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Sample records for neural crest-derived structures

  1. Regeneration of neural crest derivatives in the Xenopus tadpole tail

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    Slack Jonathan MW

    2007-05-01

    Full Text Available Abstract Background After amputation of the Xenopus tadpole tail, a functionally competent new tail is regenerated. It contains spinal cord, notochord and muscle, each of which has previously been shown to derive from the corresponding tissue in the stump. The regeneration of the neural crest derivatives has not previously been examined and is described in this paper. Results Labelling of the spinal cord by electroporation, or by orthotopic grafting of transgenic tissue expressing GFP, shows that no cells emigrate from the spinal cord in the course of regeneration. There is very limited regeneration of the spinal ganglia, but new neurons as well as fibre tracts do appear in the regenerated spinal cord and the regenerated tail also contains abundant peripheral innervation. The regenerated tail contains a normal density of melanophores. Cell labelling experiments show that melanophores do not arise from the spinal cord during regeneration, nor from the mesenchymal tissues of the skin, but they do arise by activation and proliferation of pre-existing melanophore precursors. If tails are prepared lacking melanophores, then the regenerates also lack them. Conclusion On regeneration there is no induction of a new neural crest similar to that seen in embryonic development. However there is some regeneration of neural crest derivatives. Abundant melanophores are regenerated from unpigmented precursors, and, although spinal ganglia are not regenerated, sufficient sensory systems are produced to enable essential functions to continue.

  2. Neural Crest-Derived Mesenchymal Cells Require Wnt Signaling for Their Development and Drive Invagination of the Telencephalic Midline

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    Choe, Youngshik; Zarbalis, Konstantinos S.; Pleasure, Samuel J.

    2014-01-01

    Embryonic neural crest cells contribute to the development of the craniofacial mesenchyme, forebrain meninges and perivascular cells. In this study, we investigated the function of ß-catenin signaling in neural crest cells abutting the dorsal forebrain during development. In the absence of ß-catenin signaling, neural crest cells failed to expand in the interhemispheric region and produced ectopic smooth muscle cells instead of generating dermal and calvarial mesenchyme. In contrast, constitutive expression of stabilized ß-catenin in neural crest cells increased the number of mesenchymal lineage precursors suggesting that ß-catenin signaling is necessary for the expansion of neural crest-derived mesenchymal cells. Interestingly, the loss of neural crest-derived mesenchymal stem cells (MSCs) leads to failure of telencephalic midline invagination and causes ventricular system defects. This study shows that ß-catenin signaling is required for the switch of neural crest cells to MSCs and mediates the expansion of MSCs to drive the formation of mesenchymal structures of the head. Furthermore, loss of these structures causes striking defects in forebrain morphogenesis. PMID:24516524

  3. Neural crest-derived mesenchymal cells require Wnt signaling for their development and drive invagination of the telencephalic midline.

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    Youngshik Choe

    Full Text Available Embryonic neural crest cells contribute to the development of the craniofacial mesenchyme, forebrain meninges and perivascular cells. In this study, we investigated the function of ß-catenin signaling in neural crest cells abutting the dorsal forebrain during development. In the absence of ß-catenin signaling, neural crest cells failed to expand in the interhemispheric region and produced ectopic smooth muscle cells instead of generating dermal and calvarial mesenchyme. In contrast, constitutive expression of stabilized ß-catenin in neural crest cells increased the number of mesenchymal lineage precursors suggesting that ß-catenin signaling is necessary for the expansion of neural crest-derived mesenchymal cells. Interestingly, the loss of neural crest-derived mesenchymal stem cells (MSCs leads to failure of telencephalic midline invagination and causes ventricular system defects. This study shows that ß-catenin signaling is required for the switch of neural crest cells to MSCs and mediates the expansion of MSCs to drive the formation of mesenchymal structures of the head. Furthermore, loss of these structures causes striking defects in forebrain morphogenesis.

  4. Hepatocyte growth factor/scatter factor-MET signaling in neural crest-derived melanocyte development.

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    Kos, L; Aronzon, A; Takayama, H; Maina, F; Ponzetto, C; Merlino, G; Pavan, W

    1999-02-01

    The mechanisms governing development of neural crest-derived melanocytes, and how alterations in these pathways lead to hypopigmentation disorders, are not completely understood. Hepatocyte growth factor/scatter factor (HGF/SF) signaling through the tyrosine-kinase receptor, MET, is capable of promoting the proliferation, increasing the motility, and maintaining high tyrosinase activity and melanin synthesis of melanocytes in vitro. In addition, transgenic mice that ubiquitously overexpress HGF/SF demonstrate hyperpigmentation in the skin and leptomenigenes and develop melanomas. To investigate whether HGF/ SF-MET signaling is involved in the development of neural crest-derived melanocytes, transgenic embryos, ubiquitously overexpressing HGF/SF, were analyzed. In HGF/SF transgenic embryos, the distribution of melanoblasts along the characteristic migratory pathway was not affected. However, additional ectopically localized melanoblasts were also observed in the dorsal root ganglia and neural tube, as early as 11.5 days post coitus (p.c.). We utilized an in vitro neural crest culture assay to further explore the role of HGF/SF-MET signaling in neural crest development. HGF/SF added to neural crest cultures increased melanoblast number, permitted differentiation into pigmented melanocytes, promoted melanoblast survival, and could replace mast-cell growth factor/Steel factor (MGF) in explant cultures. To examine whether HGF/SF-MET signaling is required for the proper development of melanocytes, embryos with a targeted Met null mutation (Met-/-) were analysed. In Met-/- embryos, melanoblast number and location were not overtly affected up to 14 days p.c. These results demonstrate that HGF/SF-MET signaling influences, but is not required for, the initial development of neural crest-derived melanocytes in vivo and in vitro.

  5. Modeling Cerebrovascular Pathophysiology in Amyloid-β Metabolism using Neural-Crest-Derived Smooth Muscle Cells

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    Christine Cheung

    2014-10-01

    Full Text Available Summary: There is growing recognition of cerebrovascular contributions to neurodegenerative diseases. In the walls of cerebral arteries, amyloid-beta (Aβ accumulation is evident in a majority of aged people and patients with cerebral amyloid angiopathy. Here, we leverage human pluripotent stem cells to generate vascular smooth muscle cells (SMCs from neural crest progenitors, recapitulating brain-vasculature-specific attributes of Aβ metabolism. We confirm that the lipoprotein receptor, LRP1, functions in our neural-crest-derived SMCs to mediate Aβ uptake and intracellular lysosomal degradation. Hypoxia significantly compromises the contribution of SMCs to Aβ clearance by suppressing LRP1 expression. This enabled us to develop an assay of Aβ uptake by using the neural crest-derived SMCs with hypoxia as a stress paradigm. We then tested several vascular protective compounds in a high-throughput format, demonstrating the value of stem-cell-based phenotypic screening for novel therapeutics and drug repurposing, aimed at alleviating amyloid burden. : The contribution of blood vessel pathologies to neurodegenerative disorders is relatively neglected, partly due to inadequate human tissues for research. By using human stem cells, Cheung et al. establish a method of generating vascular smooth muscle cells (SMCs from neural crest progenitors, the primary precursors that give rise to brain blood vessels. These stem-cell-derived SMCs display defective amyloid processing under chronic hypoxia, a phenomenon well documented in the cerebral vasculatures of aged people and patients with Alzheimer’s disease.

  6. Constitutively active Notch1 converts cranial neural crest-derived frontonasal mesenchyme to perivascular cells in vivo

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    Sophie R. Miller

    2017-03-01

    Full Text Available Perivascular/mural cells originate from either the mesoderm or the cranial neural crest. Regardless of their origin, Notch signalling is necessary for their formation. Furthermore, in both chicken and mouse, constitutive Notch1 activation (via expression of the Notch1 intracellular domain is sufficient in vivo to convert trunk mesoderm-derived somite cells to perivascular cells, at the expense of skeletal muscle. In experiments originally designed to investigate the effect of premature Notch1 activation on the development of neural crest-derived olfactory ensheathing glial cells (OECs, we used in ovo electroporation to insert a tetracycline-inducible NotchΔE construct (encoding a constitutively active mutant of mouse Notch1 into the genome of chicken cranial neural crest cell precursors, and activated NotchΔE expression by doxycycline injection at embryonic day 4. NotchΔE-targeted cells formed perivascular cells within the frontonasal mesenchyme, and expressed a perivascular marker on the olfactory nerve. Hence, constitutively activating Notch1 is sufficient in vivo to drive not only somite cells, but also neural crest-derived frontonasal mesenchyme and perhaps developing OECs, to a perivascular cell fate. These results also highlight the plasticity of neural crest-derived mesenchyme and glia.

  7. Distinct functional and temporal requirements for zebrafish Hdac1 during neural crest-derived craniofacial and peripheral neuron development.

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    Ignatius, Myron S; Unal Eroglu, Arife; Malireddy, Smitha; Gallagher, Glen; Nambiar, Roopa M; Henion, Paul D

    2013-01-01

    The regulation of gene expression is accomplished by both genetic and epigenetic means and is required for the precise control of the development of the neural crest. In hdac1(b382) mutants, craniofacial cartilage development is defective in two distinct ways. First, fewer hoxb3a, dlx2 and dlx3-expressing posterior branchial arch precursors are specified and many of those that are consequently undergo apoptosis. Second, in contrast, normal numbers of progenitors are present in the anterior mandibular and hyoid arches, but chondrocyte precursors fail to terminally differentiate. In the peripheral nervous system, there is a disruption of enteric, DRG and sympathetic neuron differentiation in hdac1(b382) mutants compared to wildtype embryos. Specifically, enteric and DRG-precursors differentiate into neurons in the anterior gut and trunk respectively, while enteric and DRG neurons are rarely present in the posterior gut and tail. Sympathetic neuron precursors are specified in hdac1(b382) mutants and they undergo generic neuronal differentiation but fail to undergo noradrenergic differentiation. Using the HDAC inhibitor TSA, we isolated enzyme activity and temporal requirements for HDAC function that reproduce hdac1(b382) defects in craniofacial and sympathetic neuron development. Our study reveals distinct functional and temporal requirements for zebrafish hdac1 during neural crest-derived craniofacial and peripheral neuron development.

  8. Distinct functional and temporal requirements for zebrafish Hdac1 during neural crest-derived craniofacial and peripheral neuron development.

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    Myron S Ignatius

    Full Text Available The regulation of gene expression is accomplished by both genetic and epigenetic means and is required for the precise control of the development of the neural crest. In hdac1(b382 mutants, craniofacial cartilage development is defective in two distinct ways. First, fewer hoxb3a, dlx2 and dlx3-expressing posterior branchial arch precursors are specified and many of those that are consequently undergo apoptosis. Second, in contrast, normal numbers of progenitors are present in the anterior mandibular and hyoid arches, but chondrocyte precursors fail to terminally differentiate. In the peripheral nervous system, there is a disruption of enteric, DRG and sympathetic neuron differentiation in hdac1(b382 mutants compared to wildtype embryos. Specifically, enteric and DRG-precursors differentiate into neurons in the anterior gut and trunk respectively, while enteric and DRG neurons are rarely present in the posterior gut and tail. Sympathetic neuron precursors are specified in hdac1(b382 mutants and they undergo generic neuronal differentiation but fail to undergo noradrenergic differentiation. Using the HDAC inhibitor TSA, we isolated enzyme activity and temporal requirements for HDAC function that reproduce hdac1(b382 defects in craniofacial and sympathetic neuron development. Our study reveals distinct functional and temporal requirements for zebrafish hdac1 during neural crest-derived craniofacial and peripheral neuron development.

  9. In vivo impact of Dlx3 conditional inactivation in Neural Crest-Derived Craniofacial Bones

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    Duverger, Olivier; Isaac, Juliane; Zah, Angela; Hwang, Joonsung; Berdal, Ariane; Lian, Jane B.; Morasso, Maria I.

    2012-01-01

    Mutations in DLX3 in humans lead to defects in craniofacial and appendicular bones, yet the in vivo activity related to Dlx3 function during normal skeletal development have not been fully elucidated. Here we used a conditional knockout approach to analyze the effects of neural crest deletion of Dlx3 on craniofacial bones development. At birth, mutant mice exhibit a normal overall positioning of the skull bones, but a change in the shape of the calvaria was observed. Molecular analysis of the genes affected in the frontal bones and mandibles from these mice identified several bone markers known to affect bone development, with a strong prediction for increased bone formation and mineralization in vivo. Interestingly, while a subset of these genes were similarly affected in frontal bones and mandibles (Sost, Mepe, Bglap, Alp, Ibsp, Agt), several genes, including Lect1 and Calca, were specifically affected in frontal bones. Consistent with these molecular alterations, cells isolated from the frontal bone of mutant mice exhibited increased differentiation and mineralization capacities ex vivo, supporting cell autonomous defects in neural crest cells. However, adult mutant animals exhibited decreased bone mineral density in both mandibles and calvaria, as well as a significant increase in bone porosity. Together, these observations suggest that mature osteoblasts in the adult respond to signals that regulate adult bone mass and remodeling. This study provides new downstream targets for Dlx3 in craniofacial bone, and gives additional evidence of the complex regulation of bone formation and homeostasis in the adult skeleton. PMID:22886599

  10. ADAM10 is essential for cranial neural crest-derived maxillofacial bone development

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    Tan, Yu, E-mail: tanyu2048@163.com; Fu, Runqing, E-mail: furunqing@sjtu.edu.cn; Liu, Jiaqiang, E-mail: liujqmj@163.com; Wu, Yong, E-mail: wyonger@gmail.com; Wang, Bo, E-mail: wb228@126.com; Jiang, Ning, E-mail: 179639060@qq.com; Nie, Ping, E-mail: nieping1011@sina.com; Cao, Haifeng, E-mail: 0412chf@163.com; Yang, Zhi, E-mail: wcums1981@163.com; Fang, Bing, E-mail: fangbing@sjtu.edu.cn

    2016-07-08

    Growth disorders of the craniofacial bones may lead to craniofacial deformities. The majority of maxillofacial bones are derived from cranial neural crest cells via intramembranous bone formation. Any interruption of the craniofacial skeleton development process might lead to craniofacial malformation. A disintegrin and metalloprotease (ADAM)10 plays an essential role in organ development and tissue integrity in different organs. However, little is known about its function in craniofacial bone formation. Therefore, we investigated the role of ADAM10 in the developing craniofacial skeleton, particularly during typical mandibular bone development. First, we showed that ADAM10 was expressed in a specific area of the craniofacial bone and that the expression pattern dynamically changed during normal mouse craniofacial development. Then, we crossed wnt1-cre transgenic mice with adam10-flox mice to generate ADAM10 conditional knockout mice. The stereomicroscopic, radiographic, and von Kossa staining results showed that conditional knockout of ADAM10 in cranial neural crest cells led to embryonic death, craniofacial dysmorphia and bone defects. Furthermore, we demonstrated that impaired mineralization could be triggered by decreased osteoblast differentiation, increased cell death. Overall, these findings show that ADAM10 plays an essential role in craniofacial bone development. -- Highlights: •We firstly reported that ADAM10 was essentially involved in maxillofacial bone development. •ADAM10 cKO mice present craniofacial dysmorphia and bone defects. •Impaired osteoblast differentiation,proliferation and apoptosis underlie the bone deformity.

  11. ADAM10 is essential for cranial neural crest-derived maxillofacial bone development

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    Tan, Yu; Fu, Runqing; Liu, Jiaqiang; Wu, Yong; Wang, Bo; Jiang, Ning; Nie, Ping; Cao, Haifeng; Yang, Zhi; Fang, Bing

    2016-01-01

    Growth disorders of the craniofacial bones may lead to craniofacial deformities. The majority of maxillofacial bones are derived from cranial neural crest cells via intramembranous bone formation. Any interruption of the craniofacial skeleton development process might lead to craniofacial malformation. A disintegrin and metalloprotease (ADAM)10 plays an essential role in organ development and tissue integrity in different organs. However, little is known about its function in craniofacial bone formation. Therefore, we investigated the role of ADAM10 in the developing craniofacial skeleton, particularly during typical mandibular bone development. First, we showed that ADAM10 was expressed in a specific area of the craniofacial bone and that the expression pattern dynamically changed during normal mouse craniofacial development. Then, we crossed wnt1-cre transgenic mice with adam10-flox mice to generate ADAM10 conditional knockout mice. The stereomicroscopic, radiographic, and von Kossa staining results showed that conditional knockout of ADAM10 in cranial neural crest cells led to embryonic death, craniofacial dysmorphia and bone defects. Furthermore, we demonstrated that impaired mineralization could be triggered by decreased osteoblast differentiation, increased cell death. Overall, these findings show that ADAM10 plays an essential role in craniofacial bone development. -- Highlights: •We firstly reported that ADAM10 was essentially involved in maxillofacial bone development. •ADAM10 cKO mice present craniofacial dysmorphia and bone defects. •Impaired osteoblast differentiation,proliferation and apoptosis underlie the bone deformity.

  12. Interaction of adult human neural crest-derived stem cells with a nanoporous titanium surface is sufficient to induce their osteogenic differentiation

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    Matthias Schürmann

    2014-07-01

    Full Text Available Osteogenic differentiation of various adult stem cell populations such as neural crest-derived stem cells is of great interest in the context of bone regeneration. Ideally, exogenous differentiation should mimic an endogenous differentiation process, which is partly mediated by topological cues. To elucidate the osteoinductive potential of porous substrates with different pore diameters (30 nm, 100 nm, human neural crest-derived stem cells isolated from the inferior nasal turbinate were cultivated on the surface of nanoporous titanium covered membranes without additional chemical or biological osteoinductive cues. As controls, flat titanium without any topological features and osteogenic medium was used. Cultivation of human neural crest-derived stem cells on 30 nm pores resulted in osteogenic differentiation as demonstrated by alkaline phosphatase activity after seven days as well as by calcium deposition after 3 weeks of cultivation. In contrast, cultivation on flat titanium and on membranes equipped with 100 nm pores was not sufficient to induce osteogenic differentiation. Moreover, we demonstrate an increase of osteogenic transcripts including Osterix, Osteocalcin and up-regulation of Integrin β1 and α2 in the 30 nm pore approach only. Thus, transplantation of stem cells pre-cultivated on nanostructured implants might improve the clinical outcome by support of the graft adherence and acceleration of the regeneration process.

  13. In vitro cementoblast-like differentiation of postmigratory neural crest-derived p75{sup +} stem cells with dental follicle cell conditioned medium

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    Wen, Xiujie; Liu, Luchuan; Deng, Manjing; Liu, Rui; Zhang, Li; Nie, Xin, E-mail: dr.xinnie@gmail.com

    2015-09-10

    Cranial neural crest-derived cells (CNCCs) play important role in epithelial–mesenchymal interactions during tooth morphogenesis. However, the heterogeneity of CNCCs and their tendency to spontaneously differentiate along smooth muscle or osteoblast lineages in vitro limit further understanding of their biological properties. We studied the differentiation properties of isolated rat embryonic postmigratory CNCCs, expressing p75 neurotrophin receptor (p75NTR). These p75NTR positive (p75{sup +}) CNCCs, isolated using fluorescence activated cell sorter, exhibited fibroblast-like morphology and characteristics of mesenchymal stem cells. Incubation of p75{sup +} CNCCs in dental follicle cell conditioned medium (DFCCM) combined with dentin non-collagenous proteins (dNCPs), altered their morphological features to cementoblast-like appearance. These cells also showed low proliferative activity, high ALP activity and significantly increased calcified nodule formation. Markers related to mineralization or specific to cementoblast lineage were highly expressed in dNCPs/DFCCM-treated p75{sup +} cells, suggesting their differentiation along cementoblast-like lineage. p75{sup +} stem cells selected from postmigratory CNCCs represent a pure stem cell population and could be used as a stem cell model for in vitro studies due to their intrinsic ability to differentiate to neuronal cells and transform from neuroectoderm to ectomesenchyme. They can provide a potential stem cell resource for tooth engineering studies and help to further investigate mechanisms of epithelial–mesenchymal interactions in tooth morphogenesis. - Highlights: • Cranial neural crest-derived cells (CNCCs) take part in tooth morphogenesis. • positive (p75{sup +}) CNCCs are fibroblast-like and resemble mesenchymal stem cells. • p75{sup +} CNCCs in dental follicle cell medium (DFCCM/dNCP) appear like cementoblasts. • DFCCM/dNCP-treated p75{sup +} cells express cementoblast specific mineralization

  14. In vitro cementoblast-like differentiation of postmigratory neural crest-derived p75+ stem cells with dental follicle cell conditioned medium

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    Wen, Xiujie; Liu, Luchuan; Deng, Manjing; Liu, Rui; Zhang, Li; Nie, Xin

    2015-01-01

    Cranial neural crest-derived cells (CNCCs) play important role in epithelial–mesenchymal interactions during tooth morphogenesis. However, the heterogeneity of CNCCs and their tendency to spontaneously differentiate along smooth muscle or osteoblast lineages in vitro limit further understanding of their biological properties. We studied the differentiation properties of isolated rat embryonic postmigratory CNCCs, expressing p75 neurotrophin receptor (p75NTR). These p75NTR positive (p75 + ) CNCCs, isolated using fluorescence activated cell sorter, exhibited fibroblast-like morphology and characteristics of mesenchymal stem cells. Incubation of p75 + CNCCs in dental follicle cell conditioned medium (DFCCM) combined with dentin non-collagenous proteins (dNCPs), altered their morphological features to cementoblast-like appearance. These cells also showed low proliferative activity, high ALP activity and significantly increased calcified nodule formation. Markers related to mineralization or specific to cementoblast lineage were highly expressed in dNCPs/DFCCM-treated p75 + cells, suggesting their differentiation along cementoblast-like lineage. p75 + stem cells selected from postmigratory CNCCs represent a pure stem cell population and could be used as a stem cell model for in vitro studies due to their intrinsic ability to differentiate to neuronal cells and transform from neuroectoderm to ectomesenchyme. They can provide a potential stem cell resource for tooth engineering studies and help to further investigate mechanisms of epithelial–mesenchymal interactions in tooth morphogenesis. - Highlights: • Cranial neural crest-derived cells (CNCCs) take part in tooth morphogenesis. • positive (p75 + ) CNCCs are fibroblast-like and resemble mesenchymal stem cells. • p75 + CNCCs in dental follicle cell medium (DFCCM/dNCP) appear like cementoblasts. • DFCCM/dNCP-treated p75 + cells express cementoblast specific mineralization markers. • p75 + cells are pure stem

  15. Decreased proliferative, migrative and neuro-differentiative potential of postnatal rat enteric neural crest-derived cells during culture in vitro

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    Yu, Hui; Pan, Wei-Kang; Zheng, Bai-Jun; Wang, Huai-Jie; Chen, Xin-Lin; Liu, Yong; Gao, Ya

    2016-01-01

    A growing body of evidence supports the potential use of enteric neural crest-derived cells (ENCCs) as a cell replacement therapy for Hirschsprung's disease. Based on previous observations of robust propagation of primary ENCCs, as opposed to their progeny, it is suggested that their therapeutic potential after in vitro expansion may be restricted. We therefore examined the growth and differentiation activities and phenotypic characteristics of continuous ENCC cultures. ENCCs were isolated from the intestines of postnatal rats and were identified using an immunocytochemical approach. During continuous ENCC culture expansion, proliferation, migration, apoptosis, and differentiation potentials were monitored. The Cell Counting Kit-8 was used for assessment of ENCC vitality, Transwell inserts for cell migration, immunocytochemistry for cell counts and identification, and flow cytometry for apoptosis. Over six continuous generations, ENCC proliferation potency was reduced and with prolonged culture, the ratio of migratory ENCCs was decreased. The percentage of apoptosis showed an upward trend with prolonged intragenerational culture, but showed a downward trend with prolonged culture of combined generations. Furthermore, the percentage of peripherin"+ cells decreased whilst the percentage of GFAP"+ cells increased with age. The results demonstrated that alterations in ENCC growth characteristics occur with increased culture time, which may partially account for the poor results of proposed cell therapies. - Highlights: • Differences were identified between primary and daughter ENCCs. • Daughter ENCCs had reduced proliferation, migration and differentiation. • Daughter ENCCs also had increased apoptosis. • These altered characteristics warrant further investigation.

  16. Renal excretion of iodine-131 labelled meta-iodobenzylguanidine and metabolites after therapeutic doses in patients suffering from different neural crest-derived tumours

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    Wafelman, A.R.; Hoefnagel, C.A.; Maessen, H.J.M.; Maes, R.A.A.; Beijnen, J.H.

    1997-01-01

    Iodine-131 labelled meta-iodobenzylguanidine ([ 131 I[MIBG) is used for diagnostic scintigraphy and radionuclide therapy of neural crest-derived tumours. After administration of therapeutic doses of [ 131 I[MIBG (3.1-7.5 GBq) to 17 patients (n=32 courses), aged 2-73 years, 56%±10%, 73%±11%, 80%±10% and 83%±10% of the dose was cumulatively excreted as total radioactivity in urine at t=24 h, 48 h, 72 h and 96 h, respectively. Except for two adult patients, who showed excretion of 14%-18% of [ 131 I[meta-iodohippuric acid ([ 131 I[MIHA), the cumulatively excreted radioactivity consisted of >85% [ 131 I[MIBG, with 6% of the dose excreted as free [ 131 I[iodide, 4% as [ 131 I[MIHA and 2.5% as an unknown iodine-131 labelled metabolite. Cumulative renal excretion rates of total radioactivity and of [ 131 I[MIBG appeared to be higher in neuroblastoma and phaeochromocytoma patients than in carcinoid patients. Based on the excretion of small amounts of [ 131 I[meta-iodobenzoic acid in two patients, a possible metabolic pathway for [ 131 I[MIBG is suggested. The degree of metabolism was not related to the extent of liver uptake of radioactivity. (orig.). With 2 figs., 5 tabs

  17. Combination of exogenous cell transplantation and 5-HT4 receptor agonism induce endogenous enteric neural crest-derived cells in a rat hypoganglionosis model.

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    Yu, Hui; Zheng, Bai-Jun; Pan, Wei-Kang; Wang, Huai-Jie; Xie, Chong; Zhao, Yu-Ying; Chen, Xin-Lin; Liu, Yong; Gao, Ya

    2017-02-01

    Enteric neural crest-derived cells (ENCCs) can migrate into endogenous ganglia and differentiate into progeny cells, and have even partially rescued bowel function; however, poor reliability and limited functional recovery after ENCC transplantation have yet to be addressed. Here, we investigated the induction of endogenous ENCCs by combining exogenous ENCC transplantation with a 5-HT 4 receptor agonist mosapride in a rat model of hypoganglionosis, established by benzalkonium chloride treatment. ENCCs, isolated from the gut of newborn rats, were labeled with a lentiviral eGFP reporter. ENCCs and rats were treated with the 5-HT 4 receptor agonist/antagonist. The labeled ENCCs were then transplanted into the muscular layer of benzalkonium chloride-treated colons. At given days post-intervention, colonic tissue samples were removed for histological analysis. ENCCs and neurons were detected by eGFP expression and immunoreactivity to p75 NTR and peripherin, respectively. eGFP-positive ENCCs and neurons could survive and maintain levels of fluorescence after transplantation. With longer times post-intervention, the number of peripherin-positive cells gradually increased in all groups. Significantly more peripherin-positive cells were found following ENCCs plus mosapride treatment, compared with the other groups. These results show that exogenous ENCCs combined with the 5-HT 4 receptor agonist effectively induced endogenous ENCCs proliferation and differentiation in a rat hypoganglionosis model. Copyright © 2016 Elsevier Inc. All rights reserved.

  18. Decreased proliferative, migrative and neuro-differentiative potential of postnatal rat enteric neural crest-derived cells during culture in vitro

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    Yu, Hui [Department of Pediatric Surgery, the Second Affiliated Hospital, Xi’an Jiaotong University, No 157, Xi Wu Road, Xi’an 710004, Shaanxi (China); Institute of Neurobiology, Environment and Genes Related to Diseases Key Laboratory of Chinese Ministry of Education, Xi’an Jiaotong University, No 96, Yan Ta Xi Road, Xi’an 710061, Shaanxi (China); Pan, Wei-Kang; Zheng, Bai-Jun; Wang, Huai-Jie [Department of Pediatric Surgery, the Second Affiliated Hospital, Xi’an Jiaotong University, No 157, Xi Wu Road, Xi’an 710004, Shaanxi (China); Chen, Xin-Lin; Liu, Yong [Institute of Neurobiology, Environment and Genes Related to Diseases Key Laboratory of Chinese Ministry of Education, Xi’an Jiaotong University, No 96, Yan Ta Xi Road, Xi’an 710061, Shaanxi (China); Gao, Ya, E-mail: ygao@mail.xjtu.edu.cn [Department of Pediatric Surgery, the Second Affiliated Hospital, Xi’an Jiaotong University, No 157, Xi Wu Road, Xi’an 710004, Shaanxi (China)

    2016-05-01

    A growing body of evidence supports the potential use of enteric neural crest-derived cells (ENCCs) as a cell replacement therapy for Hirschsprung's disease. Based on previous observations of robust propagation of primary ENCCs, as opposed to their progeny, it is suggested that their therapeutic potential after in vitro expansion may be restricted. We therefore examined the growth and differentiation activities and phenotypic characteristics of continuous ENCC cultures. ENCCs were isolated from the intestines of postnatal rats and were identified using an immunocytochemical approach. During continuous ENCC culture expansion, proliferation, migration, apoptosis, and differentiation potentials were monitored. The Cell Counting Kit-8 was used for assessment of ENCC vitality, Transwell inserts for cell migration, immunocytochemistry for cell counts and identification, and flow cytometry for apoptosis. Over six continuous generations, ENCC proliferation potency was reduced and with prolonged culture, the ratio of migratory ENCCs was decreased. The percentage of apoptosis showed an upward trend with prolonged intragenerational culture, but showed a downward trend with prolonged culture of combined generations. Furthermore, the percentage of peripherin{sup +} cells decreased whilst the percentage of GFAP{sup +} cells increased with age. The results demonstrated that alterations in ENCC growth characteristics occur with increased culture time, which may partially account for the poor results of proposed cell therapies. - Highlights: • Differences were identified between primary and daughter ENCCs. • Daughter ENCCs had reduced proliferation, migration and differentiation. • Daughter ENCCs also had increased apoptosis. • These altered characteristics warrant further investigation.

  19. Differentiation defect in neural crest-derived smooth muscle cells in patients with aortopathy associated with bicuspid aortic valves

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    Jiao Jiao

    2016-08-01

    Full Text Available Individuals with bicuspid aortic valves (BAV are at a higher risk of developing thoracic aortic aneurysms (TAA than patients with trileaflet aortic valves (TAV. The aneurysms associated with BAV most commonly involve the ascending aorta and spare the descending aorta. Smooth muscle cells (SMCs in the ascending and descending aorta arise from neural crest (NC and paraxial mesoderm (PM, respectively. We hypothesized defective differentiation of the neural crest stem cells (NCSCs-derived SMCs but not paraxial mesoderm cells (PMCs-derived SMCs contributes to the aortopathy associated with BAV. When induced pluripotent stem cells (iPSCs from BAV/TAA patients were differentiated into NCSC-derived SMCs, these cells demonstrated significantly decreased expression of marker of SMC differentiation (MYH11 and impaired contraction compared to normal control. In contrast, the PMC-derived SMCs were similar to control cells in these aspects. The NCSC-SMCs from the BAV/TAA also showed decreased TGF-β signaling based on phosphorylation of SMAD2, and increased mTOR signaling. Inhibition of mTOR pathway using rapamycin rescued the aberrant differentiation. Our data demonstrates that decreased differentiation and contraction of patient's NCSC-derived SMCs may contribute to that aortopathy associated with BAV.

  20. Combination of exogenous cell transplantation and 5-HT{sub 4} receptor agonism induce endogenous enteric neural crest-derived cells in a rat hypoganglionosis model

    Energy Technology Data Exchange (ETDEWEB)

    Yu, Hui [Department of Pediatric Surgery, the Second Affiliated Hospital, Xi’an Jiaotong University, No 157, Xi Wu Road, Xi’an 710004, Shaanxi (China); Institute of Neurobiology, Environment and Genes Related to Diseases Key Laboratory of Chinese Ministry of Education, Xi’an Jiaotong University, No 96, Yan Ta Xi Road, Xi’an 710061, Shaanxi (China); Zheng, Bai-Jun; Pan, Wei-Kang; Wang, Huai-Jie; Xie, Chong; Zhao, Yu-Ying [Department of Pediatric Surgery, the Second Affiliated Hospital, Xi’an Jiaotong University, No 157, Xi Wu Road, Xi’an 710004, Shaanxi (China); Chen, Xin-Lin; Liu, Yong [Institute of Neurobiology, Environment and Genes Related to Diseases Key Laboratory of Chinese Ministry of Education, Xi’an Jiaotong University, No 96, Yan Ta Xi Road, Xi’an 710061, Shaanxi (China); Gao, Ya, E-mail: ygao@mail.xjtu.edu.cn [Department of Pediatric Surgery, the Second Affiliated Hospital, Xi’an Jiaotong University, No 157, Xi Wu Road, Xi’an 710004, Shaanxi (China)

    2017-02-01

    Enteric neural crest-derived cells (ENCCs) can migrate into endogenous ganglia and differentiate into progeny cells, and have even partially rescued bowel function; however, poor reliability and limited functional recovery after ENCC transplantation have yet to be addressed. Here, we investigated the induction of endogenous ENCCs by combining exogenous ENCC transplantation with a 5-HT{sub 4} receptor agonist mosapride in a rat model of hypoganglionosis, established by benzalkonium chloride treatment. ENCCs, isolated from the gut of newborn rats, were labeled with a lentiviral eGFP reporter. ENCCs and rats were treated with the 5-HT{sub 4} receptor agonist/antagonist. The labeled ENCCs were then transplanted into the muscular layer of benzalkonium chloride-treated colons. At given days post-intervention, colonic tissue samples were removed for histological analysis. ENCCs and neurons were detected by eGFP expression and immunoreactivity to p75{sup NTR} and peripherin, respectively. eGFP-positive ENCCs and neurons could survive and maintain levels of fluorescence after transplantation. With longer times post-intervention, the number of peripherin-positive cells gradually increased in all groups. Significantly more peripherin-positive cells were found following ENCCs plus mosapride treatment, compared with the other groups. These results show that exogenous ENCCs combined with the 5-HT{sub 4} receptor agonist effectively induced endogenous ENCCs proliferation and differentiation in a rat hypoganglionosis model. - Highlights: • Survival and differentiation of exogenous ENCCs in treated colons. • With longer times post-intervention, the number of ENCCs and their progeny cells gradually increased. • Exogenous ENCCs combined with the 5-HT4 receptor agonist ffectively induced ENCCs proliferation and differentiation.

  1. Combination of exogenous cell transplantation and 5-HT4 receptor agonism induce endogenous enteric neural crest-derived cells in a rat hypoganglionosis model

    International Nuclear Information System (INIS)

    Yu, Hui; Zheng, Bai-Jun; Pan, Wei-Kang; Wang, Huai-Jie; Xie, Chong; Zhao, Yu-Ying; Chen, Xin-Lin; Liu, Yong; Gao, Ya

    2017-01-01

    Enteric neural crest-derived cells (ENCCs) can migrate into endogenous ganglia and differentiate into progeny cells, and have even partially rescued bowel function; however, poor reliability and limited functional recovery after ENCC transplantation have yet to be addressed. Here, we investigated the induction of endogenous ENCCs by combining exogenous ENCC transplantation with a 5-HT 4 receptor agonist mosapride in a rat model of hypoganglionosis, established by benzalkonium chloride treatment. ENCCs, isolated from the gut of newborn rats, were labeled with a lentiviral eGFP reporter. ENCCs and rats were treated with the 5-HT 4 receptor agonist/antagonist. The labeled ENCCs were then transplanted into the muscular layer of benzalkonium chloride-treated colons. At given days post-intervention, colonic tissue samples were removed for histological analysis. ENCCs and neurons were detected by eGFP expression and immunoreactivity to p75 NTR and peripherin, respectively. eGFP-positive ENCCs and neurons could survive and maintain levels of fluorescence after transplantation. With longer times post-intervention, the number of peripherin-positive cells gradually increased in all groups. Significantly more peripherin-positive cells were found following ENCCs plus mosapride treatment, compared with the other groups. These results show that exogenous ENCCs combined with the 5-HT 4 receptor agonist effectively induced endogenous ENCCs proliferation and differentiation in a rat hypoganglionosis model. - Highlights: • Survival and differentiation of exogenous ENCCs in treated colons. • With longer times post-intervention, the number of ENCCs and their progeny cells gradually increased. • Exogenous ENCCs combined with the 5-HT4 receptor agonist ffectively induced ENCCs proliferation and differentiation.

  2. FGF8 signaling sustains progenitor status and multipotency of cranial neural crest-derived mesenchymal cells in vivo and in vitro

    Science.gov (United States)

    Shao, Meiying; Liu, Chao; Song, Yingnan; Ye, Wenduo; He, Wei; Yuan, Guohua; Gu, Shuping; Lin, Congxin; Ma, Liang; Zhang, Yanding; Tian, Weidong; Hu, Tao; Chen, YiPing

    2015-01-01

    The cranial neural crest (CNC) cells play a vital role in craniofacial development and regeneration. They are multi-potent progenitors, being able to differentiate into various types of tissues. Both pre-migratory and post-migratory CNC cells are plastic, taking on diverse fates by responding to different inductive signals. However, what sustains the multipotency of CNC cells and derivatives remains largely unknown. In this study, we present evidence that FGF8 signaling is able to sustain progenitor status and multipotency of CNC-derived mesenchymal cells both in vivo and in vitro. We show that augmented FGF8 signaling in pre-migratory CNC cells prevents cell differentiation and organogenesis in the craniofacial region by maintaining their progenitor status. CNC-derived mesenchymal cells with Fgf8 overexpression or control cells in the presence of exogenous FGF8 exhibit prolonged survival, proliferation, and multi-potent differentiation capability in cell cultures. Remarkably, exogenous FGF8 also sustains the capability of CNC-derived mesenchymal cells to participate in organogenesis such as odontogenesis. Furthermore, FGF8-mediated signaling strongly promotes adipogenesis but inhibits osteogenesis of CNC-derived mesenchymal cells in vitro. Our results reveal a specific role for FGF8 in the maintenance of progenitor status and in fate determination of CNC cells, implicating a potential application in expansion and fate manipulation of CNC-derived cells in stem cell-based craniofacial regeneration. PMID:26243590

  3. DNA methyltransferase 3b is dispensable for mouse neural crest development.

    Directory of Open Access Journals (Sweden)

    Bridget T Jacques-Fricke

    Full Text Available The neural crest is a population of multipotent cells that migrates extensively throughout vertebrate embryos to form diverse structures. Mice mutant for the de novo DNA methyltransferase DNMT3b exhibit defects in two neural crest derivatives, the craniofacial skeleton and cardiac ventricular septum, suggesting that DNMT3b activity is necessary for neural crest development. Nevertheless, the requirement for DNMT3b specifically in neural crest cells, as opposed to interacting cell types, has not been determined. Using a conditional DNMT3b allele crossed to the neural crest cre drivers Wnt1-cre and Sox10-cre, neural crest DNMT3b mutants were generated. In both neural crest-specific and fully DNMT3b-mutant embryos, cranial neural crest cells exhibited only subtle migration defects, with increased numbers of dispersed cells trailing organized streams in the head. In spite of this, the resulting cranial ganglia, craniofacial skeleton, and heart developed normally when neural crest cells lacked DNMT3b. This indicates that DNTM3b is not necessary in cranial neural crest cells for their development. We conclude that defects in neural crest derivatives in DNMT3b mutant mice reflect a requirement for DNMT3b in lineages such as the branchial arch mesendoderm or the cardiac mesoderm that interact with neural crest cells during formation of these structures.

