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neural coding

Juanita Mathews, Michael Levin
Breakthroughs in biomedicine and synthetic bioengineering require predictive, rational control over anatomical structure and function. Recent successes in manipulating cellular and molecular hardware have not been matched by progress in understanding the patterning software implemented during embryogenesis and regeneration. A fundamental capability gap is driving desired changes in growth and form to address birth defects and traumatic injury. Here we review new tools, results, and conceptual advances in an exciting emerging field: endogenous non-neural bioelectric signaling, which enables cellular collectives to make global decisions and implement large-scale pattern homeostasis...
April 20, 2018: Current Opinion in Biotechnology
Sonia Bansal, Judith M Ford, Miriam Spering
Humans and other primates are equipped with neural mechanisms that allow them to automatically make predictions about future events, facilitating processing of expected sensations and actions. Prediction-driven control and monitoring of perceptual and motor acts are vital to normal cognitive functioning. This review provides an overview of corollary discharge mechanisms involved in predictions across sensory modalities and discusses consequences of predictive coding for cognition and behavior. Converging evidence now links impairments in corollary discharge mechanisms to neuropsychiatric symptoms such as hallucinations and delusions...
April 23, 2018: Annals of the New York Academy of Sciences
Liqun Luo, Edward M Callaway, Karel Svoboda
Tremendous progress has been made since Neuron published our Primer on genetic dissection of neural circuits 10 years ago. Since then, cell-type-specific anatomical, neurophysiological, and perturbation studies have been carried out in a multitude of invertebrate and vertebrate organisms, linking neurons and circuits to behavioral functions. New methods allow systematic classification of cell types and provide genetic access to diverse neuronal types for studies of connectivity and neural coding during behavior...
April 18, 2018: Neuron
Zsolt Turi, Ivan Alekseichuk, Walter Paulus
The ability to simultaneously process and maintain multiple pieces of information is limited. Over the past 50 years, observational methods have provided a large amount of insight regarding the neural mechanisms that underpin the mental capacity that we refer to as "working memory." More than 20 years ago, a neural coding scheme was proposed for working memory. As a result of technological developments, we can now not only observe but can also influence brain rhythms in humans. Building on these novel developments, we have begun to externally control brain oscillations in order to extend the limits of working memory...
April 19, 2018: PLoS Biology
Alice Mizrahi, Tifenn Hirtzlin, Akio Fukushima, Hitoshi Kubota, Shinji Yuasa, Julie Grollier, Damien Querlioz
In neuroscience, population coding theory demonstrates that neural assemblies can achieve fault-tolerant information processing. Mapped to nanoelectronics, this strategy could allow for reliable computing with scaled-down, noisy, imperfect devices. Doing so requires that the population components form a set of basis functions in terms of their response functions to inputs, offering a physical substrate for computing. Such a population can be implemented with CMOS technology, but the corresponding circuits have high area or energy requirements...
April 18, 2018: Nature Communications
Bernhard Pastötter, Christian Frings
Understanding the neural processes that maintain goal-directed behavior is a major challenge for the study of attentional control. Although much of the previous work on the issue has focused on prefrontal brain areas, little is known about the contribution of sensory brain processes to the regulation of attentional control. The present EEG study examined brain oscillatory activities invoked in the processing of response conflict in a lateralized Eriksen single-flanker task, in which target letters were presented at fixation and single distractor letters were presented either left or right to the targets...
April 18, 2018: Journal of Cognitive Neuroscience
Tatsuhiko Naito
Accurate splice-site prediction is essential to delineate gene structures from sequence data. Several computational techniques have been applied to create a system to predict canonical splice sites. For classification tasks, deep neural networks (DNNs) have achieved record-breaking results and often outperformed other supervised learning techniques. In this study, a new method of splice-site prediction using DNNs was proposed. The proposed system receives an input sequence data and returns an answer as to whether it is splice site...
