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neural coding

Gabriella S P Hsia, Camila M Musso, Lucas Alvizi, Luciano A Brito, Gerson S Kobayashi, Rita C M Pavanello, Mayana Zatz, Alice Gardham, Emma Wakeling, Roseli M Zechi-Ceide, Debora Bertola, Maria Rita Passos-Bueno
Repeats in coding and non-coding regions have increasingly been associated with many human genetic disorders, such as Richieri-Costa-Pereira syndrome (RCPS). RCPS, mostly characterized by midline cleft mandible, Robin sequence and limb defects, is an autosomal-recessive acrofacial dysostosis mainly reported in Brazilian patients. This disorder is caused by decreased levels of EIF4A3 , mostly due to an increased number of repeats at the EIF4A3 5'UTR. EIF4A3 5'UTR alleles are CG-rich and vary in size and organization of three types of motifs...
2018: Frontiers in Genetics
Yihong Wang, Xuying Xu, Rubin Wang
Place cells are important elements in the spatial representation system of the brain. A considerable amount of experimental data and classical models are achieved in this area. However, an important question has not been addressed, which is how the three dimensional space is represented by the place cells. This question is preliminarily surveyed by energy coding method in this research. Energy coding method argues that neural information can be expressed by neural energy and it is convenient to model and compute for neural systems due to the global and linearly addable properties of neural energy...
2018: Frontiers in Neuroscience
Fan Feng, Luhua Lai, Jianfeng Pei
With the idea of retrosynthetic analysis, which was raised in the 1960s, chemical synthesis analysis and pathway design have been transformed from a complex problem to a regular process of structural simplification. This review aims to summarize the developments of computer-assisted synthetic analysis and design in recent years, and how machine-learning algorithms contributed to them. LHASA system started the pioneering work of designing semi-empirical reaction modes in computers, with its following rule-based and network-searching work not only expanding the databases, but also building new approaches to indicating reaction rules...
2018: Frontiers in Chemistry
Samuel T Kissinger, Alexandr Pak, Yu Tang, Sotiris C Masmanidis, Alexander A Chubykin
Familiarity of the environment changes the way we perceive and encode incoming information. However, the neural substrates underlying this phenomenon are poorly understood. Here we describe a new form of experience-dependent low frequency oscillations in the primary visual cortex (V1) of awake adult male mice. The oscillations emerged in visually evoked potentials (VEPs) and single-unit activity following repeated visual stimulation. The oscillations were sensitive to the spatial frequency content of a visual stimulus and required the muscarinic acetylcholine receptors (mAChRs) for their induction and expression...
June 18, 2018: Journal of Neuroscience: the Official Journal of the Society for Neuroscience
Arnaud Leleu, Milena Dzhelyova, Bruno Rossion, Renaud Brochard, Karine Durand, Benoist Schaal, Jean-Yves Baudouin
Efficient decoding of even brief and slight intensity facial expression changes is important for social interactions. However, robust evidence for the human brain ability to automatically detect brief and subtle changes of facial expression remains limited. Here we built on a recently developed paradigm in human electrophysiology with full-blown expressions (Dzhelyova et al., 2017), to isolate and quantify a neural marker for the detection of brief and subtle changes of facial expression. Scalp electroencephalogram (EEG) was recorded from 18 participants during stimulation of a neutral face changing randomly in size at a rapid rate of 6 Hz...
June 16, 2018: NeuroImage
Zhizhong Wang, Xingyang Jiao, Songwei Wang, Xiaoke Niu, Li Shi
Reconstruction of visual input through a neuron response helps to understand the information processing mechanism of the visual system. This paper uses the amplitude and phase characteristics of the local field potential signal in the pigeon optic tectum area to reconstruct the visual input from the neuron response data by means of local information accumulation using a linear inverse filter and a back propagation neural network algorithm. The reconstructed results show that the correlation between three reconstructed images and their corresponding stimulus images (tree branches, birds, and eyeglasses) was 0...
