Search results for "LEARNING"

2016

During hippocampal sharp wave/ripple (SWR) events, previously occurring, sensory input-driven neuronal firing patterns are replayed. Such replay is thought to be important for plasticity-related processes and consolidation of memory traces. It has previously been shown that the electrical stimulation-induced disruption of SWR events interferes with learning in rodents in different experimental paradigms. On the other hand, the cognitive map theory posits that the plastic changes of the firing of hippocampal place cells constitute the electrophysiological counterpart of the spatial learning, observable at the behavioral level. Therefore, we tested whether intact SWR events occurring during t…

2016

AbstractThe different factors involved in the growth process of complex networks imprint valuable information in their observable topologies. How to exploit this information to accurately predict structural network changes is the subject of active research. A recent model of network growth sustains that the emergence of properties common to most complex systems is the result of certain trade-offs between node birth-time and similarity. This model has a geometric interpretation in hyperbolic space, where distances between nodes abstract this optimisation process. Current methods for network hyperbolic embedding search for node coordinates that maximise the likelihood that the network was pro…

Toward a direct and scalable identification of reduced models for categorical processes.

2017

The applicability of many computational approaches is dwelling on the identification of reduced models defined on a small set of collective variables (colvars). A methodology for scalable probability-preserving identification of reduced models and colvars directly from the data is derived—not relying on the availability of the full relation matrices at any stage of the resulting algorithm, allowing for a robust quantification of reduced model uncertainty and allowing us to impose a priori available physical information. We show two applications of the methodology: (i) to obtain a reduced dynamical model for a polypeptide dynamics in water and (ii) to identify diagnostic rules from a standar…

DeepWAS: Multivariate genotype-phenotype associations by directly integrating regulatory information using deep learning

2020

Genome-wide association studies (GWAS) identify genetic variants associated with traits or diseases. GWAS never directly link variants to regulatory mechanisms. Instead, the functional annotation of variants is typically inferred by post hoc analyses. A specific class of deep learning-based methods allows for the prediction of regulatory effects per variant on several cell type-specific chromatin features. We here describe “DeepWAS”, a new approach that integrates these regulatory effect predictions of single variants into a multivariate GWAS setting. Thereby, single variants associated with a trait or disease are directly coupled to their impact on a chromatin feature in a cell type. Up to…

The Effects of Static and Dynamic Visual Representations as Aids for Primary School Children in Tasks of Auditory Discrimination of Sound Patterns. A…

2018

It has been proposed that non-conventional presentations of visual information could be very useful as a scaffolding strategy in the learning of Western music notation. As a result, this study has attempted to determine if there is any effect of static and dynamic presentation modes of visual information in the recognition of sound patterns. An intervention-based quasi-experimental design was adopted with two groups of fifth-grade students in a Spanish city. Students did tasks involving discrimination, auditory recognition and symbolic association of the sound patterns with non-musical representations, either static images (S group), or dynamic images (D group). The results showed neither s…

A clustering package for nucleotide sequences using Laplacian Eigenmaps and Gaussian Mixture Model.

2018

International audience; In this article, a new Python package for nucleotide sequences clustering is proposed. This package, freely available on-line, implements a Laplacian eigenmap embedding and a Gaussian Mixture Model for DNA clustering. It takes nucleotide sequences as input, and produces the optimal number of clusters along with a relevant visualization. Despite the fact that we did not optimise the computational speed, our method still performs reasonably well in practice. Our focus was mainly on data analytics and accuracy and as a result, our approach outperforms the state of the art, even in the case of divergent sequences. Furthermore, an a priori knowledge on the number of clust…

Cell-to-Cell Communication in Learning and Memory: From Neuro- and Glio-Transmission to Information Exchange Mediated by Extracellular Vesicles

2019

Most aspects of nervous system development and function rely on the continuous crosstalk between neurons and the variegated universe of non-neuronal cells surrounding them. The most extraordinary property of this cellular community is its ability to undergo adaptive modifications in response to environmental cues originating from inside or outside the body. Such ability, known as neuronal plasticity, allows long-lasting modifications of the strength, composition and efficacy of the connections between neurons, which constitutes the biochemical base for learning and memory. Nerve cells communicate with each other through both wiring (synaptic) and volume transmission of signals. It is by now…

Cholinergic signaling controls immune functions and promotes homeostasis

2020

Abstract Acetylcholine (ACh) was created by nature as one of the first signaling molecules, expressed already in procaryotes. Based on the positively charged nitrogen, ACh could initially mediate signaling in the absence of receptors. When evolution established more and more complex organisms the new emerging organs systems, like the smooth and skeletal muscle systems, energy-generating systems, sexual reproductive system, immune system and the nervous system have further optimized the cholinergic signaling machinery. Thus, it is not surprising that ACh and the cholinergic system are expressed in the vast majority of cells. Consequently, multiple common interfaces exist, for example, betwee…

Impairment of learning and memory performances induced by BPA Evidences from the literature of a MoA mediated through an ED

2018

International audience; Many rodent studies and a few non-human primate data report impairments of spatial and non-spatial memory induced by exposure to bisphenol A (BPA), which are associated with neural modifications, particularly in processes involved in synaptic plasticity. BPA-induced alterations involve disruption of the estrogenic pathway as established by reversal of BPA-induced effects with estrogenic receptor antagonist or by interference of BPA with administered estradiol in ovariectomized animals. Sex differences in hormonal impregnation during critical periods of development and their influence on maturation of learning and memory processes may explain the sexual dimorphism obs…

Intrinsic volatility of synaptic connections — a challenge to the synaptic trace theory of memory

2017

According to the synaptic trace theory of memory, activity-induced changes in the pattern of synaptic connections underlie the storage of information for long periods. In this framework, the stability of memory critically depends on the stability of the underlying synaptic connections. Surprisingly however, synaptic connections in the living brain are highly volatile, which poses a fundamental challenge to the synaptic trace theory. Here we review recent experimental evidence that link the initial formation of a memory with changes in the pattern of connectivity, but also evidence that synaptic connections are considerably volatile even in the absence of learning. Then we consider different…