April 18, 2018: Journal of Computational Biology: a Journal of Computational Molecular Cell Biology
Haibo Shi, Yaoru Sun, Jie Li
Conventional models of motor control exploit the spatial representation of the controlled system to generate control commands. Typically, the control command is gained with the feedback state of a specific instant in time, which behaves like an optimal regulator or spatial filter to the feedback state. Yet, recent neuroscience studies found that the motor network may constitute an autonomous dynamical system and the temporal patterns of the control command can be contained in the dynamics of the motor network, that is, the dynamical system hypothesis (DSH)...
2018: Computational Intelligence and Neuroscience
Maryam Vaziri-Pashkam, Yaoda Xu
Recent studies have demonstrated the existence of rich visual representations in both occipitotemporal cortex (OTC) and posterior parietal cortex (PPC). Using fMRI decoding and a bottom-up data-driven approach, we showed that although robust object category representations exist in both OTC and PPC, there is an information-driven 2-pathway separation among these regions in the representational space, with occipitotemporal regions arranging hierarchically along 1 pathway and posterior parietal regions along another pathway...
April 12, 2018: Cerebral Cortex
Stefan Budach, Annalisa Marsico
Summary: Convolutional neural networks (CNNs) have been shown to perform exceptionally well in a variety of tasks, including biological sequence classification. Available implementations, however, are usually optimized for a particular task and difficult to reuse. To enable researchers to utilize these networks more easily we implemented pysster, a Python package for training CNNs on biological sequence data. Sequences are classified by learning sequence and structure motifs and the package offers an automated hyper-parameter optimization procedure and options to visualize learned motifs along with information about their positional and class enrichment...
April 6, 2018: Bioinformatics
Friedemann Zenke, Surya Ganguli
A vast majority of computation in the brain is performed by spiking neural networks. Despite the ubiquity of such spiking, we currently lack an understanding of how biological spiking neural circuits learn and compute in vivo, as well as how we can instantiate such capabilities in artificial spiking circuits in silico. Here we revisit the problem of supervised learning in temporally coding multilayer spiking neural networks. First, by using a surrogate gradient approach, we derive SuperSpike, a nonlinear voltage-based three-factor learning rule capable of training multilayer networks of deterministic integrate-and-fire neurons to perform nonlinear computations on spatiotemporal spike patterns...
April 13, 2018: Neural Computation
E Paxon Frady, Denis Kleyko, Friedrich T Sommer
To accommodate structured approaches of neural computation, we propose a class of recurrent neural networks for indexing and storing sequences of symbols or analog data vectors. These networks with randomized input weights and orthogonal recurrent weights implement coding principles previously described in vector symbolic architectures (VSA) and leverage properties of reservoir computing. In general, the storage in reservoir computing is lossy, and cross-talk noise limits the retrieval accuracy and information capacity...
April 13, 2018: Neural Computation
Haeme R P Park, Mariam Kostandyan, C Nico Boehler, Ruth M Krebs
Although it is clear that emotional and motivational manipulations yield a strong influence on cognition and behaviour, these domains have mostly been investigated in independent research lines. Therefore, it remains poorly understood how far these affective manipulations overlap in terms of their underlying neural activations, especially in light of previous findings that suggest a shared valence mechanism across multiple affective processing domains (e.g., monetary incentives, primary rewards, emotional events)...
April 11, 2018: Cognitive, Affective & Behavioral Neuroscience
Jun Fan, Quan Zhou, Yan Li, Xiuling Song, Jijie Hu, Zaisheng Qin, Jing Tang, Tao Tao
Propofol is a frequently used intravenous anesthetic agent. The impairment caused by propofol on the neural system, especially the hippocampus, has been widely reported. However, the molecular mechanism underlying the effects of propofol on learning and memory functions in the hippocampus is still unclear. In the present study we performed lncRNA and mRNA analysis in the hippocampi of adult mice, after propofol sedation, through RNA-Sequencing (RNA-Seq). A total of 146 differentially expressed lncRNAs and 1103 mRNAs were identified...