  4. Stage-specific control of neural crest stem cell proliferation by the small rho GTPases Cdc42 and Rac1

    DEFF Research Database (Denmark)

    Fuchs, Sebastian; Herzog, Dominik; Sumara, Grzegorz

    2009-01-01

    -renewal and proliferation of later stage, but not early migratory NCSCs. This stage-specific requirement for small Rho GTPases is due to changes in NCSCs that, during development, acquire responsiveness to mitogenic EGF acting upstream of both Cdc42 and Rac1. Thus, our data reveal distinct mechanisms for growth control......The neural crest (NC) generates a variety of neural and non-neural tissues during vertebrate development. Both migratory NC cells and their target structures contain cells with stem cell features. Here we show that these populations of neural crest-derived stem cells (NCSCs) are differentially...

  5. Diprosopia revisited in light of the recognized role of neural crest cells in facial development.

    Science.gov (United States)

    Carles, D; Weichhold, W; Alberti, E M; Léger, F; Pigeau, F; Horovitz, J

    1995-01-01

    The aim of this study is to compare the theory of embryogenesis of the face with human diprosopia. This peculiar form of conjoined twinning is of great interest because 1) only the facial structures are duplicated and 2) almost all cases have a rather monomorphic pattern. The hypothesis is that an initial duplication of the notochord leads to two neural plates and subsequently duplicated neural crests. In those conditions, derivatives of the neural crests will be partially or totally duplicated; therefore, in diprosopia, the duplicated facial structures would be considered to be neural crest derivatives. If these structures are identical to those that are experimentally demonstrated to be neural crest derivatives in animals, these findings are an argument to apply this theory of facial embryogenesis in man. Serial horizontal sections of the face of two diprosopic fetuses (11 and 21 weeks gestation) were studied macro- and microscopically to determine the external and internal structures that are duplicated. Complete postmortem examination was performed in search for additional malformations. The face of both fetuses showed a very similar morphologic pattern with duplication of ocular, nasal, and buccal structures. The nasal fossae and the anterior part of the tongue were also duplicated, albeit the posterior part and the pharyngolaryngeal structures were unique. Additional facial clefts were present in both fetuses. Extrafacial anomalies were represented by a craniorachischisis, two fused vertebral columns and, in the older fetus, by a complex cardiac malformation morphologically identical to malformations induced by removal or grafting of additional cardiac neural crest cells in animals. These pathological findings could identify the facial structures that are neural crest derivatives in man. They are similar to those experimentally demonstrated to be neural crest derivatives in animals. In this respect, diprosopia could be considered as the end of a spectrum

  6. Requirement for Foxd3 in the maintenance of neural crest progenitors.

    Science.gov (United States)

    Teng, Lu; Mundell, Nathan A; Frist, Audrey Y; Wang, Qiaohong; Labosky, Patricia A

    2008-05-01

    Understanding the molecular mechanisms of stem cell maintenance is crucial for the ultimate goal of manipulating stem cells for the treatment of disease. Foxd3 is required early in mouse embryogenesis; Foxd3(-/-) embryos fail around the time of implantation, cells of the inner cell mass cannot be maintained in vitro, and blastocyst-derived stem cell lines cannot be established. Here, we report that Foxd3 is required for maintenance of the multipotent mammalian neural crest. Using tissue-specific deletion of Foxd3 in the neural crest, we show that Foxd3(flox/-); Wnt1-Cre mice die perinatally with a catastrophic loss of neural crest-derived structures. Cranial neural crest tissues are either missing or severely reduced in size, the peripheral nervous system consists of reduced dorsal root ganglia and cranial nerves, and the entire gastrointestinal tract is devoid of neural crest derivatives. These results demonstrate a global role for this transcriptional repressor in all aspects of neural crest maintenance along the anterior-posterior axis, and establish an unprecedented molecular link between multiple divergent progenitor lineages of the mammalian embryo.

  7. Neural Networks for protein Structure Prediction

    DEFF Research Database (Denmark)

    Bohr, Henrik

    1998-01-01

    This is a review about neural network applications in bioinformatics. Especially the applications to protein structure prediction, e.g. prediction of secondary structures, prediction of surface structure, fold class recognition and prediction of the 3-dimensional structure of protein backbones...

  8. Development of teeth in chick embryos after mouse neural crest transplantations.

    Science.gov (United States)

    Mitsiadis, Thimios A; Chéraud, Yvonnick; Sharpe, Paul; Fontaine-Pérus, Josiane

    2003-05-27

    Teeth were lost in birds 70-80 million years ago. Current thinking holds that it is the avian cranial neural crest-derived mesenchyme that has lost odontogenic capacity, whereas the oral epithelium retains the signaling properties required to induce odontogenesis. To investigate the odontogenic capacity of ectomesenchyme, we have used neural tube transplantations from mice to chick embryos to replace the chick neural crest cell populations with mouse neural crest cells. The mouse/chick chimeras obtained show evidence of tooth formation showing that avian oral epithelium is able to induce a nonavian developmental program in mouse neural crest-derived mesenchymal cells.

  9. Polarized DIS Structure Functions from Neural Networks

    International Nuclear Information System (INIS)

    Del Debbio, L.; Guffanti, A.; Piccione, A.

    2007-01-01

    We present a parametrization of polarized Deep-Inelastic-Scattering (DIS) structure functions based on Neural Networks. The parametrization provides a bias-free determination of the probability measure in the space of structure functions, which retains information on experimental errors and correlations. As an example we discuss the application of this method to the study of the structure function g 1 p (x,Q 2 )

  10. Adipose stromal cells contain phenotypically distinct adipogenic progenitors derived from neural crest.

    Directory of Open Access Journals (Sweden)

    Yoshihiro Sowa

    Full Text Available Recent studies have shown that adipose-derived stromal/stem cells (ASCs contain phenotypically and functionally heterogeneous subpopulations of cells, but their developmental origin and their relative differentiation potential remain elusive. In the present study, we aimed at investigating how and to what extent the neural crest contributes to ASCs using Cre-loxP-mediated fate mapping. ASCs harvested from subcutaneous fat depots of either adult P0-Cre/or Wnt1-Cre/Floxed-reporter mice contained a few neural crest-derived ASCs (NCDASCs. This subpopulation of cells was successfully expanded in vitro under standard culture conditions and their growth rate was comparable to non-neural crest derivatives. Although NCDASCs were positive for several mesenchymal stem cell markers as non-neural crest derivatives, they exhibited a unique bipolar or multipolar morphology with higher expression of markers for both neural crest progenitors (p75NTR, Nestin, and Sox2 and preadipocytes (CD24, CD34, S100, Pref-1, GATA2, and C/EBP-delta. NCDASCs were able to differentiate into adipocytes with high efficiency but their osteogenic and chondrogenic potential was markedly attenuated, indicating their commitment to adipogenesis. In vivo, a very small proportion of adipocytes were originated from the neural crest. In addition, p75NTR-positive neural crest-derived cells were identified along the vessels within the subcutaneous adipose tissue, but they were negative for mural and endothelial markers. These results demonstrate that ASCs contain neural crest-derived adipocyte-restricted progenitors whose phenotype is distinct from that of non-neural crest derivatives.

  11. Identification of Non-Linear Structures using Recurrent Neural Networks

    DEFF Research Database (Denmark)

    Kirkegaard, Poul Henning; Nielsen, Søren R. K.; Hansen, H. I.

    Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure.......Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure....

  12. Identification of Non-Linear Structures using Recurrent Neural Networks

    DEFF Research Database (Denmark)

    Kirkegaard, Poul Henning; Nielsen, Søren R. K.; Hansen, H. I.

    1995-01-01

    Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure.......Two different partially recurrent neural networks structured as Multi Layer Perceptrons (MLP) are investigated for time domain identification of a non-linear structure....

  13. The structural neural substrate of subjective happiness.

    Science.gov (United States)

    Sato, Wataru; Kochiyama, Takanori; Uono, Shota; Kubota, Yasutaka; Sawada, Reiko; Yoshimura, Sayaka; Toichi, Motomi

    2015-11-20

    Happiness is a subjective experience that is an ultimate goal for humans. Psychological studies have shown that subjective happiness can be measured reliably and consists of emotional and cognitive components. However, the neural substrates of subjective happiness remain unclear. To investigate this issue, we used structural magnetic resonance imaging and questionnaires that assessed subjective happiness, the intensity of positive and negative emotional experiences, and purpose in life. We found a positive relationship between the subjective happiness score and gray matter volume in the right precuneus. Moreover, the same region showed an association with the combined positive and negative emotional intensity and purpose in life scores. Our findings suggest that the precuneus mediates subjective happiness by integrating the emotional and cognitive components of happiness.

  14. Psychedelics Promote Structural and Functional Neural Plasticity

    Directory of Open Access Journals (Sweden)

    Calvin Ly

    2018-06-01

    Full Text Available Summary: Atrophy of neurons in the prefrontal cortex (PFC plays a key role in the pathophysiology of depression and related disorders. The ability to promote both structural and functional plasticity in the PFC has been hypothesized to underlie the fast-acting antidepressant properties of the dissociative anesthetic ketamine. Here, we report that, like ketamine, serotonergic psychedelics are capable of robustly increasing neuritogenesis and/or spinogenesis both in vitro and in vivo. These changes in neuronal structure are accompanied by increased synapse number and function, as measured by fluorescence microscopy and electrophysiology. The structural changes induced by psychedelics appear to result from stimulation of the TrkB, mTOR, and 5-HT2A signaling pathways and could possibly explain the clinical effectiveness of these compounds. Our results underscore the therapeutic potential of psychedelics and, importantly, identify several lead scaffolds for medicinal chemistry efforts focused on developing plasticity-promoting compounds as safe, effective, and fast-acting treatments for depression and related disorders. : Ly et al. demonstrate that psychedelic compounds such as LSD, DMT, and DOI increase dendritic arbor complexity, promote dendritic spine growth, and stimulate synapse formation. These cellular effects are similar to those produced by the fast-acting antidepressant ketamine and highlight the potential of psychedelics for treating depression and related disorders. Keywords: neural plasticity, psychedelic, spinogenesis, synaptogenesis, depression, LSD, DMT, ketamine, noribogaine, MDMA

  15. Development of teeth in chick embryos after mouse neural crest transplantations

    OpenAIRE

    Mitsiadis, Thimios A.; Chéraud, Yvonnick; Sharpe, Paul; Fontaine-Pérus, Josiane

    2003-01-01

    Teeth were lost in birds 70–80 million years ago. Current thinking holds that it is the avian cranial neural crest-derived mesenchyme that has lost odontogenic capacity, whereas the oral epithelium retains the signaling properties required to induce odontogenesis. To investigate the odontogenic capacity of ectomesenchyme, we have used neural tube transplantations from mice to chick embryos to replace the chick neural crest cell populations with mouse neural crest cells. The mouse/chick ...

  16. Combining neural networks for protein secondary structure prediction

    DEFF Research Database (Denmark)

    Riis, Søren Kamaric

    1995-01-01

    In this paper structured neural networks are applied to the problem of predicting the secondary structure of proteins. A hierarchical approach is used where specialized neural networks are designed for each structural class and then combined using another neural network. The submodels are designed...... by using a priori knowledge of the mapping between protein building blocks and the secondary structure and by using weight sharing. Since none of the individual networks have more than 600 adjustable weights over-fitting is avoided. When ensembles of specialized experts are combined the performance...

  17. Modeling Broadband Microwave Structures by Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    V. Otevrel

    2004-06-01

    Full Text Available The paper describes the exploitation of feed-forward neural networksand recurrent neural networks for replacing full-wave numerical modelsof microwave structures in complex microwave design tools. Building aneural model, attention is turned to the modeling accuracy and to theefficiency of building a model. Dealing with the accuracy, we describea method of increasing it by successive completing a training set.Neural models are mutually compared in order to highlight theiradvantages and disadvantages. As a reference model for comparisons,approximations based on standard cubic splines are used. Neural modelsare used to replace both the time-domain numeric models and thefrequency-domain ones.

  18. Early perception and structural identity: neural implementation

    Science.gov (United States)

    Ligomenides, Panos A.

    1992-03-01

    It is suggested that there exists a minimal set of rules for the perceptual composition of the unending variety of spatio-temporal patterns in our perceptual world. Driven by perceptual discernment of "sudden change" and "unexpectedness", these rules specify conditions (such as co-linearity and virtual continuation) for perceptual grouping and for recursive compositions of perceptual "modalities" and "signatures". Beginning with a smallset of primitive perceptual elements, selected contextually at some relevant level of abstraction, perceptual compositions can graduate to an unlimited variety of spatiotemporal signatures, scenes and activities. Local discernible elements, often perceptually ambiguous by themselves, may be integrated into spatiotemporal compositions, which generate unambiguous perceptual separations between "figure" and "ground". The definition of computational algorithms for the effective instantiation of the rules of perceptual grouping remains a principal problem. In this paper we present our approach for solving the problem of perceptual recognition within the confines of one-D variational profiles. More specifically, concerning "early" (pre-attentive) recognition, we define the "structural identity of a k-norm, k ∈ K,"--SkID--as a tool for discerning and locating the instantiation of spatiotemporal objects or events. The SkID profile also serves a s a reference coordinate framework for the "perceptual focusing of attention" and the eventual assessment of resemblance. Neural network implementations of pre-attentive and attentive recognition are also discussed briefly. Our principles are exemplified by application to one-D perceptual profiles, which allows simplicity of definitions and of the rules of perceptual composition.

  19. Linking neural and symbolic representation and processing of conceptual structures

    NARCIS (Netherlands)

    van der Velde, Frank; Forth, Jamie; Nazareth, Deniece S.; Wiggins, Geraint A.

    2017-01-01

    We compare and discuss representations in two cognitive architectures aimed at representing and processing complex conceptual (sentence-like) structures. First is the Neural Blackboard Architecture (NBA), which aims to account for representation and processing of complex and combinatorial conceptual

  20. A Possible Neural Representation of Mathematical Group Structures.

    Science.gov (United States)

    Pomi, Andrés

    2016-09-01

    Every cognitive activity has a neural representation in the brain. When humans deal with abstract mathematical structures, for instance finite groups, certain patterns of activity are occurring in the brain that constitute their neural representation. A formal neurocognitive theory must account for all the activities developed by our brain and provide a possible neural representation for them. Associative memories are neural network models that have a good chance of achieving a universal representation of cognitive phenomena. In this work, we present a possible neural representation of mathematical group structures based on associative memory models that store finite groups through their Cayley graphs. A context-dependent associative memory stores the transitions between elements of the group when multiplied by each generator of a given presentation of the group. Under a convenient election of the vector basis mapping the elements of the group in the neural activity, the input of a vector corresponding to a generator of the group collapses the context-dependent rectangular matrix into a virtual square permutation matrix that is the matrix representation of the generator. This neural representation corresponds to the regular representation of the group, in which to each element is assigned a permutation matrix. This action of the generator on the memory matrix can also be seen as the dissection of the corresponding monochromatic subgraph of the Cayley graph of the group, and the adjacency matrix of this subgraph is the permutation matrix corresponding to the generator.

  1. Linking Neural and Symbolic Representation and Processing of Conceptual Structures

    Directory of Open Access Journals (Sweden)

    Frank van der Velde

    2017-08-01

    Full Text Available We compare and discuss representations in two cognitive architectures aimed at representing and processing complex conceptual (sentence-like structures. First is the Neural Blackboard Architecture (NBA, which aims to account for representation and processing of complex and combinatorial conceptual structures in the brain. Second is IDyOT (Information Dynamics of Thinking, which derives sentence-like structures by learning statistical sequential regularities over a suitable corpus. Although IDyOT is designed at a level more abstract than the neural, so it is a model of cognitive function, rather than neural processing, there are strong similarities between the composite structures developed in IDyOT and the NBA. We hypothesize that these similarities form the basis of a combined architecture in which the individual strengths of each architecture are integrated. We outline and discuss the characteristics of this combined architecture, emphasizing the representation and processing of conceptual structures.

  2. Structured Memory for Neural Turing Machines

    OpenAIRE

    Zhang, Wei; Yu, Yang; Zhou, Bowen

    2015-01-01

    Neural Turing Machines (NTM) contain memory component that simulates "working memory" in the brain to store and retrieve information to ease simple algorithms learning. So far, only linearly organized memory is proposed, and during experiments, we observed that the model does not always converge, and overfits easily when handling certain tasks. We think memory component is key to some faulty behaviors of NTM, and better organization of memory component could help fight those problems. In this...

  3. Neural entrainment to the rhythmic structure of music.

    Science.gov (United States)

    Tierney, Adam; Kraus, Nina

    2015-02-01

    The neural resonance theory of musical meter explains musical beat tracking as the result of entrainment of neural oscillations to the beat frequency and its higher harmonics. This theory has gained empirical support from experiments using simple, abstract stimuli. However, to date there has been no empirical evidence for a role of neural entrainment in the perception of the beat of ecologically valid music. Here we presented participants with a single pop song with a superimposed bassoon sound. This stimulus was either lined up with the beat of the music or shifted away from the beat by 25% of the average interbeat interval. Both conditions elicited a neural response at the beat frequency. However, although the on-the-beat condition elicited a clear response at the first harmonic of the beat, this frequency was absent in the neural response to the off-the-beat condition. These results support a role for neural entrainment in tracking the metrical structure of real music and show that neural meter tracking can be disrupted by the presentation of contradictory rhythmic cues.

  4. Neural-Fitted TD-Leaf Learning for Playing Othello With Structured Neural Networks

    NARCIS (Netherlands)

    van den Dries, Sjoerd; Wiering, Marco A.

    This paper describes a methodology for quickly learning to play games at a strong level. The methodology consists of a novel combination of three techniques, and a variety of experiments on the game of Othello demonstrates their usefulness. First, structures or topologies in neural network

  5. Combinatorial structures and processing in neural blackboard architectures

    NARCIS (Netherlands)

    van der Velde, Frank; van der Velde, Frank; de Kamps, Marc; Besold, Tarek R.; d'Avila Garcez, Artur; Marcus, Gary F.; Miikkulainen, Risto

    2015-01-01

    We discuss and illustrate Neural Blackboard Architectures (NBAs) as the basis for variable binding and combinatorial processing the brain. We focus on the NBA for sentence structure. NBAs are based on the notion that conceptual representations are in situ, hence cannot be copied or transported.

  6. Discriminative training of self-structuring hidden control neural models

    DEFF Research Database (Denmark)

    Sørensen, Helge Bjarup Dissing; Hartmann, Uwe; Hunnerup, Preben

    1995-01-01

    This paper presents a new training algorithm for self-structuring hidden control neural (SHC) models. The SHC models were trained non-discriminatively for speech recognition applications. Better recognition performance can generally be achieved, if discriminative training is applied instead. Thus...... we developed a discriminative training algorithm for SHC models, where each SHC model for a specific speech pattern is trained with utterances of the pattern to be recognized and with other utterances. The discriminative training of SHC neural models has been tested on the TIDIGITS database...

  7. Psychedelics Promote Structural and Functional Neural Plasticity.

    Science.gov (United States)

    Ly, Calvin; Greb, Alexandra C; Cameron, Lindsay P; Wong, Jonathan M; Barragan, Eden V; Wilson, Paige C; Burbach, Kyle F; Soltanzadeh Zarandi, Sina; Sood, Alexander; Paddy, Michael R; Duim, Whitney C; Dennis, Megan Y; McAllister, A Kimberley; Ori-McKenney, Kassandra M; Gray, John A; Olson, David E

    2018-06-12

    Atrophy of neurons in the prefrontal cortex (PFC) plays a key role in the pathophysiology of depression and related disorders. The ability to promote both structural and functional plasticity in the PFC has been hypothesized to underlie the fast-acting antidepressant properties of the dissociative anesthetic ketamine. Here, we report that, like ketamine, serotonergic psychedelics are capable of robustly increasing neuritogenesis and/or spinogenesis both in vitro and in vivo. These changes in neuronal structure are accompanied by increased synapse number and function, as measured by fluorescence microscopy and electrophysiology. The structural changes induced by psychedelics appear to result from stimulation of the TrkB, mTOR, and 5-HT2A signaling pathways and could possibly explain the clinical effectiveness of these compounds. Our results underscore the therapeutic potential of psychedelics and, importantly, identify several lead scaffolds for medicinal chemistry efforts focused on developing plasticity-promoting compounds as safe, effective, and fast-acting treatments for depression and related disorders. Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.

  8. Optimal neural networks for protein-structure prediction

    International Nuclear Information System (INIS)

    Head-Gordon, T.; Stillinger, F.H.

    1993-01-01

    The successful application of neural-network algorithms for prediction of protein structure is stymied by three problem areas: the sparsity of the database of known protein structures, poorly devised network architectures which make the input-output mapping opaque, and a global optimization problem in the multiple-minima space of the network variables. We present a simplified polypeptide model residing in two dimensions with only two amino-acid types, A and B, which allows the determination of the global energy structure for all possible sequences of pentamer, hexamer, and heptamer lengths. This model simplicity allows us to compile a complete structural database and to devise neural networks that reproduce the tertiary structure of all sequences with absolute accuracy and with the smallest number of network variables. These optimal networks reveal that the three problem areas are convoluted, but that thoughtful network designs can actually deconvolute these detrimental traits to provide network algorithms that genuinely impact on the ability of the network to generalize or learn the desired mappings. Furthermore, the two-dimensional polypeptide model shows sufficient chemical complexity so that transfer of neural-network technology to more realistic three-dimensional proteins is evident

  9. Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields.

    Science.gov (United States)

    Wang, Sheng; Peng, Jian; Ma, Jianzhu; Xu, Jinbo

    2016-01-11

    Protein secondary structure (SS) prediction is important for studying protein structure and function. When only the sequence (profile) information is used as input feature, currently the best predictors can obtain ~80% Q3 accuracy, which has not been improved in the past decade. Here we present DeepCNF (Deep Convolutional Neural Fields) for protein SS prediction. DeepCNF is a Deep Learning extension of Conditional Neural Fields (CNF), which is an integration of Conditional Random Fields (CRF) and shallow neural networks. DeepCNF can model not only complex sequence-structure relationship by a deep hierarchical architecture, but also interdependency between adjacent SS labels, so it is much more powerful than CNF. Experimental results show that DeepCNF can obtain ~84% Q3 accuracy, ~85% SOV score, and ~72% Q8 accuracy, respectively, on the CASP and CAMEO test proteins, greatly outperforming currently popular predictors. As a general framework, DeepCNF can be used to predict other protein structure properties such as contact number, disorder regions, and solvent accessibility.

  10. Parallel protein secondary structure prediction based on neural networks.

    Science.gov (United States)

    Zhong, Wei; Altun, Gulsah; Tian, Xinmin; Harrison, Robert; Tai, Phang C; Pan, Yi

    2004-01-01

    Protein secondary structure prediction has a fundamental influence on today's bioinformatics research. In this work, binary and tertiary classifiers of protein secondary structure prediction are implemented on Denoeux belief neural network (DBNN) architecture. Hydrophobicity matrix, orthogonal matrix, BLOSUM62 and PSSM (position specific scoring matrix) are experimented separately as the encoding schemes for DBNN. The experimental results contribute to the design of new encoding schemes. New binary classifier for Helix versus not Helix ( approximately H) for DBNN produces prediction accuracy of 87% when PSSM is used for the input profile. The performance of DBNN binary classifier is comparable to other best prediction methods. The good test results for binary classifiers open a new approach for protein structure prediction with neural networks. Due to the time consuming task of training the neural networks, Pthread and OpenMP are employed to parallelize DBNN in the hyperthreading enabled Intel architecture. Speedup for 16 Pthreads is 4.9 and speedup for 16 OpenMP threads is 4 in the 4 processors shared memory architecture. Both speedup performance of OpenMP and Pthread is superior to that of other research. With the new parallel training algorithm, thousands of amino acids can be processed in reasonable amount of time. Our research also shows that hyperthreading technology for Intel architecture is efficient for parallel biological algorithms.

  11. The relationship between structural and functional connectivity: graph theoretical analysis of an EEG neural mass model

    NARCIS (Netherlands)

    Ponten, S.C.; Daffertshofer, A.; Hillebrand, A.; Stam, C.J.

    2010-01-01

    We investigated the relationship between structural network properties and both synchronization strength and functional characteristics in a combined neural mass and graph theoretical model of the electroencephalogram (EEG). Thirty-two neural mass models (NMMs), each representing the lump activity

  12. Neural crest contributions to the lamprey head

    Science.gov (United States)

    McCauley, David W.; Bronner-Fraser, Marianne

    2003-01-01

    The neural crest is a vertebrate-specific cell population that contributes to the facial skeleton and other derivatives. We have performed focal DiI injection into the cranial neural tube of the developing lamprey in order to follow the migratory pathways of discrete groups of cells from origin to destination and to compare neural crest migratory pathways in a basal vertebrate to those of gnathostomes. The results show that the general pathways of cranial neural crest migration are conserved throughout the vertebrates, with cells migrating in streams analogous to the mandibular and hyoid streams. Caudal branchial neural crest cells migrate ventrally as a sheet of cells from the hindbrain and super-pharyngeal region of the neural tube and form a cylinder surrounding a core of mesoderm in each pharyngeal arch, similar to that seen in zebrafish and axolotl. In addition to these similarities, we also uncovered important differences. Migration into the presumptive caudal branchial arches of the lamprey involves both rostral and caudal movements of neural crest cells that have not been described in gnathostomes, suggesting that barriers that constrain rostrocaudal movement of cranial neural crest cells may have arisen after the agnathan/gnathostome split. Accordingly, neural crest cells from a single axial level contributed to multiple arches and there was extensive mixing between populations. There was no apparent filling of neural crest derivatives in a ventral-to-dorsal order, as has been observed in higher vertebrates, nor did we find evidence of a neural crest contribution to cranial sensory ganglia. These results suggest that migratory constraints and additional neural crest derivatives arose later in gnathostome evolution.

  13. Deep Convolutional Neural Networks: Structure, Feature Extraction and Training

    Directory of Open Access Journals (Sweden)

    Namatēvs Ivars

    2017-12-01

    Full Text Available Deep convolutional neural networks (CNNs are aimed at processing data that have a known network like topology. They are widely used to recognise objects in images and diagnose patterns in time series data as well as in sensor data classification. The aim of the paper is to present theoretical and practical aspects of deep CNNs in terms of convolution operation, typical layers and basic methods to be used for training and learning. Some practical applications are included for signal and image classification. Finally, the present paper describes the proposed block structure of CNN for classifying crucial features from 3D sensor data.

  14. Structural neural substrates of reading the mind in the eyes

    Directory of Open Access Journals (Sweden)

    Wataru eSato

    2016-04-01

    Full Text Available The ability to read the minds of others in their eyes plays an important role in human adaptation to social environments. Behavioral studies have resulted in the development of a test to measure this ability (Reading the Mind in the Eyes Test, revised version; Eyes Test, and have demonstrated that this ability is consistent over time. Although functional neuroimaging studies revealed brain activation while performing the Eyes Test, the structural neural substrates supporting consistent performance on the Eyes Test remain unclear. In this study we assessed the Eyes Test and analyzed structural magnetic resonance images using voxel-based morphometry in healthy participants. Test performance was positively associated with the gray matter volumes of the dorsomedial prefrontal cortex, inferior parietal lobule (temporoparietal junction, and precuneus in the left hemisphere. These results suggest that the fronto-temporoparietal network structures support the consistent ability to read the mind in the eyes.

  15. Anatomical study of lumbar vertebral pedicle and adjacent neural structures

    Directory of Open Access Journals (Sweden)

    Matuoka Cláudia Maria

    2002-01-01

    Full Text Available For the evaluation of the Lumbar pedicle morphometry and its relation to the neural structures, 14 male adult cadavers were dissected, and the size of the lumbar pedicle was assessed by measuring its sagittal and transversal diameter. It was found that the size of the pedicle increases from L2 to L5, both in the sagittal and transversal diameter, the first bigger. The relation of the lumbar pedicle to the neural structures was evaluated by measuring the distance between dura-mater and the pedicle medial area, the distance between the most distal area of the pedicle and the nerve root that appears under it, and , to obtain in an indirect way, the distance between the pedicle apex and the nerve root that appears over it. The acquired results showed that the distance between the most distal area of the pedicle and the nerve root that appears under it, and the distance between the pedicle medial area and dura-mater, do not increase from L2 to L5, and they are in average 1,98 and 3,02 respectively. The distance between the pedicle apex and the nerve root that appears over it, increases from L2 to L5, varying from 13,64 in L2 to 21,62 in L5. The location of the spinal ganglion in relation to the pedicle has also been found, and 87% of the spinal ganglions are located in the foraminal zone.

  16. Radiant energy during infrared neural stimulation at the target structure

    Science.gov (United States)

    Richter, Claus-Peter; Rajguru, Suhrud; Stafford, Ryan; Stock, Stuart R.

    2013-03-01

    Infrared neural stimulation (INS) describes a method, by which an infrared laser is used to stimulate neurons. The major benefit of INS over stimulating neurons with electrical current is its spatial selectivity. To translate the technique into a clinical application it is important to know the energy required to stimulate the neural structure. With this study we provide measurements of the radiant exposure, at the target structure that is required to stimulate the auditory neurons. Flat polished fibers were inserted into scala tympani so that the spiral ganglion was in front of the optical fiber. Angle polished fibers were inserted along scala tympani, and rotating the beveled surface of the fiber allowed the radiation beam to be directed perpendicular to the spiral ganglion. The radiant exposure for stimulation at the modiolus for flat and angle polished fibers averaged 6.78+/-2.15 mJ/cm2. With the angle polished fibers, a 90º change in the orientation of the optical beam from an orientation that resulted in an INS-evoked maximum response, resulted in a 50% drop in the response amplitude. When the orientation of the beam was changed by 180º, such that it was directed opposite to the orientation with the maxima, minimum response amplitude was observed.

  17. Cascaded bidirectional recurrent neural networks for protein secondary structure prediction.

    Science.gov (United States)

    Chen, Jinmiao; Chaudhari, Narendra

    2007-01-01

    Protein secondary structure (PSS) prediction is an important topic in bioinformatics. Our study on a large set of non-homologous proteins shows that long-range interactions commonly exist and negatively affect PSS prediction. Besides, we also reveal strong correlations between secondary structure (SS) elements. In order to take into account the long-range interactions and SS-SS correlations, we propose a novel prediction system based on cascaded bidirectional recurrent neural network (BRNN). We compare the cascaded BRNN against another two BRNN architectures, namely the original BRNN architecture used for speech recognition as well as Pollastri's BRNN that was proposed for PSS prediction. Our cascaded BRNN achieves an overall three state accuracy Q3 of 74.38\\%, and reaches a high Segment OVerlap (SOV) of 66.0455. It outperforms the original BRNN and Pollastri's BRNN in both Q3 and SOV. Specifically, it improves the SOV score by 4-6%.

  18. A modular structure to accident identification using neural networks

    International Nuclear Information System (INIS)

    Duque Estrada, Cassius Rodrigo

    2005-01-01

    This work uses the accident identification method based on Artificial Neural Networks (ANN) as basic blocks of a modular structure, allowing the inclusion of new accidents to be identified without modifying the ANN already trained. This structure comprises several modules for accident identification and one module for analysis. Each identification module follows the structure of the basic block. The identification modules are responsible for the recognition of an accident belonging to the specific set of events for which it were trained. The analysis module processes the output from the identification module to determine the system response. In order to test this structure it was proposed a transient identification problem comprising fifty accidents distributed in five identification modules. The results have demonstrated that the accident identification method used as basic block of a modular structure allows the inclusion of new sets of accidents, or variations of a same accident, without modifying the ANN already trained. For this, it is enough to include into the system an specific module for this new set of accidents. (author)

  19. The neural crest, a multifaceted structure of the vertebrates.

    Science.gov (United States)

    Dupin, Elisabeth; Le Douarin, Nicole M

    2014-09-01

    In this review, several features of the cells originating from the lateral borders of the primitive neural anlagen, the neural crest (NC) are considered. Among them, their multipotentiality, which together with their migratory properties, leads them to colonize the developing body and to participate in the development of many tissues and organs. The in vitro analysis of the developmental capacities of single NC cells (NCC) showed that they present several analogies with the hematopoietic cells whose differentiation involves the activity of stem cells endowed with different arrays of developmental potentialities. The permanence of such NC stem cells in the adult organism raises the problem of their role at that stage of life. The NC has appeared during evolution in the vertebrate phylum and is absent in their Protocordates ancestors. The major role of the NCC in the development of the vertebrate head points to a critical role for this structure in the remarkable diversification and radiation of this group of animals. © 2014 Wiley Periodicals, Inc.

  20. Direct Adaptive Aircraft Control Using Dynamic Cell Structure Neural Networks

    Science.gov (United States)

    Jorgensen, Charles C.

    1997-01-01

    A Dynamic Cell Structure (DCS) Neural Network was developed which learns topology representing networks (TRNS) of F-15 aircraft aerodynamic stability and control derivatives. The network is integrated into a direct adaptive tracking controller. The combination produces a robust adaptive architecture capable of handling multiple accident and off- nominal flight scenarios. This paper describes the DCS network and modifications to the parameter estimation procedure. The work represents one step towards an integrated real-time reconfiguration control architecture for rapid prototyping of new aircraft designs. Performance was evaluated using three off-line benchmarks and on-line nonlinear Virtual Reality simulation. Flight control was evaluated under scenarios including differential stabilator lock, soft sensor failure, control and stability derivative variations, and air turbulence.

  1. Simple neural substrate predicts complex rhythmic structure in duetting birds

    Science.gov (United States)

    Amador, Ana; Trevisan, M. A.; Mindlin, G. B.

    2005-09-01

    Horneros (Furnarius Rufus) are South American birds well known for their oven-looking nests and their ability to sing in couples. Previous work has analyzed the rhythmic organization of the duets, unveiling a mathematical structure behind the songs. In this work we analyze in detail an extended database of duets. The rhythms of the songs are compatible with the dynamics presented by a wide class of dynamical systems: forced excitable systems. Compatible with this nonlinear rule, we build a biologically inspired model for how the neural and the anatomical elements may interact to produce the observed rhythmic patterns. This model allows us to synthesize songs presenting the acoustic and rhythmic features observed in real songs. We also make testable predictions in order to support our hypothesis.

  2. Resolution of Singularities Introduced by Hierarchical Structure in Deep Neural Networks.

    Science.gov (United States)

    Nitta, Tohru

    2017-10-01

    We present a theoretical analysis of singular points of artificial deep neural networks, resulting in providing deep neural network models having no critical points introduced by a hierarchical structure. It is considered that such deep neural network models have good nature for gradient-based optimization. First, we show that there exist a large number of critical points introduced by a hierarchical structure in deep neural networks as straight lines, depending on the number of hidden layers and the number of hidden neurons. Second, we derive a sufficient condition for deep neural networks having no critical points introduced by a hierarchical structure, which can be applied to general deep neural networks. It is also shown that the existence of critical points introduced by a hierarchical structure is determined by the rank and the regularity of weight matrices for a specific class of deep neural networks. Finally, two kinds of implementation methods of the sufficient conditions to have no critical points are provided. One is a learning algorithm that can avoid critical points introduced by the hierarchical structure during learning (called avoidant learning algorithm). The other is a neural network that does not have some critical points introduced by the hierarchical structure as an inherent property (called avoidant neural network).

  3. Effects of turbidity on the neural structures of two closely related ...

    African Journals Online (AJOL)

    The neural structures of the sister species Pseudobarbus afer and P. asper were compared. P. afer, a redfin minnow which inhabits clear, perennial mountain streams, was found to have larger neural structures related to vision than P. asper, which inhabits turbid, intermittent streams of the Gamtoos River system, ...

  4. Learning Orthographic Structure With Sequential Generative Neural Networks.

    Science.gov (United States)

    Testolin, Alberto; Stoianov, Ivilin; Sperduti, Alessandro; Zorzi, Marco

    2016-04-01

    Learning the structure of event sequences is a ubiquitous problem in cognition and particularly in language. One possible solution is to learn a probabilistic generative model of sequences that allows making predictions about upcoming events. Though appealing from a neurobiological standpoint, this approach is typically not pursued in connectionist modeling. Here, we investigated a sequential version of the restricted Boltzmann machine (RBM), a stochastic recurrent neural network that extracts high-order structure from sensory data through unsupervised generative learning and can encode contextual information in the form of internal, distributed representations. We assessed whether this type of network can extract the orthographic structure of English monosyllables by learning a generative model of the letter sequences forming a word training corpus. We show that the network learned an accurate probabilistic model of English graphotactics, which can be used to make predictions about the letter following a given context as well as to autonomously generate high-quality pseudowords. The model was compared to an extended version of simple recurrent networks, augmented with a stochastic process that allows autonomous generation of sequences, and to non-connectionist probabilistic models (n-grams and hidden Markov models). We conclude that sequential RBMs and stochastic simple recurrent networks are promising candidates for modeling cognition in the temporal domain. Copyright © 2015 Cognitive Science Society, Inc.

  5. Convolution neural-network-based detection of lung structures

    Science.gov (United States)

    Hasegawa, Akira; Lo, Shih-Chung B.; Freedman, Matthew T.; Mun, Seong K.

    1994-05-01

    Chest radiography is one of the most primary and widely used techniques in diagnostic imaging. Nowadays with the advent of digital radiology, the digital medical image processing techniques for digital chest radiographs have attracted considerable attention, and several studies on the computer-aided diagnosis (CADx) as well as on the conventional image processing techniques for chest radiographs have been reported. In the automatic diagnostic process for chest radiographs, it is important to outline the areas of the lungs, the heart, and the diaphragm. This is because the original chest radiograph is composed of important anatomic structures and, without knowing exact positions of the organs, the automatic diagnosis may result in unexpected detections. The automatic extraction of an anatomical structure from digital chest radiographs can be a useful tool for (1) the evaluation of heart size, (2) automatic detection of interstitial lung diseases, (3) automatic detection of lung nodules, and (4) data compression, etc. Based on the clearly defined boundaries of heart area, rib spaces, rib positions, and rib cage extracted, one should be able to use this information to facilitate the tasks of the CADx on chest radiographs. In this paper, we present an automatic scheme for the detection of lung field from chest radiographs by using a shift-invariant convolution neural network. A novel algorithm for smoothing boundaries of lungs is also presented.

  6. Protein 8-class secondary structure prediction using conditional neural fields.

    Science.gov (United States)

    Wang, Zhiyong; Zhao, Feng; Peng, Jian; Xu, Jinbo

    2011-10-01

    Compared with the protein 3-class secondary structure (SS) prediction, the 8-class prediction gains less attention and is also much more challenging, especially for proteins with few sequence homologs. This paper presents a new probabilistic method for 8-class SS prediction using conditional neural fields (CNFs), a recently invented probabilistic graphical model. This CNF method not only models the complex relationship between sequence features and SS, but also exploits the interdependency among SS types of adjacent residues. In addition to sequence profiles, our method also makes use of non-evolutionary information for SS prediction. Tested on the CB513 and RS126 data sets, our method achieves Q8 accuracy of 64.9 and 64.7%, respectively, which are much better than the SSpro8 web server (51.0 and 48.0%, respectively). Our method can also be used to predict other structure properties (e.g. solvent accessibility) of a protein or the SS of RNA. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  7. The Energy Coding of a Structural Neural Network Based on the Hodgkin-Huxley Model.