June 15, 2018: Neuroreport
David M Devilbiss
Flexible and adaptive behaviors have evolved with increasing complexity and numbers of neuromodulator systems. The neuromodulatory locus coeruleus-norepinephrine (LC-NE) system is central to regulating cognitive function in a behaviorally-relevant and arousal-dependent manner. Through its nearly ubiquitous efferent projections, the LC-NE system acts to modulate neuron function on a cell-by-cell basis and exert a spectrum of actions across different brain regions to optimize target circuit function. As LC neuron activity, NE signaling, and arousal level increases, cognitive performance improves over an inverted-U shaped curve...
June 13, 2018: Brain Research
Ning Mei, Michael D Grossberg, Kenneth Ng, Karen T Navarro, Timothy M Ellmore
There is growing interest in understanding how specific neural events that occur during sleep, including characteristic spindle oscillations between 10 and 16 Hz (Hz), are related to learning and memory. Neural events can be recorded during sleep using the well-known method of scalp electroencephalography (EEG). While publicly available sleep EEG datasets exist, most consist of only a few channels collected in specific patient groups being evaluated overnight for sleep disorders in clinical settings. The dataset described in this Data in Brief includes 22 participants who each participated in EEG recordings on two separate days...
June 2018: Data in Brief
Samantha R Mattheiss, Hillary Levinson, William W Graves
Studies of the neural substrates of semantic (word meaning) processing have typically focused on semantic manipulations, with less consideration for potential differences in difficulty across conditions. While the idea that particular brain regions can support multiple functions is widely accepted, studies of specific cognitive domains rarely test for co-location with other functions. Here we start with standard univariate analyses comparing words to meaningless nonwords, replicating our recent finding that this contrast can activate task-positive regions for words, and default-mode regions in the putative semantic network for nonwords, pointing to difficulty effects...
July 1, 2018: Cerebral Cortex
Weiming Hu, Yabo Fan, Junliang Xing, Liang Sun, Zhaoquan Cai, Stephen Maybank
We construct a new efficient near duplicate image detection method using a hierarchical hash code learning neural network and load-balanced locality-sensitive hashing (LSH) indexing. We propose a deep constrained siamese hash coding neural network combined with deep feature learning. Our neural network is able to extract effective features for near duplicate image detection. The extracted features are used to construct a LSH-based index. We propose a load-balanced LSH method to produce load-balanced buckets in the hashing process...
September 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Amirhossein Tavanaei, Timothée Masquelier, Anthony Maida
Although representation learning methods developed within the framework of traditional neural networks are relatively mature, developing a spiking representation model remains a challenging problem. This paper proposes an event-based method to train a feedforward spiking neural network (SNN) layer for extracting visual features. The method introduces a novel spike-timing-dependent plasticity (STDP) learning rule and a threshold adjustment rule both derived from a vector quantization-like objective function subject to a sparsity constraint...
June 1, 2018: Neural Networks: the Official Journal of the International Neural Network Society
Jiayi Zhang, Yang Liu, Laijin Lu
Peripheral nerve injury is one of the most common clinical diseases. Although the regeneration of the peripheral nerve is better than that of the nerves of the central nervous system, because of its growth rate restrictions after damage. Hence, the outcome of repair after injury is not favorable. Small RNA, a type of non-coding RNA, has recently been gaining attention in neural injury. It is widely distributed in the nervous system in vivo and a significant change in the expression of small RNAs has been observed in a neural injury model...
June 9, 2018: Life Sciences
Mehedi Hasan, Alexander Kotov, April Idalski Carcone, Ming Dong, Sylvie Naar
The problem of analyzing temporally ordered sequences of observations generated by molecular, physiological or psychological processes to make predictions about the outcome of these processes arises in many domains of clinical informatics. In this paper, we focus on predicting the outcome of patient-provider communication sequences in the context of the clinical dialog. Specifically, we consider prediction of the motivational interview success (i.e. eliciting a particular type of patient behavioral response) based on an observed sequence of coded patient-provider communication exchanges as a sequence classification problem...
2018: AMIA Summits on Translational Science Proceedings
Yoshio Sakurai, Yuma Osako, Yuta Tanisumi, Eriko Ishihara, Junya Hirokawa, Hiroyuki Manabe
In this review article we focus on research methodologies for detecting the actual activity of cell assemblies, which are populations of functionally connected neurons that encode information in the brain. We introduce and discuss traditional and novel experimental methods and those currently in development and briefly discuss their advantages and disadvantages for the detection of cell-assembly activity. First, we introduce the electrophysiological method, i.e., multineuronal recording, and review former and recent examples of studies showing models of dynamic coding by cell assemblies in behaving rodents and monkeys...
