Marshall University Music Department Presents a Graduate Recital, Edward M. Vineyard

https://mds.marshall.edu/music_perf/1062/thumbnail.jp

A novel mutation in isoform 3 of the plasma membrane Ca2+ pump impairs cellular Ca2+ homeostasis in a patient with cerebellar ataxia and laminin subunit 1\u3b1 mutations.

The particular importance of Ca2+ signaling to neurons demands its precise regulation within their cytoplasm. Isoform 3 of the plasma membrane Ca2+ ATPase (the PMCA3 pump), which is highly expressed in brain and cerebellum, plays an important role in the regulation of neuronal Ca2+. A genetic defect of the function of the PMCA3 pump has been described in one family with X-linked congenital cerebellar ataxia. Here we describe a novel mutation of the PMCA3 pump (ATP2B3) in a patient with global developmental delay, generalized hypotonia and cerebellar ataxia. The mutation (a R482H replacement) impairs the Ca2+ ejection function of the pump. It reduces the ability of the pump expressed in model cells to control Ca2+ transients generated by cell stimulation and impairs its Ca2+ extrusion function under conditions of low resting cytosolic Ca2+ as well. In silico analysis of the structural effect of the mutation suggests a reduced stabilization of the portion of the pump surrounding the mutated residue in the Ca2+-bound state. The patient also carries two missense mutations in LAMA1, encoding for laminin subunit 1\u3b1. On the basis of the family pedigree of the patient, the presence of both PMCA3 and LAMA1 mutations appears to be necessary for the development of the disease. Considering the observed defect in cellular Ca2+ homeostasis and the previous finding that PMCAs act as digenic modulators in Ca2+-linked pathologies, the PMCA3 dysfunction along with LAMA1 mutations could act synergistically to cause the neurological phenotype

Neurogenesis Deep Learning

Neural machine learning methods, such as deep neural networks (DNN), have achieved remarkable success in a number of complex data processing tasks. These methods have arguably had their strongest impact on tasks such as image and audio processing - data processing domains in which humans have long held clear advantages over conventional algorithms. In contrast to biological neural systems, which are capable of learning continuously, deep artificial networks have a limited ability for incorporating new information in an already trained network. As a result, methods for continuous learning are potentially highly impactful in enabling the application of deep networks to dynamic data sets. Here, inspired by the process of adult neurogenesis in the hippocampus, we explore the potential for adding new neurons to deep layers of artificial neural networks in order to facilitate their acquisition of novel information while preserving previously trained data representations. Our results on the MNIST handwritten digit dataset and the NIST SD 19 dataset, which includes lower and upper case letters and digits, demonstrate that neurogenesis is well suited for addressing the stability-plasticity dilemma that has long challenged adaptive machine learning algorithms.Comment: 8 pages, 8 figures, Accepted to 2017 International Joint Conference on Neural Networks (IJCNN 2017

A new chelonioid turtle from the Paleocene of Cabinda, Angola

Sem PDF.We report a new chelonioid turtle on the basis of a nearly complete skull collected in lower Paleocene, shallow marine deposits, equivalent to the offshore Landana Formation, near the town of Landana in Cabinda Province, Angola. Chelonioid material previously reported from this locality is likely referable to this new taxon. The well-preserved skull is missing the left quadrate, squamosal, and prootic, both opisthotics, and the mandible. The skull possesses a rod-like basisphenoid rostrum, which is a synapomorphy of Chelonioidea, but it differs from other chelonioid skulls in that the contact between the parietal and squamosal is absent, and the posterior palatine foramen is present. Phylogenetic analysis recovers the new taxon as a basal chelonioid. The Paleocenetextendash Eocene strata near Landana have produced a wealth of turtle fossils, including the holotype of the pleurodire Taphrosphys congolensis. A turtle humerus collected from the Landana locality differs morphologically from the humeri of chelonioids and Taphrosphys, indicating the presence of a third taxon. Chelonioid fossil material in the Landana assemblage is rare compared to the abundant fragmentary remains of Taphrosphys that are found throughout the stratigraphic section. This disparity in abundance suggests the new chelonioid taxon preferred open marine habitats, whereas Taphrosphys frequented nearshore environments.publishe

A Digital Neuromorphic Architecture Efficiently Facilitating Complex Synaptic Response Functions Applied to Liquid State Machines

Information in neural networks is represented as weighted connections, or synapses, between neurons. This poses a problem as the primary computational bottleneck for neural networks is the vector-matrix multiply when inputs are multiplied by the neural network weights. Conventional processing architectures are not well suited for simulating neural networks, often requiring large amounts of energy and time. Additionally, synapses in biological neural networks are not binary connections, but exhibit a nonlinear response function as neurotransmitters are emitted and diffuse between neurons. Inspired by neuroscience principles, we present a digital neuromorphic architecture, the Spiking Temporal Processing Unit (STPU), capable of modeling arbitrary complex synaptic response functions without requiring additional hardware components. We consider the paradigm of spiking neurons with temporally coded information as opposed to non-spiking rate coded neurons used in most neural networks. In this paradigm we examine liquid state machines applied to speech recognition and show how a liquid state machine with temporal dynamics maps onto the STPU-demonstrating the flexibility and efficiency of the STPU for instantiating neural algorithms.Comment: 8 pages, 4 Figures, Preprint of 2017 IJCN