    Science.gov (United States)

    Zhu, Zhenyu; Wang, Rubin; Zhu, Fengyun

    2018-01-01

    Based on the Hodgkin-Huxley model, the present study established a fully connected structural neural network to simulate the neural activity and energy consumption of the network by neural energy coding theory. The numerical simulation result showed that the periodicity of the network energy distribution was positively correlated to the number of neurons and coupling strength, but negatively correlated to signal transmitting delay. Moreover, a relationship was established between the energy distribution feature and the synchronous oscillation of the neural network, which showed that when the proportion of negative energy in power consumption curve was high, the synchronous oscillation of the neural network was apparent. In addition, comparison with the simulation result of structural neural network based on the Wang-Zhang biophysical model of neurons showed that both models were essentially consistent.

  8. Diagnostic Classifiers: Revealing how Neural Networks Process Hierarchical Structure

    NARCIS (Netherlands)

    Veldhoen, S.; Hupkes, D.; Zuidema, W.

    2016-01-01

    We investigate how neural networks can be used for hierarchical, compositional semantics. To this end, we define the simple but nontrivial artificial task of processing nested arithmetic expressions and study whether different types of neural networks can learn to add and subtract. We find that

  9. Identification of neural structures involved in stuttering using vibrotactile feedback.

    Science.gov (United States)

    Cheadle, Oliver; Sorger, Clarissa; Howell, Peter

    Feedback delivered over auditory and vibratory afferent pathways has different effects on the fluency of people who stutter (PWS). These features were exploited to investigate the neural structures involved in stuttering. The speech signal vibrated locations on the body (vibrotactile feedback, VTF). Eleven PWS read passages under VTF and control (no-VTF) conditions. All combinations of vibration amplitude, synchronous or delayed VTF and vibrator position (hand, sternum or forehead) were presented. Control conditions were performed at the beginning, middle and end of test sessions. Stuttering rate, but not speaking rate, differed between the control and VTF conditions. Notably, speaking rate did not change between when VTF was delayed versus when it was synchronous in contrast with what happens with auditory feedback. This showed that cerebellar mechanisms, which are affected when auditory feedback is delayed, were not implicated in the fluency-enhancing effects of VTF, suggesting that there is a second fluency-enhancing mechanism. Copyright © 2018 Elsevier Inc. All rights reserved.

  10. Interactive extraction of neural structures with user-guided morphological diffusion

    KAUST Repository

    Yong Wan,; Otsuna, H.; Chi-Bin Chien,; Hansen, C.

    2012-01-01

    Extracting neural structures with their fine details from confocal volumes is essential to quantitative analysis in neurobiology research. Despite the abundance of various segmentation methods and tools, for complex neural structures, both manual and semi-automatic methods are ine ective either in full 3D or when user interactions are restricted to 2D slices. Novel interaction techniques and fast algorithms are demanded by neurobiologists to interactively and intuitively extract neural structures from confocal data. In this paper, we present such an algorithm-technique combination, which lets users interactively select desired structures from visualization results instead of 2D slices. By integrating the segmentation functions with a confocal visualization tool neurobiologists can easily extract complex neural structures within their typical visualization workflow.

  11. Interactive extraction of neural structures with user-guided morphological diffusion

    KAUST Repository

    Yong Wan,

    2012-10-01

    Extracting neural structures with their fine details from confocal volumes is essential to quantitative analysis in neurobiology research. Despite the abundance of various segmentation methods and tools, for complex neural structures, both manual and semi-automatic methods are ine ective either in full 3D or when user interactions are restricted to 2D slices. Novel interaction techniques and fast algorithms are demanded by neurobiologists to interactively and intuitively extract neural structures from confocal data. In this paper, we present such an algorithm-technique combination, which lets users interactively select desired structures from visualization results instead of 2D slices. By integrating the segmentation functions with a confocal visualization tool neurobiologists can easily extract complex neural structures within their typical visualization workflow.

  12. Neurally Derived Tissues in Xenopus laevis Embryos Exhibit a Consistent Bioelectrical Left-Right Asymmetry

    Directory of Open Access Journals (Sweden)

    Vaibhav P. Pai

    2012-01-01

    Full Text Available Consistent left-right asymmetry in organ morphogenesis is a fascinating aspect of bilaterian development. Although embryonic patterning of asymmetric viscera, heart, and brain is beginning to be understood, less is known about possible subtle asymmetries present in anatomically identical paired structures. We investigated two important developmental events: physiological controls of eye development and specification of neural crest derivatives, in Xenopus laevis embryos. We found that the striking hyperpolarization of transmembrane potential (Vmem demarcating eye induction usually occurs in the right eye field first. This asymmetry is randomized by perturbing visceral left-right patterning, suggesting that eye asymmetry is linked to mechanisms establishing primary laterality. Bilateral misexpression of a depolarizing channel mRNA affects primarily the right eye, revealing an additional functional asymmetry in the control of eye patterning by Vmem. The ATP-sensitive K+ channel subunit transcript, SUR1, is asymmetrically expressed in the eye primordia, thus being a good candidate for the observed physiological asymmetries. Such subtle asymmetries are not only seen in the eye: consistent asymmetry was also observed in the migration of differentiated melanocytes on the left and right sides. These data suggest that even anatomically symmetrical structures may possess subtle but consistent laterality and interact with other developmental left-right patterning pathways.

  13. Structural reliability calculation method based on the dual neural network and direct integration method.

    Science.gov (United States)

    Li, Haibin; He, Yun; Nie, Xiaobo

    2018-01-01

    Structural reliability analysis under uncertainty is paid wide attention by engineers and scholars due to reflecting the structural characteristics and the bearing actual situation. The direct integration method, started from the definition of reliability theory, is easy to be understood, but there are still mathematics difficulties in the calculation of multiple integrals. Therefore, a dual neural network method is proposed for calculating multiple integrals in this paper. Dual neural network consists of two neural networks. The neural network A is used to learn the integrand function, and the neural network B is used to simulate the original function. According to the derivative relationships between the network output and the network input, the neural network B is derived from the neural network A. On this basis, the performance function of normalization is employed in the proposed method to overcome the difficulty of multiple integrations and to improve the accuracy for reliability calculations. The comparisons between the proposed method and Monte Carlo simulation method, Hasofer-Lind method, the mean value first-order second moment method have demonstrated that the proposed method is an efficient and accurate reliability method for structural reliability problems.

  14. Neural Crossroads in the Hematopoietic Stem Cell Niche.

    Science.gov (United States)

    Agarwala, Sobhika; Tamplin, Owen J

    2018-05-29

    The hematopoietic stem cell (HSC) niche supports steady-state hematopoiesis and responds to changing needs during stress and disease. The nervous system is an important regulator of the niche, and its influence is established early in development when stem cells are specified. Most research has focused on direct innervation of the niche, however recent findings show there are different modes of neural control, including globally by the central nervous system (CNS) and hormone release, locally by neural crest-derived mesenchymal stem cells, and intrinsically by hematopoietic cells that express neural receptors and neurotransmitters. Dysregulation between neural and hematopoietic systems can contribute to disease, however new therapeutic opportunities may be found among neuroregulator drugs repurposed to support hematopoiesis. Copyright © 2018 Elsevier Ltd. All rights reserved.

  15. Application of structured support vector machine backpropagation to a convolutional neural network for human pose estimation.

    Science.gov (United States)

    Witoonchart, Peerajak; Chongstitvatana, Prabhas

    2017-08-01

    In this study, for the first time, we show how to formulate a structured support vector machine (SSVM) as two layers in a convolutional neural network, where the top layer is a loss augmented inference layer and the bottom layer is the normal convolutional layer. We show that a deformable part model can be learned with the proposed structured SVM neural network by backpropagating the error of the deformable part model to the convolutional neural network. The forward propagation calculates the loss augmented inference and the backpropagation calculates the gradient from the loss augmented inference layer to the convolutional layer. Thus, we obtain a new type of convolutional neural network called an Structured SVM convolutional neural network, which we applied to the human pose estimation problem. This new neural network can be used as the final layers in deep learning. Our method jointly learns the structural model parameters and the appearance model parameters. We implemented our method as a new layer in the existing Caffe library. Copyright © 2017 Elsevier Ltd. All rights reserved.

  16. An object recognition using structured light and neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Byeong Gab; Kim, Dong Gi; Kang, E Sok [Chungnam National Univ., Taejon (Korea, Republic of); Yoon, Ji Sup [Korea Atomic Energy Research Institute, Taejon (Korea, Republic of)

    1998-12-31

    This paper presents a 3D image processing which uses neural networks to combine a 2D vision camera and a laser slit beam. A laser slit beam from laser source is slitted by a set of cylindrical lenses and the line image of the networks allow to get the 3D image parameters such as the size, the position and the orientation from the line image without knowing the camera intrinsic parameters. (author). 7 refs., 3 tabs., 5 figs.

  17. An object recognition using structured light and neural networks

    International Nuclear Information System (INIS)

    Kim, Byeong Gab; Kim, Dong Gi; Kang, E Sok; Yoon, Ji Sup

    1997-01-01

    This paper presents a 3D image processing which uses neural networks to combine a 2D vision camera and a laser slit beam. A laser slit beam from laser source is slitted by a set of cylindrical lenses and the line image of the networks allow to get the 3D image parameters such as the size, the position and the orientation from the line image without knowing the camera intrinsic parameters. (author). 7 refs., 3 tabs., 5 figs

  18. An object recognition using structured light and neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Byeong Gab; Kim, Dong Gi; Kang, E Sok [Chungnam National Univ., Taejon (Korea, Republic of); Yoon, Ji Sup [Korea Atomic Energy Research Institute, Taejon (Korea, Republic of)

    1997-12-31

    This paper presents a 3D image processing which uses neural networks to combine a 2D vision camera and a laser slit beam. A laser slit beam from laser source is slitted by a set of cylindrical lenses and the line image of the networks allow to get the 3D image parameters such as the size, the position and the orientation from the line image without knowing the camera intrinsic parameters. (author). 7 refs., 3 tabs., 5 figs.

  19. Deep Neural Network for Structural Prediction and Lane Detection in Traffic Scene.

    Science.gov (United States)

    Li, Jun; Mei, Xue; Prokhorov, Danil; Tao, Dacheng

    2017-03-01

    Hierarchical neural networks have been shown to be effective in learning representative image features and recognizing object classes. However, most existing networks combine the low/middle level cues for classification without accounting for any spatial structures. For applications such as understanding a scene, how the visual cues are spatially distributed in an image becomes essential for successful analysis. This paper extends the framework of deep neural networks by accounting for the structural cues in the visual signals. In particular, two kinds of neural networks have been proposed. First, we develop a multitask deep convolutional network, which simultaneously detects the presence of the target and the geometric attributes (location and orientation) of the target with respect to the region of interest. Second, a recurrent neuron layer is adopted for structured visual detection. The recurrent neurons can deal with the spatial distribution of visible cues belonging to an object whose shape or structure is difficult to explicitly define. Both the networks are demonstrated by the practical task of detecting lane boundaries in traffic scenes. The multitask convolutional neural network provides auxiliary geometric information to help the subsequent modeling of the given lane structures. The recurrent neural network automatically detects lane boundaries, including those areas containing no marks, without any explicit prior knowledge or secondary modeling.

  20. Lunar Circular Structure Classification from Chang 'e 2 High Resolution Lunar Images with Convolutional Neural Network

    Science.gov (United States)

    Zeng, X. G.; Liu, J. J.; Zuo, W.; Chen, W. L.; Liu, Y. X.

    2018-04-01

    Circular structures are widely distributed around the lunar surface. The most typical of them could be lunar impact crater, lunar dome, et.al. In this approach, we are trying to use the Convolutional Neural Network to classify the lunar circular structures from the lunar images.

  1. A Neural Network Model of the Structure and Dynamics of Human Personality

    Science.gov (United States)

    Read, Stephen J.; Monroe, Brian M.; Brownstein, Aaron L.; Yang, Yu; Chopra, Gurveen; Miller, Lynn C.

    2010-01-01

    We present a neural network model that aims to bridge the historical gap between dynamic and structural approaches to personality. The model integrates work on the structure of the trait lexicon, the neurobiology of personality, temperament, goal-based models of personality, and an evolutionary analysis of motives. It is organized in terms of two…

  2. A loop-based neural architecture for structured behavior encoding and decoding.

    Science.gov (United States)

    Gisiger, Thomas; Boukadoum, Mounir

    2018-02-01

    We present a new type of artificial neural network that generalizes on anatomical and dynamical aspects of the mammal brain. Its main novelty lies in its topological structure which is built as an array of interacting elementary motifs shaped like loops. These loops come in various types and can implement functions such as gating, inhibitory or executive control, or encoding of task elements to name a few. Each loop features two sets of neurons and a control region, linked together by non-recurrent projections. The two neural sets do the bulk of the loop's computations while the control unit specifies the timing and the conditions under which the computations implemented by the loop are to be performed. By functionally linking many such loops together, a neural network is obtained that may perform complex cognitive computations. To demonstrate the potential offered by such a system, we present two neural network simulations. The first illustrates the structure and dynamics of a single loop implementing a simple gating mechanism. The second simulation shows how connecting four loops in series can produce neural activity patterns that are sufficient to pass a simplified delayed-response task. We also show that this network reproduces electrophysiological measurements gathered in various regions of the brain of monkeys performing similar tasks. We also demonstrate connections between this type of neural network and recurrent or long short-term memory network models, and suggest ways to generalize them for future artificial intelligence research. Copyright © 2017 Elsevier Ltd. All rights reserved.

  3. Puzzle Pieces: Neural Structure and Function in Prader-Willi Syndrome

    Directory of Open Access Journals (Sweden)

    Katherine E. Manning

    2015-12-01

    Full Text Available Prader-Willi syndrome (PWS is a neurodevelopmental disorder of genomic imprinting, presenting with a behavioural phenotype encompassing hyperphagia, intellectual disability, social and behavioural difficulties, and propensity to psychiatric illness. Research has tended to focus on the cognitive and behavioural investigation of these features, and, with the exception of eating behaviour, the neural physiology is currently less well understood. A systematic review was undertaken to explore findings relating to neural structure and function in PWS, using search terms designed to encompass all published articles concerning both in vivo and post-mortem studies of neural structure and function in PWS. This supported the general paucity of research in this area, with many articles reporting case studies and qualitative descriptions or focusing solely on the overeating behaviour, although a number of systematic investigations were also identified. Research to date implicates a combination of subcortical and higher order structures in PWS, including those involved in processing reward, motivation, affect and higher order cognitive functions, with both anatomical and functional investigations indicating abnormalities. It appears likely that PWS involves aberrant activity across distributed neural networks. The characterisation of neural structure and function warrants both replication and further systematic study.

  4. Puzzle Pieces: Neural Structure and Function in Prader-Willi Syndrome

    Science.gov (United States)

    Manning, Katherine E.; Holland, Anthony J.

    2015-01-01

    Prader-Willi syndrome (PWS) is a neurodevelopmental disorder of genomic imprinting, presenting with a behavioural phenotype encompassing hyperphagia, intellectual disability, social and behavioural difficulties, and propensity to psychiatric illness. Research has tended to focus on the cognitive and behavioural investigation of these features, and, with the exception of eating behaviour, the neural physiology is currently less well understood. A systematic review was undertaken to explore findings relating to neural structure and function in PWS, using search terms designed to encompass all published articles concerning both in vivo and post-mortem studies of neural structure and function in PWS. This supported the general paucity of research in this area, with many articles reporting case studies and qualitative descriptions or focusing solely on the overeating behaviour, although a number of systematic investigations were also identified. Research to date implicates a combination of subcortical and higher order structures in PWS, including those involved in processing reward, motivation, affect and higher order cognitive functions, with both anatomical and functional investigations indicating abnormalities. It appears likely that PWS involves aberrant activity across distributed neural networks. The characterisation of neural structure and function warrants both replication and further systematic study. PMID:28943631

  5. Shared memories reveal shared structure in neural activity across individuals

    Science.gov (United States)

    Chen, J.; Leong, Y.C.; Honey, C.J.; Yong, C.H.; Norman, K.A.; Hasson, U.

    2016-01-01

    Our lives revolve around sharing experiences and memories with others. When different people recount the same events, how similar are their underlying neural representations? Participants viewed a fifty-minute movie, then verbally described the events during functional MRI, producing unguided detailed descriptions lasting up to forty minutes. As each person spoke, event-specific spatial patterns were reinstated in default-network, medial-temporal, and high-level visual areas. Individual event patterns were both highly discriminable from one another and similar between people, suggesting consistent spatial organization. In many high-order areas, patterns were more similar between people recalling the same event than between recall and perception, indicating systematic reshaping of percept into memory. These results reveal the existence of a common spatial organization for memories in high-level cortical areas, where encoded information is largely abstracted beyond sensory constraints; and that neural patterns during perception are altered systematically across people into shared memory representations for real-life events. PMID:27918531

  6. Adaptive neural network/expert system that learns fault diagnosis for different structures

    Science.gov (United States)

    Simon, Solomon H.

    1992-08-01

    Corporations need better real-time monitoring and control systems to improve productivity by watching quality and increasing production flexibility. The innovative technology to achieve this goal is evolving in the form artificial intelligence and neural networks applied to sensor processing, fusion, and interpretation. By using these advanced Al techniques, we can leverage existing systems and add value to conventional techniques. Neural networks and knowledge-based expert systems can be combined into intelligent sensor systems which provide real-time monitoring, control, evaluation, and fault diagnosis for production systems. Neural network-based intelligent sensor systems are more reliable because they can provide continuous, non-destructive monitoring and inspection. Use of neural networks can result in sensor fusion and the ability to model highly, non-linear systems. Improved models can provide a foundation for more accurate performance parameters and predictions. We discuss a research software/hardware prototype which integrates neural networks, expert systems, and sensor technologies and which can adapt across a variety of structures to perform fault diagnosis. The flexibility and adaptability of the prototype in learning two structures is presented. Potential applications are discussed.

  7. Hierarchical modular structure enhances the robustness of self-organized criticality in neural networks

    International Nuclear Information System (INIS)

    Wang Shengjun; Zhou Changsong

    2012-01-01

    One of the most prominent architecture properties of neural networks in the brain is the hierarchical modular structure. How does the structure property constrain or improve brain function? It is thought that operating near criticality can be beneficial for brain function. Here, we find that networks with modular structure can extend the parameter region of coupling strength over which critical states are reached compared to non-modular networks. Moreover, we find that one aspect of network function—dynamical range—is highest for the same parameter region. Thus, hierarchical modularity enhances robustness of criticality as well as function. However, too much modularity constrains function by preventing the neural networks from reaching critical states, because the modular structure limits the spreading of avalanches. Our results suggest that the brain may take advantage of the hierarchical modular structure to attain criticality and enhanced function. (paper)

  8. Classification of crystal structure using a convolutional neural network.

    Science.gov (United States)

    Park, Woon Bae; Chung, Jiyong; Jung, Jaeyoung; Sohn, Keemin; Singh, Satendra Pal; Pyo, Myoungho; Shin, Namsoo; Sohn, Kee-Sun

    2017-07-01

    A deep machine-learning technique based on a convolutional neural network (CNN) is introduced. It has been used for the classification of powder X-ray diffraction (XRD) patterns in terms of crystal system, extinction group and space group. About 150 000 powder XRD patterns were collected and used as input for the CNN with no handcrafted engineering involved, and thereby an appropriate CNN architecture was obtained that allowed determination of the crystal system, extinction group and space group. In sharp contrast with the traditional use of powder XRD pattern analysis, the CNN never treats powder XRD patterns as a deconvoluted and discrete peak position or as intensity data, but instead the XRD patterns are regarded as nothing but a pattern similar to a picture. The CNN interprets features that humans cannot recognize in a powder XRD pattern. As a result, accuracy levels of 81.14, 83.83 and 94.99% were achieved for the space-group, extinction-group and crystal-system classifications, respectively. The well trained CNN was then used for symmetry identification of unknown novel inorganic compounds.

  9. Chronic stress disrupts neural coherence between cortico-limbic structures

    Directory of Open Access Journals (Sweden)

    João Filipe Oliveira

    2013-02-01

    Full Text Available Chronic stress impairs cognitive function, namely on tasks that rely on the integrity of cortico-limbic networks. To unravel the functional impact of progressive stress in cortico-limbic networks we measured neural activity and spectral coherences between the ventral hippocampus (vHIP and the medial prefrontal cortex (mPFC in rats subjected to short term (STS and chronic unpredictable stress (CUS. CUS exposure consistently disrupted the spectral coherence between both areas for a wide range of frequencies, whereas STS exposure failed to trigger such effect. The chronic stress-induced coherence decrease correlated inversely with the vHIP power spectrum, but not with the mPFC power spectrum, which supports the view that hippocampal dysfunction is the primary event after stress exposure. Importantly, we additionally show that the variations in vHIP-to-mPFC coherence and power spectrum in the vHIP correlated with stress-induced behavioral deficits in a spatial reference memory task. Altogether, these findings result in an innovative readout to measure, and follow, the functional events that underlie the stress-induced reference memory impairments.

  10. Invariant 2D object recognition using the wavelet transform and structured neural networks

    Science.gov (United States)

    Khalil, Mahmoud I.; Bayoumi, Mohamed M.

    1999-03-01

    This paper applies the dyadic wavelet transform and the structured neural networks approach to recognize 2D objects under translation, rotation, and scale transformation. Experimental results are presented and compared with traditional methods. The experimental results showed that this refined technique successfully classified the objects and outperformed some traditional methods especially in the presence of noise.

  11. Vibration Based Damage Assessment of a Civil Engineering Structures using a Neural Networks

    DEFF Research Database (Denmark)

    Kirkegaard, Poul Henning; Rytter, A.

    In this paper the possibility of using a Multilayer Perceptron (MLP) network trained with the Backpropagation Algorith as a non-destructive damage assessment technique to locate and quantify a damage in Civil Engineering structures is investigated. Since artificial neural networks are proving...

  12. Delineating Neural Structures of Developmental Human Brains with Diffusion Tensor Imaging

    Directory of Open Access Journals (Sweden)

    Hao Huang

    2010-01-01

    Full Text Available The human brain anatomy is characterized by dramatic structural changes during fetal development. It is extraordinarily complex and yet its origin is a simple tubular structure. Revealing detailed anatomy at different stages of brain development not only aids in understanding this highly ordered process, but also provides clues to detect abnormalities caused by genetic or environmental factors. However, anatomical studies of human brain development during the fetal period are surprisingly scarce and histology-based atlases have become available only recently. Diffusion tensor imaging (DTI measures water diffusion to delineate the underlying neural structures. The high contrasts derived from DTI can be used to establish the brain atlas. With DTI tractography, coherent neural structures, such as white matter tracts, can be three-dimensionally reconstructed. The primary eigenvector of the diffusion tensor can be further explored to characterize microstructures in the cerebral wall of the developmental brains. In this mini-review, the application of DTI in order to reveal the structures of developmental fetal brains has been reviewed in the above-mentioned aspects. The fetal brain DTI provides a unique insight for delineating the neural structures in both macroscopic and microscopic levels. The resultant DTI database will provide structural guidance for the developmental study of human fetal brains in basic neuroscience, and reference standards for diagnostic radiology of premature newborns.

  13. Explicitly integrating parameter, input, and structure uncertainties into Bayesian Neural Networks for probabilistic hydrologic forecasting

    KAUST Repository

    Zhang, Xuesong

    2011-11-01

    Estimating uncertainty of hydrologic forecasting is valuable to water resources and other relevant decision making processes. Recently, Bayesian Neural Networks (BNNs) have been proved powerful tools for quantifying uncertainty of streamflow forecasting. In this study, we propose a Markov Chain Monte Carlo (MCMC) framework (BNN-PIS) to incorporate the uncertainties associated with parameters, inputs, and structures into BNNs. This framework allows the structure of the neural networks to change by removing or adding connections between neurons and enables scaling of input data by using rainfall multipliers. The results show that the new BNNs outperform BNNs that only consider uncertainties associated with parameters and model structures. Critical evaluation of posterior distribution of neural network weights, number of effective connections, rainfall multipliers, and hyper-parameters shows that the assumptions held in our BNNs are not well supported. Further understanding of characteristics of and interactions among different uncertainty sources is expected to enhance the application of neural networks for uncertainty analysis of hydrologic forecasting. © 2011 Elsevier B.V.

  14. Knowledge base and neural network approach for protein secondary structure prediction.

    Science.gov (United States)

    Patel, Maulika S; Mazumdar, Himanshu S

    2014-11-21

    Protein structure prediction is of great relevance given the abundant genomic and proteomic data generated by the genome sequencing projects. Protein secondary structure prediction is addressed as a sub task in determining the protein tertiary structure and function. In this paper, a novel algorithm, KB-PROSSP-NN, which is a combination of knowledge base and modeling of the exceptions in the knowledge base using neural networks for protein secondary structure prediction (PSSP), is proposed. The knowledge base is derived from a proteomic sequence-structure database and consists of the statistics of association between the 5-residue words and corresponding secondary structure. The predicted results obtained using knowledge base are refined with a Backpropogation neural network algorithm. Neural net models the exceptions of the knowledge base. The Q3 accuracy of 90% and 82% is achieved on the RS126 and CB396 test sets respectively which suggest improvement over existing state of art methods. Copyright © 2014 Elsevier Ltd. All rights reserved.

  15. Learning Orthographic Structure with Sequential Generative Neural Networks

    Science.gov (United States)

    Testolin, Alberto; Stoianov, Ivilin; Sperduti, Alessandro; Zorzi, Marco

    2016-01-01

    Learning the structure of event sequences is a ubiquitous problem in cognition and particularly in language. One possible solution is to learn a probabilistic generative model of sequences that allows making predictions about upcoming events. Though appealing from a neurobiological standpoint, this approach is typically not pursued in…

  16. Pax7 lineage contributions to the mammalian neural crest.

    Directory of Open Access Journals (Sweden)

    Barbara Murdoch

    Full Text Available Neural crest cells are vertebrate-specific multipotent cells that contribute to a variety of tissues including the peripheral nervous system, melanocytes, and craniofacial bones and cartilage. Abnormal development of the neural crest is associated with several human maladies including cleft/lip palate, aggressive cancers such as melanoma and neuroblastoma, and rare syndromes, like Waardenburg syndrome, a complex disorder involving hearing loss and pigment defects. We previously identified the transcription factor Pax7 as an early marker, and required component for neural crest development in chick embryos. In mammals, Pax7 is also thought to play a role in neural crest development, yet the precise contribution of Pax7 progenitors to the neural crest lineage has not been determined.Here we use Cre/loxP technology in double transgenic mice to fate map the Pax7 lineage in neural crest derivates. We find that Pax7 descendants contribute to multiple tissues including the cranial, cardiac and trunk neural crest, which in the cranial cartilage form a distinct regional pattern. The Pax7 lineage, like the Pax3 lineage, is additionally detected in some non-neural crest tissues, including a subset of the epithelial cells in specific organs.These results demonstrate a previously unappreciated widespread distribution of Pax7 descendants within and beyond the neural crest. They shed light regarding the regionally distinct phenotypes observed in Pax3 and Pax7 mutants, and provide a unique perspective into the potential roles of Pax7 during disease and development.

  17. Bayesian Inference using Neural Net Likelihood Models for Protein Secondary Structure Prediction

    Directory of Open Access Journals (Sweden)

    Seong-Gon Kim

    2011-06-01

    Full Text Available Several techniques such as Neural Networks, Genetic Algorithms, Decision Trees and other statistical or heuristic methods have been used to approach the complex non-linear task of predicting Alpha-helicies, Beta-sheets and Turns of a proteins secondary structure in the past. This project introduces a new machine learning method by using an offline trained Multilayered Perceptrons (MLP as the likelihood models within a Bayesian Inference framework to predict secondary structures proteins. Varying window sizes are used to extract neighboring amino acid information and passed back and forth between the Neural Net models and the Bayesian Inference process until there is a convergence of the posterior secondary structure probability.

  18. Research on FBG-Based CFRP Structural Damage Identification Using BP Neural Network

    Science.gov (United States)

    Geng, Xiangyi; Lu, Shizeng; Jiang, Mingshun; Sui, Qingmei; Lv, Shanshan; Xiao, Hang; Jia, Yuxi; Jia, Lei

    2018-06-01

    A damage identification system of carbon fiber reinforced plastics (CFRP) structures is investigated using fiber Bragg grating (FBG) sensors and back propagation (BP) neural network. FBG sensors are applied to construct the sensing network to detect the structural dynamic response signals generated by active actuation. The damage identification model is built based on the BP neural network. The dynamic signal characteristics extracted by the Fourier transform are the inputs, and the damage states are the outputs of the model. Besides, damages are simulated by placing lumped masses with different weights instead of inducing real damages, which is confirmed to be feasible by finite element analysis (FEA). At last, the damage identification system is verified on a CFRP plate with 300 mm × 300 mm experimental area, with the accurate identification of varied damage states. The system provides a practical way for CFRP structural damage identification.

  19. Training verb argument structure production in agrammatic aphasia: Behavioral and neural recovery patterns

    Science.gov (United States)

    Thompson, Cynthia K.; Riley, Ellyn A.; den Ouden, Dirk-Bart; Meltzer-Asscher, Aya; Lukic, Sladjana

    2013-01-01

    Introduction Neuroimaging and lesion studies indicate a left hemisphere network for verb and verb argument structure processing, involving both frontal and temporoparietal brain regions. Although their verb comprehension is generally unimpaired, it is well known that individuals with agrammatic aphasia often present with verb production deficits, characterized by an argument structure complexity hierarchy, indicating faulty access to argument structure representations for production and integration into syntactic contexts. Recovery of verb processing in agrammatism, however, has received little attention and no studies have examined the neural mechanisms associated with improved verb and argument structure processing. In the present study we trained agrammatic individuals on verbs with complex argument structure in sentence contexts and examined generalization to verbs with less complex argument structure. The neural substrates of improved verb production were examined using functional magnetic resonance imaging (fMRI). Methods Eight individuals with chronic agrammatic aphasia participated in the study (four experimental and four control participants). Production of three-argument verbs in active sentences was trained using a sentence generation task emphasizing the verb’s argument structure and the thematic roles of sentential noun phrases. Before and after training, production of trained and untrained verbs was tested in naming and sentence production and fMRI scans were obtained, using an action naming task. Results Significant pre- to post-training improvement in trained and untrained (one- and two-argument) verbs was found for treated, but not control, participants, with between-group differences found for verb naming, production of verbs in sentences, and production of argument structure. fMRI activation derived from post-treatment compared to pre-treatment scans revealed upregulation in cortical regions implicated for verb and argument structure processing

  20. Protein secondary structure prediction using modular reciprocal bidirectional recurrent neural networks.

    Science.gov (United States)

    Babaei, Sepideh; Geranmayeh, Amir; Seyyedsalehi, Seyyed Ali

    2010-12-01

    The supervised learning of recurrent neural networks well-suited for prediction of protein secondary structures from the underlying amino acids sequence is studied. Modular reciprocal recurrent neural networks (MRR-NN) are proposed to model the strong correlations between adjacent secondary structure elements. Besides, a multilayer bidirectional recurrent neural network (MBR-NN) is introduced to capture the long-range intramolecular interactions between amino acids in formation of the secondary structure. The final modular prediction system is devised based on the interactive integration of the MRR-NN and the MBR-NN structures to arbitrarily engage the neighboring effects of the secondary structure types concurrent with memorizing the sequential dependencies of amino acids along the protein chain. The advanced combined network augments the percentage accuracy (Q₃) to 79.36% and boosts the segment overlap (SOV) up to 70.09% when tested on the PSIPRED dataset in three-fold cross-validation. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

  1. The functional and structural neural basis of individual differences in loss aversion.

    Science.gov (United States)

    Canessa, Nicola; Crespi, Chiara; Motterlini, Matteo; Baud-Bovy, Gabriel; Chierchia, Gabriele; Pantaleo, Giuseppe; Tettamanti, Marco; Cappa, Stefano F

    2013-09-04

    Decision making under risk entails the anticipation of prospective outcomes, typically leading to the greater sensitivity to losses than gains known as loss aversion. Previous studies on the neural bases of choice-outcome anticipation and loss aversion provided inconsistent results, showing either bidirectional mesolimbic responses of activation for gains and deactivation for losses, or a specific amygdala involvement in processing losses. Here we focused on loss aversion with the aim to address interindividual differences in the neural bases of choice-outcome anticipation. Fifty-six healthy human participants accepted or rejected 104 mixed gambles offering equal (50%) chances of gaining or losing different amounts of money while their brain activity was measured with functional magnetic resonance imaging (fMRI). We report both bidirectional and gain/loss-specific responses while evaluating risky gambles, with amygdala and posterior insula specifically tracking the magnitude of potential losses. At the individual level, loss aversion was reflected both in limbic fMRI responses and in gray matter volume in a structural amygdala-thalamus-striatum network, in which the volume of the "output" centromedial amygdala nuclei mediating avoidance behavior was negatively correlated with monetary performance. We conclude that outcome anticipation and ensuing loss aversion involve multiple neural systems, showing functional and structural individual variability directly related to the actual financial outcomes of choices. By supporting the simultaneous involvement of both appetitive and aversive processing in economic decision making, these results contribute to the interpretation of existing inconsistencies on the neural bases of anticipating choice outcomes.

  2. Optimizing the De-Noise Neural Network Model for GPS Time-Series Monitoring of Structures

    Directory of Open Access Journals (Sweden)

    Mosbeh R. Kaloop

    2015-09-01

    Full Text Available The Global Positioning System (GPS is recently used widely in structures and other applications. Notwithstanding, the GPS accuracy still suffers from the errors afflicting the measurements, particularly the short-period displacement of structural components. Previously, the multi filter method is utilized to remove the displacement errors. This paper aims at using a novel application for the neural network prediction models to improve the GPS monitoring time series data. Four prediction models for the learning algorithms are applied and used with neural network solutions: back-propagation, Cascade-forward back-propagation, adaptive filter and extended Kalman filter, to estimate which model can be recommended. The noise simulation and bridge’s short-period GPS of the monitoring displacement component of one Hz sampling frequency are used to validate the four models and the previous method. The results show that the Adaptive neural networks filter is suggested for de-noising the observations, specifically for the GPS displacement components of structures. Also, this model is expected to have significant influence on the design of structures in the low frequency responses and measurements’ contents.

  3. Learning Spatiotemporally Encoded Pattern Transformations in Structured Spiking Neural Networks.

    Science.gov (United States)

    Gardner, Brian; Sporea, Ioana; Grüning, André

    2015-12-01

    Information encoding in the nervous system is supported through the precise spike timings of neurons; however, an understanding of the underlying processes by which such representations are formed in the first place remains an open question. Here we examine how multilayered networks of spiking neurons can learn to encode for input patterns using a fully temporal coding scheme. To this end, we introduce a new supervised learning rule, MultilayerSpiker, that can train spiking networks containing hidden layer neurons to perform transformations between spatiotemporal input and output spike patterns. The performance of the proposed learning rule is demonstrated in terms of the number of pattern mappings it can learn, the complexity of network structures it can be used on, and its classification accuracy when using multispike-based encodings. In particular, the learning rule displays robustness against input noise and can generalize well on an example data set. Our approach contributes to both a systematic understanding of how computations might take place in the nervous system and a learning rule that displays strong technical capability.

  4. Prediction of Optimal Design and Deflection of Space Structures Using Neural Networks

    Directory of Open Access Journals (Sweden)

    Reza Kamyab Moghadas

    2012-01-01

    Full Text Available The main aim of the present work is to determine the optimal design and maximum deflection of double layer grids spending low computational cost using neural networks. The design variables of the optimization problem are cross-sectional area of the elements as well as the length of the span and height of the structures. In this paper, a number of double layer grids with various random values of length and height are selected and optimized by simultaneous perturbation stochastic approximation algorithm. Then, radial basis function (RBF and generalized regression (GR neural networks are trained to predict the optimal design and maximum deflection of the structures. The numerical results demonstrate the efficiency of the proposed methodology.

  5. Forecasting the term structure of crude oil futures prices with neural networks

    Czech Academy of Sciences Publication Activity Database

    Baruník, Jozef; Malinská, B.

    2016-01-01

    Roč. 164, č. 1 (2016), s. 366-379 ISSN 0306-2619 R&D Projects: GA ČR(CZ) GBP402/12/G097 Institutional support: RVO:67985556 Keywords : Term structure * Nelson–Siegel model * Dynamic neural networks * Crude oil futures Subject RIV: AH - Economics Impact factor: 7.182, year: 2016 http://library.utia.cas.cz/separaty/2016/E/barunik-0453168.pdf

  6. Deep neural nets as a method for quantitative structure-activity relationships.

    Science.gov (United States)

    Ma, Junshui; Sheridan, Robert P; Liaw, Andy; Dahl, George E; Svetnik, Vladimir

    2015-02-23

    Neural networks were widely used for quantitative structure-activity relationships (QSAR) in the 1990s. Because of various practical issues (e.g., slow on large problems, difficult to train, prone to overfitting, etc.), they were superseded by more robust methods like support vector machine (SVM) and random forest (RF), which arose in the early 2000s. The last 10 years has witnessed a revival of neural networks in the machine learning community thanks to new methods for preventing overfitting, more efficient training algorithms, and advancements in computer hardware. In particular, deep neural nets (DNNs), i.e. neural nets with more than one hidden layer, have found great successes in many applications, such as computer vision and natural language processing. Here we show that DNNs can routinely make better prospective predictions than RF on a set of large diverse QSAR data sets that are taken from Merck's drug discovery effort. The number of adjustable parameters needed for DNNs is fairly large, but our results show that it is not necessary to optimize them for individual data sets, and a single set of recommended parameters can achieve better performance than RF for most of the data sets we studied. The usefulness of the parameters is demonstrated on additional data sets not used in the calibration. Although training DNNs is still computationally intensive, using graphical processing units (GPUs) can make this issue manageable.

  7. AKT signaling displays multifaceted functions in neural crest development.

    Science.gov (United States)

    Sittewelle, Méghane; Monsoro-Burq, Anne H

    2018-05-31

    AKT signaling is an essential intracellular pathway controlling cell homeostasis, cell proliferation and survival, as well as cell migration and differentiation in adults. Alterations impacting the AKT pathway are involved in many pathological conditions in human disease. Similarly, during development, multiple transmembrane molecules, such as FGF receptors, PDGF receptors or integrins, activate AKT to control embryonic cell proliferation, migration, differentiation, and also cell fate decisions. While many studies in mouse embryos have clearly implicated AKT signaling in the differentiation of several neural crest derivatives, information on AKT functions during the earliest steps of neural crest development had remained relatively scarce until recently. However, recent studies on known and novel regulators of AKT signaling demonstrate that this pathway plays critical roles throughout the development of neural crest progenitors. Non-mammalian models such as fish and frog embryos have been instrumental to our understanding of AKT functions in neural crest development, both in neural crest progenitors and in the neighboring tissues. This review combines current knowledge acquired from all these different vertebrate animal models to describe the various roles of AKT signaling related to neural crest development in vivo. We first describe the importance of AKT signaling in patterning the tissues involved in neural crest induction, namely the dorsal mesoderm and the ectoderm. We then focus on AKT signaling functions in neural crest migration and differentiation. Copyright © 2018 Elsevier Inc. All rights reserved.

