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. This study aims to investigate the serotonergic modulation of visual responses of neurons to stimulus orientation in the primary visual cortex. Methods: Eight-week-old naive mice were anesthetized and a craniotomy was done on the region of interest in the primary visual cortex. Spontaneous and visual-evoked activities of neurons were recorded before and during the electrical stimulation of the dorsal raphe nucleus using in vivo whole-cell patch-clamp recording. The square-wave grating of 12 orientations was presented. The data were analyzed and the Wilcoxon signed-rank test was used to compare the data of two conditions that belong to the same neurons, with or without electrical stimulation. Results: The serotonergic system changed the orientation tuning of nearly 60% of recorded neurons by decreasing the mean firing rate in two independent visual response components, namely gain and baseline response. It also increased the mean firing rate in a small number of neurons (about 20%). Additionally, it left the preferred orientation and sensitivity of neurons unchanged. Conclusion: Serotonergic modulation showed a bidirectional effect. It causes predominately divisive and subtractive decreases in the visual responses of the neurons in the primary visual cortex that can modify the balance between internal and external sensory signals and result in disorders. Highlights: The serotonergic system predominantly decreased the mean firing rate of neurons in the primary visual cortex.The serotonergic system decreased responses of visual cortical neurons by subtractive and divisive changes of orientation tuning.The serotonergic system leaves the spontaneous activity of visual cortical neurons unchanged. Plain Language Summary: Serotonin is one of the well-known neuromodulators involved in many physiological functions of the brain, such as sensory processing. It can play an essential role in producing perceptual psychotic episodes following the use of psychedelic drugs. Neural mechanisms of changes in cortical processing by the serotonergic system are not elucidated enough. In this study, we showed the electrical stimulation of the dorsal raphe nucleus as the main resource for projecting serotonergic neurons to the visual cortex, causing to decrease in visual-evoked responses of neurons in the primary visual cortex without changing the spontaneous activity. This effect may lead to an imbalance between the brain's intrinsic and stimulus-evoked activity and result in various kinds of psychiatric disorders, such as visual hallucinogenic experiences in schizophrenia and autism. Accordingly, it is crucial to understand the mechanisms by which serotonin affects the rapid and long-term activity of neocortical circuits. Such studies can be helpful in the diagnosis and treatment of disorders related to the neuromodulatory roles of the serotonergic system by providing new methods for rebalancing these intricate components.

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. Here, we review evidence suggesting that midbrain dopaminergic neurons signal reward prediction error, driving synaptic plasticity in the striatum underlying learning. We focus on phasic increases in action potential firing of midbrain dopamine neurons in response to unexpected rewards. These dopamine neurons prominently innervate the dorsal and ventral striatum. In the striatum, the released dopamine binds to dopamine receptors, where it regulates the plasticity of glutamatergic synapses. The increase of striatal dopamine accompanying an unexpected reward activates dopamine type 1 receptors (D1Rs) initiating a signaling cascade that promotes long-term potentiation of recently active glutamatergic input onto striatonigral neurons. Sensorimotor-evoked glutamatergic input, which is active immediately before reward delivery will thus be strengthened onto neurons in the striatum expressing D1Rs. In turn, these neurons cause disinhibition of brainstem motor centers and disinhibition of the motor thalamus, thus promoting motor output to reinforce rewarded stimulus-action outcomes. Although many details of the hypothesis need further investigation, altogether, it seems likely that dopamine signals in the striatum might underlie important aspects of goal-directed reward-based 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. These features were tested for the classification of single tasks as well as of two tasks concurrently performed. For single tasks, correlation-based feature selection was used, and the features were then classified using linear discriminant analysis (LDA), non-linear support vector machines, and multi-layer perceptron. The eXtreme gradient boosting (XGBoost) classifier was used for the classification of two movements simultaneously performed. The second and third versions of the Ninapro dataset (conventional control) and Ameri's movement dataset (simultaneous control) were used to test the proposed features. For the Ninapro dataset, the overall accuracy of LDA using the TMD feature was 98.99 ± 1.36% and 92.25 ± 9.48% for able-bodied and amputee subjects, respectively. Using ensemble learning of the three classifiers, the average macro and micro-F-score, macro recall, and precision on the validation sets were 98.23 ± 2.02, 98.32 ± 1.93, 98.32 ± 1.93, and 98.88 ± 1.31%, respectively, for the intact subjects. The movement misclassification percentage was 1.75 ± 1.73 and 3.44 ± 2.23 for the intact subjects and amputees. The proposed features were significantly correlated with the movement classes [Generalized Linear Model (GLM); P-value < 0.05]. An accurate online implementation of the proposed algorithm was also presented. For the simultaneous control, the overall accuracy was 99.71 ± 0.08 and 97.85 ± 0.10 for the XGBoost and LDA classifiers, respectively. The proposed features are thus promising for conventional and simultaneous myoelectric control.

