Search results for " neural"
showing 10 items of 1611 documents
Reinforcement learning in synthetic gene circuits.
2020
Synthetic gene circuits allow programming in DNA the expression of a phenotype at a given environmental condition. The recent integration of memory systems with gene circuits opens the door to their adaptation to new conditions and their re-programming. This lays the foundation to emulate neuromorphic behaviour and solve complex problems similarly to artificial neural networks. Cellular products such as DNA or proteins can be used to store memory in both digital and analog formats, allowing cells to be turned into living computing devices able to record information regarding their previous states. In particular, synthetic gene circuits with memory can be engineered into living systems to al…
Human Pluripotent Stem Cell-Derived Neuronal Networks:Their Electrical Functionality and Usability for Modelling and Toxicology
2011
Micro electrode array (MEA)-based platforms have been used to study neuronal networks for decades. The used cells have, for the most part, been rodent primary neurons. The gained knowledge has indeed increased the understanding of neuronal network development and maturation both in vitro and in vivo. If aiming to understand the development of human brain, however, the used cell type should preferably be of human origin due to difficult interpolation from the rodent cell data. In addition, the development of functional human neuronal networks would open up a new era for, e.g., toxicology testing, drug screening and disease modelling. The use of MEA with bioelectrically active cells was first…
Stage-specific control of oligodendrocyte survival and morphogenesis by TDP-43
2021
AbstractGeneration of oligodendrocytes in the adult brain enables both adaptive changes in neural circuits and regeneration of myelin sheaths destroyed by injury, disease, and normal aging. This transformation of oligodendrocyte precursor cells (OPCs) into myelinating oligodendrocytes requires processing of distinct mRNAs at different stages of cell maturation. Although mislocalization and aggregation of the RNA binding protein TDP-43 occur in both neurons and glia in neurodegenerative diseases, the consequences of TDP-43 loss within different stages of the oligodendrocyte lineage are not well understood. By performing stage-specific genetic inactivation of Tardbp in vivo, we show that olig…
Interaction between odorants and proteins involved in the perception of smell: the case of odorant-binding proteins probed by molecular modelling and…
2012
A joint approach that combines molecular modelling and fluorescence spectroscopy is used to study the affinity of an odorant binding protein towards various odorant molecules. We focus on the capability of molecular modelling to rank odorants according to their affinity with this protein, which is involved in the sense of smell. Although ligand-based approaches are unable to propose an accurate model attending to the strength of the bond with the odorant-binding protein, receptor-based structures considering either static or dynamic structure of the protein perform equally to discriminate between good, medium and low affinity odorants. Such approaches will be useful for further rational des…
Impact of ethanol on the perception of wine odorant mixtures
2007
International audience; Several studies have focused on perceptual interactions in binary odor mixtures, but few on more complex mixtures. The aroma of wine is an example of a complex odor mixture. Our aim was to assess the impact of ethanol on the perception of mixtures of Woody (whiskey lactone) and Fruity (isoamyl acetate) odorants commonly found, physico-chemically and perceptually, in wine. Physico-chemically, reduced whiskey lactone volatility was observed in hydro-alcoholic solutions. Perceptually, a synergy effect by the Woody on the Fruity odor was observed in aqueous solutions, which disappeared with the addition of ethanol. Conversely, the Woody odor was masked in both aqueous an…
Improving Speaker-Independent Lipreading with Domain-Adversarial Training
2017
We present a Lipreading system, i.e. a speech recognition system using only visual features, which uses domain-adversarial training for speaker independence. Domain-adversarial training is integrated into the optimization of a lipreader based on a stack of feedforward and LSTM (Long Short-Term Memory) recurrent neural networks, yielding an end-to-end trainable system which only requires a very small number of frames of untranscribed target data to substantially improve the recognition accuracy on the target speaker. On pairs of different source and target speakers, we achieve a relative accuracy improvement of around 40% with only 15 to 20 seconds of untranscribed target speech data. On mul…
Deep Neural Network Frontend for Continuous EMG-Based Speech Recognition
2016
Attention-based Model for Evaluating the Complexity of Sentences in English Language
2020
The automation of text complexity evaluation (ATCE) is an emerging problem which has been tackled by means of different methodologies. We present an effective deep learning- based solution which leverages both Recurrent Neural and the Attention mechanism. The developed system is capable of classifying sentences written in the English language by analysing their syntactical and lexical complexity. An accurate test phase has been carried out, and the system has been compared with a baseline tool based on the Support Vector Machine. This paper represents an extension of a previous deep learning model, which allows showing the suitability of Neural Networks to evaluate sentence complexity in tw…
Deep neural attention-based model for the evaluation of italian sentences complexity
2020
In this paper, the Automatic Text Complexity Evaluation problem is modeled as a binary classification task tackled by a Neural Network based system. It exploits Recurrent Neural Units and the Attention mechanism to measure the complexity of sentences written in the Italian language. An accurate test phase has been carried out, and the system has been compared with state-of-art tools that tackle the same problem. The computed performances proof the model suitability to evaluate sentence complexity improving the results achieved by other state-of-the-art systems.
Multi-class Text Complexity Evaluation via Deep Neural Networks
2019
Automatic Text Complexity Evaluation (ATE) is a natural language processing task which aims to assess texts difficulty taking into account many facets related to complexity. A large number of papers tackle the problem of ATE by means of machine learning algorithms in order to classify texts into complex or simple classes. In this paper, we try to go beyond the methodologies presented so far by introducing a preliminary system based on a deep neural network model whose objective is to classify sentences into more of two classes. Experiments have been carried out on a manually annotated corpus which has been preprocessed in order to make it suitable for the scope of the paper. The results sho…