  8. Flexible deep brain neural probes based on a parylene tube structure

    Science.gov (United States)

    Zhao, Zhiguo; Kim, Eric; Luo, Hao; Zhang, Jinsheng; Xu, Yong

    2018-01-01

    Most microfabricated neural probes have limited shank length, which prevents them from reaching many deep brain structures. This paper reports deep brain neural probes with ultra-long penetrating shanks based on a simple but novel parylene tube structure. The mechanical strength of the parylene tube shank is temporarily enhanced during implantation by inserting a metal wire. The metal wire can be removed after implantation, making the implanted probe very flexible and thus minimizing the stress caused by micromotions of brain tissues. Optogenetic stimulation and chemical delivery capabilities can be potentially integrated by taking advantage of the tube structure. Single-shank prototypes with a shank length of 18.2 mm have been developed. The microfabrication process comprises of deep reactive ion etching (DRIE) of silicon, parylene conformal coating/refilling, and XeF2 isotropic silicon etching. In addition to bench-top insertion characterization, the functionality of developed probes has been preliminarily demonstrated by implanting into the amygdala of a rat and recording neural signals.

  9. The Elementary Nature of Purposive Behavior: Evolving Minimal Neural Structures that Display Intrinsic Intentionality

    Directory of Open Access Journals (Sweden)

    John S. Watson

    2005-01-01

    Full Text Available A study of the evolution of agency in artificial life was designed to access the potential emergence of purposiveness and intentionality as these attributes of behavior have been defined in psychology and philosophy. The study involved Darwinian evolution of mobile neural nets (autonomous agents in terms of their adaptive weight patterning and structure (number of sensory, hidden, and memory units that controlled movement. An agent was embedded in a 10 × 10 toroidal matrix along with “containers” that held benefit or harm if entered. Sensory exposure to content of a container was only briefly available at a distance so that adaptive response to a nearby container required use of relevant memory. The best 20% of each generation of agents, based on net benefit consumed during limited lifetime, were selected to parent the following generation. Purposiveness emerged for all selected agents by 300 generations. By 4000 generations, 90% passed a test of purposive intentionality based on Piaget's criteria for Stage IV object permanence in human infants. An additional test of these agents confirmed that the behavior of 67% of them was consistent with the philosophical criterion of intention being “about” the container's contents. Given that the evolved neural structure of more than half of the successful agents had only 1 hidden and 1 memory node, it is argued that, contrary to common assumption, purposive and intentional aspects of adaptive behavior require an evolution of minimal complexity of supportive neural structure.

  10. Musical intervention enhances infants' neural processing of temporal structure in music and speech.

    Science.gov (United States)

    Zhao, T Christina; Kuhl, Patricia K

    2016-05-10

    Individuals with music training in early childhood show enhanced processing of musical sounds, an effect that generalizes to speech processing. However, the conclusions drawn from previous studies are limited due to the possible confounds of predisposition and other factors affecting musicians and nonmusicians. We used a randomized design to test the effects of a laboratory-controlled music intervention on young infants' neural processing of music and speech. Nine-month-old infants were randomly assigned to music (intervention) or play (control) activities for 12 sessions. The intervention targeted temporal structure learning using triple meter in music (e.g., waltz), which is difficult for infants, and it incorporated key characteristics of typical infant music classes to maximize learning (e.g., multimodal, social, and repetitive experiences). Controls had similar multimodal, social, repetitive play, but without music. Upon completion, infants' neural processing of temporal structure was tested in both music (tones in triple meter) and speech (foreign syllable structure). Infants' neural processing was quantified by the mismatch response (MMR) measured with a traditional oddball paradigm using magnetoencephalography (MEG). The intervention group exhibited significantly larger MMRs in response to music temporal structure violations in both auditory and prefrontal cortical regions. Identical results were obtained for temporal structure changes in speech. The intervention thus enhanced temporal structure processing not only in music, but also in speech, at 9 mo of age. We argue that the intervention enhanced infants' ability to extract temporal structure information and to predict future events in time, a skill affecting both music and speech processing.

  11. Artificial neural networks aided conceptual stage design of water harvesting structures

    Directory of Open Access Journals (Sweden)

    Vinay Chandwani

    2016-09-01

    Full Text Available The paper presents artificial neural networks (ANNs based methodology for ascertaining the structural parameters of water harvesting structures (WHS at the conceptual stage of design. The ANN is trained using exemplar patterns generated using an in-house MSExcel based design program, to draw a functional relationship between the five inputs design parameters namely, peak flood discharge, safe bearing capacity of strata, length of structure, height of structure and silt factor and four outputs namely, top width, bottom width, foundation depth and flood lift representing the structural parameters of WHS. The results of the study show that, the structural parameters of the WHS predicted using ANN model are in close agreement with the actual field parameters. The versatility of ANN to map complex or complex unknown relationships has been proven in the study. A parametric sensitivity study is also performed to assess the most significant design parameter. The study holistically presents a neural network based decision support tool that can be used to accurately estimate the major design parameters of the WHS at the conceptual stage of design in quick time, aiding the engineer-in-charge to conveniently forecast the budget requirements and minimize the labor involved during the subsequent phases of analysis and design.

  12. Neural crest stem cell population in craniomaxillofacial development and tissue repair

    Directory of Open Access Journals (Sweden)

    M La Noce

    2014-10-01

    Full Text Available Neural crest cells, delaminating from the neural tube during migration, undergo an epithelial-mesenchymal transition and differentiate into several cell types strongly reinforcing the mesoderm of the craniofacial body area – giving rise to bone, cartilage and other tissues and cells of this human body area. Recent studies on craniomaxillofacial neural crest-derived cells have provided evidence for the tremendous plasticity of these cells. Actually, neural crest cells can respond and adapt to the environment in which they migrate and the cranial mesoderm plays an important role toward patterning the identity of the migrating neural crest cells. In our experience, neural crest-derived stem cells, such as dental pulp stem cells, can actively proliferate, repair bone and give rise to other tissues and cytotypes, including blood vessels, smooth muscle, adipocytes and melanocytes, highlighting that their use in tissue engineering is successful. In this review, we provide an overview of the main pathways involved in neural crest formation, delamination, migration and differentiation; and, in particular, we concentrate our attention on the translatability of the latest scientific progress. Here we try to suggest new ideas and strategies that are needed to fully develop the clinical use of these cells. This effort should involve both researchers/clinicians and improvements in good manufacturing practice procedures. It is important to address studies towards clinical application or take into consideration that studies must have an effective therapeutic prospect for humans. New approaches and ideas must be concentrated also toward stem cell recruitment and activation within the human body, overcoming the classical grafting.

  13. Elements of an algorithm for optimizing a parameter-structural neural network

    Science.gov (United States)

    Mrówczyńska, Maria

    2016-06-01

    The field of processing information provided by measurement results is one of the most important components of geodetic technologies. The dynamic development of this field improves classic algorithms for numerical calculations in the aspect of analytical solutions that are difficult to achieve. Algorithms based on artificial intelligence in the form of artificial neural networks, including the topology of connections between neurons have become an important instrument connected to the problem of processing and modelling processes. This concept results from the integration of neural networks and parameter optimization methods and makes it possible to avoid the necessity to arbitrarily define the structure of a network. This kind of extension of the training process is exemplified by the algorithm called the Group Method of Data Handling (GMDH), which belongs to the class of evolutionary algorithms. The article presents a GMDH type network, used for modelling deformations of the geometrical axis of a steel chimney during its operation.

  14. Distributed collaborative probabilistic design of multi-failure structure with fluid-structure interaction using fuzzy neural network of regression

    Science.gov (United States)

    Song, Lu-Kai; Wen, Jie; Fei, Cheng-Wei; Bai, Guang-Chen

    2018-05-01

    To improve the computing efficiency and precision of probabilistic design for multi-failure structure, a distributed collaborative probabilistic design method-based fuzzy neural network of regression (FR) (called as DCFRM) is proposed with the integration of distributed collaborative response surface method and fuzzy neural network regression model. The mathematical model of DCFRM is established and the probabilistic design idea with DCFRM is introduced. The probabilistic analysis of turbine blisk involving multi-failure modes (deformation failure, stress failure and strain failure) was investigated by considering fluid-structure interaction with the proposed method. The distribution characteristics, reliability degree, and sensitivity degree of each failure mode and overall failure mode on turbine blisk are obtained, which provides a useful reference for improving the performance and reliability of aeroengine. Through the comparison of methods shows that the DCFRM reshapes the probability of probabilistic analysis for multi-failure structure and improves the computing efficiency while keeping acceptable computational precision. Moreover, the proposed method offers a useful insight for reliability-based design optimization of multi-failure structure and thereby also enriches the theory and method of mechanical reliability design.

  15. Neural systems supporting linguistic structure, linguistic experience, and symbolic communication in sign language and gesture.

    Science.gov (United States)

    Newman, Aaron J; Supalla, Ted; Fernandez, Nina; Newport, Elissa L; Bavelier, Daphne

    2015-09-15

    Sign languages used by deaf communities around the world possess the same structural and organizational properties as spoken languages: In particular, they are richly expressive and also tightly grammatically constrained. They therefore offer the opportunity to investigate the extent to which the neural organization for language is modality independent, as well as to identify ways in which modality influences this organization. The fact that sign languages share the visual-manual modality with a nonlinguistic symbolic communicative system-gesture-further allows us to investigate where the boundaries lie between language and symbolic communication more generally. In the present study, we had three goals: to investigate the neural processing of linguistic structure in American Sign Language (using verbs of motion classifier constructions, which may lie at the boundary between language and gesture); to determine whether we could dissociate the brain systems involved in deriving meaning from symbolic communication (including both language and gesture) from those specifically engaged by linguistically structured content (sign language); and to assess whether sign language experience influences the neural systems used for understanding nonlinguistic gesture. The results demonstrated that even sign language constructions that appear on the surface to be similar to gesture are processed within the left-lateralized frontal-temporal network used for spoken languages-supporting claims that these constructions are linguistically structured. Moreover, although nonsigners engage regions involved in human action perception to process communicative, symbolic gestures, signers instead engage parts of the language-processing network-demonstrating an influence of experience on the perception of nonlinguistic stimuli.

  16. Tracting the neural basis of music: Deficient structural connectivity underlying acquired amusia.

    Science.gov (United States)

    Sihvonen, Aleksi J; Ripollés, Pablo; Särkämö, Teppo; Leo, Vera; Rodríguez-Fornells, Antoni; Saunavaara, Jani; Parkkola, Riitta; Soinila, Seppo

    2017-12-01

    Acquired amusia provides a unique opportunity to investigate the fundamental neural architectures of musical processing due to the transition from a functioning to defective music processing system. Yet, the white matter (WM) deficits in amusia remain systematically unexplored. To evaluate which WM structures form the neural basis for acquired amusia and its recovery, we studied 42 stroke patients longitudinally at acute, 3-month, and 6-month post-stroke stages using DTI [tract-based spatial statistics (TBSS) and deterministic tractography (DT)] and the Scale and Rhythm subtests of the Montreal Battery of Evaluation of Amusia (MBEA). Non-recovered amusia was associated with structural damage and subsequent degeneration in multiple WM tracts including the right inferior fronto-occipital fasciculus (IFOF), arcuate fasciculus (AF), inferior longitudinal fasciculus (ILF), uncinate fasciculus (UF), and frontal aslant tract (FAT), as well as in the corpus callosum (CC) and its posterior part (tapetum). In a linear regression analysis, the volume of the right IFOF was the main predictor of MBEA performance across time. Overall, our results provide a comprehensive picture of the large-scale deficits in intra- and interhemispheric structural connectivity underlying amusia, and conversely highlight which pathways are crucial for normal music perception. Copyright © 2017 Elsevier Ltd. All rights reserved.

  17. Augmented neural networks and problem structure-based heuristics for the bin-packing problem

    Science.gov (United States)

    Kasap, Nihat; Agarwal, Anurag

    2012-08-01

    In this article, we report on a research project where we applied augmented-neural-networks (AugNNs) approach for solving the classical bin-packing problem (BPP). AugNN is a metaheuristic that combines a priority rule heuristic with the iterative search approach of neural networks to generate good solutions fast. This is the first time this approach has been applied to the BPP. We also propose a decomposition approach for solving harder BPP, in which subproblems are solved using a combination of AugNN approach and heuristics that exploit the problem structure. We discuss the characteristics of problems on which such problem structure-based heuristics could be applied. We empirically show the effectiveness of the AugNN and the decomposition approach on many benchmark problems in the literature. For the 1210 benchmark problems tested, 917 problems were solved to optimality and the average gap between the obtained solution and the upper bound for all the problems was reduced to under 0.66% and computation time averaged below 33 s per problem. We also discuss the computational complexity of our approach.

  18. Structural Analysis of Three-dimensional Human Neural Tissue derived from Induced Pluripotent Stem Cells

    DEFF Research Database (Denmark)

    Terrence Brooks, Patrick; Rasmussen, Mikkel Aabech; Hyttel, Poul

    2016-01-01

    Objective: The present study aimed at establishing a method for production of a three-dimensional (3D) human neural tissue derived from induced pluripotent stem cells (iPSCs) and analyzing the outcome by a combination of tissue ultrastructure and expression of neural markers. Methods: A two......-step cell culture procedure was implemented by subjecting human iPSCs to a 3D scaffoldbased neural differentiation protocol. First, neural fate-inducing small molecules were used to create a neuroepithelial monolayer. Second, the monolayer was trypsinized into single cells and seeded into a porous...... polystyrene scaffold and further cultured to produce a 3D neural tissue. The neural tissue was characterized by a combination of immunohistochemistry and transmission electron microscopy (TEM). Results: iPSCs developed into a 3D neural tissue expressing markers for neural progenitor cells, early neural...

  19. Crystal structure of the Ig1 domain of the neural cell adhesion molecule NCAM2 displays domain swapping

    DEFF Research Database (Denmark)

    Rasmussen, Kim Krighaar; Kulahin, Nikolaj; Kristensen, Ole

    2008-01-01

    The crystal structure of the first immunoglobulin (Ig1) domain of neural cell adhesion molecule 2 (NCAM2/OCAM/RNCAM) is presented at a resolution of 2.7 A. NCAM2 is a member of the immunoglobulin superfamily of cell adhesion molecules (IgCAMs). In the structure, two Ig domains interact by domain...

  20. Structural neural correlates of multitasking: A voxel-based morphometry study.

    Science.gov (United States)

    Zhang, Rui-Ting; Yang, Tian-Xiao; Wang, Yi; Sui, Yuxiu; Yao, Jingjing; Zhang, Chen-Yuan; Cheung, Eric F C; Chan, Raymond C K

    2016-12-01

    Multitasking refers to the ability to organize assorted tasks efficiently in a short period of time, which plays an important role in daily life. However, the structural neural correlates of multitasking performance remain unclear. The present study aimed at exploring the brain regions associated with multitasking performance using global correlation analysis. Twenty-six healthy participants first underwent structural brain scans and then performed the modified Six Element Test, which required participants to attempt six subtasks in 10 min while obeying a specific rule. Voxel-based morphometry of the whole brain was used to detect the structural correlates of multitasking ability. Grey matter volume of the anterior cingulate cortex (ACC) was positively correlated with the overall performance and time monitoring in multitasking. In addition, white matter volume of the anterior thalamic radiation (ATR) was also positively correlated with time monitoring during multitasking. Other related brain regions associated with multitasking included the superior frontal gyrus, the inferior occipital gyrus, the lingual gyrus, and the inferior longitudinal fasciculus. No significant correlation was found between grey matter volume of the prefrontal cortex (Brodmann Area 10) and multitasking performance. Using a global correlation analysis to examine various aspects of multitasking performance, this study provided new insights into the structural neural correlates of multitasking ability. In particular, the ACC was identified as an important brain region that played both a general and a specific time-monitoring role in multitasking, extending the role of the ACC from lesioned populations to healthy populations. The present findings also support the view that the ATR may influence multitasking performance by affecting time-monitoring abilities. © 2016 The Institute of Psychology, Chinese Academy of Sciences and John Wiley & Sons Australia, Ltd.

  1. Prediction of the Fundamental Period of Infilled RC Frame Structures Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Panagiotis G. Asteris

    2016-01-01

    Full Text Available The fundamental period is one of the most critical parameters for the seismic design of structures. There are several literature approaches for its estimation which often conflict with each other, making their use questionable. Furthermore, the majority of these approaches do not take into account the presence of infill walls into the structure despite the fact that infill walls increase the stiffness and mass of structure leading to significant changes in the fundamental period. In the present paper, artificial neural networks (ANNs are used to predict the fundamental period of infilled reinforced concrete (RC structures. For the training and the validation of the ANN, a large data set is used based on a detailed investigation of the parameters that affect the fundamental period of RC structures. The comparison of the predicted values with analytical ones indicates the potential of using ANNs for the prediction of the fundamental period of infilled RC frame structures taking into account the crucial parameters that influence its value.

  2. Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks

    Science.gov (United States)

    Abdeljaber, Osama; Avci, Onur; Kiranyaz, Serkan; Gabbouj, Moncef; Inman, Daniel J.

    2017-02-01

    Structural health monitoring (SHM) and vibration-based structural damage detection have been a continuous interest for civil, mechanical and aerospace engineers over the decades. Early and meticulous damage detection has always been one of the principal objectives of SHM applications. The performance of a classical damage detection system predominantly depends on the choice of the features and the classifier. While the fixed and hand-crafted features may either be a sub-optimal choice for a particular structure or fail to achieve the same level of performance on another structure, they usually require a large computation power which may hinder their usage for real-time structural damage detection. This paper presents a novel, fast and accurate structural damage detection system using 1D Convolutional Neural Networks (CNNs) that has an inherent adaptive design to fuse both feature extraction and classification blocks into a single and compact learning body. The proposed method performs vibration-based damage detection and localization of the damage in real-time. The advantage of this approach is its ability to extract optimal damage-sensitive features automatically from the raw acceleration signals. Large-scale experiments conducted on a grandstand simulator revealed an outstanding performance and verified the computational efficiency of the proposed real-time damage detection method.

  3. Effect of synapse dilution on the memory retrieval in structured attractor neural networks

    Science.gov (United States)

    Brunel, N.

    1993-08-01

    We investigate a simple model of structured attractor neural network (ANN). In this network a module codes for the category of the stored information, while another group of neurons codes for the remaining information. The probability distribution of stabilities of the patterns and the prototypes of the categories are calculated, for two different synaptic structures. The stability of the prototypes is shown to increase when the fraction of neurons coding for the category goes down. Then the effect of synapse destruction on the retrieval is studied in two opposite situations : first analytically in sparsely connected networks, then numerically in completely connected ones. In both cases the behaviour of the structured network and that of the usual homogeneous networks are compared. When lesions increase, two transitions are shown to appear in the behaviour of the structured network when one of the patterns is presented to the network. After the first transition the network recognizes the category of the pattern but not the individual pattern. After the second transition the network recognizes nothing. These effects are similar to syndromes caused by lesions in the central visual system, namely prosopagnosia and agnosia. In both types of networks (structured or homogeneous) the stability of the prototype is greater than the stability of individual patterns, however the first transition, for completely connected networks, occurs only when the network is structured.

  4. Community structure analysis of rejection sensitive personality profiles: A common neural response to social evaluative threat?

    Science.gov (United States)

    Kortink, Elise D; Weeda, Wouter D; Crowley, Michael J; Gunther Moor, Bregtje; van der Molen, Melle J W

    2018-06-01

    Monitoring social threat is essential for maintaining healthy social relationships, and recent studies suggest a neural alarm system that governs our response to social rejection. Frontal-midline theta (4-8 Hz) oscillatory power might act as a neural correlate of this system by being sensitive to unexpected social rejection. Here, we examined whether frontal-midline theta is modulated by individual differences in personality constructs sensitive to social disconnection. In addition, we examined the sensitivity of feedback-related brain potentials (i.e., the feedback-related negativity and P3) to social feedback. Sixty-five undergraduate female participants (mean age = 19.69 years) participated in the Social Judgment Paradigm, a fictitious peer-evaluation task in which participants provided expectancies about being liked/disliked by peer strangers. Thereafter, they received feedback signaling social acceptance/rejection. A community structure analysis was employed to delineate personality profiles in our data. Results provided evidence of two subgroups: one group scored high on attachment-related anxiety and fear of negative evaluation, whereas the other group scored high on attachment-related avoidance and low on fear of negative evaluation. In both groups, unexpected rejection feedback yielded a significant increase in theta power. The feedback-related negativity was sensitive to unexpected feedback, regardless of valence, and was largest for unexpected rejection feedback. The feedback-related P3 was significantly enhanced in response to expected social acceptance feedback. Together, these findings confirm the sensitivity of frontal midline theta oscillations to the processing of social threat, and suggest that this alleged neural alarm system behaves similarly in individuals that differ in personality constructs relevant to social evaluation.

  5. Crustal block structure by GPS data using neural network in the Northern Tien Shan

    Science.gov (United States)

    Kostuk, A.; Carmenate, D.

    2010-05-01

    For over ten years regular GPS measurements have been carried out by Research Station RAS in the Central Asia. The results of these measurements have not only proved the conclusion that the Earth's crust meridional compression equals in total about 17 mm/year from the Tarim massif to the Kazakh shield, but have also allowed estimating deformation behavior in the region. As is known, deformation behavior of continental crust is an actively discussed issue. On the one hand, the Earth's crust is presented as a set of microplates (blocks) and deformation here is a result of shifting along the blocks boundaries, on the other hand, lithospheric deformation is distributed by volume and meets the rheological model of nonlinear viscous fluid. This work represents an attempt to detect the block structure of the surface of the Northern Tien Shan using GPS velocity fields. As a significant difference from analogous works, appears the vector field clustering with the help of neural network used as a classifier by many criteria that allows dividing input space into areas and using of all three components of GPS velocity. In this case, we use such a feature of neural networks as self-organization. Among the mechanisms of self-organization there are two main classes: self-organization based on the Hebb associative rule and the mechanism of neuronal competition based on the generalized Kohonen rule. In this case, we use an approach of self-organizing networks in which we take neuronal competition as an algorithm for their training. As a rule, these are single-layer networks where each neuron is connected to all components of m-dimensional input vector. GPS vectors of the Central Asian velocity field located within the territory of the Northern Tien Shan were used as input patterns. Measurements at GPS sites were fulfilled in 36 hour-long sessions by double-frequency receivers Trimble and Topcon. In so doing, measurement discreteness equaled 30 seconds; the data were processed by

  6. Optogenetic interrogation of neural circuits: technology for probing mammalian brain structures

    Science.gov (United States)

    Zhang, Feng; Gradinaru, Viviana; Adamantidis, Antoine R; Durand, Remy; Airan, Raag D; de Lecea, Luis; Deisseroth, Karl

    2015-01-01

    Elucidation of the neural substrates underlying complex animal behaviors depends on precise activity control tools, as well as compatible readout methods. Recent developments in optogenetics have addressed this need, opening up new possibilities for systems neuroscience. Interrogation of even deep neural circuits can be conducted by directly probing the necessity and sufficiency of defined circuit elements with millisecond-scale, cell type-specific optical perturbations, coupled with suitable readouts such as electrophysiology, optical circuit dynamics measures and freely moving behavior in mammals. Here we collect in detail our strategies for delivering microbial opsin genes to deep mammalian brain structures in vivo, along with protocols for integrating the resulting optical control with compatible readouts (electrophysiological, optical and behavioral). The procedures described here, from initial virus preparation to systems-level functional readout, can be completed within 4–5 weeks. Together, these methods may help in providing circuit-level insight into the dynamics underlying complex mammalian behaviors in health and disease. PMID:20203662

  7. CNNdel: Calling Structural Variations on Low Coverage Data Based on Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Jing Wang

    2017-01-01

    Full Text Available Many structural variations (SVs detection methods have been proposed due to the popularization of next-generation sequencing (NGS. These SV calling methods use different SV-property-dependent features; however, they all suffer from poor accuracy when running on low coverage sequences. The union of results from these tools achieves fairly high sensitivity but still produces low accuracy on low coverage sequence data. That is, these methods contain many false positives. In this paper, we present CNNdel, an approach for calling deletions from paired-end reads. CNNdel gathers SV candidates reported by multiple tools and then extracts features from aligned BAM files at the positions of candidates. With labeled feature-expressed candidates as a training set, CNNdel trains convolutional neural networks (CNNs to distinguish true unlabeled candidates from false ones. Results show that CNNdel works well with NGS reads from 26 low coverage genomes of the 1000 Genomes Project. The paper demonstrates that convolutional neural networks can automatically assign the priority of SV features and reduce the false positives efficaciously.

  8. A simple structure wavelet transform circuit employing function link neural networks and SI filters

    Science.gov (United States)

    Mu, Li; Yigang, He

    2016-12-01

    Signal processing by means of analog circuits offers advantages from a power consumption viewpoint. Implementing wavelet transform (WT) using analog circuits is of great interest when low-power consumption becomes an important issue. In this article, a novel simple structure WT circuit in analog domain is presented by employing functional link neural network (FLNN) and switched-current (SI) filters. First, the wavelet base is approximated using FLNN algorithms for giving a filter transfer function that is suitable for simple structure WT circuit implementation. Next, the WT circuit is constructed with the wavelet filter bank, whose impulse response is the approximated wavelet and its dilations. The filter design that follows is based on a follow-the-leader feedback (FLF) structure with multiple output bilinear SI integrators and current mirrors as the main building blocks. SI filter is well suited for this application since the dilation constant across different scales of the transform can be precisely implemented and controlled by the clock frequency of the circuit with the same system architecture. Finally, to illustrate the design procedure, a seventh-order FLNN-approximated Gaussian wavelet is implemented as an example. Simulations have successfully verified that the designed simple structure WT circuit has low sensitivity, low-power consumption and litter effect to the imperfections.

  9. The necessity of connection structures in neural models of variable binding.

    Science.gov (United States)

    van der Velde, Frank; de Kamps, Marc

    2015-08-01

    In his review of neural binding problems, Feldman (Cogn Neurodyn 7:1-11, 2013) addressed two types of models as solutions of (novel) variable binding. The one type uses labels such as phase synchrony of activation. The other ('connectivity based') type uses dedicated connections structures to achieve novel variable binding. Feldman argued that label (synchrony) based models are the only possible candidates to handle novel variable binding, whereas connectivity based models lack the flexibility required for that. We argue and illustrate that Feldman's analysis is incorrect. Contrary to his conclusion, connectivity based models are the only viable candidates for models of novel variable binding because they are the only type of models that can produce behavior. We will show that the label (synchrony) based models analyzed by Feldman are in fact examples of connectivity based models. Feldman's analysis that novel variable binding can be achieved without existing connection structures seems to result from analyzing the binding problem in a wrong frame of reference, in particular in an outside instead of the required inside frame of reference. Connectivity based models can be models of novel variable binding when they possess a connection structure that resembles a small-world network, as found in the brain. We will illustrate binding with this type of model with episode binding and the binding of words, including novel words, in sentence structures.

  10. Identification of crystalline structures using Moessbauer parameters and artificial neural network

    International Nuclear Information System (INIS)

    Salles, E.O.T.; Souza Junior, P.A. De; Garg, V.K.

    1995-01-01

    Moessbauer spectroscopy is a useful technique for characterizing the valences, electronic and magnetic states, coordination symmetric and site occupancies of Fe cations. The Moessbauer parameters of Isomer Shift (I.S.) and Quadrupole Splitting (Q.S.) are useful to distinguish paramagnetic ferrous and ferric ions in several substances, while the internal magnetic field provides information on the crystallinity. A correlation is being sought between Moessbauer parameters and several structure properties of some iron-containing minerals using Artificial Neural Networks (ANN). Distinct regions of crystalline structures are defined when any two parameters are plotted, but in several cases superposition of these regions leads to erroneous conclusions. We have tried to eliminate this difficulty by using convenient axes. These axes form n-dimensional vectors as input to our ANN. In recent years ANN has shown to be a powerful technique to solve problems as pattern recognition, optimization, preview ups and downs in stock market, automatic control and identification of a mineral from a Moessbauer spectrum of Moessbauer data bank. Using ANN we have been successful in identification of crystalline structures from plots of Moessbauer spectral parameters of I.S., Q.S., and structure using Moessbauer parameters of I.S., Q.S., and polyhedral volume of a coordination site are presented. (author) 28 refs.; 4 figs.; 2 tabs

  11. Relationship among visual field, blood flow, and neural structure measurements in glaucoma.

    Science.gov (United States)

    Hwang, John C; Konduru, Ranjith; Zhang, Xinbo; Tan, Ou; Francis, Brian A; Varma, Rohit; Sehi, Mitra; Greenfield, David S; Sadda, Srinivas R; Huang, David

    2012-05-17

    To determine the relationship among visual field, neural structural, and blood flow measurements in glaucoma. Case-control study. Forty-seven eyes of 42 patients with perimetric glaucoma were age-matched with 27 normal eyes of 27 patients. All patients underwent Doppler Fourier-domain optical coherence tomography to measure retinal blood flow and standard glaucoma evaluation with visual field testing and quantitative structural imaging. Linear regression analysis was performed to analyze the relationship among visual field, blood flow, and structure, after all variables were converted to logarithmic decibel scale. Retinal blood flow was reduced in glaucoma eyes compared to normal eyes (P flow and structural loss of rim area and retinal nerve fiber layer (RNFL). There was no correlation or paradoxical correlation between blood flow and structure. Multivariate regression analysis revealed that reduced blood flow and structural loss are independent predictors of visual field loss. Each dB decrease in blood flow was associated with at least 1.62 dB loss in mean deviation (P ≤ 0.001), whereas each dB decrease in rim area and RNFL was associated with 1.15 dB and 2.56 dB loss in mean deviation, respectively (P ≤ 0.03). There is a close link between reduced retinal blood flow and visual field loss in glaucoma that is largely independent of structural loss. Further studies are needed to elucidate the causes of the vascular dysfunction and potential avenues for therapeutic intervention. Blood flow measurement may be useful as an independent assessment of glaucoma severity.

  12. Cell death in neural precursor cells and neurons before neurite formation prevents the emergence of abnormal neural structures in the Drosophila optic lobe.

    Science.gov (United States)

    Hara, Yusuke; Sudo, Tatsuya; Togane, Yu; Akagawa, Hiromi; Tsujimura, Hidenobu

    2018-04-01

    Programmed cell death is a conserved strategy for neural development both in vertebrates and invertebrates and is recognized at various developmental stages in the brain from neurogenesis to adulthood. To understand the development of the central nervous system, it is essential to reveal not only molecular mechanisms but also the role of neural cell death (Pinto-Teixeira et al., 2016). To understand the role of cell death in neural development, we investigated the effect of inhibition of cell death on optic lobe development. Our data demonstrate that, in the optic lobe of Drosophila, cell death occurs in neural precursor cells and neurons before neurite formation and functions to prevent various developmental abnormalities. When neuronal cell death was inhibited by an effector caspase inhibitor, p35, multiple abnormal neuropil structures arose during optic lobe development-e.g., enlarged or fused neuropils, misrouted neurons and abnormal neurite lumps. Inhibition of cell death also induced morphogenetic defects in the lamina and medulla development-e.g., failures in the separation of the lamina and medulla cortices and the medulla rotation. These defects were reproduced in the mutant of an initiator caspase, dronc. If cell death was a mechanism for removing the abnormal neuropil structures, we would also expect to observe them in mutants defective for corpse clearance. However, they were not observed in these mutants. When dead cell-membranes were visualized with Apoliner, they were observed only in cortices and not in neuropils. These results suggest that the cell death occurs before mature neurite formation. Moreover, we found that inhibition of cell death induced ectopic neuroepithelial cells, neuroblasts and ganglion mother cells in late pupal stages, at sites where the outer and inner proliferation centers were located at earlier developmental stages. Caspase-3 activation was observed in the neuroepithelial cells and neuroblasts in the proliferation centers

  13. Adaptive Sliding Mode Control of Dynamic Systems Using Double Loop Recurrent Neural Network Structure.

    Science.gov (United States)

    Fei, Juntao; Lu, Cheng

    2018-04-01

    In this paper, an adaptive sliding mode control system using a double loop recurrent neural network (DLRNN) structure is proposed for a class of nonlinear dynamic systems. A new three-layer RNN is proposed to approximate unknown dynamics with two different kinds of feedback loops where the firing weights and output signal calculated in the last step are stored and used as the feedback signals in each feedback loop. Since the new structure has combined the advantages of internal feedback NN and external feedback NN, it can acquire the internal state information while the output signal is also captured, thus the new designed DLRNN can achieve better approximation performance compared with the regular NNs without feedback loops or the regular RNNs with a single feedback loop. The new proposed DLRNN structure is employed in an equivalent controller to approximate the unknown nonlinear system dynamics, and the parameters of the DLRNN are updated online by adaptive laws to get favorable approximation performance. To investigate the effectiveness of the proposed controller, the designed adaptive sliding mode controller with the DLRNN is applied to a -axis microelectromechanical system gyroscope to control the vibrating dynamics of the proof mass. Simulation results demonstrate that the proposed methodology can achieve good tracking property, and the comparisons of the approximation performance between radial basis function NN, RNN, and DLRNN show that the DLRNN can accurately estimate the unknown dynamics with a fast speed while the internal states of DLRNN are more stable.

  14. Nonlinear dynamic systems identification using recurrent interval type-2 TSK fuzzy neural network - A novel structure.

    Science.gov (United States)

    El-Nagar, Ahmad M

    2018-01-01

    In this study, a novel structure of a recurrent interval type-2 Takagi-Sugeno-Kang (TSK) fuzzy neural network (FNN) is introduced for nonlinear dynamic and time-varying systems identification. It combines the type-2 fuzzy sets (T2FSs) and a recurrent FNN to avoid the data uncertainties. The fuzzy firing strengths in the proposed structure are returned to the network input as internal variables. The interval type-2 fuzzy sets (IT2FSs) is used to describe the antecedent part for each rule while the consequent part is a TSK-type, which is a linear function of the internal variables and the external inputs with interval weights. All the type-2 fuzzy rules for the proposed RIT2TSKFNN are learned on-line based on structure and parameter learning, which are performed using the type-2 fuzzy clustering. The antecedent and consequent parameters of the proposed RIT2TSKFNN are updated based on the Lyapunov function to achieve network stability. The obtained results indicate that our proposed network has a small root mean square error (RMSE) and a small integral of square error (ISE) with a small number of rules and a small computation time compared with other type-2 FNNs. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

  15. Preschool externalizing behavior predicts gender-specific variation in adolescent neural structure.

    Directory of Open Access Journals (Sweden)

    Jessica Z K Caldwell

    Full Text Available Dysfunction in the prefrontal cortex, amygdala, and hippocampus is believed to underlie the development of much psychopathology. However, to date only limited longitudinal data relate early behavior with neural structure later in life. Our objective was to examine the relationship of early life externalizing behavior with adolescent brain structure. We report here the first longitudinal study linking externalizing behavior during preschool to brain structure during adolescence. We examined the relationship of preschool externalizing behavior with amygdala, hippocampus, and prefrontal cortex volumes at age 15 years in a community sample of 76 adolescents followed longitudinally since their mothers' pregnancy. A significant gender by externalizing behavior interaction revealed that males-but not females-with greater early childhood externalizing behavior had smaller amygdala volumes at adolescence (t = 2.33, p = .023. No significant results were found for the hippocampus or the prefrontal cortex. Greater early externalizing behavior also related to smaller volume of a cluster including the angular gyrus and tempoparietal junction across genders. Results were not attributable to the impact of preschool anxiety, preschool maternal stress, school-age internalizing or externalizing behaviors, or adolescent substance use. These findings demonstrate a novel, gender-specific relationship between early-childhood externalizing behavior and adolescent amygdala volume, as well as a cross-gender result for the angular gyrus and tempoparietal junction.

  16. Analysis of Neural Stem Cells from Human Cortical Brain Structures In Vitro.

    Science.gov (United States)

    Aleksandrova, M A; Poltavtseva, R A; Marei, M V; Sukhikh, G T

    2016-05-01

    Comparative immunohistochemical analysis of the neocortex from human fetuses showed that neural stem and progenitor cells are present in the brain throughout the gestation period, at least from week 8 through 26. At the same time, neural stem cells from the first and second trimester fetuses differed by the distribution, morphology, growth, and quantity. Immunocytochemical analysis of neural stem cells derived from fetuses at different gestation terms and cultured under different conditions showed their differentiation capacity. Detailed analysis of neural stem cell populations derived from fetuses on gestation weeks 8-9, 18-20, and 26 expressing Lex/SSEA1 was performed.

  17. Aebp2 as an epigenetic regulator for neural crest cells.

    Directory of Open Access Journals (Sweden)

    Hana Kim

    Full Text Available Aebp2 is a potential targeting protein for the mammalian Polycomb Repression Complex 2 (PRC2. We generated a mutant mouse line disrupting the transcription of Aebp2 to investigate its in vivo roles. Aebp2-mutant homozygotes were embryonic lethal while heterozygotes survived to adulthood with fertility. In developing mouse embryos, Aebp2 is expressed mainly within cells of neural crest origin. In addition, many heterozygotes display a set of phenotypes, enlarged colon and hypopigmentation, similar to those observed in human patients with Hirschsprung's disease and Waardenburg syndrome. These phenotypes are usually caused by the absence of the neural crest-derived ganglia in hindguts and melanocytes. ChIP analyses demonstrated that the majority of the genes involved in the migration and development process of neural crest cells are downstream target genes of AEBP2 and PRC2. Furthermore, expression analyses confirmed that some of these genes are indeed affected in the Aebp2 heterozygotes. Taken together, these results suggest that Aebp2 may regulate the migration and development of the neural crest cells through the PRC2-mediated epigenetic mechanism.

  18. Multi-Level Interval Estimation for Locating damage in Structures by Using Artificial Neural Networks

    International Nuclear Information System (INIS)

    Pan Danguang; Gao Yanhua; Song Junlei

    2010-01-01

    A new analysis technique, called multi-level interval estimation method, is developed for locating damage in structures. In this method, the artificial neural networks (ANN) analysis method is combined with the statistics theory to estimate the range of damage location. The ANN is multilayer perceptron trained by back-propagation. Natural frequencies and modal shape at a few selected points are used as input to identify the location and severity of damage. Considering the large-scale structures which have lots of elements, multi-level interval estimation method is developed to reduce the estimation range of damage location step-by-step. Every step, estimation range of damage location is obtained from the output of ANN by using the method of interval estimation. The next ANN training cases are selected from the estimation range after linear transform, and the output of new ANN estimation range of damage location will gained a reduced estimation range. Two numerical example analyses on 10-bar truss and 100-bar truss are presented to demonstrate the effectiveness of the proposed method.

  19. A Damage Prognosis Method of Girder Structures Based on Wavelet Neural Networks

    Directory of Open Access Journals (Sweden)

    Rumian Zhong

    2014-01-01

    Full Text Available Based on the basic theory of wavelet neural networks and finite element model updating method, a basic framework of damage prognosis method is proposed in this paper. Firstly, a damaged I-steel beam model testing is used to verify the feasibility and effectiveness of the proposed damage prognosis method. The results show that the predicted results of the damage prognosis method and the measured results are very well consistent, and the maximum error is less than 5%. Furthermore, Xinyihe Bridge in the Beijing-Shanghai Highway is selected as the engineering background, and the damage prognosis is conducted based on the data from the structural health monitoring system. The results show that the traffic volume will increase and seasonal differences will decrease in the next year and a half. The displacement has a slight increase and seasonal characters in the critical section of mid span, but the strain will increase distinctly. The analysis results indicate that the proposed method can be applied to the damage prognosis of girder bridge structures and has the potential for the bridge health monitoring and safety prognosis.

  20. Acoustic leak detection at complicated geometrical structures using fuzzy logic and neural networks

    International Nuclear Information System (INIS)

    Hessel, G.; Schmitt, W.; Weiss, F.P.

