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Hernandez, V.

Publications and source records attributed to Hernandez, V..

2 recordsLinked to original sources

Npas1+-Nkx2.1+ Neurons Form A Unique Pallidal Neuron Subclass

Within the basal ganglia circuit, the external globus pallidus (GPe) is critically involved in motor control. Aside from Foxp2+ neurons and ChAT+ neurons that have been established as unique neuron types, there is little consensus on the classification of GPe neurons. Properties of the remaining neuron types are poorly-defined. In this study, we leverage new mouse lines, viral tools, and molecular markers to better define GPe neuron subtypes. We found that Sox6 represents a novel, defining marker for GPe neuron subtypes. Lhx6+ neurons that lack the expression of Sox6 were devoid of both parvalbumin and Npas1. This result confirms previous assertions of the existence of a unique Lhx6+ population. Neurons that arise from the Dbx1+ lineage were similarly abundant in the GPe and displayed a heterogeneous makeup. Importantly, tracing experiments revealed that Npas1+-Nkx2.1+ neurons represent the principal non-cholinergic, cortically-projecting neurons. In other words, they form the pallido-cortical arm of the cortico-pallido-cortical loop. Our data further described that pyramidal-tract neurons in the cortex collateralized within the GPe, forming a closed-loop system between the two brain structures. Overall, our findings reconcile some of the discrepancies that arose from differences in techniques or the reliance on pre-existing tools. While spatial distribution and electrophysiological properties of GPe neurons reaffirm the diversification of GPe subtypes, statistical analyses strongly support the notion that these neuron subtypes can be categorized under the two principal neuron classes--i.e., PV+ neurons and Npas1+ neurons.\n\nSignificance statementThe poor understanding of the neuronal composition in the GPe undermines our ability to interrogate its precise behavioral and disease involvements. In this study, twelve different genetic crosses were used, hundreds of neurons were electrophysiologically-characterized, and over 100,000 neurons were histologically- and/or anatomically-profiled. Our current study further establishes the segregation of GPe neuron classes and illustrates the complexity of GPe neurons in adult mice. Our results support the idea that Npas1+-Nkx2.1+ neurons are a distinct GPe neuron subclass. By providing a detailed analysis of the organization of the cortico-pallidal-cortical projection, our findings establish the cellular and circuit substrates that can be important for motor function and dysfunction.

neuroscience

Convolutional and Recurrent Neural Network for Human Action Recognition: application on American Sign Language

Human Action Recognition (HAR) is an important and difficult topic because of the important variability between tasks repeated several times by a subject and between subjects. This work is motivated by providing time-series signal classification and a robust validation and test approaches. This study proposes to classify 60 American Sign Language signs from data provided by the LeapMotion sensor by using a combined approach with Convolutional Neural Network (ConvNet) and Recurrent Neural Network with Long-Short Term Memory cells (LSTM) called ConvNet-LSTM. Moreover, a complete kinematic model of the right and left forearm/hand/fingers/thumb is proposed as well as the use of a simple data augmentation technique to improve the generalization of neural networks. Results showed an accuracy of 89.3% on a user-independent test set with data augmentation when using the ConvNet-LSTM, while LSTM alone provided an accuracy of 85.0% on the same test set. The result dropped respectively to 85.9% and 81.4% without data augmentation.

bioengineering