Investigating the Serotonergic Modulation of Orientation Tuning of Neurons in Primary Visual Cortex of Anesthetized Mice.

Introduction: Sensory processing is profoundly regulated by brain neuromodulatory systems. One of the main neuromodulators is serotonin which influences higher cognitive functions, such as different aspects of perceptual processing. Accordingly, malfunction in the serotonergic system may lead to visual illusion in psychiatric disorders, such as autism and schizophrenia.

Striatal Dopamine Signals and Reward Learning.

We are constantly bombarded by sensory information and constantly making decisions on how to act. In order to optimally adapt behavior, we must judge which sequences of sensory inputs and actions lead to successful outcomes in specific circumstances. Neuronal circuits of the basal ganglia have been strongly implicated in action selection, as well as the learning and execution of goal-directed behaviors, with accumulating evidence supporting the hypothesis that midbrain dopamine neurons might encode a reward signal useful for learning.

Kernel Density Estimation of Electromyographic Signals and Ensemble Learning for Highly Accurate Classification of a Large Set of Hand/Wrist Motions.

The performance of myoelectric control highly depends on the features extracted from surface electromyographic (sEMG) signals. We propose three new sEMG features based on the kernel density estimation. The trimmed mean of density (TMD), the entropy of density, and the trimmed mean absolute value of derivative density were computed for each sEMG channel.

A community-based transcriptomics classification and nomenclature of neocortical cell types.

To understand the function of cortical circuits, it is necessary to catalog their cellular diversity. Past attempts to do so using anatomical, physiological or molecular features of cortical cells have not resulted in a unified taxonomy of neuronal or glial cell types, partly due to limited data. Single-cell transcriptomics is enabling, for the first time, systematic high-throughput measurements of cortical cells and generation of datasets that hold the promise of being complete, accurate and permanent.

Electrophysiological Profiling of Neocortical Neural Subtypes: A Semi-Supervised Method Applied to Whole-Cell Patch-Clamp Data.

A lot of efforts have been made to understand the structure and function of neocortical circuits. In fact, a promising way to understand the functions of cortical circuits is the classification of the neural types, based on their different properties. Recent studies focused on applying modern computational methods to classify neurons based on molecular, morphological, physiological, or mixed of these criteria.

Muscle Activity Map Reconstruction from High Density Surface EMG Signals With Missing Channels Using Image Inpainting and Surface Reconstruction Methods.

Objective: The aim of this study was to reconstruct low-quality High-density surface EMG (HDsEMG) signals, recorded with 2-D electrode arrays, using image inpainting and surface reconstruction methods.

Methods: It is common that some fraction of the electrodes may provide low-quality signals. We used variety of image inpainting methods, based on partial differential equations (PDEs), and surface reconstruction methods to reconstruct the time-averaged or instantaneous muscle activity maps of those outlier channels.