    1993-10-01

    An acoustic method based on pattern recognition is being developed. During the learning phase, the localization classifier is trained with sound patterns that are generated with simulated leaks at all locations endangered by leak. The patterns are extracted from the signals of an appropriate sensor array. After training unknown leak positions can be recognized through comparison with the training patterns. The experimental part is performed at an acoustic 1:3 model of the reactor vessel and head and at an original VVER-440 reactor in the former NPP Greifswald. The leaks were simulated at the vessel head using mobile sound sources driven either by compressed air, a piezoelectric transmitter or by a thin metal blade excited through a jet of compressed air. The sound patterns of the simulated leaks are simultaneously detected with an AE-sensor array and with high frequency microphones measuring structure-borne sound and airborne sound, respectively. Pattern classifiers based on Fuzzy Pattern Classification (FPC) and Artificial Neural Networks (ANN) are currently tested for validation of the acoustic emission-sensor array (FPC), leak localization via structure-borne sound (FPC) and the leak localization using microphones (ANN). The initial results show the used classifiers principally to be capable of detecting and locating leaks, but they also show that further investigations are necessary to develop a reliable method applicable at NPPs. (orig./HP)

  1. Improving quantitative structure-activity relationship models using Artificial Neural Networks trained with dropout.

    Science.gov (United States)

    Mendenhall, Jeffrey; Meiler, Jens

    2016-02-01

    Dropout is an Artificial Neural Network (ANN) training technique that has been shown to improve ANN performance across canonical machine learning (ML) datasets. Quantitative Structure Activity Relationship (QSAR) datasets used to relate chemical structure to biological activity in Ligand-Based Computer-Aided Drug Discovery pose unique challenges for ML techniques, such as heavily biased dataset composition, and relatively large number of descriptors relative to the number of actives. To test the hypothesis that dropout also improves QSAR ANNs, we conduct a benchmark on nine large QSAR datasets. Use of dropout improved both enrichment false positive rate and log-scaled area under the receiver-operating characteristic curve (logAUC) by 22-46 % over conventional ANN implementations. Optimal dropout rates are found to be a function of the signal-to-noise ratio of the descriptor set, and relatively independent of the dataset. Dropout ANNs with 2D and 3D autocorrelation descriptors outperform conventional ANNs as well as optimized fingerprint similarity search methods.

  2. Frontal Structural Neural Correlates of Working Memory Performance in Older Adults.

    Science.gov (United States)

    Nissim, Nicole R; O'Shea, Andrew M; Bryant, Vaughn; Porges, Eric C; Cohen, Ronald; Woods, Adam J

    2016-01-01

    Working memory is an executive memory process that allows transitional information to be held and manipulated temporarily in memory stores before being forgotten or encoded into long-term memory. Working memory is necessary for everyday decision-making and problem solving, making it a fundamental process in the daily lives of older adults. Working memory relies heavily on frontal lobe structures and is known to decline with age. The current study aimed to determine the neural correlates of decreased working memory performance in the frontal lobes by comparing cortical thickness and cortical surface area from two demographically matched groups of healthy older adults, free from cognitive impairment, with high versus low N-Back working memory performance ( N = 56; average age = 70.29 ± 10.64). High-resolution structural T1-weighted images (1 mm isotropic voxels) were obtained on a 3T Philips MRI scanner. When compared to high performers, low performers exhibited significantly decreased cortical surface area in three frontal lobe regions lateralized to the right hemisphere: medial orbital frontal gyrus, inferior frontal gyrus, and superior frontal gyrus (FDR p frontal regions may underlie age-related decline of working memory function.

  3. Prediction of municipal solid waste generation using artificial neural network approach enhanced by structural break analysis.

    Science.gov (United States)

    Adamović, Vladimir M; Antanasijević, Davor Z; Ristić, Mirjana Đ; Perić-Grujić, Aleksandra A; Pocajt, Viktor V

    2017-01-01

    This paper presents the development of a general regression neural network (GRNN) model for the prediction of annual municipal solid waste (MSW) generation at the national level for 44 countries of different size, population and economic development level. Proper modelling of MSW generation is essential for the planning of MSW management system as well as for the simulation of various environmental impact scenarios. The main objective of this work was to examine the potential influence of economy crisis (global or local) on the forecast of MSW generation obtained by the GRNN model. The existence of the so-called structural breaks that occur because of the economic crisis in the studied period (2000-2012) for each country was determined and confirmed using the Chow test and Quandt-Andrews test. Two GRNN models, one which did not take into account the influence of the economic crisis (GRNN) and another one which did (SB-GRNN), were developed. The novelty of the applied method is that it uses broadly available social, economic and demographic indicators and indicators of sustainability, together with GRNN and structural break testing for the prediction of MSW generation at the national level. The obtained results demonstrate that the SB-GRNN model provide more accurate predictions than the model which neglected structural breaks, with a mean absolute percentage error (MAPE) of 4.0 % compared to 6.7 % generated by the GRNN model. The proposed model enhanced with structural breaks can be a viable alternative for a more accurate prediction of MSW generation at the national level, especially for developing countries for which a lack of MSW data is notable.

  4. Revisiting the Neural Basis of Acquired Amusia: Lesion Patterns and Structural Changes Underlying Amusia Recovery.

    Science.gov (United States)

    Sihvonen, Aleksi J; Ripollés, Pablo; Rodríguez-Fornells, Antoni; Soinila, Seppo; Särkämö, Teppo

    2017-01-01

    Although, acquired amusia is a common deficit following stroke, relatively little is still known about its precise neural basis, let alone to its recovery. Recently, we performed a voxel-based lesion-symptom mapping (VLSM) and morphometry (VBM) study which revealed a right lateralized lesion pattern, and longitudinal gray matter volume (GMV) and white matter volume (WMV) changes that were specifically associated with acquired amusia after stroke. In the present study, using a larger sample of stroke patients ( N = 90), we aimed to replicate and extend the previous structural findings as well as to determine the lesion patterns and volumetric changes associated with amusia recovery. Structural MRIs were acquired at acute and 6-month post-stroke stages. Music perception was behaviorally assessed at acute and 3-month post-stroke stages using the Scale and Rhythm subtests of the Montreal Battery of Evaluation of Amusia (MBEA). Using these scores, the patients were classified as non-amusic, recovered amusic, and non-recovered amusic. The results of the acute stage VLSM analyses and the longitudinal VBM analyses converged to show that more severe and persistent (non-recovered) amusia was associated with an extensive pattern of lesions and GMV/WMV decrease in right temporal, frontal, parietal, striatal, and limbic areas. In contrast, less severe and transient (recovered) amusia was linked to lesions specifically in left inferior frontal gyrus as well as to a GMV decrease in right parietal areas. Separate continuous analyses of MBEA Scale and Rhythm scores showed extensively overlapping lesion pattern in right temporal, frontal, and subcortical structures as well as in the right insula. Interestingly, the recovered pitch amusia was related to smaller GMV decreases in the temporoparietal junction whereas the recovered rhythm amusia was associated to smaller GMV decreases in the inferior temporal pole. Overall, the results provide a more comprehensive picture of the lesions

  5. Revisiting the Neural Basis of Acquired Amusia: Lesion Patterns and Structural Changes Underlying Amusia Recovery

    Science.gov (United States)

    Sihvonen, Aleksi J.; Ripollés, Pablo; Rodríguez-Fornells, Antoni; Soinila, Seppo; Särkämö, Teppo

    2017-01-01

    Although, acquired amusia is a common deficit following stroke, relatively little is still known about its precise neural basis, let alone to its recovery. Recently, we performed a voxel-based lesion-symptom mapping (VLSM) and morphometry (VBM) study which revealed a right lateralized lesion pattern, and longitudinal gray matter volume (GMV) and white matter volume (WMV) changes that were specifically associated with acquired amusia after stroke. In the present study, using a larger sample of stroke patients (N = 90), we aimed to replicate and extend the previous structural findings as well as to determine the lesion patterns and volumetric changes associated with amusia recovery. Structural MRIs were acquired at acute and 6-month post-stroke stages. Music perception was behaviorally assessed at acute and 3-month post-stroke stages using the Scale and Rhythm subtests of the Montreal Battery of Evaluation of Amusia (MBEA). Using these scores, the patients were classified as non-amusic, recovered amusic, and non-recovered amusic. The results of the acute stage VLSM analyses and the longitudinal VBM analyses converged to show that more severe and persistent (non-recovered) amusia was associated with an extensive pattern of lesions and GMV/WMV decrease in right temporal, frontal, parietal, striatal, and limbic areas. In contrast, less severe and transient (recovered) amusia was linked to lesions specifically in left inferior frontal gyrus as well as to a GMV decrease in right parietal areas. Separate continuous analyses of MBEA Scale and Rhythm scores showed extensively overlapping lesion pattern in right temporal, frontal, and subcortical structures as well as in the right insula. Interestingly, the recovered pitch amusia was related to smaller GMV decreases in the temporoparietal junction whereas the recovered rhythm amusia was associated to smaller GMV decreases in the inferior temporal pole. Overall, the results provide a more comprehensive picture of the lesions

  6. Revisiting the Neural Basis of Acquired Amusia: Lesion Patterns and Structural Changes Underlying Amusia Recovery

    Directory of Open Access Journals (Sweden)

    Aleksi J. Sihvonen

    2017-07-01

    Full Text Available Although, acquired amusia is a common deficit following stroke, relatively little is still known about its precise neural basis, let alone to its recovery. Recently, we performed a voxel-based lesion-symptom mapping (VLSM and morphometry (VBM study which revealed a right lateralized lesion pattern, and longitudinal gray matter volume (GMV and white matter volume (WMV changes that were specifically associated with acquired amusia after stroke. In the present study, using a larger sample of stroke patients (N = 90, we aimed to replicate and extend the previous structural findings as well as to determine the lesion patterns and volumetric changes associated with amusia recovery. Structural MRIs were acquired at acute and 6-month post-stroke stages. Music perception was behaviorally assessed at acute and 3-month post-stroke stages using the Scale and Rhythm subtests of the Montreal Battery of Evaluation of Amusia (MBEA. Using these scores, the patients were classified as non-amusic, recovered amusic, and non-recovered amusic. The results of the acute stage VLSM analyses and the longitudinal VBM analyses converged to show that more severe and persistent (non-recovered amusia was associated with an extensive pattern of lesions and GMV/WMV decrease in right temporal, frontal, parietal, striatal, and limbic areas. In contrast, less severe and transient (recovered amusia was linked to lesions specifically in left inferior frontal gyrus as well as to a GMV decrease in right parietal areas. Separate continuous analyses of MBEA Scale and Rhythm scores showed extensively overlapping lesion pattern in right temporal, frontal, and subcortical structures as well as in the right insula. Interestingly, the recovered pitch amusia was related to smaller GMV decreases in the temporoparietal junction whereas the recovered rhythm amusia was associated to smaller GMV decreases in the inferior temporal pole. Overall, the results provide a more comprehensive picture of

  7. Chaotic diagonal recurrent neural network

    International Nuclear Information System (INIS)

    Wang Xing-Yuan; Zhang Yi

    2012-01-01

    We propose a novel neural network based on a diagonal recurrent neural network and chaos, and its structure and learning algorithm are designed. The multilayer feedforward neural network, diagonal recurrent neural network, and chaotic diagonal recurrent neural network are used to approach the cubic symmetry map. The simulation results show that the approximation capability of the chaotic diagonal recurrent neural network is better than the other two neural networks. (interdisciplinary physics and related areas of science and technology)

  8. A neural network approach to the study of dynamics and structure of molecular systems

    International Nuclear Information System (INIS)

    Getino, C.; Sumpter, B.G.; Noid, D.W.

    1994-01-01

    Neural networks are used to study intramolecular energy flow in molecular systems (tetratomics to macromolecules), developing new techniques for efficient analysis of data obtained from molecular-dynamics and quantum mechanics calculations. Neural networks can map phase space points to intramolecular vibrational energies along a classical trajectory (example of complicated coordinate transformation), producing reasonably accurate values for any region of the multidimensional phase space of a tetratomic molecule. Neural network energy flow predictions are found to significantly enhance the molecular-dynamics method to longer time-scales and extensive averaging of trajectories for macromolecular systems. Pattern recognition abilities of neural networks can be used to discern phase space features. Neural networks can also expand model calculations by interpolation of costly quantum mechanical ab initio data, used to develop semiempirical potential energy functions

  9. Development of relative humidity models by using optimized neural network structures

    Energy Technology Data Exchange (ETDEWEB)

    Martinez-romero, A.; Ortega, J. F.; Juan, J. A.; Tarjuelo, J. M.; Moreno, M. A.

    2010-07-01

    Climate has always had a very important role in life on earth, as well as human activity and health. The influence of relative humidity (RH) in controlled environments (e.g. industrial processes in agro-food processing, cold storage of foods such as fruits, vegetables and meat, or controls in greenhouses) is very important. Relative humidity is a main factor in agricultural production and crop yield (due to the influence on crop water demand or the development and distribution of pests and diseases, for example). The main objective of this paper is to estimate RH [maximum (RHmax), average (RHave), and minimum (RHmin)] data in a specific area, being applied to the Region of Castilla-La Mancha (C-LM) in this case, from available data at thermo-pluviometric weather stations. In this paper Artificial neural networks (ANN) are used to generate RH considering maximum and minimum temperatures and extraterrestrial solar radiation data. Model validation and generation is based on data from the years 2000 to 2008 from 44 complete agroclimatic weather stations. Relative errors are estimated as 1) spatial errors of 11.30%, 6.80% and 10.27% and 2) temporal errors of 10.34%, 6.59% and 9.77% for RHmin, RHmax and RHave, respectively. The use of ANNs is interesting in generating climate parameters from available climate data. For determining optimal ANN structure in estimating RH values, model calibration and validation is necessary, considering spatial and temporal variability. (Author) 44 refs.

  10. Artificial neural network prediction of quantitative structure - retention relationships of polycyclic aromatic hydocarbons in gas chromatography

    Directory of Open Access Journals (Sweden)

    SNEZANA SREMAC

    2005-11-01

    Full Text Available A feed-forward artificial neural network (ANN model was used to link molecular structures (boiling points, connectivity indices and molecular weights and retention indices of polycyclic aromatic hydrocarbons (PAHs in linear temperature-programmed gas chromatography. A randomly taken subset of PAH retention data reported by Lee et al. [Anal. Chem. 51 (1979 768], containing retention index data for 30 PAHs, was used to make the ANN model. The prediction ability of the trained ANN was tested on unseen data for 18 PAHs from the same article, as well as on the retention data for 7 PAHs experimentally obtained in this work. In addition, two different data sets with known retention indices taken from the literature were analyzed by the same ANN model. It has been shown that the relative accuracy as the degree of agreement between the measured and the predicted retention indices in all testing sets, for most of the studied PAHs, were within the experimental error margins (±3%.

  11. Emotional face processing and flat affect in schizophrenia: functional and structural neural correlates.

    Science.gov (United States)

    Lepage, M; Sergerie, K; Benoit, A; Czechowska, Y; Dickie, E; Armony, J L

    2011-09-01

    There is a general consensus in the literature that schizophrenia causes difficulties with facial emotion perception and discrimination. Functional brain imaging studies have observed reduced limbic activity during facial emotion perception but few studies have examined the relation to flat affect severity. A total of 26 people with schizophrenia and 26 healthy controls took part in this event-related functional magnetic resonance imaging study. Sad, happy and neutral faces were presented in a pseudo-random order and participants indicated the gender of the face presented. Manual segmentation of the amygdala was performed on a structural T1 image. Both the schizophrenia group and the healthy control group rated the emotional valence of facial expressions similarly. Both groups exhibited increased brain activity during the perception of emotional faces relative to neutral ones in multiple brain regions, including multiple prefrontal regions bilaterally, the right amygdala, right cingulate cortex and cuneus. Group comparisons, however, revealed increased activity in the healthy group in the anterior cingulate, right parahippocampal gyrus and multiple visual areas. In schizophrenia, the severity of flat affect correlated significantly with neural activity in several brain areas including the amygdala and parahippocampal region bilaterally. These results suggest that many of the brain regions involved in emotional face perception, including the amygdala, are equally recruited in both schizophrenia and controls, but flat affect can also moderate activity in some other brain regions, notably in the left amygdala and parahippocampal gyrus bilaterally. There were no significant group differences in the volume of the amygdala.

  12. Bayesian inversion of surface wave data for discontinuities and velocity structure in the upper mantle using Neural Networks. Geologica Ultraiectina (287)

    NARCIS (Netherlands)

    Meier, U.

    2008-01-01

    We present a neural network approach to invert surface wave data for discontinuities and velocity structure in the upper mantle. We show how such a neural network can be trained on a set of random samples to give a continuous approximation to the inverse relation in a compact and computationally

  13. Do neural tube defects lead to structural alterations in the human bladder?

    Science.gov (United States)

    Pazos, Helena M F; Lobo, Márcio Luiz de P; Costa, Waldemar S; Sampaio, Francisco J B; Cardoso, Luis Eduardo M; Favorito, Luciano Alves

    2011-05-01

    Anencephaly is the most severe neural tube defect in human fetuses. The objective of this paper is to analyze the structure of the bladder in anencephalic human fetuses. We studied 40 bladders of normal human fetuses (20 male and 20 female, aged 14 to 23 WPC) and 12 bladders of anencephalic fetuses (5 male and 7 female, aged 18 to 22 WPC). The bladders were removed and processed by routine histological techniques. Stereological analysis of collagen, elastic system fibers and smooth muscle was performed in sections. Data were expressed as volumetric density (Vv-%). The images were captured with Olympus BX51 microscopy and Olympus DP70 camera. The stereological analysis was done using the software Image Pro and Image J. For biochemical analysis, samples were fixed in acetone, and collagen concentrations were expressed as micrograms of hydroxyproline per mg of dry tissue. Means were statistically compared using the unpaired t-test (p<0.05). We observed a significant increase (p<0.0001) in the Vv of collagen in the bladders of anencephalic fetuses (69.71%) when compared to normal fetuses (52.74%), and a significant decrease (p<0.0001) in the Vv of smooth muscle cells in the bladders of anencephalic fetuses (23.96%) when compared to normal fetuses (38.35%). The biochemical analyses showed a higher concentration of total collagen in the bladders of anencephalic fetuses (37354 µg/mg) when compared to normal fetuses (48117 µg/mg, p<0.02). The structural alterations of the bladder found in this study may suggest the existence of functional alterations in the bladder of anencephalic human fetuses.

  14. A phenotypic structure and neural correlates of compulsive behaviors in adolescents.

    Directory of Open Access Journals (Sweden)

    Chantale Montigny

    Full Text Available A compulsivity spectrum has been hypothesized to exist across Obsessive-Compulsive disorder (OCD, Eating Disorders (ED, substance abuse (SA and binge-drinking (BD. The objective was to examine the validity of this compulsivity spectrum, and differentiate it from an externalizing behaviors dimension, but also to look at hypothesized personality and neural correlates.A community-sample of adolescents (N=1938; mean age 14.5 years, and their parents were recruited via high-schools in 8 European study sites. Data on adolescents' psychiatric symptoms, DSM diagnoses (DAWBA and substance use behaviors (AUDIT and ESPAD were collected through adolescent- and parent-reported questionnaires and interviews. The phenotypic structure of compulsive behaviors was then tested using structural equation modeling. The model was validated using personality variables (NEO-FFI and TCI, and Voxel-Based Morphometry (VBM analysis.Compulsivity symptoms best fit a higher-order two factor model, with ED and OCD loading onto a compulsivity factor, and BD and SA loading onto an externalizing factor, composed also of ADHD and conduct disorder symptoms. The compulsivity construct correlated with neuroticism (r=0.638; p ≤ 0.001, conscientiousness (r=0.171; p ≤ 0.001, and brain gray matter volume in left and right orbitofrontal cortex, right ventral striatum and right dorsolateral prefrontal cortex. The externalizing factor correlated with extraversion (r=0.201; p ≤ 0.001, novelty-seeking (r=0.451; p ≤ 0.001, and negatively with gray matter volume in the left inferior and middle frontal gyri.Results suggest that a compulsivity spectrum exists in an adolescent, preclinical sample and accounts for variance in both OCD and ED, but not substance-related behaviors, and can be differentiated from an externalizing spectrum.

  15. A phenotypic structure and neural correlates of compulsive behaviors in adolescents.

    Science.gov (United States)

    Montigny, Chantale; Castellanos-Ryan, Natalie; Whelan, Robert; Banaschewski, Tobias; Barker, Gareth J; Büchel, Christian; Gallinat, Jürgen; Flor, Herta; Mann, Karl; Paillère-Martinot, Marie-Laure; Nees, Frauke; Lathrop, Mark; Loth, Eva; Paus, Tomas; Pausova, Zdenka; Rietschel, Marcella; Schumann, Gunter; Smolka, Michael N; Struve, Maren; Robbins, Trevor W; Garavan, Hugh; Conrod, Patricia J

    2013-01-01

    A compulsivity spectrum has been hypothesized to exist across Obsessive-Compulsive disorder (OCD), Eating Disorders (ED), substance abuse (SA) and binge-drinking (BD). The objective was to examine the validity of this compulsivity spectrum, and differentiate it from an externalizing behaviors dimension, but also to look at hypothesized personality and neural correlates. A community-sample of adolescents (N=1938; mean age 14.5 years), and their parents were recruited via high-schools in 8 European study sites. Data on adolescents' psychiatric symptoms, DSM diagnoses (DAWBA) and substance use behaviors (AUDIT and ESPAD) were collected through adolescent- and parent-reported questionnaires and interviews. The phenotypic structure of compulsive behaviors was then tested using structural equation modeling. The model was validated using personality variables (NEO-FFI and TCI), and Voxel-Based Morphometry (VBM) analysis. Compulsivity symptoms best fit a higher-order two factor model, with ED and OCD loading onto a compulsivity factor, and BD and SA loading onto an externalizing factor, composed also of ADHD and conduct disorder symptoms. The compulsivity construct correlated with neuroticism (r=0.638; p ≤ 0.001), conscientiousness (r=0.171; p ≤ 0.001), and brain gray matter volume in left and right orbitofrontal cortex, right ventral striatum and right dorsolateral prefrontal cortex. The externalizing factor correlated with extraversion (r=0.201; p ≤ 0.001), novelty-seeking (r=0.451; p ≤ 0.001), and negatively with gray matter volume in the left inferior and middle frontal gyri. Results suggest that a compulsivity spectrum exists in an adolescent, preclinical sample and accounts for variance in both OCD and ED, but not substance-related behaviors, and can be differentiated from an externalizing spectrum.

  16. A Phenotypic Structure and Neural Correlates of Compulsive Behaviors in Adolescents

    Science.gov (United States)

    Montigny, Chantale; Castellanos-Ryan, Natalie; Whelan, Robert; Banaschewski, Tobias; Barker, Gareth J.; Büchel, Christian; Gallinat, Jürgen; Flor, Herta; Mann, Karl; Paillère-Martinot, Marie-Laure; Nees, Frauke; Lathrop, Mark; Loth, Eva; Paus, Tomas; Pausova, Zdenka; Rietschel, Marcella; Schumann, Gunter; Smolka, Michael N.; Struve, Maren; Robbins, Trevor W.; Garavan, Hugh; Conrod, Patricia J.

    2013-01-01

    Background A compulsivity spectrum has been hypothesized to exist across Obsessive-Compulsive disorder (OCD), Eating Disorders (ED), substance abuse (SA) and binge-drinking (BD). The objective was to examine the validity of this compulsivity spectrum, and differentiate it from an externalizing behaviors dimension, but also to look at hypothesized personality and neural correlates. Method A community-sample of adolescents (N=1938; mean age 14.5 years), and their parents were recruited via high-schools in 8 European study sites. Data on adolescents’ psychiatric symptoms, DSM diagnoses (DAWBA) and substance use behaviors (AUDIT and ESPAD) were collected through adolescent- and parent-reported questionnaires and interviews. The phenotypic structure of compulsive behaviors was then tested using structural equation modeling. The model was validated using personality variables (NEO-FFI and TCI), and Voxel-Based Morphometry (VBM) analysis. Results Compulsivity symptoms best fit a higher-order two factor model, with ED and OCD loading onto a compulsivity factor, and BD and SA loading onto an externalizing factor, composed also of ADHD and conduct disorder symptoms. The compulsivity construct correlated with neuroticism (r=0.638; p≤0.001), conscientiousness (r=0.171; p≤0.001), and brain gray matter volume in left and right orbitofrontal cortex, right ventral striatum and right dorsolateral prefrontal cortex. The externalizing factor correlated with extraversion (r=0.201; p≤0.001), novelty-seeking (r=0.451; p≤0.001), and negatively with gray matter volume in the left inferior and middle frontal gyri. Conclusions Results suggest that a compulsivity spectrum exists in an adolescent, preclinical sample and accounts for variance in both OCD and ED, but not substance-related behaviors, and can be differentiated from an externalizing spectrum. PMID:24244633

  17. Modular Adaptive System Based on a Multi-Stage Neural Structure for Recognition of 2D Objects of Discontinuous Production

    Directory of Open Access Journals (Sweden)

    I. Topalova

    2005-03-01

    Full Text Available This is a presentation of a new system for invariant recognition of 2D objects with overlapping classes, that can not be effectively recognized with the traditional methods. The translation, scale and partial rotation invariant contour object description is transformed in a DCT spectrum space. The obtained frequency spectrums are decomposed into frequency bands in order to feed different BPG neural nets (NNs. The NNs are structured in three stages - filtering and full rotation invariance; partial recognition; general classification. The designed multi-stage BPG Neural Structure shows very good accuracy and flexibility when tested with 2D objects used in the discontinuous production. The reached speed and the opportunuty for an easy restructuring and reprogramming of the system makes it suitable for application in different applied systems for real time work.

  18. Modular Adaptive System Based on a Multi-Stage Neural Structure for Recognition of 2D Objects of Discontinuous Production

    Directory of Open Access Journals (Sweden)

    I. Topalova

    2008-11-01

    Full Text Available This is a presentation of a new system for invariant recognition of 2D objects with overlapping classes, that can not be effectively recognized with the traditional methods. The translation, scale and partial rotation invariant contour object description is transformed in a DCT spectrum space. The obtained frequency spectrums are decomposed into frequency bands in order to feed different BPG neural nets (NNs. The NNs are structured in three stages - filtering and full rotation invariance; partial recognition; general classification. The designed multi-stage BPG Neural Structure shows very good accuracy and flexibility when tested with 2D objects used in the discontinuous production. The reached speed and the opportunuty for an easy restructuring and reprogramming of the system makes it suitable for application in different applied systems for real time work.

  19. Explicitly integrating parameter, input, and structure uncertainties into Bayesian Neural Networks for probabilistic hydrologic forecasting

    KAUST Repository

    Zhang, Xuesong; Liang, Faming; Yu, Beibei; Zong, Ziliang

    2011-01-01

    Estimating uncertainty of hydrologic forecasting is valuable to water resources and other relevant decision making processes. Recently, Bayesian Neural Networks (BNNs) have been proved powerful tools for quantifying uncertainty of streamflow

  20. Self-awareness in neurodegenerative disease relies on neural structures mediating reward-driven attention.

    Science.gov (United States)

    Shany-Ur, Tal; Lin, Nancy; Rosen, Howard J; Sollberger, Marc; Miller, Bruce L; Rankin, Katherine P

    2014-08-01

    Accurate self-awareness is essential for adapting one's tasks and goals to one's actual abilities. Patients with neurodegenerative diseases, particularly those with right frontal involvement, often present with poor self-awareness of their functional limitations that may exacerbate their already jeopardized decision-making and behaviour. We studied the structural neuroanatomical basis for impaired self-awareness among patients with neurodegenerative disease and healthy older adults. One hundred and twenty-four participants (78 patients with neurodegenerative diseases including Alzheimer's disease, behavioural variant frontotemporal dementia, right-temporal frontotemporal dementia, semantic variant and non-fluent variant primary progressive aphasia, and 46 healthy controls) described themselves on the Patient Competency Rating Scale, rating observable functioning across four domains (daily living activities, cognitive, emotional control, interpersonal). All participants underwent structural magnetic resonance imaging. Informants also described subjects' functioning on the same scale. Self-awareness was measured by comparing self and informant ratings. Group differences in discrepancy scores were analysed using general linear models, controlling for age, sex and disease severity. Compared with controls, patients with behavioural variant frontotemporal dementia overestimated their functioning in all domains, patients with Alzheimer's disease overestimated cognitive and emotional functioning, patients with right-temporal frontotemporal dementia overestimated interpersonal functioning, and patients with non-fluent aphasia overestimated emotional and interpersonal functioning. Patients with semantic variant aphasia did not overestimate functioning on any domain. To examine the neuroanatomic correlates of impaired self-awareness, discrepancy scores were correlated with brain volume using voxel-based morphometry. To identify the unique neural correlates of overlooking

  1. Prolonged Expansion Induces Spontaneous Neural Progenitor Differentiation from Human Gingiva-Derived Mesenchymal Stem Cells.

    Science.gov (United States)

    Rajan, Thangavelu Soundara; Scionti, Domenico; Diomede, Francesca; Piattelli, Adriano; Bramanti, Placido; Mazzon, Emanuela; Trubiani, Oriana

    2017-12-01

    Neural crest-derived mesenchymal stem cells (MSCs) obtained from dental tissues received considerable interest in regenerative medicine, particularly in nerve regeneration owing to their embryonic origin and ease of harvest. Proliferation efficacy and differentiation capacity into diverse cell lineages propose dental MSCs as an in vitro tool for disease modeling. In this study, we investigated the spontaneous differentiation efficiency of dental MSCs obtained from human gingiva tissue (hGMSCs) into neural progenitor cells after extended passaging. At passage 41, the morphology of hGMSCs changed from typical fibroblast-like shape into sphere-shaped cells with extending processes. Next-generation transcriptomics sequencing showed increased expression of neural progenitor markers such as NES, MEIS2, and MEST. In addition, de novo expression of neural precursor genes, such as NRN1, PHOX2B, VANGL2, and NTRK3, was noticed in passage 41. Immunocytochemistry results showed suppression of neurogenesis repressors TP53 and p21, whereas Western blot results revealed the expression of neurotrophic factors BDNF and NT3 at passage 41. Our results showed the spontaneous efficacy of hGMSCs to differentiate into neural precursor cells over prolonged passages and that these cells may assist in producing novel in vitro disease models that are associated with neural development.

  2. The Effect of an Enrichment Reading Program on the Cognitive Processes and Neural Structures of Children Having Reading Difficulties

    Directory of Open Access Journals (Sweden)

    Hayriye Gül KURUYER

    2017-06-01

    Full Text Available The main purpose of the current study is to explain the effect of an enrichment reading program on the cognitive processes and neural structures of children experiencing reading difficulties. The current study was carried out in line with a single-subject research method and the between-subjects multiple probe design belonging to this method. This research focuses on a group of eight students with reading difficulties. Within the context of the study, memory capacities, attention spans, reading-related activation and white matter pathways of the students were determined before and after the application of the enrichment reading program. This determination process was carried out in two stages. Neuro-imaging was performed in the first stage and in the second stage the students’ cognitive processes and neural structures were investigated in terms of focusing attention and memory capacities by using the following tools: Stroop Test TBAG Form, Auditory Verbal Digit Span Test-Form B, Cancellation Test and Number Order Learning Test. The results obtained show that the enrichment reading program resulted in an improvement in the reading profiles of the students having reading difficulties in terms of their cognitive processes and neural structures.

  3. Structural and neural network analyses of fracture systems at the Aespoe Hard Rock Laboratory, SE Sweden

    International Nuclear Information System (INIS)

    Sirat, M.

    1999-01-01

    The > 10,000 fractures documented in the 450 m deep Aespoe Hard Rock Laboratory (HRL) provide a unique opportunity to study brittle deformation of a Swedish bedrock mass. The fracture population consists of six major sets, one sub-horizontal and five sub-vertical. A classical structural analysis explored the interrelations between geometry and frequency of both dry and wet fractures with respect to depth and in-situ stresses. Three main findings are: In-situ stresses govern frequency distributions of dilated, hence water-bearing fractures. About 68.5% of sub-horizontal fractures are dilated in the thrust regime above a depth of ca. 230 m while 53% of sub-vertical fractures are dilated in the underlying wrench regime. Fractures curve both horizontally and vertically, a finding confirmed by the application of artificial neural networks that included Back-Propagation and Self-Organizing (Kohonen) networks. The asymmetry of the total fracture population and tilts of the sub-Cambrian peneplain demonstrates that multiple reactivations of fractures have tilted the Aespoe rock mass 6 deg to the west. The potential space problem raised by this tilt is negated by systematic curvature of steep fractures, some of which sole out to gently dipping fracture zones. Fractures probably developed their curvature when they formed deep in crystalline crust in Precambrian times but have since reactivated at shallow depths. These findings add significantly to the conceptual model of Aespoe and should be taken into account in future studies regarding the isolation of Sweden's high-grade radioactive waste in crystalline bedrock

  4. The Burden of Binge and Heavy Drinking on the Brain: Effects on Adolescent and Young Adult Neural Structure and Function

    Directory of Open Access Journals (Sweden)

    Anita Cservenka

    2017-06-01

    Full Text Available Introduction: Adolescence and young adulthood are periods of continued biological and psychosocial maturation. Thus, there may be deleterious effects of consuming large quantities of alcohol on neural development and associated cognition during this time. The purpose of this mini review is to highlight neuroimaging research that has specifically examined the effects of binge and heavy drinking on adolescent and young adult brain structure and function.Methods: We review cross-sectional and longitudinal studies of young binge and heavy drinkers that have examined brain structure (e.g., gray and white matter volume, cortical thickness, white matter microstructure and investigated brain response using functional magnetic resonance imaging (fMRI.Results: Binge and heavy-drinking adolescents and young adults have systematically thinner and lower volume in prefrontal cortex and cerebellar regions, and attenuated white matter development. They also show elevated brain activity in fronto-parietal regions during working memory, verbal learning, and inhibitory control tasks. In response to alcohol cues, relative to controls or light-drinking individuals, binge and heavy drinkers show increased neural response mainly in mesocorticolimbic regions, including the striatum, anterior cingulate cortex (ACC, hippocampus, and amygdala. Mixed findings are present in risky decision-making tasks, which could be due to large variation in task design and analysis.Conclusions: These findings suggest altered neural structure and activity in binge and heavy-drinking youth may be related to the neurotoxic effects of consuming alcohol in large quantities during a highly plastic neurodevelopmental period, which could result in neural reorganization, and increased risk for developing an alcohol use disorder (AUD.

  5. Effects of bursting dynamic features on the generation of multi-clustered structure of neural network with symmetric spike-timing-dependent plasticity learning rule

    Energy Technology Data Exchange (ETDEWEB)

    Liu, Hui; Song, Yongduan; Xue, Fangzheng; Li, Xiumin, E-mail: xmli@cqu.edu.cn [Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education, Chongqing University, Chongqing 400044 (China); College of Automation, Chongqing University, Chongqing 400044 (China)

    2015-11-15

    In this paper, the generation of multi-clustered structure of self-organized neural network with different neuronal firing patterns, i.e., bursting or spiking, has been investigated. The initially all-to-all-connected spiking neural network or bursting neural network can be self-organized into clustered structure through the symmetric spike-timing-dependent plasticity learning for both bursting and spiking neurons. However, the time consumption of this clustering procedure of the burst-based self-organized neural network (BSON) is much shorter than the spike-based self-organized neural network (SSON). Our results show that the BSON network has more obvious small-world properties, i.e., higher clustering coefficient and smaller shortest path length than the SSON network. Also, the results of larger structure entropy and activity entropy of the BSON network demonstrate that this network has higher topological complexity and dynamical diversity, which benefits for enhancing information transmission of neural circuits. Hence, we conclude that the burst firing can significantly enhance the efficiency of clustering procedure and the emergent clustered structure renders the whole network more synchronous and therefore more sensitive to weak input. This result is further confirmed from its improved performance on stochastic resonance. Therefore, we believe that the multi-clustered neural network which self-organized from the bursting dynamics has high efficiency in information processing.

  6. Effects of bursting dynamic features on the generation of multi-clustered structure of neural network with symmetric spike-timing-dependent plasticity learning rule

    International Nuclear Information System (INIS)

    Liu, Hui; Song, Yongduan; Xue, Fangzheng; Li, Xiumin

    2015-01-01

    In this paper, the generation of multi-clustered structure of self-organized neural network with different neuronal firing patterns, i.e., bursting or spiking, has been investigated. The initially all-to-all-connected spiking neural network or bursting neural network can be self-organized into clustered structure through the symmetric spike-timing-dependent plasticity learning for both bursting and spiking neurons. However, the time consumption of this clustering procedure of the burst-based self-organized neural network (BSON) is much shorter than the spike-based self-organized neural network (SSON). Our results show that the BSON network has more obvious small-world properties, i.e., higher clustering coefficient and smaller shortest path length than the SSON network. Also, the results of larger structure entropy and activity entropy of the BSON network demonstrate that this network has higher topological complexity and dynamical diversity, which benefits for enhancing information transmission of neural circuits. Hence, we conclude that the burst firing can significantly enhance the efficiency of clustering procedure and the emergent clustered structure renders the whole network more synchronous and therefore more sensitive to weak input. This result is further confirmed from its improved performance on stochastic resonance. Therefore, we believe that the multi-clustered neural network which self-organized from the bursting dynamics has high efficiency in information processing

  7. An application of neural network for Structural Health Monitoring of an adaptive wing with an array of FBG sensors

    International Nuclear Information System (INIS)

    Mieloszyk, Magdalena; Skarbek, Lukasz; Ostachowicz, Wieslaw; Krawczuk, Marek

    2011-01-01

    This paper presents an application of neural networks to determinate the level of activation of shape memory alloy actuators of an adaptive wing. In this concept the shape of the wing can be controlled and altered thanks to the wing design and the use of integrated shape memory alloy actuators. The wing is assumed as assembled from a number of wing sections that relative positions can be controlled independently by thermal activation of shape memory actuators. The investigated wing is employed with an array of Fibre Bragg Grating sensors. The Fibre Bragg Grating sensors with combination of a neural network have been used to Structural Health Monitoring of the wing condition. The FBG sensors are a great tool to control the condition of composite structures due to their immunity to electromagnetic fields as well as their small size and weight. They can be mounted onto the surface or embedded into the wing composite material without any significant influence on the wing strength. The paper concentrates on analysis of the determination of the twisting moment produced by an activated shape memory alloy actuator. This has been analysed both numerically using the finite element method by a commercial code ABAQUS (registered) and experimentally using Fibre Bragg Grating sensor measurements. The results of the analysis have been then used by a neural network to determine twisting moments produced by each shape memory alloy actuator.

  8. The artist emerges: visual art learning alters neural structure and function.

    Science.gov (United States)

    Schlegel, Alexander; Alexander, Prescott; Fogelson, Sergey V; Li, Xueting; Lu, Zhengang; Kohler, Peter J; Riley, Enrico; Tse, Peter U; Meng, Ming

    2015-01-15

    How does the brain mediate visual artistic creativity? Here we studied behavioral and neural changes in drawing and painting students compared to students who did not study art. We investigated three aspects of cognition vital to many visual artists: creative cognition, perception, and perception-to-action. We found that the art students became more creative via the reorganization of prefrontal white matter but did not find any significant changes in perceptual ability or related neural activity in the art students relative to the control group. Moreover, the art students improved in their ability to sketch human figures from observation, and multivariate patterns of cortical and cerebellar activity evoked by this drawing task became increasingly separable between art and non-art students. Our findings suggest that the emergence of visual artistic skills is supported by plasticity in neural pathways that enable creative cognition and mediate perceptuomotor integration. Copyright © 2014 Elsevier Inc. All rights reserved.