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. Statistical analyses of these data reveal clusters that often correspond to cell types previously defined by morphological or physiological criteria and that appear conserved across cortical areas and species. To capitalize on these new methods, we propose the adoption of a transcriptome-based taxonomy of cell types for mammalian neocortex. This classification should be hierarchical and use a standardized nomenclature. It should be based on a probabilistic definition of a cell type and incorporate data from different approaches, developmental stages and species. A community-based classification and data aggregation model, such as a knowledge graph, could provide a common foundation for the study of cortical circuits. This community-based classification, nomenclature and data aggregation could serve as an example for cell type atlases in other parts of the body.

Electrophysiological Profiling of Neocortical Neural Subtypes: A Semi-Supervised Method Applied to in vivo 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. Although there are studies in the literature on in vitro/vivo extracellular or in vitro intracellular recordings, a study on the classification of neuronal types using in vivo whole-cell patch-clamp recordings is still lacking. We thus proposed a novel semi-supervised classification method based on waveform shape of neurons' spikes using in vivo whole-cell patch-clamp recordings. We, first, detected spike candidates. Then discriminative features were extracted from the time samples of the spikes using discrete cosine transform. We then extracted the center of clusters using fuzzy c-mean clustering and finally, the neurons were classified using the minimum distance classifier. We distinguished three types of neurons: excitatory pyramidal cells (Pyr) and two types of inhibitory neurons: GABAergic- parvalbumin positive (PV), and somatostatin positive (SST) non-pyramidal cells in layer II/III of the mice primary visual cortex. We used 10-fold cross validation in our study. The classification accuracy for PV, Pyr, and SST was 91.59 ± 1.69, 97.47 ± 0.67, and 89.06 ± 1.99, respectively. Overall, the algorithm correctly classified 92.67 ± 0.54% of the cells, confirming the relative robustness of the discriminant functions. The performance of the method was further assessed on in vitro recordings by using a pool of 50 neurons from Allen institute Cell Types Database (5 major subtypes of neurons: Pyr, PV, SST, 5HT3a, and vasoactive intestinal peptide (VIP) cells). Its overall accuracy was 84.13 ± 0.81% on this data set using cross validation framework. The proposed algorithm is thus a promising new tool in recognizing cell's type with high accuracy in laboratories using in vivo/vitro whole-cell patch-clamp recording technique. The developed programs and the entire dataset are available online to interested readers.

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. Two novel reconstruction algorithms were also proposed. HDsEMG signals were recorded from the biceps femoris and brachial biceps muscles during low-to-moderate-level isometric contractions, and some of the channels (5-25%) were randomly marked as outliers. The root-mean-square error (RMSE) between the original and reconstructed maps was then calculated. RESULTS: Overall, the proposed Poisson and wave PDE outperformed the other methods (average RMSE 8.7 µVrms ± 6.1 µVrms and 7.5 µVrms ± 5.9 µVrms) for the time-averaged single-differential and monopolar map reconstruction, respectively. Biharmonic Spline, the discrete cosine transform, and the Poisson PDE outperformed the other methods for the instantaneous map reconstruction. The running time of the proposed Poisson and wave PDE methods, implemented using a Vectorization package, was 4.6 ± 5.7 ms and 0.6 ± 0.5 ms, respectively, for each signal epoch or time sample in each channel. CONCLUSION: The proposed reconstruction algorithms could be promising new tools for reconstructing muscle activity maps in real-time applications. SIGNIFICANCE: Proper reconstruction methods could recover the information of low-quality recorded channels in HDsEMG signals.