Spontaneous dissection of the intrapetrous internal carotid artery

Two cases of cervicocephalic arterial dissection of the intrapetrous carotid artery are described. One patient presented with intolerable objective pulsatile tinnitus, the other with a cerebral infarction. Both were successfully treated with anticoagulants. The significance of minor degrees of trauma and of neck extension in the aetiology of these apparently spontaneous lesions is discussed

Bio-Inspired Multi-Layer Spiking Neural Network Extracts Discriminative Features from Speech Signals

Spiking neural networks (SNNs) enable power-efficient implementations due to their sparse, spike-based coding scheme. This paper develops a bio-inspired SNN that uses unsupervised learning to extract discriminative features from speech signals, which can subsequently be used in a classifier. The architecture consists of a spiking convolutional/pooling layer followed by a fully connected spiking layer for feature discovery. The convolutional layer of leaky, integrate-and-fire (LIF) neurons represents primary acoustic features. The fully connected layer is equipped with a probabilistic spike-timing-dependent plasticity learning rule. This layer represents the discriminative features through probabilistic, LIF neurons. To assess the discriminative power of the learned features, they are used in a hidden Markov model (HMM) for spoken digit recognition. The experimental results show performance above 96% that compares favorably with popular statistical feature extraction methods. Our results provide a novel demonstration of unsupervised feature acquisition in an SNN

Legislative History: An Act to Amend the Date to Determine Age for Kindergarten Eligibility (HP284)(LD 367)

https://digitalmaine.com/legishist113/1366/thumbnail.jp

Temporal Convolution in Spiking Neural Networks: a Bio-mimetic Paradigm

Abstract Recent spectacular advances in Artificial Intelligence (AI), in large, be attributed to developments in Deep Learning (DL). In essence, DL is not a new concept. In many respects, DL shares characteristics of “traditional” types of Neural Network (NN). The main distinguishing feature is that it uses many more layers in order to learn increasingly complex features. Each layer convolutes into the previous by simplifying and applying a function upon a subsection of that layer. Deep Learning’s fantastic success can be attributed to dedicated researchers experimenting with many different groundbreaking techniques, but also some of its triumph can also be attributed to fortune. It was the right technique at the right time. To function effectively, DL mainly requires two things: (a) vast amounts of training data and (b) a very specific type of computational capacity. These two respective requirements have been amply met with the growth of the internet and the rapid development of GPUs. As such DL is an almost perfect fit for today’s technologies. However, DL is only a very rough approximation of how the brain works. More recently, Spiking Neural Networks (SNNs) have tried to simulate biological phenomena in a more realistic way. In SNNs information is transmitted as discreet spikes of data rather than a continuous weight or a differentiable activation function. In practical terms this means that far more nuanced interactions can occur between neurons and that the network can run far more efficiently (e.g. in terms of calculations needed and therefore overall power requirements). Nevertheless, the big problem with SNNs is that unlike DL it does not “fit” well with existing technologies. Worst still is that no one has yet come up with definitive way to make SNNs function at a “deep” level. The difficulty is that in essence "deep" and "spiking" refer to fundamentally different characteristics of a neural network: "spiking" focuses on the activation of individual neurons, whereas "deep" concerns itself to the network architecture itself [1]. However, these two methods are in fact not contradictory, but have so far been developed in isolation from each other due to the prevailing technology driving each technique and the fundamental conceptual distance between each of the two biological paradigms. If advances in AI are to continue at the present rate that new technologies are going to be developed and the contradictory aspects of DL and SNN are going to have to be reconciled. Very recently, there have been a handful of attempts to amalgamate DL and SNN in a variety of ways [2]-one of the most exciting being the creation of a specific hierarchical learning paradigm in Recurrent SNN (RSNNs) called e-prop [3]. However, this paper posits that this has been made problematic because a fundamental agent in the way the biological brain functions has been missing from each paradigm, and that if this is included in a new model then the union between DL and RSNN can be made in a more harmonious manner. The missing piece to the jigsaw, in fact, is the glial cell and the unacknowledged function it plays in neural processing. In this context, this paper examines how DL and SNN can be combined, and how glial dynamics cannot only address outstanding issues with the existing individual paradigms - for example the “weight transport” problem - but also act as the “glue” – e.g. pun intended - between these two paradigms. This idea has direct parallel with the idea of convolution in DL but has the added dimension of time. It is important not only where events happen but also when events occur in this new paradigm. The synergy between these two powerful paradigms give hints at the direction and potential of what could be an important part of the next wave of development in AI