  9. Dissecting CNBP, a zinc-finger protein required for neural crest development, in its structural and functional domains.

    Science.gov (United States)

    Armas, Pablo; Agüero, Tristán H; Borgognone, Mariana; Aybar, Manuel J; Calcaterra, Nora B

    2008-10-17

    Cellular nucleic-acid-binding protein (CNBP) plays an essential role in forebrain and craniofacial development by controlling cell proliferation and survival to mediate neural crest expansion. CNBP binds to single-stranded nucleic acids and displays nucleic acid chaperone activity in vitro. The CNBP family shows a conserved modular organization of seven Zn knuckles and an arginine-glycine-glycine (RGG) box between the first and second Zn knuckles. The participation of these structural motifs in CNBP biochemical activities has still not been addressed. Here, we describe the generation of CNBP mutants that dissect the protein into regions with structurally and functionally distinct properties. Mutagenesis approaches were followed to generate: (i) an amino acid replacement that disrupted the fifth Zn knuckle; (ii) N-terminal deletions that removed the first Zn knuckle and the RGG box, or the RGG box alone; and (iii) a C-terminal deletion that eliminated the three last Zn knuckles. Mutant proteins were overexpressed in Escherichia coli, purified, and used to analyze their biochemical features in vitro, or overexpressed in Xenopus laevis embryos to study their function in vivo during neural crest cell development. We found that the Zn knuckles are required, but not individually essential, for CNBP biochemical activities, whereas the RGG box is essential for RNA-protein binding and nucleic acid chaperone activity. Removal of the RGG box allowed CNBP to preserve a weak single-stranded-DNA-binding capability. A mutant mimicking the natural N-terminal proteolytic CNBP form behaved as the RGG-deleted mutant. By gain-of-function and loss-of-function experiments in Xenopus embryos, we confirmed the participation of CNBP in neural crest development, and we demonstrated that the CNBP mutants lacking the N-terminal region or the RGG box alone may act as dominant negatives in vivo. Based on these data, we speculate about the existence of a specific proteolytic mechanism for the

  10. Structure-from-motion: dissociating perception, neural persistence, and sensory memory of illusory depth and illusory rotation.

    Science.gov (United States)

    Pastukhov, Alexander; Braun, Jochen

    2013-02-01

    In the structure-from-motion paradigm, physical motion on a screen produces the vivid illusion of an object rotating in depth. Here, we show how to dissociate illusory depth and illusory rotation in a structure-from-motion stimulus using a rotationally asymmetric shape and reversals of physical motion. Reversals of physical motion create a conflict between the original illusory states and the new physical motion: Either illusory depth remains constant and illusory rotation reverses, or illusory rotation stays the same and illusory depth reverses. When physical motion reverses after the interruption in presentation, we find that illusory rotation tends to remain constant for long blank durations (T (blank) ≥ 0.5 s), but illusory depth is stabilized if interruptions are short (T (blank) ≤ 0.1 s). The stability of illusory depth over brief interruptions is consistent with the effect of neural persistence. When this is curtailed using a mask, stability of ambiguous vision (for either illusory depth or illusory rotation) is disrupted. We also examined the selectivity of the neural persistence of illusory depth. We found that it relies on a static representation of an interpolated illusory object, since changes to low-level display properties had little detrimental effect. We discuss our findings with respect to other types of history dependence in multistable displays (sensory stabilization memory, neural fatigue, etc.). Our results suggest that when brief interruptions are used during the presentation of multistable displays, switches in perception are likely to rely on the same neural mechanisms as spontaneous switches, rather than switches due to the initial percept choice at the stimulus onset.

  11. Neural networkbased semi-active control strategy for structural vibration mitigation with magnetorheological damper

    DEFF Research Database (Denmark)

    Bhowmik, Subrata

    2011-01-01

    This paper presents a neural network based semi-active control method for a rotary type magnetorheological (MR) damper. The characteristics of the MR damper are described by the classic Bouc-Wen model, and the performance of the proposed control method is evaluated in terms of a base exited shear...... to determine the damper current based on the derived optimal damper force. For that reason an inverse MR damper model is also designed based on the neural network identification of the particular rotary MR damper. The performance of the proposed controller is compared to that of an optimal pure viscous damper...

  12. Comparison of Neural Network Error Measures for Simulation of Slender Marine Structures

    DEFF Research Database (Denmark)

    Christiansen, Niels H.; Voie, Per Erlend Torbergsen; Winther, Ole

    2014-01-01

    Training of an artificial neural network (ANN) adjusts the internal weights of the network in order to minimize a predefined error measure. This error measure is given by an error function. Several different error functions are suggested in the literature. However, the far most common measure...

  13. Travelling waves in models of neural tissue: from localised structures to periodic waves

    NARCIS (Netherlands)

    Meijer, Hil Gaétan Ellart; Coombes, Stephen

    2014-01-01

    We consider travelling waves (fronts, pulses and periodics) in spatially extended one dimensional neural field models. We demonstrate for an excitatory field with linear adaptation that, in addition to an expected stable pulse solution, a stable anti-pulse can exist. Varying the adaptation strength

  14. Neural signature of developmental coordination disorder in the structural connectome independent of comorbid autism.

    Science.gov (United States)

    Caeyenberghs, Karen; Taymans, Tom; Wilson, Peter H; Vanderstraeten, Guy; Hosseini, Hadi; van Waelvelde, Hilde

    2016-07-01

    Children with autism spectrum disorders (ASD) often exhibit motor clumsiness (Developmental Coordination Disorder, DCD), i.e. they struggle with everyday tasks that require motor coordination like dressing, self-care, and participating in sport and leisure activities. Previous studies in these neurodevelopmental disorders have demonstrated functional abnormalities and alterations of white matter microstructural integrity in specific brain regions. These findings suggest that the global organization of brain networks is affected in DCD and ASD and support the hypothesis of a 'dys-connectivity syndrome' from a network perspective. No studies have compared the structural covariance networks between ASD and DCD in order to look for the signature of DCD independent of comorbid autism. Here, we aimed to address the question of whether abnormal connectivity in DCD overlaps that seen in autism or comorbid DCD-autism. Using graph theoretical analysis, we investigated differences in global and regional topological properties of structural brain networks in 53 children: 8 ASD children with DCD (DCD+ASD), 15 ASD children without DCD (ASD), 11 with DCD only, and 19 typically developing (TD) children. We constructed separate structural correlation networks based on cortical thickness derived from Freesurfer. The children were assessed on the Movement-ABC and the Beery Test of Visual Motor Integration. Behavioral results demonstrated that the DCD group and DCD+ASD group scored on average poorer than the TD and ASD groups on various motor measures. Furthermore, although the brain networks of all groups exhibited small-world properties, the topological architecture of the networks was significantly altered in children with ASD compared with DCD and TD. ASD children showed increased normalized path length and higher values of clustering coefficient. Also, paralimbic regions exhibited nodal clustering coefficient alterations in singular disorders. These changes were disorder

  15. Krox20 defines a subpopulation of cardiac neural crest cells contributing to arterial valves and bicuspid aortic valve.

    Science.gov (United States)

    Odelin, Gaëlle; Faure, Emilie; Coulpier, Fanny; Di Bonito, Maria; Bajolle, Fanny; Studer, Michèle; Avierinos, Jean-François; Charnay, Patrick; Topilko, Piotr; Zaffran, Stéphane

    2018-01-03

    Although cardiac neural crest cells are required at early stages of arterial valve development, their contribution during valvular leaflet maturation remains poorly understood. Here, we show in mouse that neural crest cells from pre-otic and post-otic regions make distinct contributions to the arterial valve leaflets. Genetic fate-mapping analysis of Krox20-expressing neural crest cells shows a large contribution to the borders and the interleaflet triangles of the arterial valves. Loss of Krox20 function results in hyperplastic aortic valve and partially penetrant bicuspid aortic valve formation. Similar defects are observed in neural crest Krox20 -deficient embryos. Genetic lineage tracing in Krox20 -/- mutant mice shows that endothelial-derived cells are normal, whereas neural crest-derived cells are abnormally increased in number and misplaced in the valve leaflets. In contrast, genetic ablation of Krox20 -expressing cells is not sufficient to cause an aortic valve defect, suggesting that adjacent cells can compensate this depletion. Our findings demonstrate a crucial role for Krox20 in arterial valve development and reveal that an excess of neural crest cells may be associated with bicuspid aortic valve. © 2018. Published by The Company of Biologists Ltd.

  16. Neural Integration of Information Specifying Human Structure from Form, Motion, and Depth

    Science.gov (United States)

    Jackson, Stuart; Blake, Randolph

    2010-01-01

    Recent computational models of biological motion perception operate on ambiguous two-dimensional representations of the body (e.g., snapshots, posture templates) and contain no explicit means for disambiguating the three-dimensional orientation of a perceived human figure. Are there neural mechanisms in the visual system that represent a moving human figure’s orientation in three dimensions? To isolate and characterize the neural mechanisms mediating perception of biological motion, we used an adaptation paradigm together with bistable point-light (PL) animations whose perceived direction of heading fluctuates over time. After exposure to a PL walker with a particular stereoscopically defined heading direction, observers experienced a consistent aftereffect: a bistable PL walker, which could be perceived in the adapted orientation or reversed in depth, was perceived predominantly reversed in depth. A phase-scrambled adaptor produced no aftereffect, yet when adapting and test walkers differed in size or appeared on opposite sides of fixation aftereffects did occur. Thus, this heading direction aftereffect cannot be explained by local, disparity-specific motion adaptation, and the properties of scale and position invariance imply higher-level origins of neural adaptation. Nor is disparity essential for producing adaptation: when suspended on top of a stereoscopically defined, rotating globe, a context-disambiguated “globetrotter” was sufficient to bias the bistable walker’s direction, as were full-body adaptors. In sum, these results imply that the neural signals supporting biomotion perception integrate information on the form, motion, and three-dimensional depth orientation of the moving human figure. Models of biomotion perception should incorporate mechanisms to disambiguate depth ambiguities in two-dimensional body representations. PMID:20089892

  17. Modeling Distillation Column Using ARX Model Structure and Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Reza Pirmoradi

    2012-04-01

    Full Text Available Distillation is a complex and highly nonlinear industrial process. In general it is not always possible to obtain accurate first principles models for high-purity distillation columns. On the other hand the development of first principles models is usually time consuming and expensive. To overcome these problems, empirical models such as neural networks can be used. One major drawback of empirical models is that the prediction is valid only inside the data domain that is sufficiently covered by measurement data. Modeling distillation columns by means of neural networks is reported in literature by using recursive networks. The recursive networks are proper for modeling purpose, but such models have the problems of high complexity and high computational cost. The objective of this paper is to propose a simple and reliable model for distillation column. The proposed model uses feed forward neural networks which results in a simple model with less parameters and faster training time. Simulation results demonstrate that predictions of the proposed model in all regions are close to outputs of the dynamic model and the error in negligible. This implies that the model is reliable in all regions.

  18. Quantitative Live Imaging of Human Embryonic Stem Cell Derived Neural Rosettes Reveals Structure-Function Dynamics Coupled to Cortical Development.

    Science.gov (United States)

    Ziv, Omer; Zaritsky, Assaf; Yaffe, Yakey; Mutukula, Naresh; Edri, Reuven; Elkabetz, Yechiel

    2015-10-01

    Neural stem cells (NSCs) are progenitor cells for brain development, where cellular spatial composition (cytoarchitecture) and dynamics are hypothesized to be linked to critical NSC capabilities. However, understanding cytoarchitectural dynamics of this process has been limited by the difficulty to quantitatively image brain development in vivo. Here, we study NSC dynamics within Neural Rosettes--highly organized multicellular structures derived from human pluripotent stem cells. Neural rosettes contain NSCs with strong epithelial polarity and are expected to perform apical-basal interkinetic nuclear migration (INM)--a hallmark of cortical radial glial cell development. We developed a quantitative live imaging framework to characterize INM dynamics within rosettes. We first show that the tendency of cells to follow the INM orientation--a phenomenon we referred to as radial organization, is associated with rosette size, presumably via mechanical constraints of the confining structure. Second, early forming rosettes, which are abundant with founder NSCs and correspond to the early proliferative developing cortex, show fast motions and enhanced radial organization. In contrast, later derived rosettes, which are characterized by reduced NSC capacity and elevated numbers of differentiated neurons, and thus correspond to neurogenesis mode in the developing cortex, exhibit slower motions and decreased radial organization. Third, later derived rosettes are characterized by temporal instability in INM measures, in agreement with progressive loss in rosette integrity at later developmental stages. Finally, molecular perturbations of INM by inhibition of actin or non-muscle myosin-II (NMII) reduced INM measures. Our framework enables quantification of cytoarchitecture NSC dynamics and may have implications in functional molecular studies, drug screening, and iPS cell-based platforms for disease modeling.

  19. Quantitative Live Imaging of Human Embryonic Stem Cell Derived Neural Rosettes Reveals Structure-Function Dynamics Coupled to Cortical Development.

    Directory of Open Access Journals (Sweden)

    Omer Ziv

    2015-10-01

    Full Text Available Neural stem cells (NSCs are progenitor cells for brain development, where cellular spatial composition (cytoarchitecture and dynamics are hypothesized to be linked to critical NSC capabilities. However, understanding cytoarchitectural dynamics of this process has been limited by the difficulty to quantitatively image brain development in vivo. Here, we study NSC dynamics within Neural Rosettes--highly organized multicellular structures derived from human pluripotent stem cells. Neural rosettes contain NSCs with strong epithelial polarity and are expected to perform apical-basal interkinetic nuclear migration (INM--a hallmark of cortical radial glial cell development. We developed a quantitative live imaging framework to characterize INM dynamics within rosettes. We first show that the tendency of cells to follow the INM orientation--a phenomenon we referred to as radial organization, is associated with rosette size, presumably via mechanical constraints of the confining structure. Second, early forming rosettes, which are abundant with founder NSCs and correspond to the early proliferative developing cortex, show fast motions and enhanced radial organization. In contrast, later derived rosettes, which are characterized by reduced NSC capacity and elevated numbers of differentiated neurons, and thus correspond to neurogenesis mode in the developing cortex, exhibit slower motions and decreased radial organization. Third, later derived rosettes are characterized by temporal instability in INM measures, in agreement with progressive loss in rosette integrity at later developmental stages. Finally, molecular perturbations of INM by inhibition of actin or non-muscle myosin-II (NMII reduced INM measures. Our framework enables quantification of cytoarchitecture NSC dynamics and may have implications in functional molecular studies, drug screening, and iPS cell-based platforms for disease modeling.

  20. Spatio-temporal neural stem cell behavior that leads to both perfect and imperfect structural brain regeneration in adult newts.

    Science.gov (United States)

    Urata, Yuko; Yamashita, Wataru; Inoue, Takeshi; Agata, Kiyokazu

    2018-06-14

    Adult newts can regenerate large parts of their brain from adult neural stem cells (NSCs), but how adult NSCs reorganize brain structures during regeneration remains unclear. In development, elaborate brain structures are produced under broadly coordinated regulations of embryonic NSCs in the neural tube, whereas brain regeneration entails exquisite control of the reestablishment of certain brain parts, suggesting a yet-unknown mechanism directs NSCs upon partial brain excision. Here we report that upon one-quarter excision of the adult newt ( Pleurodeles waltl ) mesencephalon, active participation of local NSCs around specific brain subregions' boundaries leads to some imperfect and some perfect brain regeneration along an individual's rostrocaudal axis. Regeneration phenotypes depend on how the wound closing occurs using local NSCs, and perfect regeneration replicates development-like processes but takes more than one year. Our findings indicate that newt brain regeneration is supported by modularity of boundary-domain NSCs with self-organizing ability in neighboring fields. © 2018. Published by The Company of Biologists Ltd.

  1. Applications of Mesenchymal Stem Cells and Neural Crest Cells in Craniofacial Skeletal Research

    Directory of Open Access Journals (Sweden)

    Satoru Morikawa

    2016-01-01

    Full Text Available Craniofacial skeletal tissues are composed of tooth and bone, together with nerves and blood vessels. This composite material is mainly derived from neural crest cells (NCCs. The neural crest is transient embryonic tissue present during neural tube formation whose cells have high potential for migration and differentiation. Thus, NCCs are promising candidates for craniofacial tissue regeneration; however, the clinical application of NCCs is hindered by their limited accessibility. In contrast, mesenchymal stem cells (MSCs are easily accessible in adults, have similar potential for self-renewal, and can differentiate into skeletal tissues, including bones and cartilage. Therefore, MSCs may represent good sources of stem cells for clinical use. MSCs are classically identified under adherent culture conditions, leading to contamination with other cell lineages. Previous studies have identified mouse- and human-specific MSC subsets using cell surface markers. Additionally, some studies have shown that a subset of MSCs is closely related to neural crest derivatives and endothelial cells. These MSCs may be promising candidates for regeneration of craniofacial tissues from the perspective of developmental fate. Here, we review the fundamental biology of MSCs in craniofacial research.

  2. Neural plasticity expressed in central auditory structures with and without tinnitus

    Directory of Open Access Journals (Sweden)

    Larry E Roberts

    2012-05-01

    Full Text Available Sensory training therapies for tinnitus are based on the assumption that, notwithstanding neural changes related to tinnitus, auditory training can alter the response properties of neurons in auditory pathways. To address this question, we investigated whether brain changes induced by sensory training in tinnitus sufferers and measured by EEG are similar to those induced in age and hearing loss matched individuals without tinnitus trained on the same auditory task. Auditory training was given using a 5 kHz 40-Hz amplitude-modulated sound that was in the tinnitus frequency region of the tinnitus subjects and enabled extraction of the 40-Hz auditory steady-state response (ASSR and P2 transient response known to localize to primary and nonprimary auditory cortex, respectively. P2 amplitude increased with training equally in participants with tinnitus and in control subjects, suggesting normal remodeling of nonprimary auditory regions in tinnitus. However, training-induced changes in the ASSR differed between the tinnitus and control groups. In controls ASSR phase advanced toward the stimulus waveform by about ten degrees over training, in agreement with previous results obtained in young normal hearing individuals. However, ASSR phase did not change significantly with training in the tinnitus group, although some participants showed phase shifts resembling controls. On the other hand, ASSR amplitude increased with training in the tinnitus group, whereas in controls this response (which is difficult to remodel in young normal hearing subjects did not change with training. These results suggest that neural changes related to tinnitus altered how neural plasticity was expressed in the region of primary but not nonprimary auditory cortex. Auditory training did not reduce tinnitus loudness although a small effect on the tinnitus spectrum was detected.

  3. Neural network AE source location apart from structure size and material

    Czech Academy of Sciences Publication Activity Database

    Chlada, Milan; Převorovský, Zdeněk; Blaháček, Michal

    2010-01-01

    Roč. 28, - (2010), s. 99-107 ISSN 0730-0050. [European Conference on Acoustic Emission Testing 2010 /29./. Vídeň, 08.09.2010-10.09.2010] R&D Projects: GA ČR(CZ) GAP104/10/1430; GA MPO(CZ) FR-TI1/274 Institutional research plan: CEZ:AV0Z20760514 Keywords : AE source location * artificial neural network * arrival time profiles Subject RIV: BI - Acoustics http://www.ndt.net/search/abstract.php3?AbsID=10828

  4. Structures based on semi-degradable biomaterials for neural regeneration in the central nervous system

    OpenAIRE

    Perez Garnes, Manuel

    2015-01-01

    Se pretende obtener un material semibiodegradable basado en ácido hialurónico químicamente enlazado a cadenas de polímeros acrílicos. Los hidrogeles de ácido hialurónico presentan en general buenas características para su utilización en regeneración del sistema nervioso central: es biodegradable, es un componente importante del tejido neural, sus propiedades mecánicas son semejantes a las del tejido cerebral, promueve la formación de nuevos capilares (angiogénesis), y limita la inflamación. C...

  5. Classification of Forest Vertical Structure in South Korea from Aerial Orthophoto and Lidar Data Using an Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Soo-Kyung Kwon

    2017-10-01

    Full Text Available Every vegetation colony has its own vertical structure. Forest vertical structure is considered as an important indicator of a forest’s diversity and vitality. The vertical structure of a forest has typically been investigated by field survey, which is the traditional method of forest inventory. However, this method is very time- and cost-consuming due to poor accessibility. Remote sensing data such as satellite imagery, aerial photography, and lidar data can be a viable alternative to the traditional field-based forestry survey. In this study, we classified forest vertical structures from red-green-blue (RGB aerial orthophotos and lidar data using an artificial neural network (ANN, which is a powerful machine learning technique. The test site was Gongju province in South Korea, which contains single-, double-, and triple-layered forest structures. The performance of the proposed method was evaluated by comparing the results with field survey data. The overall accuracy achieved was about 70%. It means that the proposed approach can classify the forest vertical structures from the aerial orthophotos and lidar data.

  6. An Electricity Price Forecasting Model by Hybrid Structured Deep Neural Networks

    Directory of Open Access Journals (Sweden)

    Ping-Huan Kuo

    2018-04-01

    Full Text Available Electricity price is a key influencer in the electricity market. Electricity market trades by each participant are based on electricity price. The electricity price adjusted with the change in supply and demand relationship can reflect the real value of electricity in the transaction process. However, for the power generating party, bidding strategy determines the level of profit, and the accurate prediction of electricity price could make it possible to determine a more accurate bidding price. This cannot only reduce transaction risk, but also seize opportunities in the electricity market. In order to effectively estimate electricity price, this paper proposes an electricity price forecasting system based on the combination of 2 deep neural networks, the Convolutional Neural Network (CNN and the Long Short Term Memory (LSTM. In order to compare the overall performance of each algorithm, the Mean Absolute Error (MAE and Root-Mean-Square error (RMSE evaluating measures were applied in the experiments of this paper. Experiment results show that compared with other traditional machine learning methods, the prediction performance of the estimating model proposed in this paper is proven to be the best. By combining the CNN and LSTM models, the feasibility and practicality of electricity price prediction is also confirmed in this paper.

  7. Structured chaos shapes spike-response noise entropy in balanced neural networks

    Directory of Open Access Journals (Sweden)

    Guillaume eLajoie

    2014-10-01

    Full Text Available Large networks of sparsely coupled, excitatory and inhibitory cells occur throughout the brain. For many models of these networks, a striking feature is that their dynamics are chaotic and thus, are sensitive to small perturbations. How does this chaos manifest in the neural code? Specifically, how variable are the spike patterns that such a network produces in response to an input signal? To answer this, we derive a bound for a general measure of variability -- spike-train entropy. This leads to important insights on the variability of multi-cell spike pattern distributions in large recurrent networks of spiking neurons responding to fluctuating inputs. The analysis is based on results from random dynamical systems theory and is complemented by detailed numerical simulations. We find that the spike pattern entropy is an order of magnitude lower than what would be extrapolated from single cells. This holds despite the fact that network coupling becomes vanishingly sparse as network size grows -- a phenomenon that depends on ``extensive chaos, as previously discovered for balanced networks without stimulus drive. Moreover, we show how spike pattern entropy is controlled by temporal features of the inputs. Our findings provide insight into how neural networks may encode stimuli in the presence of inherently chaotic dynamics.

  8. Neural network system and methods for analysis of organic materials and structures using spectral data

    Science.gov (United States)

    Meyer, Bernd J.; Sellers, Jeffrey P.; Thomsen, Jan U.

    1993-01-01

    Apparatus and processes for recognizing and identifying materials. Characteristic spectra are obtained for the materials via spectroscopy techniques including nuclear magnetic resonance spectroscopy, infrared absorption analysis, x-ray analysis, mass spectroscopy and gas chromatography. Desired portions of the spectra may be selected and then placed in proper form and format for presentation to a number of input layer neurons in an offline neural network. The network is first trained according to a predetermined training process; it may then be employed to identify particular materials. Such apparatus and processes are particularly useful for recognizing and identifying organic compounds such as complex carbohydrates, whose spectra conventionally require a high level of training and many hours of hard work to identify, and are frequently indistinguishable from one another by human interpretation.

  9. Role of cranial neural crest cells in visceral arch muscle positioning and morphogenesis in the Mexican axolotl, Ambystoma mexicanum.

    Science.gov (United States)

    Ericsson, Rolf; Cerny, Robert; Falck, Pierre; Olsson, Lennart

    2004-10-01

    The role of cranial neural crest cells in the formation of visceral arch musculature was investigated in the Mexican axolotl, Ambystoma mexicanum. DiI (1,1'-dioctadecyl-3,3,3',3'-tetramethylindocarbocyanine, perchlorate) labeling and green fluorescent protein (GFP) mRNA injections combined with unilateral transplantations of neural folds showed that neural crest cells contribute to the connective tissues but not the myofibers of developing visceral arch muscles in the mandibular, hyoid, and branchial arches. Extirpations of individual cranial neural crest streams demonstrated that neural crest cells are necessary for correct morphogenesis of visceral arch muscles. These do, however, initially develop in their proper positions also in the absence of cranial neural crest. Visceral arch muscles forming in the absence of neural crest cells start to differentiate at their origins but fail to extend toward their insertions and may have a frayed appearance. Our data indicate that visceral arch muscle positioning is controlled by factors that do not have a neural crest origin. We suggest that the cranial neural crest-derived connective tissues provide directional guidance important for the proper extension of the cranial muscles and the subsequent attachment to the insertion on the correct cartilage. In a comparative context, our data from the Mexican axolotl support the view that the cranial neural crest plays a fundamental role in the development of not only the skeleton of the vertebrate head but also in the morphogenesis of the cranial muscles and that this might be a primitive feature of cranial development in vertebrates. 2004 Wiley-Liss, Inc.

  10. On structure-exploiting trust-region regularized nonlinear least squares algorithms for neural-network learning.

    Science.gov (United States)

    Mizutani, Eiji; Demmel, James W

    2003-01-01

    This paper briefly introduces our numerical linear algebra approaches for solving structured nonlinear least squares problems arising from 'multiple-output' neural-network (NN) models. Our algorithms feature trust-region regularization, and exploit sparsity of either the 'block-angular' residual Jacobian matrix or the 'block-arrow' Gauss-Newton Hessian (or Fisher information matrix in statistical sense) depending on problem scale so as to render a large class of NN-learning algorithms 'efficient' in both memory and operation costs. Using a relatively large real-world nonlinear regression application, we shall explain algorithmic strengths and weaknesses, analyzing simulation results obtained by both direct and iterative trust-region algorithms with two distinct NN models: 'multilayer perceptrons' (MLP) and 'complementary mixtures of MLP-experts' (or neuro-fuzzy modular networks).

  11. Investigation of the various structure parameters for predicting impact sensitivity of energetic molecules via artificial neural network

    Energy Technology Data Exchange (ETDEWEB)

    Keshavarz, Mohammad Hossein; Jaafari, Mohammad [Department of Chemistry, Malek-Ashtar University of Technology, Shahin-Shahr, P.O. Box 83145/115 (Iran)

    2006-06-15

    A generalized scheme is introduced for predicting impact sensitivity of any explosives by using artificial neural networks. Experimental values for the impact sensitivity for 291 compounds containing C, H, N and O have been used for training and testing sets. The input descriptors include aromatic character, heteroaromatic character, the number of N-NO{sub 2} bonds and the number of {alpha}-hydrogen atoms as well as the number of carbon, hydrogen, nitrogen, and oxygen divided by molecular weight. The reliability of the proposed model was assessed by comparing the results against measured values as well as five models of complicated quantum mechanical computed values of 14 CHNO explosives from a variety of chemical structures. The model gives root mean squares errors of 41 cm and 56 cm for training and test sets, respectively, of the H{sub 50} quantity. (Abstract Copyright [2006], Wiley Periodicals, Inc.)

  12. A mathematical analysis of the effects of Hebbian learning rules on the dynamics and structure of discrete-time random recurrent neural networks.

    Science.gov (United States)

    Siri, Benoît; Berry, Hugues; Cessac, Bruno; Delord, Bruno; Quoy, Mathias

    2008-12-01

    We present a mathematical analysis of the effects of Hebbian learning in random recurrent neural networks, with a generic Hebbian learning rule, including passive forgetting and different timescales, for neuronal activity and learning dynamics. Previous numerical work has reported that Hebbian learning drives the system from chaos to a steady state through a sequence of bifurcations. Here, we interpret these results mathematically and show that these effects, involving a complex coupling between neuronal dynamics and synaptic graph structure, can be analyzed using Jacobian matrices, which introduce both a structural and a dynamical point of view on neural network evolution. Furthermore, we show that sensitivity to a learned pattern is maximal when the largest Lyapunov exponent is close to 0. We discuss how neural networks may take advantage of this regime of high functional interest.

  13. Oxytocin receptor polymorphism and childhood social experiences shape adult personality, brain structure and neural correlates of mentalizing.

    Science.gov (United States)

    Schneider-Hassloff, H; Straube, B; Jansen, A; Nuscheler, B; Wemken, G; Witt, S H; Rietschel, M; Kircher, T

    2016-07-01

    The oxytocin system is involved in human social behavior and social cognition such as attachment, emotion recognition and mentalizing (i.e. the ability to represent mental states of oneself and others). It is shaped by social experiences in early life, especially by parent-infant interactions. The single nucleotid polymorphism rs53576 in the oxytocin receptor (OXTR) gene has been linked to social behavioral phenotypes. In 195 adult healthy subjects we investigated the interaction of OXTR rs53576 and childhood attachment security (CAS) on the personality traits "adult attachment style" and "alexithymia" (i.e. emotional self-awareness), on brain structure (voxel-based morphometry) and neural activation (fMRI) during an interactive mentalizing paradigm (prisoner's dilemma game; subgroup: n=163). We found that in GG-homozygotes, but not in A-allele carriers, insecure childhood attachment is - in adulthood - associated with a) higher attachment-related anxiety and alexithymia, b) higher brain gray matter volume of left amygdala and lower volumes in right superior parietal lobule (SPL), left temporal pole (TP), and bilateral frontal regions, and c) higher mentalizing-related neural activity in bilateral TP and precunei, and right middle and superior frontal gyri. Interaction effects of genotype and CAS on brain volume and/or function were associated with individual differences in alexithymia and attachment-related anxiety. Interactive effects were in part sexually dimorphic. The interaction of OXTR genotype and CAS modulates adult personality as well as brain structure and function of areas implicated in salience processing and mentalizing. Rs53576 GG-homozygotes are partially more susceptible to childhood attachment experiences than A-allele carriers. Copyright © 2016 Elsevier Inc. All rights reserved.

  14. Correlations between vascular invasion, neural structures invasion and microvessel density with clinicopathological parameters in gastric cancer

    Directory of Open Access Journals (Sweden)

    Bădescu Alina

    2014-06-01

    Full Text Available Scopul: Scopul studiului efectuat a fost acela de a estima prezenţa invaziei tumorale la nivelul vaselor limfatice, sanguine şi la nivel neural în carcinoamele gastrice pe preparatele colorate hematoxilină-eozină (H-E şi, de asemenea, densitatea microvascularizatiei tumorale (MVD, detectată imunohistochimic, precum şi relaţia acestora cu parametrii clinico-patologici şi biologici ai tumorilor. Material şi metodă. Pentru evaluarea invaziei limfo-vasculare şi neurale am inclus în studiu 367 de pacienţi diagnosticaţi cu carcinoame gastrice. Pentru studiul imunohistochimic al MVD, au fost selectaţi 28 de pacienţi, din care 16 pacienţi cu gastrectomomie totală, în urma căreia s-a stabilit stadiul TNM al tumorii primare şi 12 pacienţi cu biopsie gastrică. Biopsiile gastrice şi probele chirurgicale au fost procesate folosind tehnica de includere la parafină şi coloraţia hematoxilină-eozină, iar pentru evaluarea imunohistochimică a MVD s-au utilizat anticorpii anti-CD31 şi anti-CD34. Rezultate: Prezenţa invaziei tumorale la nivelul vaselor sanguine a fost semnificativ asociată cu stadiile avansate de boală (p<0,01 şi cu carcinoamele gastrice slab diferenţiate (p<0,01, în timp ce invazia vaselor limfatice s-a asociat semnificativ doar cu stadiul avansat al tumorilor (p<0,001. În ceea ce priveşte invazia tumorală peri- sau intraneurală, s-a observat o corelaţie semnificativă a acesteia cu sexul feminin (p<0,05, cu stadiile avansate de boală (p<0,001, cu tipul difuz al carcinoamelor gastrice (p<0,05 şi cu tumorile slab diferenţiate (p<0,05. S-a observat o legătură strânsă între valoarea MVD determinată cu anticorpul anti CD34 şi doi dintre parametrii histopatologici importanţi: tipul histologic al carcinoamelor gastrice conform clasificării Lauren (tipul difuz; p<0,05 şi gradul de diferenţiere al tumorilor (tumorile slab diferenţiate; p<0,05. S-a observat de asemenea o corelaţie semnificativă a

  15. Spherical harmonics based descriptor for neural network potentials: Structure and dynamics of Au147 nanocluster.

    Science.gov (United States)

    Jindal, Shweta; Chiriki, Siva; Bulusu, Satya S

    2017-05-28

    We propose a highly efficient method for fitting the potential energy surface of a nanocluster using a spherical harmonics based descriptor integrated with an artificial neural network. Our method achieves the accuracy of quantum mechanics and speed of empirical potentials. For large sized gold clusters (Au 147 ), the computational time for accurate calculation of energy and forces is about 1.7 s, which is faster by several orders of magnitude compared to density functional theory (DFT). This method is used to perform the global minimum optimizations and molecular dynamics simulations for Au 147 , and it is found that its global minimum is not an icosahedron. The isomer that can be regarded as the global minimum is found to be 4 eV lower in energy than the icosahedron and is confirmed from DFT. The geometry of the obtained global minimum contains 105 atoms on the surface and 42 atoms in the core. A brief study on the fluxionality in Au 147 is performed, and it is concluded that Au 147 has a dynamic surface, thus opening a new window for studying its reaction dynamics.

  16. Impact of pH on the structure and function of neural cadherin.

    Science.gov (United States)

    Jungles, Jared M; Dukes, Matthew P; Vunnam, Nagamani; Pedigo, Susan

    2014-12-02

    Neural (N-) cadherin is a transmembrane protein within adherens junctions that mediates cell-cell adhesion. It has 5 modular extracellular domains (EC1-EC5) that bind 3 calcium ions between each of the modules. Calcium binding is required for dimerization. N-Cadherin is involved in diverse processes including tissue morphogenesis, excitatory synapse formation and dynamics, and metastasis of cancer. During neurotransmission and tumorigenesis, fluctuations in extracellular pH occur, causing tissue acidosis with associated physiological consequences. Studies reported here aim to determine the effect of pH on the dimerization properties of a truncated construct of N-cadherin containing EC1-EC2. Since N-cadherin is an anionic protein, we hypothesized that acidification of solution would cause an increase in stability of the apo protein, a decrease in the calcium-binding affinity, and a concomitant decrease in the formation of adhesive dimer. The stability of the apo monomer was increased and the calcium-binding affinity was decreased at reduced pH, consistent with our hypothesis. Surprisingly, analytical SEC studies showed an increase in calcium-induced dimerization as solution pH decreased from 7.4 to 5.0. Salt-dependent dimerization studies indicated that electrostatic repulsion attenuates dimerization affinity. These results point to a possible electrostatic mechanism for moderating dimerization affinity of the Type I cadherin family. Extrapolating these results to cell adhesion in vivo leads to the assertion that decreased pH promotes adhesion by N-cadherin, thereby stabilizing synaptic junctions.

  17. Human neural progenitor cells decrease photoreceptor degeneration, normalize opsin distribution and support synapse structure in cultured porcine retina.

    Science.gov (United States)

    Mollick, Tanzina; Mohlin, Camilla; Johansson, Kjell

    2016-09-01

    Retinal neurodegenerative disorders like retinitis pigmentosa, age-related macular degeneration, diabetic retinopathy and retinal detachment decrease retinal functionality leading to visual impairment. The pathological events are characterized by photoreceptor degeneration, synaptic disassembly, remodeling of postsynaptic neurons and activation of glial cells. Despite intense research, no effective treatment has been found for these disorders. The current study explores the potential of human neural progenitor cell (hNPC) derived factors to slow the degenerative processes in adult porcine retinal explants. Retinas were cultured for 3 days with or without hNPCs as a feeder layer and investigated by terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL), immunohistochemical, western blot and quantitative real time-polymerase chain reaction (qRT-PCR) techniques. TUNEL showed that hNPCs had the capacity to limit photoreceptor cell death. Among cone photoreceptors, hNPC coculture resulted in better maintenance of cone outer segments and reduced opsin mislocalization. Additionally, maintained synaptic structural integrity and preservation of second order calbindin positive horizontal cells was also observed. However, Müller cell gliosis only seemed to be alleviated in terms of reduced Müller cell density. Our observations indicate that at 3 days of coculture, hNPC derived factors had the capacity to protect photoreceptors, maintain synaptic integrity and support horizontal cell survival. Human neural progenitor cell applied treatment modalities may be an effective strategy to help maintain retinal functionality in neurodegenerative pathologies. Whether hNPCs can independently hinder Müller cell gliosis by utilizing higher concentrations or by combination with other pharmacological agents still needs to be determined. Copyright © 2016 Elsevier B.V. All rights reserved.

  18. Equilibrium point control of a monkey arm simulator by a fast learning tree structured artificial neural network.

    Science.gov (United States)

    Dornay, M; Sanger, T D

    1993-01-01

    A planar 17 muscle model of the monkey's arm based on realistic biomechanical measurements was simulated on a Symbolics Lisp Machine. The simulator implements the equilibrium point hypothesis for the control of arm movements. Given initial and final desired positions, it generates a minimum-jerk desired trajectory of the hand and uses the backdriving algorithm to determine an appropriate sequence of motor commands to the muscles (Flash 1987; Mussa-Ivaldi et al. 1991; Dornay 1991b). These motor commands specify a temporal sequence of stable (attractive) equilibrium positions which lead to the desired hand movement. A strong disadvantage of the simulator is that it has no memory of previous computations. Determining the desired trajectory using the minimum-jerk model is instantaneous, but the laborious backdriving algorithm is slow, and can take up to one hour for some trajectories. The complexity of the required computations makes it a poor model for biological motor control. We propose a computationally simpler and more biologically plausible method for control which achieves the benefits of the backdriving algorithm. A fast learning, tree-structured network (Sanger 1991c) was trained to remember the knowledge obtained by the backdriving algorithm. The neural network learned the nonlinear mapping from a 2-dimensional cartesian planar hand position (x,y) to a 17-dimensional motor command space (u1, . . ., u17). Learning 20 training trajectories, each composed of 26 sample points [[x,y], [u1, . . ., u17] took only 20 min on a Sun-4 Sparc workstation. After the learning stage, new, untrained test trajectories as well as the original trajectories of the hand were given to the neural network as input. The network calculated the required motor commands for these movements. The resulting movements were close to the desired ones for both the training and test cases.

  19. Consciousness and neural plasticity

    DEFF Research Database (Denmark)

    changes or to abandon the strong identity thesis altogether. Were one to pursue a theory according to which consciousness is not an epiphenomenon to brain processes, consciousness may in fact affect its own neural basis. The neural correlate of consciousness is often seen as a stable structure, that is...

