stimuli, however, are sensed in retinotopic coordinates, which shift with eye movements. Furthermore, sen-sorimotor delays necessitate predictive processing. How does the brain compute timely gaze-invariant target locations? The dorsal visual pathway encodes spa-tial intentions, yet the underlying dynamic mechanisms remain elusive. Using multilevel analysis, we characterized intention coding in area V6A of the Pos-terior Parietal Cortex during delayed reaching tasks under diverse gaze-target conditions. We revealed a consistent population-level intention coding in V6A as eye positions changed. Next, we identified differential single-cell encoding of gaze and reaching targets in retinotopic, gaze-posture, and body-centered coordinates and elucidated the dynamical spatial normalization. Finally, we demonstrated context-dependent predictive spatial encoding in V6A, advancing our understand-ing of the temporal evolution of predictive visuomotor transformations during motor planning.

neuroscience↗

More or less latent variables in the high-dimensional data space? That is the question

Dimensionality reduction is widely used in modern Neuro-science to process massive neural recordings data. Despite the development of complex non-linear techniques, linear algorithms, in particular Principal Component Analysis (PCA), are still the gold standard. However, there is no consensus on how to estimate the optimal number of latent variables to retain. In this study, we addressed this issue by testing different criteria on simulated data. Parallel analysis and cross validation proved to be the best methods, being largely unaffected by the number of units and the amount of noise. Parallel analysis was quite conservative and tended to underestimate the number of dimensions especially in low-noise regimes, whereas in these conditions cross validation provided slightly better estimates. Both criteria consistently estimate the ground truth when 100+ units were available. As an exemplary application to real data, we estimated the dimensionality of the spiking activity in two macaque parietal areas during different phases of a delayed reaching task. We show that different criteria can lead to different trends in the estimated dimensionality. These apparently contrasting results are reconciled when the implicit definition of dimensionality underlying the different criteria is considered. Our findings suggest that the term dimensionality needs to be defined carefully and, more importantly, that the most robust criteria for choosing the number of dimensions should be adopted in future works. To help other researchers with the implementation of such an approach on their data, we provide a simple software package, and we present the results of our simulations through a simple Web based app to guide the choice of latent variables in a variety of new studies. Key pointsO_LIParallel analysis and cross-validation are the most effective criteria for principal components retention, with parallel analysis being slightly more conservative in low-noise conditions, but being more robust with larger noise. C_LIO_LIThe size of data matrix as well as the decay rate of the explained variance decreasing curve strongly limit the number of latent components that should be considered. C_LIO_LIWhen analyzing real spiking data, the estimated dimensionality depends dramatically on the criterion used, leading to apparently different results. However, these differences stem, in large part, from the implicit definitions of dimensionality underlying each criterion. C_LIO_LIThis study emphasizes the need for careful definition of dimensionality in population spiking activity and suggests the use of parallel analysis and cross-validation methods for future research. C_LI

neuroscience↗