2018: Frontiers in Systems Neuroscience
Shaun Gallagher, Micah Allen
We distinguish between three philosophical views on the neuroscience of predictive models: predictive coding (associated with internal Bayesian models and prediction error minimization), predictive processing (associated with radical connectionism and 'simple' embodiment) and predictive engagement (associated with enactivist approaches to cognition). We examine the concept of active inference under each model and then ask how this concept informs discussions of social cognition. In this context we consider Frith and Friston's proposal for a neural hermeneutics, and we explore the alternative model of enactivist hermeneutics...
2018: Synthese
Steve Majerus
The concept of modality specific buffers for the temporary storage of information is a fundamental characteristic of the Working memory model proposed by Baddeley and Hitch (1974). The phonological input buffer does not make an explicit distinction between the identity and the serial order of memoranda, both relying on phonological codes. This review provides a critical examination of the codes and processes involved in item and serial order maintenance capabilities. On the one hand, an increasing number of studies indicate that brain injury can lead to selective impairment for the short-term retention of item versus serial order information...
May 15, 2018: Cortex; a Journal Devoted to the Study of the Nervous System and Behavior
Christiane Ahlheim, Bradley C Love
Recent advances in multivariate fMRI analysis stress the importance of information inherent to voxel patterns. Key to interpreting these patterns is estimating the underlying dimensionality of neural representations. Dimensions may correspond to psychological dimensions, such as length and orientation, or involve other coding schemes. Unfortunately, the noise structure of fMRI data inflates dimensionality estimates and thus makes it difficult to assess the true underlying dimensionality of a pattern. To address this challenge, we developed a novel approach to identify brain regions that carry reliable task-modulated signal and to derive an estimate of the signal's functional dimensionality...
June 7, 2018: NeuroImage
Silvia Maggi, Adrien Peyrache, Mark D Humphries
The prefrontal cortex is implicated in learning the rules of an environment through trial and error. But it is unclear how such learning is related to the prefrontal cortex's role in short-term memory. Here we ask if the encoding of short-term memory in prefrontal cortex is used by rats learning decision rules in a Y-maze task. We find that a similar pattern of neural ensemble activity is selectively recalled after reinforcement for a correct decision. This reinforcement-selective recall only reliably occurs immediately before the abrupt behavioural transitions indicating successful learning of the current rule, and fades quickly thereafter...
June 7, 2018: Nature Communications
G H Roshani, A Karami, A Khazaei, A Olfateh, E Nazemi, M Omidi
Gamma ray source has very important role in precision of multi-phase flow metering. In this study, different combination of gamma ray sources ((133 Ba-137 Cs), (133 Ba-60 Co), (241 Am-137 Cs), (241 Am-60 Co), (133 Ba-241 Am) and (60 Co-137 Cs)) were investigated in order to optimize the three-phase flow meter. Three phases were water, oil and gas and the regime was considered annular. The required data was numerically generated using MCNP-X code which is a Monte-Carlo code. Indeed, the present study devotes to forecast the volume fractions in the annular three-phase flow, based on a multi energy metering system including various radiation sources and also one NaI detector, using a hybrid model of artificial neural network and Jaya Optimization algorithm...
May 17, 2018: Applied Radiation and Isotopes
Sanjay Purushotham, Chuizheng Meng, Zhengping Che, Yan Liu
Deep learning models (aka Deep Neural Networks) have revolutionized many fields including computer vision, natural language processing, speech recognition, and is being increasingly used in clinical healthcare applications. However, few works exist which have benchmarked the performance of the deep learning models with respect to the state-of-the-art machine learning models and prognostic scoring systems on publicly available healthcare datasets. In this paper, we present the benchmarking results for several clinical prediction tasks such as mortality prediction, length of stay prediction, and ICD-9 code group prediction using Deep Learning models, ensemble of machine learning models (Super Learner algorithm), SAPS II and SOFA scores...
June 4, 2018: Journal of Biomedical Informatics
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