  20. Structure Crack Identification Based on Surface-mounted Active Sensor Network with Time-Domain Feature Extraction and Neural Network

    Directory of Open Access Journals (Sweden)

    Chunling DU

    2012-03-01

    Full Text Available In this work the condition of metallic structures are classified based on the acquired sensor data from a surface-mounted piezoelectric sensor/actuator network. The structures are aluminum plates with riveted holes and possible crack damage at these holes. A 400 kHz sine wave burst is used as diagnostic signals. The combination of time-domain S0 waves from received sensor signals is directly used as features and preprocessing is not needed for the dam age detection. Since the time sequence of the extracted S0 has a high dimension, principal component estimation is applied to reduce its dimension before entering NN (neural network training for classification. An LVQ (learning vector quantization NN is used to classify the conditions as healthy or damaged. A number of FEM (finite element modeling results are taken as inputs to the NN for training, since the simulated S0 waves agree well with the experimental results on real plates. The performance of the classification is then validated by using these testing results.

  1. Evolvable Neural Software System

    Science.gov (United States)

    Curtis, Steven A.

    2009-01-01

    The Evolvable Neural Software System (ENSS) is composed of sets of Neural Basis Functions (NBFs), which can be totally autonomously created and removed according to the changing needs and requirements of the software system. The resulting structure is both hierarchical and self-similar in that a given set of NBFs may have a ruler NBF, which in turn communicates with other sets of NBFs. These sets of NBFs may function as nodes to a ruler node, which are also NBF constructs. In this manner, the synthetic neural system can exhibit the complexity, three-dimensional connectivity, and adaptability of biological neural systems. An added advantage of ENSS over a natural neural system is its ability to modify its core genetic code in response to environmental changes as reflected in needs and requirements. The neural system is fully adaptive and evolvable and is trainable before release. It continues to rewire itself while on the job. The NBF is a unique, bilevel intelligence neural system composed of a higher-level heuristic neural system (HNS) and a lower-level, autonomic neural system (ANS). Taken together, the HNS and the ANS give each NBF the complete capabilities of a biological neural system to match sensory inputs to actions. Another feature of the NBF is the Evolvable Neural Interface (ENI), which links the HNS and ANS. The ENI solves the interface problem between these two systems by actively adapting and evolving from a primitive initial state (a Neural Thread) to a complicated, operational ENI and successfully adapting to a training sequence of sensory input. This simulates the adaptation of a biological neural system in a developmental phase. Within the greater multi-NBF and multi-node ENSS, self-similar ENI s provide the basis for inter-NBF and inter-node connectivity.

  2. Revisiting Earth's radial seismic structure using a Bayesian neural network approach

    NARCIS (Netherlands)

    de Wit, R.W.L.

    2015-01-01

    The gross features of seismic observations can be explained by relatively simple spherically symmetric (1-D) models of wave velocities, density and attenuation, which describe the Earth's average(radial) structure. 1-D earth models are often used as a reference for studies on Earth's thermo-chemical

  3. Prediction of protein structural features by use of artificial neural networks

    DEFF Research Database (Denmark)

    Petersen, Bent

    . There is a huge over-representation of DNA sequences when comparing the amount of experimentally verified proteins with the amount of DNA sequences. The academic and industrial research community therefore has to rely on structure predictions instead of waiting for the time consuming experimentally determined...

  4. Learning to Perceive Structure from Motion and Neural Plasticity in Patients with Alzheimer's Disease

    Science.gov (United States)

    Kim, Nam-Gyoon; Park, Jong-Hee

    2010-01-01

    Recent research has demonstrated that Alzheimer's disease (AD) affects the visual sensory pathways, producing a variety of visual deficits, including the capacity to perceive structure-from-motion (SFM). Because the sensory areas of the adult brain are known to retain a large degree of plasticity, the present study was conducted to explore whether…

  5. Neural correlates of emotional personality: a structural and functional magnetic resonance imaging study.

    Directory of Open Access Journals (Sweden)

    Stefan Koelsch

    Full Text Available Studies addressing brain correlates of emotional personality have remained sparse, despite the involvement of emotional personality in health and well-being. This study investigates structural and functional brain correlates of psychological and physiological measures related to emotional personality. Psychological measures included neuroticism, extraversion, and agreeableness scores, as assessed using a standard personality questionnaire. As a physiological measure we used a cardiac amplitude signature, the so-called E κ value (computed from the electrocardiogram which has previously been related to tender emotionality. Questionnaire scores and E κ values were related to both functional (eigenvector centrality mapping, ECM and structural (voxel-based morphometry, VBM neuroimaging data. Functional magnetic resonance imaging (fMRI data were obtained from 22 individuals (12 females while listening to music (joy, fear, or neutral music. ECM results showed that agreeableness scores correlated with centrality values in the dorsolateral prefrontal cortex, the anterior cingulate cortex, and the ventral striatum (nucleus accumbens. Individuals with higher E κ values (indexing higher tender emotionality showed higher centrality values in the subiculum of the right hippocampal formation. Structural MRI data from an independent sample of 59 individuals (34 females showed that neuroticism scores correlated with volume of the left amygdaloid complex. In addition, individuals with higher E κ showed larger gray matter volume in the same portion of the subiculum in which individuals with higher E κ showed higher centrality values. Our results highlight a role of the amygdala in neuroticism. Moreover, they indicate that a cardiac signature related to emotionality (E κ correlates with both function (increased network centrality and structure (grey matter volume of the subiculum of the hippocampal formation, suggesting a role of the hippocampal formation for

  6. Neural correlates of emotional personality: a structural and functional magnetic resonance imaging study.

    Science.gov (United States)

    Koelsch, Stefan; Skouras, Stavros; Jentschke, Sebastian

    2013-01-01

    Studies addressing brain correlates of emotional personality have remained sparse, despite the involvement of emotional personality in health and well-being. This study investigates structural and functional brain correlates of psychological and physiological measures related to emotional personality. Psychological measures included neuroticism, extraversion, and agreeableness scores, as assessed using a standard personality questionnaire. As a physiological measure we used a cardiac amplitude signature, the so-called E κ value (computed from the electrocardiogram) which has previously been related to tender emotionality. Questionnaire scores and E κ values were related to both functional (eigenvector centrality mapping, ECM) and structural (voxel-based morphometry, VBM) neuroimaging data. Functional magnetic resonance imaging (fMRI) data were obtained from 22 individuals (12 females) while listening to music (joy, fear, or neutral music). ECM results showed that agreeableness scores correlated with centrality values in the dorsolateral prefrontal cortex, the anterior cingulate cortex, and the ventral striatum (nucleus accumbens). Individuals with higher E κ values (indexing higher tender emotionality) showed higher centrality values in the subiculum of the right hippocampal formation. Structural MRI data from an independent sample of 59 individuals (34 females) showed that neuroticism scores correlated with volume of the left amygdaloid complex. In addition, individuals with higher E κ showed larger gray matter volume in the same portion of the subiculum in which individuals with higher E κ showed higher centrality values. Our results highlight a role of the amygdala in neuroticism. Moreover, they indicate that a cardiac signature related to emotionality (E κ) correlates with both function (increased network centrality) and structure (grey matter volume) of the subiculum of the hippocampal formation, suggesting a role of the hippocampal formation for

  7. CNNH_PSS: protein 8-class secondary structure prediction by convolutional neural network with highway.

    Science.gov (United States)

    Zhou, Jiyun; Wang, Hongpeng; Zhao, Zhishan; Xu, Ruifeng; Lu, Qin

    2018-05-08

    Protein secondary structure is the three dimensional form of local segments of proteins and its prediction is an important problem in protein tertiary structure prediction. Developing computational approaches for protein secondary structure prediction is becoming increasingly urgent. We present a novel deep learning based model, referred to as CNNH_PSS, by using multi-scale CNN with highway. In CNNH_PSS, any two neighbor convolutional layers have a highway to deliver information from current layer to the output of the next one to keep local contexts. As lower layers extract local context while higher layers extract long-range interdependencies, the highways between neighbor layers allow CNNH_PSS to have ability to extract both local contexts and long-range interdependencies. We evaluate CNNH_PSS on two commonly used datasets: CB6133 and CB513. CNNH_PSS outperforms the multi-scale CNN without highway by at least 0.010 Q8 accuracy and also performs better than CNF, DeepCNF and SSpro8, which cannot extract long-range interdependencies, by at least 0.020 Q8 accuracy, demonstrating that both local contexts and long-range interdependencies are indeed useful for prediction. Furthermore, CNNH_PSS also performs better than GSM and DCRNN which need extra complex model to extract long-range interdependencies. It demonstrates that CNNH_PSS not only cost less computer resource, but also achieves better predicting performance. CNNH_PSS have ability to extracts both local contexts and long-range interdependencies by combing multi-scale CNN and highway network. The evaluations on common datasets and comparisons with state-of-the-art methods indicate that CNNH_PSS is an useful and efficient tool for protein secondary structure prediction.

  8. Impaired neural structure and function contributing to autonomic symptoms in congenital central hypoventilation syndrome.

    Science.gov (United States)

    Harper, Ronald M; Kumar, Rajesh; Macey, Paul M; Harper, Rebecca K; Ogren, Jennifer A

    2015-01-01

    Congenital central hypoventilation syndrome (CCHS) patients show major autonomic alterations in addition to their better-known breathing deficiencies. The processes underlying CCHS, mutations in the PHOX2B gene, target autonomic neuronal development, with frame shift extent contributing to symptom severity. Many autonomic characteristics, such as impaired pupillary constriction and poor temperature regulation, reflect parasympathetic alterations, and can include disturbed alimentary processes, with malabsorption and intestinal motility dyscontrol. The sympathetic nervous system changes can exert life-threatening outcomes, with dysregulation of sympathetic outflow leading to high blood pressure, time-altered and dampened heart rate and breathing responses to challenges, cardiac arrhythmia, profuse sweating, and poor fluid regulation. The central mechanisms contributing to failed autonomic processes are readily apparent from structural and functional magnetic resonance imaging studies, which reveal substantial cortical thinning, tissue injury, and disrupted functional responses in hypothalamic, hippocampal, posterior thalamic, and basal ganglia sites and their descending projections, as well as insular, cingulate, and medial frontal cortices, which influence subcortical autonomic structures. Midbrain structures are also compromised, including the raphe system and its projections to cerebellar and medullary sites, the locus coeruleus, and medullary reflex integrating sites, including the dorsal and ventrolateral medullary nuclei. The damage to rostral autonomic sites overlaps metabolic, affective and cognitive regulatory regions, leading to hormonal disruption, anxiety, depression, behavioral control, and sudden death concerns. The injuries suggest that interventions for mitigating hypoxic exposure and nutrient loss may provide cellular protection, in the same fashion as interventions in other conditions with similar malabsorption, fluid turnover, or hypoxic exposure.

  9. Impaired Neural Structure and Function Contributing to Autonomic Symptoms in Congenital Central Hypoventilation Syndrome

    Directory of Open Access Journals (Sweden)

    Ronald M Harper

    2015-10-01

    Full Text Available Congenital central hypoventilation syndrome (CCHS patients show major autonomic alterations in addition to their better-known breathing deficiencies. The processes underlying CCHS, mutations in the PHOX2B gene, target autonomic neuronal development, with frame shift extent contributing to symptom severity. Many autonomic characteristics, such as impaired pupillary constriction and poor temperature regulation, reflect parasympathetic alterations, and can include disturbed alimentary processes, with malabsorption and intestinal motility dyscontrol. The sympathetic nervous system changes can exert life-threatening outcomes, with dysregulation of sympathetic outflow leading to high blood pressure, time-altered and dampened heart rate and breathing responses to challenges, cardiac arrhythmia, profuse sweating, and poor fluid regulation. The central mechanisms contributing to failed autonomic processes are readily apparent from structural and functional magnetic resonance imaging studies, which reveal substantial cortical thinning, tissue injury, and disrupted functional responses in hypothalamic, hippocampal, posterior thalamic, and basal ganglia sites and their descending projections, as well as insular, cingulate, and medial frontal cortices, which influence subcortical autonomic structures. Midbrain structures are also compromised, including the raphe system and its projections to cerebellar and medullary sites, the locus coeruleus, and medullary reflex integrating sites, including the dorsal and ventrolateral medullary nuclei. The damage to rostral autonomic sites overlaps metabolic, affective and cognitive regulatory regions, leading to hormonal disruption, anxiety, depression, behavioral control, and sudden death concerns. The injuries suggest that interventions for mitigating hypoxic exposure and nutrient loss may provide cellular protection, in the same fashion as interventions in other conditions with similar malabsorption, fluid turnover

  10. Sequential neural models with stochastic layers

    DEFF Research Database (Denmark)

    Fraccaro, Marco; Sønderby, Søren Kaae; Paquet, Ulrich

    2016-01-01

    How can we efficiently propagate uncertainty in a latent state representation with recurrent neural networks? This paper introduces stochastic recurrent neural networks which glue a deterministic recurrent neural network and a state space model together to form a stochastic and sequential neural...... generative model. The clear separation of deterministic and stochastic layers allows a structured variational inference network to track the factorization of the model's posterior distribution. By retaining both the nonlinear recursive structure of a recurrent neural network and averaging over...

  11. Establishing structure-property correlations and classification of base oils using statistical techniques and artificial neural networks

    International Nuclear Information System (INIS)

    Kapur, G.S.; Sastry, M.I.S.; Jaiswal, A.K.; Sarpal, A.S.

    2004-01-01

    The present paper describes various classification techniques like cluster analysis, principal component (PC)/factor analysis to classify different types of base stocks. The API classification of base oils (Group I-III) has been compared to a more detailed NMR derived chemical compositional and molecular structural parameters based classification in order to point out the similarities of the base oils in the same group and the differences between the oils placed in different groups. The detailed compositional parameters have been generated using 1 H and 13 C nuclear magnetic resonance (NMR) spectroscopic methods. Further, oxidation stability, measured in terms of rotating bomb oxidation test (RBOT) life, of non-conventional base stocks and their blends with conventional base stocks, has been quantitatively correlated with their 1 H NMR and elemental (sulphur and nitrogen) data with the help of multiple linear regression (MLR) and artificial neural networks (ANN) techniques. The MLR based model developed using NMR and elemental data showed a high correlation between the 'measured' and 'estimated' RBOT values for both training (R=0.859) and validation (R=0.880) data sets. The ANN based model, developed using fewer number of input variables (only 1 H NMR data) also showed high correlation between the 'measured' and 'estimated' RBOT values for training (R=0.881), validation (R=0.860) and test (R=0.955) data sets

  12. Neural responses to nostalgia-evoking music modeled by elements of dynamic musical structure and individual differences in affective traits.

    Science.gov (United States)

    Barrett, Frederick S; Janata, Petr

    2016-10-01

    Nostalgia is an emotion that is most commonly associated with personally and socially relevant memories. It is primarily positive in valence and is readily evoked by music. It is also an idiosyncratic experience that varies between individuals based on affective traits. We identified frontal, limbic, paralimbic, and midbrain brain regions in which the strength of the relationship between ratings of nostalgia evoked by music and blood-oxygen-level-dependent (BOLD) signal was predicted by affective personality measures (nostalgia proneness and the sadness scale of the Affective Neuroscience Personality Scales) that are known to modulate the strength of nostalgic experiences. We also identified brain areas including the inferior frontal gyrus, substantia nigra, cerebellum, and insula in which time-varying BOLD activity correlated more strongly with the time-varying tonal structure of nostalgia-evoking music than with music that evoked no or little nostalgia. These findings illustrate one way in which the reward and emotion regulation networks of the brain are recruited during the experiencing of complex emotional experiences triggered by music. These findings also highlight the importance of considering individual differences when examining the neural responses to strong and idiosyncratic emotional experiences. Finally, these findings provide a further demonstration of the use of time-varying stimulus-specific information in the investigation of music-evoked experiences. Copyright © 2016 Elsevier Ltd. All rights reserved.

  13. Effects of Time-Compressed Speech Training on Multiple Functional and Structural Neural Mechanisms Involving the Left Superior Temporal Gyrus.

    Science.gov (United States)

    Maruyama, Tsukasa; Takeuchi, Hikaru; Taki, Yasuyuki; Motoki, Kosuke; Jeong, Hyeonjeong; Kotozaki, Yuka; Nakagawa, Seishu; Nouchi, Rui; Iizuka, Kunio; Yokoyama, Ryoichi; Yamamoto, Yuki; Hanawa, Sugiko; Araki, Tsuyoshi; Sakaki, Kohei; Sasaki, Yukako; Magistro, Daniele; Kawashima, Ryuta

    2018-01-01

    Time-compressed speech is an artificial form of rapidly presented speech. Training with time-compressed speech (TCSSL) in a second language leads to adaptation toward TCSSL. Here, we newly investigated the effects of 4 weeks of training with TCSSL on diverse cognitive functions and neural systems using the fractional amplitude of spontaneous low-frequency fluctuations (fALFF), resting-state functional connectivity (RSFC) with the left superior temporal gyrus (STG), fractional anisotropy (FA), and regional gray matter volume (rGMV) of young adults by magnetic resonance imaging. There were no significant differences in change of performance of measures of cognitive functions or second language skills after training with TCSSL compared with that of the active control group. However, compared with the active control group, training with TCSSL was associated with increased fALFF, RSFC, and FA and decreased rGMV involving areas in the left STG. These results lacked evidence of a far transfer effect of time-compressed speech training on a wide range of cognitive functions and second language skills in young adults. However, these results demonstrated effects of time-compressed speech training on gray and white matter structures as well as on resting-state intrinsic activity and connectivity involving the left STG, which plays a key role in listening comprehension.

  14. Tinnitus Neural Mechanisms and Structural Changes in the Brain: The Contribution of Neuroimaging Research

    Directory of Open Access Journals (Sweden)

    Simonetti, Patricia

    2015-03-01

    Full Text Available Introduction Tinnitus is an abnormal perception of sound in the absence of an external stimulus. Chronic tinnitus usually has a high impact in many aspects of patients' lives, such as emotional stress, sleep disturbance, concentration difficulties, and so on. These strong reactions are usually attributed to central nervous system involvement. Neuroimaging has revealed the implication of brain structures in the auditory system. Objective This systematic review points out neuroimaging studies that contribute to identifying the structures involved in the pathophysiological mechanism of generation and persistence of various forms of tinnitus. Data Synthesis Functional imaging research reveals that tinnitus perception is associated with the involvement of the nonauditory brain areas, including the front parietal area; the limbic system, which consists of the anterior cingulate cortex, anterior insula, and amygdala; and the hippocampal and parahippocampal area. Conclusion The neuroimaging research confirms the involvement of the mechanisms of memory and cognition in the persistence of perception, anxiety, distress, and suffering associated with tinnitus.

  15. Effects of Neuromodulation on Excitatory-Inhibitory Neural Network Dynamics Depend on Network Connectivity Structure

    Science.gov (United States)

    Rich, Scott; Zochowski, Michal; Booth, Victoria

    2018-01-01

    Acetylcholine (ACh), one of the brain's most potent neuromodulators, can affect intrinsic neuron properties through blockade of an M-type potassium current. The effect of ACh on excitatory and inhibitory cells with this potassium channel modulates their membrane excitability, which in turn affects their tendency to synchronize in networks. Here, we study the resulting changes in dynamics in networks with inter-connected excitatory and inhibitory populations (E-I networks), which are ubiquitous in the brain. Utilizing biophysical models of E-I networks, we analyze how the network connectivity structure in terms of synaptic connectivity alters the influence of ACh on the generation of synchronous excitatory bursting. We investigate networks containing all combinations of excitatory and inhibitory cells with high (Type I properties) or low (Type II properties) modulatory tone. To vary network connectivity structure, we focus on the effects of the strengths of inter-connections between excitatory and inhibitory cells (E-I synapses and I-E synapses), and the strengths of intra-connections among excitatory cells (E-E synapses) and among inhibitory cells (I-I synapses). We show that the presence of ACh may or may not affect the generation of network synchrony depending on the network connectivity. Specifically, strong network inter-connectivity induces synchronous excitatory bursting regardless of the cellular propensity for synchronization, which aligns with predictions of the PING model. However, when a network's intra-connectivity dominates its inter-connectivity, the propensity for synchrony of either inhibitory or excitatory cells can determine the generation of network-wide bursting.

  16. A role for chemokine signaling in neural crest cell migration and craniofacial development

    Science.gov (United States)

    Killian, Eugenia C. Olesnicky; Birkholz, Denise A.; Artinger, Kristin Bruk

    2009-01-01

    Neural crest cells (NCCs) are a unique population of multipotent cells that migrate along defined pathways throughout the embryo and give rise to many diverse cell types including pigment cells, craniofacial cartilage and the peripheral nervous system (PNS). Aberrant migration of NCCs results in a wide variety of congenital birth defects including craniofacial abnormalities. The chemokine Sdf1 and its receptors, Cxcr4 and Cxcr7, have been identified as key components in the regulation of cell migration in a variety of tissues. Here we describe a novel role for the zebrafish chemokine receptor Cxcr4a in the development and migration of cranial NCCs (CNCCs). We find that loss of Cxcr4a, but not Cxcr7b results in aberrant CNCC migration, defects in the neurocranium, as well as cranial ganglia dismorphogenesis. Moreover, overexpression of either Sdf1b or Cxcr4a causes aberrant CNCC migration and results in ectopic craniofacial cartilages. We propose a model in which Sdf1b signaling from the pharyngeal arch endoderm and optic stalk to Cxcr4a expressing CNCCs is important for both the proper condensation of the CNCCs into pharyngeal arches and the subsequent patterning and morphogenesis of the neural crest derived tissues. PMID:19576198

  17. Common neural structures activated by epidural and transcutaneous lumbar spinal cord stimulation: Elicitation of posterior root-muscle reflexes.

    Directory of Open Access Journals (Sweden)

    Ursula S Hofstoetter

    Full Text Available Epidural electrical stimulation of the lumbar spinal cord is currently regaining momentum as a neuromodulation intervention in spinal cord injury (SCI to modify dysregulated sensorimotor functions and augment residual motor capacity. There is ample evidence that it engages spinal circuits through the electrical stimulation of large-to-medium diameter afferent fibers within lumbar and upper sacral posterior roots. Recent pilot studies suggested that the surface electrode-based method of transcutaneous spinal cord stimulation (SCS may produce similar neuromodulatory effects as caused by epidural SCS. Neurophysiological and computer modeling studies proposed that this noninvasive technique stimulates posterior-root fibers as well, likely activating similar input structures to the spinal cord as epidural stimulation. Here, we add a yet missing piece of evidence substantiating this assumption. We conducted in-depth analyses and direct comparisons of the electromyographic (EMG characteristics of short-latency responses in multiple leg muscles to both stimulation techniques derived from ten individuals with SCI each. Post-activation depression of responses evoked by paired pulses applied either epidurally or transcutaneously confirmed the reflex nature of the responses. The muscle responses to both techniques had the same latencies, EMG peak-to-peak amplitudes, and waveforms, except for smaller responses with shorter onset latencies in the triceps surae muscle group and shorter offsets of the responses in the biceps femoris muscle during epidural stimulation. Responses obtained in three subjects tested with both methods at different time points had near-identical waveforms per muscle group as well as same onset latencies. The present results strongly corroborate the activation of common neural input structures to the lumbar spinal cord-predominantly primary afferent fibers within multiple posterior roots-by both techniques and add to unraveling the

  18. Error and attack tolerance of synchronization in Hindmarsh–Rose neural networks with community structure

    International Nuclear Information System (INIS)

    Li, Chun-Hsien; Yang, Suh-Yuh

    2014-01-01

    Synchronization is one of the most important features observed in large-scale complex networks of interacting dynamical systems. As is well known, there is a close relation between the network topology and the network synchronizability. Using the coupled Hindmarsh–Rose neurons with community structure as a model network, in this paper we explore how failures of the nodes due to random errors or intentional attacks affect the synchronizability of community networks. The intentional attacks are realized by removing a fraction of the nodes with high values in some centrality measure such as the centralities of degree, eigenvector, betweenness and closeness. According to the master stability function method, we employ the algebraic connectivity of the considered community network as an indicator to examine the network synchronizability. Numerical evidences show that the node failure strategy based on the betweenness centrality has the most influence on the synchronizability of community networks. With this node failure strategy for a given network with a fixed number of communities, we find that the larger the degree of communities, the worse the network synchronizability; however, for a given network with a fixed degree of communities, we observe that the more the number of communities, the better the network synchronizability.

  19. Prediction of phenotypic susceptibility to antiretroviral drugs using physiochemical properties of the primary enzymatic structure combined with artificial neural networks

    DEFF Research Database (Denmark)

    Kjaer, J; Høj, L; Fox, Z

    2008-01-01

    OBJECTIVES: Genotypic interpretation systems extrapolate observed associations in datasets to predict viral susceptibility to antiretroviral drugs (ARVs) for given isolates. We aimed to develop and validate an approach using artificial neural networks (ANNs) that employ descriptors...

  20. Influence and timing of arrival of murine neural crest on pancreatic beta cell development and maturation.

    Science.gov (United States)

    Plank, Jennifer L; Mundell, Nathan A; Frist, Audrey Y; LeGrone, Alison W; Kim, Thomas; Musser, Melissa A; Walter, Teagan J; Labosky, Patricia A

    2011-01-15

    Interactions between cells from the ectoderm and mesoderm influence development of the endodermally-derived pancreas. While much is known about how mesoderm regulates pancreatic development, relatively little is understood about how and when the ectodermally-derived neural crest regulates pancreatic development and specifically, beta cell maturation. A previous study demonstrated that signals from the neural crest regulate beta cell proliferation and ultimately, beta cell mass. Here, we expand on that work to describe timing of neural crest arrival at the developing pancreatic bud and extend our knowledge of the non-cell autonomous role for neural crest derivatives in the process of beta cell maturation. We demonstrated that murine neural crest entered the pancreatic mesenchyme between the 26 and 27 somite stages (approximately 10.0 dpc) and became intermingled with pancreatic progenitors as the epithelium branched into the surrounding mesenchyme. Using a neural crest-specific deletion of the Forkhead transcription factor Foxd3, we ablated neural crest cells that migrate to the pancreatic primordium. Consistent with previous data, in the absence of Foxd3, and therefore the absence of neural crest cells, proliferation of insulin-expressing cells and insulin-positive area are increased. Analysis of endocrine cell gene expression in the absence of neural crest demonstrated that, although the number of insulin-expressing cells was increased, beta cell maturation was significantly impaired. Decreased MafA and Pdx1 expression illustrated the defect in beta cell maturation; we discovered that without neural crest, there was a reduction in the percentage of insulin-positive cells that co-expressed Glut2 and Pdx1 compared to controls. In addition, transmission electron microscopy analyses revealed decreased numbers of characteristic insulin granules and the presence of abnormal granules in insulin-expressing cells from mutant embryos. Together, these data demonstrate that

  1. The deeper structure of the southern Dead Sea basin derived from neural network analysis of velocity and attenuation tomography

    Science.gov (United States)

    Braeuer, Benjamin; Haberland, Christian; Bauer, Klaus; Weber, Michael

    2014-05-01

    The Dead Sea basin is a pull-apart basin at the Dead Sea transform fault, the boundary between the African and the Arabian plates. Though the DSB has been studied for a long time, the available knowledge - based mainly on surface geology, drilling and seismic reflection surveys - gives only a partial picture of its shallow structure. Therefore, within the framework of the international DESIRE (DEad Sea Integrated REsearch) project, a dense temporary local seismological network was operated in the southern Dead Sea area. Within 18 month of recording 650 events were detected. In addition to an already published tomography study revealing the distribution of P velocities and the Vp/Vs ratios a 2D P-wave attenuation tomography (parameter Qp) was performed. The neural network technique of Self-organizing maps (SOM) is used for the joint interpretation of these three parameters (Vp, Vp/Vs, Qp). The resulting clusters in the petrophysical parameter space are assigned to the main lithological units below the southern part of the Dead Sea basin: (1) The basin sediments characterized by strong attenuation, high vp/vs ratios and low P velocities. (2) The pre-basin sediments characterized by medium to strong attenuation, low Vp/Vs ratios and medium P velocities. (3) The basement characterized by low to moderate attenuation, medium vp/vs ratios and high P velocities. Thus, the asymmetric southern Dead Sea basin is filled with basin sediments down to depth of 7 to 12 km. Below the basin sediments, the pre-basin sediments are extending to a depth between 13 and 18 km.

  2. SpikeTemp: An Enhanced Rank-Order-Based Learning Approach for Spiking Neural Networks With Adaptive Structure.

    Science.gov (United States)

    Wang, Jinling; Belatreche, Ammar; Maguire, Liam P; McGinnity, Thomas Martin

    2017-01-01

    This paper presents an enhanced rank-order-based learning algorithm, called SpikeTemp, for spiking neural networks (SNNs) with a dynamically adaptive structure. The trained feed-forward SNN consists of two layers of spiking neurons: 1) an encoding layer which temporally encodes real-valued features into spatio-temporal spike patterns and 2) an output layer of dynamically grown neurons which perform spatio-temporal classification. Both Gaussian receptive fields and square cosine population encoding schemes are employed to encode real-valued features into spatio-temporal spike patterns. Unlike the rank-order-based learning approach, SpikeTemp uses the precise times of the incoming spikes for adjusting the synaptic weights such that early spikes result in a large weight change and late spikes lead to a smaller weight change. This removes the need to rank all the incoming spikes and, thus, reduces the computational cost of SpikeTemp. The proposed SpikeTemp algorithm is demonstrated on several benchmark data sets and on an image recognition task. The results show that SpikeTemp can achieve better classification performance and is much faster than the existing rank-order-based learning approach. In addition, the number of output neurons is much smaller when the square cosine encoding scheme is employed. Furthermore, SpikeTemp is benchmarked against a selection of existing machine learning algorithms, and the results demonstrate the ability of SpikeTemp to classify different data sets after just one presentation of the training samples with comparable classification performance.

  3. ANT Advanced Neural Tool

    Energy Technology Data Exchange (ETDEWEB)

    Labrador, I.; Carrasco, R.; Martinez, L.

    1996-07-01

    This paper describes a practical introduction to the use of Artificial Neural Networks. Artificial Neural Nets are often used as an alternative to the traditional symbolic manipulation and first order logic used in Artificial Intelligence, due the high degree of difficulty to solve problems that can not be handled by programmers using algorithmic strategies. As a particular case of Neural Net a Multilayer Perception developed by programming in C language on OS9 real time operating system is presented. A detailed description about the program structure and practical use are included. Finally, several application examples that have been treated with the tool are presented, and some suggestions about hardware implementations. (Author) 15 refs.

  4. ANT Advanced Neural Tool

    International Nuclear Information System (INIS)

    Labrador, I.; Carrasco, R.; Martinez, L.

    1996-01-01

    This paper describes a practical introduction to the use of Artificial Neural Networks. Artificial Neural Nets are often used as an alternative to the traditional symbolic manipulation and first order logic used in Artificial Intelligence, due the high degree of difficulty to solve problems that can not be handled by programmers using algorithmic strategies. As a particular case of Neural Net a Multilayer Perception developed by programming in C language on OS9 real time operating system is presented. A detailed description about the program structure and practical use are included. Finally, several application examples that have been treated with the tool are presented, and some suggestions about hardware implementations. (Author) 15 refs

  5. Analysis of the dynamic behavior of structures using the high-rate GNSS-PPP method combined with a wavelet-neural model: Numerical simulation and experimental tests

    Science.gov (United States)

    Kaloop, Mosbeh R.; Yigit, Cemal O.; Hu, Jong W.

    2018-03-01

    Recently, the high rate global navigation satellite system-precise point positioning (GNSS-PPP) technique has been used to detect the dynamic behavior of structures. This study aimed to increase the accuracy of the extraction oscillation properties of structural movements based on the high-rate (10 Hz) GNSS-PPP monitoring technique. A developmental model based on the combination of wavelet package transformation (WPT) de-noising and neural network prediction (NN) was proposed to improve the dynamic behavior of structures for GNSS-PPP method. A complicated numerical simulation involving highly noisy data and 13 experimental cases with different loads were utilized to confirm the efficiency of the proposed model design and the monitoring technique in detecting the dynamic behavior of structures. The results revealed that, when combined with the proposed model, GNSS-PPP method can be used to accurately detect the dynamic behavior of engineering structures as an alternative to relative GNSS method.

  6. Genetic algorithm based on optimization of neural network structure for fault diagnosis of the clutch retainer mechanism of MF 285 tractor

    Directory of Open Access Journals (Sweden)

    S. F Mousavi

    2016-09-01

    Full Text Available Introduction The diagnosis of agricultural machinery faults must be performed at an opportune time, in order to fulfill the agricultural operations in a timely manner and to optimize the accuracy and the integrity of a system, proper monitoring and fault diagnosis of the rotating parts is required. With development of fault diagnosis methods of rotating equipment, especially bearing failure, the security, performance and availability of machines has been increasing. In general, fault detection is conducted through a specific procedure which starts with data acquisition and continues with features extraction, and subsequently failure of the machine would be detected. Several practical methods have been introduced for fault detection in rotating parts of machineries. The review of the literature shows that both Artificial Neural Networks (ANN and Support Vector Machines (SVM have been used for this purpose. However, the results show that SVM is more effective than Artificial Neural Networks in fault detection of such machineries. In some smart detection systems, incorporating an optimized method such as Genetic Algorithm in the Neural Network model, could improve the fault detection procedure. Consequently, the fault detection performance of neural networks may also be improved by combining with the Genetic Algorithm and hence will be comparable with the performance of the Support Vector Machine. In this study, the so called Genetic Algorithm (GA method was used to optimize the structure of the Artificial Neural Networks (ANN for fault detection of the clutch retainer mechanism of Massey Ferguson 285 tractor. Materials and Methods The test rig consists of some electro mechanical parts including the clutch retainer mechanism of Massey Ferguson 285 tractor, a supporting shaft, a single-phase electric motor, a loading mechanism to model the load of the tractor clutch and the corresponding power train gears. The data acquisition section consists of a

  7. Modeling a Neural Network as a Teaching Tool for the Learning of the Structure-Function Relationship

    Science.gov (United States)

    Salinas, Dino G.; Acevedo, Cristian; Gomez, Christian R.

    2010-01-01

    The authors describe an activity they have created in which students can visualize a theoretical neural network whose states evolve according to a well-known simple law. This activity provided an uncomplicated approach to a paradigm commonly represented through complex mathematical formulation. From their observations, students learned many basic…

  8. Application of neural networks in coastal engineering

    Digital Repository Service at National Institute of Oceanography (India)

    Mandal, S.

    the neural network attractive. A neural network is an information processing system modeled on the structure of the dynamic process. It can solve the complex/nonlinear problems quickly once trained by operating on problems using an interconnected number...

  9. Neural networks

    International Nuclear Information System (INIS)

    Denby, Bruce; Lindsey, Clark; Lyons, Louis

    1992-01-01

    The 1980s saw a tremendous renewal of interest in 'neural' information processing systems, or 'artificial neural networks', among computer scientists and computational biologists studying cognition. Since then, the growth of interest in neural networks in high energy physics, fueled by the need for new information processing technologies for the next generation of high energy proton colliders, can only be described as explosive

  10. Are neural crest stem cells the missing link between hematopoietic and neurogenic niches?

    Directory of Open Access Journals (Sweden)

    Cécile eCoste

    2015-06-01

    Full Text Available Hematopoietic niches are defined as cellular and molecular microenvironments that regulate hematopoietic stem cell (HSC function together with stem cell autonomous mechanisms. Many different cell types have been characterized as contributors to the formation of HSC niches, such as osteoblasts, endothelial cells, Schwann cells, and mesenchymal progenitors. These mesenchymal progenitors have themselves been classified as CXC chemokine ligand (CXCL12-abundant reticular (CAR cells, stem cell factor expressing cells, or nestin-positive mesenchymal stem cells (MSCs, which have been recently identified as neural crest-derived cells (NCSCs. Together, these cells are spatially associated with HSCs and believed to provide appropriate microenvironments for HSC self-renewal, differentiation, mobilization and hibernation both by cell-to-cell contact and soluble factors. Interestingly, it appears that regulatory pathways governing the hematopoietic niche homeostasis are operating in the neurogenic niche as well. Therefore, this review paper aims to compare both the regulation of hematopoietic and neurogenic niches, in order to highlight the role of NCSCs and nervous system components in the development and the regulation of the hematopoietic system.

  11. Are neural crest stem cells the missing link between hematopoietic and neurogenic niches?

    Science.gov (United States)

    Coste, Cécile; Neirinckx, Virginie; Gothot, André; Wislet, Sabine; Rogister, Bernard

    2015-01-01

    Hematopoietic niches are defined as cellular and molecular microenvironments that regulate hematopoietic stem cell (HSC) function together with stem cell autonomous mechanisms. Many different cell types have been characterized as contributors to the formation of HSC niches, such as osteoblasts, endothelial cells, Schwann cells, and mesenchymal progenitors. These mesenchymal progenitors have themselves been classified as CXC chemokine ligand (CXCL) 12-abundant reticular (CAR) cells, stem cell factor expressing cells, or nestin-positive mesenchymal stem cells (MSCs), which have been recently identified as neural crest-derived cells (NCSCs). Together, these cells are spatially associated with HSCs and believed to provide appropriate microenvironments for HSC self-renewal, differentiation, mobilization and hibernation both by cell-cell contact and soluble factors. Interestingly, it appears that regulatory pathways governing the hematopoietic niche homeostasis are operating in the neurogenic niche as well. Therefore, this review paper aims to compare both the regulation of hematopoietic and neurogenic niches, in order to highlight the role of NCSCs and nervous system components in the development and the regulation of the hematopoietic system.

  12. Structural Reliability: An Assessment Using a New and Efficient Two-Phase Method Based on Artificial Neural Network and a Harmony Search Algorithm

    Directory of Open Access Journals (Sweden)

    Naser Kazemi Elaki

    2016-06-01

    Full Text Available In this research, a two-phase algorithm based on the artificial neural network (ANN and a harmony search (HS algorithm has been developed with the aim of assessing the reliability of structures with implicit limit state functions. The proposed method involves the generation of datasets to be used specifically for training by Finite Element analysis, to establish an ANN model using a proven ANN model in the reliability assessment process as an analyzer for structures, and finally estimate the reliability index and failure probability by using the HS algorithm, without any requirements for the explicit form of limit state function. The proposed algorithm is investigated here, and its accuracy and efficiency are demonstrated by using several numerical examples. The results obtained show that the proposed algorithm gives an appropriate estimate for the assessment of reliability of structures.