Cosmic rays and molecular clouds

This paper deals with the cosmic-ray penetration into molecular clouds and with the related gamma--ray emission. High energy cosmic rays interact with the dense gas and produce neutral pions which in turn decay into two gamma rays. This makes molecular clouds potential sources of gamma rays, especially if they are located in the vicinity of a powerful accelerator that injects cosmic rays in the interstellar medium. The amplitude and duration in time of the cosmic--ray overdensity around a given source depend on how quickly cosmic rays diffuse in the turbulent galactic magnetic field. For these reasons, gamma-ray observations of molecular clouds can be used both to locate the sources of cosmic rays and to constrain the properties of cosmic-ray diffusion in the Galaxy.Comment: To appear in the proceedings of the San Cugat Forum on Astrophysics 2012, 27 pages, 10 figure

Inhibition of Iron Uptake Is Responsible for Differential Sensitivity to V-ATPase Inhibitors in Several Cancer Cell Lines

Many cell lines derived from tumors as well as transformed cell lines are far more sensitive to V-ATPase inhibitors than normal counterparts. The molecular mechanisms underlying these differences in sensitivity are not known. Using global gene expression data, we show that the most sensitive responses to HeLa cells to low doses of V-ATPase inhibitors involve genes responsive to decreasing intracellular iron or decreasing cholesterol and that sensitivity to iron uptake is an important determinant of V-ATPase sensitivity in several cancer cell lines. One of the most sensitive cell lines, melanoma derived SK-Mel-5, over-expresses the iron efflux transporter ferroportin and has decreased expression of proteins involved in iron uptake, suggesting that it actively suppresses cytoplasmic iron. SK-Mel-5 cells have increased production of reactive oxygen species and may be seeking to limit additional production of ROS by iron

Probabilistic Inference in General Graphical Models through Sampling in Stochastic Networks of Spiking Neurons

An important open problem of computational neuroscience is the generic organization of computations in networks of neurons in the brain. We show here through rigorous theoretical analysis that inherent stochastic features of spiking neurons, in combination with simple nonlinear computational operations in specific network motifs and dendritic arbors, enable networks of spiking neurons to carry out probabilistic inference through sampling in general graphical models. In particular, it enables them to carry out probabilistic inference in Bayesian networks with converging arrows (“explaining away”) and with undirected loops, that occur in many real-world tasks. Ubiquitous stochastic features of networks of spiking neurons, such as trial-to-trial variability and spontaneous activity, are necessary ingredients of the underlying computational organization. We demonstrate through computer simulations that this approach can be scaled up to neural emulations of probabilistic inference in fairly large graphical models, yielding some of the most complex computations that have been carried out so far in networks of spiking neurons

Deletion of PKBα/Akt1 Affects Thymic Development

BACKGROUND: The thymus constitutes the primary lymphoid organ for the majority of T cells. The phosphatidyl-inositol 3 kinase (PI3K) signaling pathway is involved in lymphoid development. Defects in single components of this pathway prevent thymocytes from progressing beyond early T cell developmental stages. Protein kinase B (PKB) is the main effector of the PI3K pathway. METHODOLOGY/PRINCIPAL FINDINGS: To determine whether PKB mediates PI3K signaling in the thymus, we characterized PKB knockout thymi. Our results reveal a significant thymic hypocellularity in PKBalpha(-/-) neonates and an accumulation of early thymocyte subsets in PKBalpha(-/-) adult mice. Using thymic grafting and fetal liver cell transfer experiments, the latter finding was specifically attributed to the lack of PKBalpha within the lymphoid component of the thymus. Microarray analyses show that the absence of PKBalpha in early thymocyte subsets modifies the expression of genes known to be involved in pre-TCR signaling, in T cell activation, and in the transduction of interferon-mediated signals. CONCLUSIONS/SIGNIFICANCE: This report highlights the specific requirements of PKBalpha for thymic development and opens up new prospects as to the mechanism downstream of PKBalpha in early thymocytes