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Tejada, A.

Publications and source records attributed to Tejada, A..

2 recordsLinked to original sources

Lateral Propagation from Sensory Cortex Provides Differential Gain for Sensory Selection

Selectively responding to target stimuli while ignoring distractors is a fundamental cognitive ability. In neocortex, such selection is associated with enhanced responses to target stimuli relative to distractor stimuli, yet the gain mechanisms underlying this differential responsivity are poorly understood. Most proposed gain mechanisms emphasize modulation within the center response field of a stimulus. In contrast, here we investigate modulation of lateral propagation from the center response field as a potential gain mechanism during a whisker-based selective detection task in mice. Both target (Go) and distractor (NoGo) stimuli evoked activity that propagated beyond their center response fields, with substantially greater lateral propagation for target stimuli. Target-evoked propagation increased with task learning and, in expert mice, exhibited the spatiotemporal characteristics of a traveling cortical wave preferentially involving infragranular layers. Furthermore, we demonstrate that frontal cortex actively suppresses distractor-evoked sensory responses, including both center region activity and lateral cortical recruitment. Computational modeling showed that modulation of lateral excitation could reproduce key features of these experimental findings. Together, our results support the regulation of lateral cortical propagation as a mechanism for differentially amplifying behaviorally relevant sensory representations during sensory selection.

neuroscience↗

Pertpy: an end-to-end framework for perturbation analysis

Advances in single-cell technology have enabled the measurement of cell-resolved molecular states across a variety of cell lines and tissues under a plethora of genetic, chemical, environmental, or disease perturbations. Current methods focus on differential comparison or are specific to a particular task in a multi-condition setting with purely statistical perspectives. The quickly growing number, size, and complexity of such studies requires a scalable analysis framework that takes existing biological context into account. Here, we present pertpy, a Python-based modular framework for the analysis of large-scale perturbation single-cell experiments. Pertpy provides access to harmonized perturbation datasets and metadata databases along with numerous fast and user-friendly implementations of both established and novel methods such as automatic metadata annotation or perturbation distances to efficiently analyze perturbation data. As part of the scverse ecosystem, pertpy interoperates with existing libraries for the analysis of single-cell data and is designed to be easily extended.

bioinformatics↗