On Entropic Learning from Noisy Time Series in the Small Data Regime.

In this work, we present a novel methodology for performing the supervised classification of time-ordered noisy data; we call this methodology Entropic Sparse Probabilistic Approximation with Markov regularization (eSPA-Markov). It is an extension of entropic learning methodologies, allowing the simultaneous learning of segmentation patterns, entropy-optimal feature space discretizations, and Bayesian classification rules. We prove the conditions for the existence and uniqueness of the learning problem solution and propose a one-shot numerical learning algorithm that-in the leading order-scales linearly in dimension.

Gauge-Optimal Approximate Learning for Small Data Classification.

Small data learning problems are characterized by a significant discrepancy between the limited number of response variable observations and the large feature space dimension. In this setting, the common learning tools struggle to identify the features important for the classification task from those that bear no relevant information and cannot derive an appropriate learning rule that allows discriminating among different classes. As a potential solution to this problem, here we exploit the idea of reducing and rotating the feature space in a lower-dimensional gauge and propose the gauge-optimal approximate learning (GOAL) algorithm, which provides an analytically tractable joint solution to the dimension reduction, feature segmentation, and classification problems for small data learning problems.

NAPE-PLD deletion in stress-TRAPed neurons results in an anxiogenic phenotype.

Anandamide (AEA) is an endogenous ligand of the cannabinoid CB1 and CB2 receptors, being a component of the endocannabinoid signaling system, which supports the maintenance or regaining of neural homeostasis upon internal and external challenges. AEA is thought to play a protective role against the development of pathological states after prolonged stress exposure, including depression and generalized anxiety disorder. Here, we used the chronic social defeat (CSD) stress as an ethologically valid model of chronic stress in male mice.

Spontaneous Activity Predicts Survival of Developing Cortical Neurons.

Spontaneous activity plays a crucial role in brain development by coordinating the integration of immature neurons into emerging cortical networks. High levels and complex patterns of spontaneous activity are generally associated with low rates of apoptosis in the cortex. However, whether spontaneous activity patterns directly encode for survival of individual cortical neurons during development remains an open question.

Keeping the Balance: GABA Receptors in the Developing Brain and Beyond.

The main neurotransmitter in the brain responsible for the inhibition of neuronal activity is γ-aminobutyric acid (GABA). It plays a crucial role in circuit formation during development, both via its primary effects as a neurotransmitter and also as a trophic factor. The GABA receptors (GABARs) are G protein-coupled metabotropic receptors; on one hand, they can influence proliferation and migration; and, on the other, they can inhibit cells by modulating the function of K and Ca channels, doing so on a slower time scale and with a longer-lasting effect compared to ionotropic GABA receptors.