2018: Frontiers in Molecular Neuroscience
Jake P Stroud, Tim P Vogels
The neural code of cortical processing remains uncracked; however, it must necessarily rely on faithful signal propagation between cortical areas. In this issue of Neuron, Joglekar et al. (2018) show that strong inter-areal excitation balanced by local inhibition can enable reliable signal propagation in data-constrained network models of macaque cortex.
April 4, 2018: Neuron
Chaohui Guo, Eydie Moses-Kolko, Mary Phillips, James E Swain, Alison E Hipwell
Neuroimaging research has suggested that activity in the amygdala, center of the socioemotional network, and functional connectivity between the amygdala and cortical regions are associated with caregiving behaviors in postpartum mothers. Anxiety is common in the early postpartum period, with severity ranging from healthy maternal preoccupation to clinical disorder. However, little is known about the influence of anxiety on the neural correlates of early caregiving. We examined these relationships in a community cohort of 75 postpartum women (ages 18-22; predominantly low-SES, minority race) who listened to infant cry sounds while undergoing an fMRI assessment...
April 4, 2018: Cognitive, Affective & Behavioral Neuroscience
Dave Neale, Kaili Clackson, Stanimira Georgieva, Hatice Dedetas, Melissa Scarpate, Sam Wass, Victoria Leong
Play during early life is a ubiquitous activity, and an individual's propensity for play is positively related to cognitive development and emotional well-being. Play behavior (which may be solitary or shared with a social partner) is diverse and multi-faceted. A challenge for current research is to converge on a common definition and measurement system for play - whether examined at a behavioral, cognitive or neurological level. Combining these different approaches in a multimodal analysis could yield significant advances in understanding the neurocognitive mechanisms of play, and provide the basis for developing biologically grounded play models...
2018: Frontiers in Psychology
Zhongzhong Chen, Yunping Lei, Xuanye Cao, Yufang Zheng, Fang Wang, Yihua Bao, Rui Peng, Richard H Finnell, Ting Zhang, Hongyan Wang
BACKGROUND: Mouse homozygous mutants in Wnt/planar cell polarity (PCP) pathway genes have been shown to cause neural tube defects (NTDs) through the disruption of normal morphogenetic processes critical to neural tube closure (NTC). Knockout mice that are heterozygotes of single PCP genes likely fail to produce NTD phenotypes, yet damaging variants detected in human NTDs are almost always heterozygous, suggesting that other deleterious interacting variants are likely to be present. Nonetheless, the Wnt/PCP pathway remains a genetic hotspot...
April 4, 2018: BMC Medical Genomics
Meng Wang, Cheng Tai, Weinan E, Liping Wei
The complex system of gene expression is regulated by the cell type-specific binding of transcription factors (TFs) to regulatory elements. Identifying variants that disrupt TF binding and lead to human diseases remains a great challenge. To address this, we implement sequence-based deep learning models that accurately predict the TF binding intensities to given DNA sequences. In addition to accurately classifying TF-DNA binding or unbinding, our models are capable of accurately predicting real-valued TF binding intensities by leveraging large-scale TF ChIP-seq data...
April 2, 2018: Nucleic Acids Research
Alla Brodski-Guerniero, Marcus J Naumer, Vera Moliadze, Jason Chan, Heike Althen, Fernando Ferreira-Santos, Joseph T Lizier, Sabine Schlitt, Janina Kitzerow, Magdalena Schütz, Anne Langer, Jochen Kaiser, Christine M Freitag, Michael Wibral
The neurophysiological underpinnings of the nonsocial symptoms of autism spectrum disorder (ASD) which include sensory and perceptual atypicalities remain poorly understood. Well-known accounts of less dominant top-down influences and more dominant bottom-up processes compete to explain these characteristics. These accounts have been recently embedded in the popular framework of predictive coding theory. To differentiate between competing accounts, we studied altered information dynamics in ASD by quantifying predictable information in neural signals...
April 4, 2018: Human Brain Mapping
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