  13. Optical imaging of neuronal activity and visualization of fine neural structures in non-desheathed nervous systems.

    Directory of Open Access Journals (Sweden)

    Christopher John Goldsmith

    Full Text Available Locating circuit neurons and recording from them with single-cell resolution is a prerequisite for studying neural circuits. Determining neuron location can be challenging even in small nervous systems because neurons are densely packed, found in different layers, and are often covered by ganglion and nerve sheaths that impede access for recording electrodes and neuronal markers. We revisited the voltage-sensitive dye RH795 for its ability to stain and record neurons through the ganglion sheath. Bath-application of RH795 stained neuronal membranes in cricket, earthworm and crab ganglia without removing the ganglion sheath, revealing neuron cell body locations in different ganglion layers. Using the pyloric and gastric mill central pattern generating neurons in the stomatogastric ganglion (STG of the crab, Cancer borealis, we found that RH795 permeated the ganglion without major residue in the sheath and brightly stained somatic, axonal and dendritic membranes. Visibility improved significantly in comparison to unstained ganglia, allowing the identification of somata location and number of most STG neurons. RH795 also stained axons and varicosities in non-desheathed nerves, and it revealed the location of sensory cell bodies in peripheral nerves. Importantly, the spike activity of the sensory neuron AGR, which influences the STG motor patterns, remained unaffected by RH795, while desheathing caused significant changes in AGR activity. With respect to recording neural activity, RH795 allowed us to optically record membrane potential changes of sub-sheath neuronal membranes without impairing sensory activity. The signal-to-noise ratio was comparable with that previously observed in desheathed preparations and sufficiently high to identify neurons in single-sweep recordings and synaptic events after spike-triggered averaging. In conclusion, RH795 enabled staining and optical recording of neurons through the ganglion sheath and is therefore both a

  14. Structural alterations in rat liver proteins due to streptozotocin-induced diabetes and the recovery effect of selenium: Fourier transform infrared microspectroscopy and neural network study

    Science.gov (United States)

    Bozkurt, Ozlem; Haman Bayari, Sevgi; Severcan, Mete; Krafft, Christoph; Popp, Jürgen; Severcan, Feride

    2012-07-01

    The relation between protein structural alterations and tissue dysfunction is a major concern as protein fibrillation and/or aggregation due to structural alterations has been reported in many disease states. In the current study, Fourier transform infrared microspectroscopic imaging has been used to investigate diabetes-induced changes on protein secondary structure and macromolecular content in streptozotocin-induced diabetic rat liver. Protein secondary structural alterations were predicted using neural network approach utilizing the amide I region. Moreover, the role of selenium in the recovery of diabetes-induced alterations on macromolecular content and protein secondary structure was also studied. The results revealed that diabetes induced a decrease in lipid to protein and glycogen to protein ratios in diabetic livers. Significant alterations in protein secondary structure were observed with a decrease in α-helical and an increase in β-sheet content. Both doses of selenium restored diabetes-induced changes in lipid to protein and glycogen to protein ratios. However, low-dose selenium supplementation was not sufficient to recover the effects of diabetes on protein secondary structure, while a higher dose of selenium fully restored diabetes-induced alterations in protein structure.

  15. Neural stem cells and neuro/gliogenesis in the central nervous system: understanding the structural and functional plasticity of the developing, mature, and diseased brain.

    Science.gov (United States)

    Yamaguchi, Masahiro; Seki, Tatsunori; Imayoshi, Itaru; Tamamaki, Nobuaki; Hayashi, Yoshitaka; Tatebayashi, Yoshitaka; Hitoshi, Seiji

    2016-05-01

    Neurons and glia in the central nervous system (CNS) originate from neural stem cells (NSCs). Knowledge of the mechanisms of neuro/gliogenesis from NSCs is fundamental to our understanding of how complex brain architecture and function develop. NSCs are present not only in the developing brain but also in the mature brain in adults. Adult neurogenesis likely provides remarkable plasticity to the mature brain. In addition, recent progress in basic research in mental disorders suggests an etiological link with impaired neuro/gliogenesis in particular brain regions. Here, we review the recent progress and discuss future directions in stem cell and neuro/gliogenesis biology by introducing several topics presented at a joint meeting of the Japanese Association of Anatomists and the Physiological Society of Japan in 2015. Collectively, these topics indicated that neuro/gliogenesis from NSCs is a common event occurring in many brain regions at various ages in animals. Given that significant structural and functional changes in cells and neural networks are accompanied by neuro/gliogenesis from NSCs and the integration of newly generated cells into the network, stem cell and neuro/gliogenesis biology provides a good platform from which to develop an integrated understanding of the structural and functional plasticity that underlies the development of the CNS, its remodeling in adulthood, and the recovery from diseases that affect it.

  16. Recycling signals in the neural crest

    OpenAIRE

    Taneyhill, Lisa A.; Bronner-Fraser, Marianne E.

    2006-01-01

    Vertebrate neural crest cells are multipotent and differentiate into structures that include cartilage and the bones of the face, as well as much of the peripheral nervous system. Understanding how different model vertebrates utilize signaling pathways reiteratively during various stages of neural crest formation and differentiation lends insight into human disorders associated with the neural crest.

  17. Recycling signals in the neural crest.

    Science.gov (United States)

    Taneyhill, Lisa A; Bronner-Fraser, Marianne

    2005-01-01

    Vertebrate neural crest cells are multipotent and differentiate into structures that include cartilage and the bones of the face, as well as much of the peripheral nervous system. Understanding how different model vertebrates utilize signaling pathways reiteratively during various stages of neural crest formation and differentiation lends insight into human disorders associated with the neural crest.

  18. Neural coding of image structure and contrast polarity of Cartesian, hyperbolic, and polar gratings in the primary and secondary visual cortex of the tree shrew.

    Science.gov (United States)

    Poirot, Jordan; De Luna, Paolo; Rainer, Gregor

    2016-04-01

    We comprehensively characterize spiking and visual evoked potential (VEP) activity in tree shrew V1 and V2 using Cartesian, hyperbolic, and polar gratings. Neural selectivity to structure of Cartesian gratings was higher than other grating classes in both visual areas. From V1 to V2, structure selectivity of spiking activity increased, whereas corresponding VEP values tended to decrease, suggesting that single-neuron coding of Cartesian grating attributes improved while the cortical columnar organization of these neurons became less precise from V1 to V2. We observed that neurons in V2 generally exhibited similar selectivity for polar and Cartesian gratings, suggesting that structure of polar-like stimuli might be encoded as early as in V2. This hypothesis is supported by the preference shift from V1 to V2 toward polar gratings of higher spatial frequency, consistent with the notion that V2 neurons encode visual scene borders and contours. Neural sensitivity to modulations of polarity of hyperbolic gratings was highest among all grating classes and closely related to the visual receptive field (RF) organization of ON- and OFF-dominated subregions. We show that spatial RF reconstructions depend strongly on grating class, suggesting that intracortical contributions to RF structure are strongest for Cartesian and polar gratings. Hyperbolic gratings tend to recruit least cortical elaboration such that the RF maps are similar to those generated by sparse noise, which most closely approximate feedforward inputs. Our findings complement previous literature in primates, rodents, and carnivores and highlight novel aspects of shape representation and coding occurring in mammalian early visual cortex. Copyright © 2016 the American Physiological Society.

  19. Neural network to diagnose lining condition

    Science.gov (United States)

    Yemelyanov, V. A.; Yemelyanova, N. Y.; Nedelkin, A. A.; Zarudnaya, M. V.

    2018-03-01

    The paper presents data on the problem of diagnosing the lining condition at the iron and steel works. The authors describe the neural network structure and software that are designed and developed to determine the lining burnout zones. The simulation results of the proposed neural networks are presented. The authors note the low learning and classification errors of the proposed neural networks. To realize the proposed neural network, the specialized software has been developed.

  20. Comparing success levels of different neural network structures in extracting discriminative information from the response patterns of a temperature-modulated resistive gas sensor

    Science.gov (United States)

    Hosseini-Golgoo, S. M.; Bozorgi, H.; Saberkari, A.

    2015-06-01

    Performances of three neural networks, consisting of a multi-layer perceptron, a radial basis function, and a neuro-fuzzy network with local linear model tree training algorithm, in modeling and extracting discriminative features from the response patterns of a temperature-modulated resistive gas sensor are quantitatively compared. For response pattern recording, a voltage staircase containing five steps each with a 20 s plateau is applied to the micro-heater of the sensor, when 12 different target gases, each at 11 concentration levels, are present. In each test, the hidden layer neuron weights are taken as the discriminatory feature vector of the target gas. These vectors are then mapped to a 3D feature space using linear discriminant analysis. The discriminative information content of the feature vectors are determined by the calculation of the Fisher’s discriminant ratio, affording quantitative comparison among the success rates achieved by the different neural network structures. The results demonstrate a superior discrimination ratio for features extracted from local linear neuro-fuzzy and radial-basis-function networks with recognition rates of 96.27% and 90.74%, respectively.

  1. Comparing success levels of different neural network structures in extracting discriminative information from the response patterns of a temperature-modulated resistive gas sensor

    International Nuclear Information System (INIS)

    Hosseini-Golgoo, S M; Bozorgi, H; Saberkari, A

    2015-01-01

    Performances of three neural networks, consisting of a multi-layer perceptron, a radial basis function, and a neuro-fuzzy network with local linear model tree training algorithm, in modeling and extracting discriminative features from the response patterns of a temperature-modulated resistive gas sensor are quantitatively compared. For response pattern recording, a voltage staircase containing five steps each with a 20 s plateau is applied to the micro-heater of the sensor, when 12 different target gases, each at 11 concentration levels, are present. In each test, the hidden layer neuron weights are taken as the discriminatory feature vector of the target gas. These vectors are then mapped to a 3D feature space using linear discriminant analysis. The discriminative information content of the feature vectors are determined by the calculation of the Fisher’s discriminant ratio, affording quantitative comparison among the success rates achieved by the different neural network structures. The results demonstrate a superior discrimination ratio for features extracted from local linear neuro-fuzzy and radial-basis-function networks with recognition rates of 96.27% and 90.74%, respectively. (paper)

  2. Neural Networks

    International Nuclear Information System (INIS)

    Smith, Patrick I.

    2003-01-01

    Physicists use large detectors to measure particles created in high-energy collisions at particle accelerators. These detectors typically produce signals indicating either where ionization occurs along the path of the particle, or where energy is deposited by the particle. The data produced by these signals is fed into pattern recognition programs to try to identify what particles were produced, and to measure the energy and direction of these particles. Ideally, there are many techniques used in this pattern recognition software. One technique, neural networks, is particularly suitable for identifying what type of particle caused by a set of energy deposits. Neural networks can derive meaning from complicated or imprecise data, extract patterns, and detect trends that are too complex to be noticed by either humans or other computer related processes. To assist in the advancement of this technology, Physicists use a tool kit to experiment with several neural network techniques. The goal of this research is interface a neural network tool kit into Java Analysis Studio (JAS3), an application that allows data to be analyzed from any experiment. As the final result, a physicist will have the ability to train, test, and implement a neural network with the desired output while using JAS3 to analyze the results or output. Before an implementation of a neural network can take place, a firm understanding of what a neural network is and how it works is beneficial. A neural network is an artificial representation of the human brain that tries to simulate the learning process [5]. It is also important to think of the word artificial in that definition as computer programs that use calculations during the learning process. In short, a neural network learns by representative examples. Perhaps the easiest way to describe the way neural networks learn is to explain how the human brain functions. The human brain contains billions of neural cells that are responsible for processing

  3. Evolvable synthetic neural system

    Science.gov (United States)

    Curtis, Steven A. (Inventor)

    2009-01-01

    An evolvable synthetic neural system includes an evolvable neural interface operably coupled to at least one neural basis function. Each neural basis function includes an evolvable neural interface operably coupled to a heuristic neural system to perform high-level functions and an autonomic neural system to perform low-level functions. In some embodiments, the evolvable synthetic neural system is operably coupled to one or more evolvable synthetic neural systems in a hierarchy.

  4. What are artificial neural networks?

    DEFF Research Database (Denmark)

    Krogh, Anders

    2008-01-01

    Artificial neural networks have been applied to problems ranging from speech recognition to prediction of protein secondary structure, classification of cancers and gene prediction. How do they work and what might they be good for? Udgivelsesdato: 2008-Feb......Artificial neural networks have been applied to problems ranging from speech recognition to prediction of protein secondary structure, classification of cancers and gene prediction. How do they work and what might they be good for? Udgivelsesdato: 2008-Feb...

  5. Joint stiffness identification of body structure using neural network. Jointed part composed of 2 beams; Neural network ni yoru shatai kozo no ketsugo gosei dotei. Buzai 2 hon kara naru ketsugobu no baai

    Energy Technology Data Exchange (ETDEWEB)

    Okabe, A; Tomioka, N [Nihon University, Tokyo (Japan)

    1997-10-01

    The method to obtain a joint stiffness value from displacements of jointed part using hierarchical neural networks in case of a jointed part composed of two beams were proposed. First, the sample data of displacements of jointed part vs. joint stiffness are prepared as learned data. Second, the relations between displacements of jointed part and joint stiffness are constructed from these learned data using a hierarchical neural networks. It was found that the value of joint stiffness can be obtained from displacement of jointed part by the trained neural network. 4 refs., 9 figs., 2 tabs.

  6. Modeling of biologically motivated self-learning equivalent-convolutional recurrent-multilayer neural structures (BLM_SL_EC_RMNS) for image fragments clustering and recognition

    Science.gov (United States)

    Krasilenko, Vladimir G.; Lazarev, Alexander A.; Nikitovich, Diana V.

    2018-03-01

    The biologically-motivated self-learning equivalence-convolutional recurrent-multilayer neural structures (BLM_SL_EC_RMNS) for fragments images clustering and recognition will be discussed. We shall consider these neural structures and their spatial-invariant equivalental models (SIEMs) based on proposed equivalent two-dimensional functions of image similarity and the corresponding matrix-matrix (or tensor) procedures using as basic operations of continuous logic and nonlinear processing. These SIEMs can simply describe the signals processing during the all training and recognition stages and they are suitable for unipolar-coding multilevel signals. The clustering efficiency in such models and their implementation depends on the discriminant properties of neural elements of hidden layers. Therefore, the main models and architecture parameters and characteristics depends on the applied types of non-linear processing and function used for image comparison or for adaptive-equivalent weighing of input patterns. We show that these SL_EC_RMNSs have several advantages, such as the self-study and self-identification of features and signs of the similarity of fragments, ability to clustering and recognize of image fragments with best efficiency and strong mutual correlation. The proposed combined with learning-recognition clustering method of fragments with regard to their structural features is suitable not only for binary, but also color images and combines self-learning and the formation of weight clustered matrix-patterns. Its model is constructed and designed on the basis of recursively continuous logic and nonlinear processing algorithms and to k-average method or method the winner takes all (WTA). The experimental results confirmed that fragments with a large numbers of elements may be clustered. For the first time the possibility of generalization of these models for space invariant case is shown. The experiment for an images of different dimensions (a reference

  7. Neural Crest Cells Isolated from the Bone Marrow of Transgenic Mice Express JCV T-Antigen.

    Directory of Open Access Journals (Sweden)

    Jennifer Gordon

    Full Text Available JC virus (JCV, a common human polyomavirus, is the etiological agent of the demyelinating disease, progressive multifocal leukoencephalopathy (PML. In addition to its role in PML, studies have demonstrated the transforming ability of the JCV early protein, T-antigen, and its association with some human cancers. JCV infection occurs in childhood and latent virus is thought to be maintained within the bone marrow, which harbors cells of hematopoietic and non-hematopoietic lineages. Here we show that non-hematopoietic mesenchymal stem cells (MSCs isolated from the bone marrow of JCV T-antigen transgenic mice give rise to JCV T-antigen positive cells when cultured under neural conditions. JCV T-antigen positive cells exhibited neural crest characteristics and demonstrated p75, SOX-10 and nestin positivity. When cultured in conditions typical for mesenchymal cells, a population of T-antigen negative cells, which did not express neural crest markers arose from the MSCs. JCV T-antigen positive cells could be cultured long-term while maintaining their neural crest characteristics. When these cells were induced to differentiate into neural crest derivatives, JCV T-antigen was downregulated in cells differentiating into bone and maintained in glial cells expressing GFAP and S100. We conclude that JCV T-antigen can be stably expressed within a fraction of bone marrow cells differentiating along the neural crest/glial lineage when cultured in vitro. These findings identify a cell population within the bone marrow permissible for JCV early gene expression suggesting the possibility that these cells could support persistent viral infection and thus provide clues toward understanding the role of the bone marrow in JCV latency and reactivation. Further, our data provides an excellent experimental model system for studying the cell-type specificity of JCV T-antigen expression, the role of bone marrow-derived stem cells in the pathogenesis of JCV-related diseases

  8. The neural crest is a source of mesenchymal stem cells with specialized hematopoietic stem cell niche function.

    Science.gov (United States)

    Isern, Joan; García-García, Andrés; Martín, Ana M; Arranz, Lorena; Martín-Pérez, Daniel; Torroja, Carlos; Sánchez-Cabo, Fátima; Méndez-Ferrer, Simón

    2014-09-25

    Mesenchymal stem cells (MSCs) and osteolineage cells contribute to the hematopoietic stem cell (HSC) niche in the bone marrow of long bones. However, their developmental relationships remain unclear. In this study, we demonstrate that different MSC populations in the developing marrow of long bones have distinct functions. Proliferative mesoderm-derived nestin(-) MSCs participate in fetal skeletogenesis and lose MSC activity soon after birth. In contrast, quiescent neural crest-derived nestin(+) cells preserve MSC activity, but do not generate fetal chondrocytes. Instead, they differentiate into HSC niche-forming MSCs, helping to establish the HSC niche by secreting Cxcl12. Perineural migration of these cells to the bone marrow requires the ErbB3 receptor. The neonatal Nestin-GFP(+) Pdgfrα(-) cell population also contains Schwann cell precursors, but does not comprise mature Schwann cells. Thus, in the developing bone marrow HSC niche-forming MSCs share a common origin with sympathetic peripheral neurons and glial cells, and ontogenically distinct MSCs have non-overlapping functions in endochondrogenesis and HSC niche formation.

  9. The neural correlates of highly iconic structures and topographic discourse in French Sign Language as observed in six hearing native signers.

    Science.gov (United States)

    Courtin, C; Hervé, P-Y; Petit, L; Zago, L; Vigneau, M; Beaucousin, V; Jobard, G; Mazoyer, B; Mellet, E; Tzourio-Mazoyer, N

    2010-09-01

    "Highly iconic" structures in Sign Language enable a narrator to act, switch characters, describe objects, or report actions in four-dimensions. This group of linguistic structures has no real spoken-language equivalent. Topographical descriptions are also achieved in a sign-language specific manner via the use of signing-space and spatial-classifier signs. We used functional magnetic resonance imaging (fMRI) to compare the neural correlates of topographic discourse and highly iconic structures in French Sign Language (LSF) in six hearing native signers, children of deaf adults (CODAs), and six LSF-naïve monolinguals. LSF materials consisted of videos of a lecture excerpt signed without spatially organized discourse or highly iconic structures (Lect LSF), a tale signed using highly iconic structures (Tale LSF), and a topographical description using a diagrammatic format and spatial-classifier signs (Topo LSF). We also presented texts in spoken French (Lect French, Tale French, Topo French) to all participants. With both languages, the Topo texts activated several different regions that are involved in mental navigation and spatial working memory. No specific correlate of LSF spatial discourse was evidenced. The same regions were more activated during Tale LSF than Lect LSF in CODAs, but not in monolinguals, in line with the presence of signing-space structure in both conditions. Motion processing areas and parts of the fusiform gyrus and precuneus were more active during Tale LSF in CODAs; no such effect was observed with French or in LSF-naïve monolinguals. These effects may be associated with perspective-taking and acting during personal transfers. 2010 Elsevier Inc. All rights reserved.

  10. A pilot study for distinguishing chromophobe renal cell carcinoma and oncocytoma using second harmonic generation imaging and convolutional neural network analysis of collagen fibrillar structure

    Science.gov (United States)

    Judd, Nicolas; Smith, Jason; Jain, Manu; Mukherjee, Sushmita; Icaza, Michael; Gallagher, Ryan; Szeligowski, Richard; Wu, Binlin

    2018-02-01

    A clear distinction between oncocytoma and chromophobe renal cell carcinoma (chRCC) is critically important for clinical management of patients. But it may often be difficult to distinguish the two entities based on hematoxylin and eosin (H and E) stained sections alone. In this study, second harmonic generation (SHG) signals which are very specific to collagen were used to image collagen fibril structure. We conduct a pilot study to develop a new diagnostic method based on the analysis of collagen associated with kidney tumors using convolutional neural networks (CNNs). CNNs comprise a type of machine learning process well-suited for drawing information out of images. This study examines a CNN model's ability to differentiate between oncocytoma (benign), and chRCC (malignant) kidney tumor images acquired with second harmonic generation (SHG), which is very specific for collagen matrix. To the best of our knowledge, this is the first study that attempts to distinguish the two entities based on their collagen structure. The model developed from this study demonstrated an overall classification accuracy of 68.7% with a specificity of 66.3% and sensitivity of 74.6%. While these results reflect an ability to classify the kidney tumors better than chance, further studies will be carried out to (a) better realize the tumor classification potential of this method with a larger sample size and (b) combining SHG with two-photon excited intrinsic fluorescence signal to achieve better classification.

  11. Neural Networks for Non-linear Control

    DEFF Research Database (Denmark)

    Sørensen, O.

    1994-01-01

    This paper describes how a neural network, structured as a Multi Layer Perceptron, is trained to predict, simulate and control a non-linear process.......This paper describes how a neural network, structured as a Multi Layer Perceptron, is trained to predict, simulate and control a non-linear process....

  12. Developing a Graphical User Interface to Automate the Estimation and Prediction of Risk Values for Flood Protective Structures using Artificial Neural Network

    Science.gov (United States)

    Hasan, M.; Helal, A.; Gabr, M.

    2014-12-01

    In this project, we focus on providing a computer-automated platform for a better assessment of the potential failures and retrofit measures of flood-protecting earth structures, e.g., dams and levees. Such structures play an important role during extreme flooding events as well as during normal operating conditions. Furthermore, they are part of other civil infrastructures such as water storage and hydropower generation. Hence, there is a clear need for accurate evaluation of stability and functionality levels during their service lifetime so that the rehabilitation and maintenance costs are effectively guided. Among condition assessment approaches based on the factor of safety, the limit states (LS) approach utilizes numerical modeling to quantify the probability of potential failures. The parameters for LS numerical modeling include i) geometry and side slopes of the embankment, ii) loading conditions in terms of rate of rising and duration of high water levels in the reservoir, and iii) cycles of rising and falling water levels simulating the effect of consecutive storms throughout the service life of the structure. Sample data regarding the correlations of these parameters are available through previous research studies. We have unified these criteria and extended the risk assessment in term of loss of life through the implementation of a graphical user interface to automate input parameters that divides data into training and testing sets, and then feeds them into Artificial Neural Network (ANN) tool through MATLAB programming. The ANN modeling allows us to predict risk values of flood protective structures based on user feedback quickly and easily. In future, we expect to fine-tune the software by adding extensive data on variations of parameters.

  13. Artificial neural network modelling

    CERN Document Server

    Samarasinghe, Sandhya

    2016-01-01

    This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems. It presents recent results of ANNs in modelling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation, Robustness and Stochasticity 2) Advances in Modelling Biological and Environmental Systems and 3) Advances in Modelling Social and Economic Systems. The book aims at serving undergraduates, postgraduates and researchers in ANN computational modelling. .

  14. Modeling and possible implementation of self-learning equivalence-convolutional neural structures for auto-encoding-decoding and clusterization of images

    Science.gov (United States)

    Krasilenko, Vladimir G.; Lazarev, Alexander A.; Nikitovich, Diana V.

    2017-08-01

    Self-learning equivalent-convolutional neural structures (SLECNS) for auto-coding-decoding and image clustering are discussed. The SLECNS architectures and their spatially invariant equivalent models (SI EMs) using the corresponding matrix-matrix procedures with basic operations of continuous logic and non-linear processing are proposed. These SI EMs have several advantages, such as the ability to recognize image fragments with better efficiency and strong cross correlation. The proposed clustering method of fragments with regard to their structural features is suitable not only for binary, but also color images and combines self-learning and the formation of weight clustered matrix-patterns. Its model is constructed and designed on the basis of recursively processing algorithms and to k-average method. The experimental results confirmed that larger images and 2D binary fragments with a large numbers of elements may be clustered. For the first time the possibility of generalization of these models for space invariant case is shown. The experiment for an image with dimension of 256x256 (a reference array) and fragments with dimensions of 7x7 and 21x21 for clustering is carried out. The experiments, using the software environment Mathcad, showed that the proposed method is universal, has a significant convergence, the small number of iterations is easily, displayed on the matrix structure, and confirmed its prospects. Thus, to understand the mechanisms of self-learning equivalence-convolutional clustering, accompanying her to the competitive processes in neurons, and the neural auto-encoding-decoding and recognition principles with the use of self-learning cluster patterns is very important which used the algorithm and the principles of non-linear processing of two-dimensional spatial functions of images comparison. These SIEMs can simply describe the signals processing during the all training and recognition stages and they are suitable for unipolar

  15. Neural Networks

    Directory of Open Access Journals (Sweden)

    Schwindling Jerome

    2010-04-01

    Full Text Available This course presents an overview of the concepts of the neural networks and their aplication in the framework of High energy physics analyses. After a brief introduction on the concept of neural networks, the concept is explained in the frame of neuro-biology, introducing the concept of multi-layer perceptron, learning and their use as data classifer. The concept is then presented in a second part using in more details the mathematical approach focussing on typical use cases faced in particle physics. Finally, the last part presents the best way to use such statistical tools in view of event classifers, putting the emphasis on the setup of the multi-layer perceptron. The full article (15 p. corresponding to this lecture is written in french and is provided in the proceedings of the book SOS 2008.

  16. Principal Component Analysis Coupled with Artificial Neural Networks—A Combined Technique Classifying Small Molecular Structures Using a Concatenated Spectral Database

    Directory of Open Access Journals (Sweden)

    Mihail Lucian Birsa

    2011-10-01

    Full Text Available In this paper we present several expert systems that predict the class identity of the modeled compounds, based on a preprocessed spectral database. The expert systems were built using Artificial Neural Networks (ANN and are designed to predict if an unknown compound has the toxicological activity of amphetamines (stimulant and hallucinogen, or whether it is a nonamphetamine. In attempts to circumvent the laws controlling drugs of abuse, new chemical structures are very frequently introduced on the black market. They are obtained by slightly modifying the controlled molecular structures by adding or changing substituents at various positions on the banned molecules. As a result, no substance similar to those forming a prohibited class may be used nowadays, even if it has not been specifically listed. Therefore, reliable, fast and accessible systems capable of modeling and then identifying similarities at molecular level, are highly needed for epidemiological, clinical, and forensic purposes. In order to obtain the expert systems, we have preprocessed a concatenated spectral database, representing the GC-FTIR (gas chromatography-Fourier transform infrared spectrometry and GC-MS (gas chromatography-mass spectrometry spectra of 103 forensic compounds. The database was used as input for a Principal Component Analysis (PCA. The scores of the forensic compounds on the main principal components (PCs were then used as inputs for the ANN systems. We have built eight PC-ANN systems (principal component analysis coupled with artificial neural network with a different number of input variables: 15 PCs, 16 PCs, 17 PCs, 18 PCs, 19 PCs, 20 PCs, 21 PCs and 22 PCs. The best expert system was found to be the ANN network built with 18 PCs, which accounts for an explained variance of 77%. This expert system has the best sensitivity (a rate of classification C = 100% and a rate of true positives TP = 100%, as well as a good selectivity (a rate of true negatives TN

  17. The neural cell adhesion molecule

    DEFF Research Database (Denmark)

    Berezin, V; Bock, E; Poulsen, F M

    2000-01-01

    During the past year, the understanding of the structure and function of neural cell adhesion has advanced considerably. The three-dimensional structures of several of the individual modules of the neural cell adhesion molecule (NCAM) have been determined, as well as the structure of the complex...... between two identical fragments of the NCAM. Also during the past year, a link between homophilic cell adhesion and several signal transduction pathways has been proposed, connecting the event of cell surface adhesion to cellular responses such as neurite outgrowth. Finally, the stimulation of neurite...

  18. High serotonin levels during brain development alter the structural input-output connectivity of neural networks in the rat somatosensory layer IV

    Directory of Open Access Journals (Sweden)

    Stéphanie eMiceli

    2013-06-01

    Full Text Available Homeostatic regulation of serotonin (5-HT concentration is critical for normal topographical organization and development of thalamocortical (TC afferent circuits. Down-regulation of the serotonin transporter (SERT and the consequent impaired reuptake of 5-HT at the synapse, results in a reduced terminal branching of developing TC afferents within the primary somatosensory cortex (S1. Despite the presence of multiple genetic models, the effect of high extracellular 5-HT levels on the structure and function of developing intracortical neural networks is far from being understood. Here, using juvenile SERT knockout (SERT-/- rats we investigated, in vitro, the effect of increased 5-HT levels on the structural organization of (i the thalamocortical projections of the ventroposteromedial thalamic nucleus towards S1, (ii the general barrel-field pattern and (iii the electrophysiological and morphological properties of the excitatory cell population in layer IV of S1 (spiny stellate and pyramidal cells. Our results confirmed previous findings that high levels of 5-HT during development lead to a reduction of the topographical precision of TCA projections towards the barrel cortex. Also, the barrel pattern was altered but not abolished in SERT-/- rats. In layer IV, both excitatory spiny stellate and pyramidal cells showed a significantly reduced intracolumnar organization of their axonal projections. In addition, the layer IV spiny stellate cells gave rise to a prominent projection towards the infragranular layer Vb. Our findings point to a structural and functional reorganization, of TCAs, as well as early stage intracortical microcircuitry, following the disruption of 5-HT reuptake during critical developmental periods. The increased projection pattern of the layer IV neurons suggests that the intracortical network changes are not limited to the main entry layer IV but may also affect the subsequent stages of the canonical circuits of the barrel

  19. Evolution of the vertebrate neurocranium: problems of the premandibular domain and the origin of the trabecula

    OpenAIRE

    Kuratani, Shigeru; Ahlberg, Per. E.

    2018-01-01

    The subdivision of the gnathostome neurocranium into an anterior neural crest-derived moiety and a posterior mesodermal moiety has attracted the interest of researchers for nearly two centuries. We present a synthetic scenario for the evolution of this structure, uniting developmental data from living cyclostomes and gnathostomes with morphological data from fossil stem gnathostomes in a common phylogenetic framework. Ancestrally, vertebrates had an anteroposteriorly short forebrain, and the ...

  20. Evolution of the vertebrate neurocranium : problems of the premandibular domain and the origin of the trabecula

    OpenAIRE

    Kuratani, Shigeru; Ahlberg, Per E.

    2018-01-01

    The subdivision of the gnathostome neurocranium into an anterior neural crest-derived moiety and a posterior mesodermal moiety has attracted the interest of researchers for nearly two centuries. We present a synthetic scenario for the evolution of this structure, uniting developmental data from living cyclostomes and gnathostomes with morphological data from fossil stem gnathostomes in a common phylogenetic framework. Ancestrally, vertebrates had an anteroposteriorly short forebrain, and the ...

  1. Neural Networks in Control Applications

    DEFF Research Database (Denmark)

    Sørensen, O.

    The intention of this report is to make a systematic examination of the possibilities of applying neural networks in those technical areas, which are familiar to a control engineer. In other words, the potential of neural networks in control applications is given higher priority than a detailed...... study of the networks themselves. With this end in view the following restrictions have been made: - Amongst numerous neural network structures, only the Multi Layer Perceptron (a feed-forward network) is applied. - Amongst numerous training algorithms, only four algorithms are examined, all...... in a recursive form (sample updating). The simplest is the Back Probagation Error Algorithm, and the most complex is the recursive Prediction Error Method using a Gauss-Newton search direction. - Over-fitting is often considered to be a serious problem when training neural networks. This problem is specifically...

  2. Direct adaptive control using feedforward neural networks

    OpenAIRE

    Cajueiro, Daniel Oliveira; Hemerly, Elder Moreira

    2003-01-01

    ABSTRACT: This paper proposes a new scheme for direct neural adaptive control that works efficiently employing only one neural network, used for simultaneously identifying and controlling the plant. The idea behind this structure of adaptive control is to compensate the control input obtained by a conventional feedback controller. The neural network training process is carried out by using two different techniques: backpropagation and extended Kalman filter algorithm. Additionally, the conver...

  3. Lack of the central nervous system- and neural crest-expressed forkhead gene Foxs1 affects motor function and body weight.

    Science.gov (United States)

    Heglind, Mikael; Cederberg, Anna; Aquino, Jorge; Lucas, Guilherme; Ernfors, Patrik; Enerbäck, Sven

    2005-07-01

    To gain insight into the expression pattern and functional importance of the forkhead transcription factor Foxs1, we constructed a Foxs1-beta-galactosidase reporter gene "knock-in" (Foxs1beta-gal/beta-gal) mouse, in which the wild-type (wt) Foxs1 allele has been inactivated and replaced by a beta-galactosidase reporter gene. Staining for beta-galactosidase activity reveals an expression pattern encompassing neural crest-derived cells, e.g., cranial and dorsal root ganglia as well as several other cell populations in the central nervous system (CNS), most prominently the internal granule layer of cerebellum. Other sites of expression include the lachrymal gland, outer nuclear layer of retina, enteric ganglion neurons, and a subset of thalamic and hypothalamic nuclei. In the CNS, blood vessel-associated smooth muscle cells and pericytes stain positive for Foxs1. Foxs1beta-gal/beta-gal mice perform significantly better (P fat diet, and we speculate that dorsomedial hypothalamic neurons, expressing Foxs1, could play a role in regulating body weight via regulation of sympathetic outflow. In support of this, we observed increased levels of uncoupling protein 1 mRNA in Foxs1beta-gal/beta-gal mice. This points toward a role for Foxs1 in the integration and processing of neuronal signals of importance for energy turnover and motor function.

  4. In vitro verification of a 3-D regenerative neural interface design: examination of neurite growth and electrical properties within a bifurcating microchannel structure

    NARCIS (Netherlands)

    Wieringa, P.A.; Wiertz, Remy; de Weerd, Eddy L; Rutten, Wim

    2010-01-01

    Toward the development of neuroprosthesis, we propose a 3-D regenerative neural interface design for connecting with the peripheral nervous system. This approach relies on bifurcating microstructures to achieve defasciculated ingrowth patterns and, consequently, high selectivity. In vitro studies

  5. The neural network approach to parton fitting

    International Nuclear Information System (INIS)

    Rojo, Joan; Latorre, Jose I.; Del Debbio, Luigi; Forte, Stefano; Piccione, Andrea

    2005-01-01

    We introduce the neural network approach to global fits of parton distribution functions. First we review previous work on unbiased parametrizations of deep-inelastic structure functions with faithful estimation of their uncertainties, and then we summarize the current status of neural network parton distribution fits

  6. Neural plasticity of development and learning.

    Science.gov (United States)

    Galván, Adriana

    2010-06-01

    Development and learning are powerful agents of change across the lifespan that induce robust structural and functional plasticity in neural systems. An unresolved question in developmental cognitive neuroscience is whether development and learning share the same neural mechanisms associated with experience-related neural plasticity. In this article, I outline the conceptual and practical challenges of this question, review insights gleaned from adult studies, and describe recent strides toward examining this topic across development using neuroimaging methods. I suggest that development and learning are not two completely separate constructs and instead, that they exist on a continuum. While progressive and regressive changes are central to both, the behavioral consequences associated with these changes are closely tied to the existing neural architecture of maturity of the system. Eventually, a deeper, more mechanistic understanding of neural plasticity will shed light on behavioral changes across development and, more broadly, about the underlying neural basis of cognition. (c) 2010 Wiley-Liss, Inc.

  7. Adaptive Graph Convolutional Neural Networks

    OpenAIRE

    Li, Ruoyu; Wang, Sheng; Zhu, Feiyun; Huang, Junzhou

    2018-01-01

    Graph Convolutional Neural Networks (Graph CNNs) are generalizations of classical CNNs to handle graph data such as molecular data, point could and social networks. Current filters in graph CNNs are built for fixed and shared graph structure. However, for most real data, the graph structures varies in both size and connectivity. The paper proposes a generalized and flexible graph CNN taking data of arbitrary graph structure as input. In that way a task-driven adaptive graph is learned for eac...

  8. Neural networks in signal processing

    International Nuclear Information System (INIS)

    Govil, R.

    2000-01-01

    Nuclear Engineering has matured during the last decade. In research and design, control, supervision, maintenance and production, mathematical models and theories are used extensively. In all such applications signal processing is embedded in the process. Artificial Neural Networks (ANN), because of their nonlinear, adaptive nature are well suited to such applications where the classical assumptions of linearity and second order Gaussian noise statistics cannot be made. ANN's can be treated as nonparametric techniques, which can model an underlying process from example data. They can also adopt their model parameters to statistical change with time. Algorithms in the framework of Neural Networks in Signal processing have found new applications potentials in the field of Nuclear Engineering. This paper reviews the fundamentals of Neural Networks in signal processing and their applications in tasks such as recognition/identification and control. The topics covered include dynamic modeling, model based ANN's, statistical learning, eigen structure based processing and generalization structures. (orig.)

  9. The principles of artificial neural network information processing

    International Nuclear Information System (INIS)

    Dai, Ru-Wei

    1993-01-01

    In this article, the basic structure of an artificial neuron is first introduced. In addition, principles of artificial neural network as well as several important artificial neural models such as perception, back propagation model, Hopfield net, and ART model are briefly discussed and analyzed. Finally the application of artificial neural network for Chinese character recognition is also given. (author)

  10. The principles of artificial neural network information processing

    International Nuclear Information System (INIS)

    Dai, Ru-Wei

    1993-01-01

    In this article, the basic structure of an artificial neuron is first introduced. In addition, principles of artificial neural network as well as several important artificial neural models such as Perceptron, Back propagation model, Hopfield net, and ART model are briefly discussed and analyzed. Finally, the application of artificial neural network for Chinese Character Recognition is also given. (author)

  11. Estimation of network structure for signal propagations by the analysis of multichannel action potentials in cultured neural networks; Ta channel katsudo den`i kaiseki ni yoru baiyo shinkei kairomonai kofun denpa keiro no suitei

    Energy Technology Data Exchange (ETDEWEB)

    Konno, N.; Fukami, T.; Shiina, T. [University of Tsukuba, Tsukuba (Japan); Jinbo, Y. [Nippon Telegraph and Telephone Corp., Tokyo (Japan)

    1998-07-01

    We have fabricated a 64 embedded microelectrode-array substrate using semiconductor technology to investigate the biological signal processing in brain by using cultured neural networks of fetal rat neocortex in vitro. We analyzed temporal and spatial neural networks patterns cultured on electrode-array substrate and attempted to examine the network structure constituted by neurons and the propagating patterns of electrical activity induced by the electric stimulus. In the experiments, each microelectrode size was 30 {mu}m squared and 150{mu} m spaced. For stimulation, one of the electrodes was selected and current pulses were applied through an isolated circuit. After the network was cultured in about 50 days, responses of neurons to electric stimulus were monitored extracellularly through 64-channel electrode array. Data recorded at each electrode consist of several spike trains generated by different cells. Therefore, these trains were separated by using wavelet transform and template matching for each electrode. We referred the temporal patterns of generated spikes for each electrode to as `spike sequences`. Next, we compared With the spike sequences among multichannel data and visualized the Cultured neural networks structure by identifying the directions of propagations and cell connections. 15 refs., 9 figs.

  12. Estimation of Conditional Quantile using Neural Networks

    DEFF Research Database (Denmark)

    Kulczycki, P.; Schiøler, Henrik

    1999-01-01

    The problem of estimating conditional quantiles using neural networks is investigated here. A basic structure is developed using the methodology of kernel estimation, and a theory guaranteeing con-sistency on a mild set of assumptions is provided. The constructed structure constitutes a basis...... for the design of a variety of different neural networks, some of which are considered in detail. The task of estimating conditional quantiles is related to Bayes point estimation whereby a broad range of applications within engineering, economics and management can be suggested. Numerical results illustrating...... the capabilities of the elaborated neural network are also given....

  13. Deep Gate Recurrent Neural Network

    Science.gov (United States)

    2016-11-22

    and Fred Cummins. Learning to forget: Continual prediction with lstm . Neural computation, 12(10):2451–2471, 2000. Alex Graves. Generating sequences...DSGU) and Simple Gated Unit (SGU), which are structures for learning long-term dependencies. Compared to traditional Long Short-Term Memory ( LSTM ) and...Gated Recurrent Unit (GRU), both structures require fewer parameters and less computation time in sequence classification tasks. Unlike GRU and LSTM

  14. Dynamic decomposition of spatiotemporal neural signals.

    Directory of Open Access Journals (Sweden)

    Luca Ambrogioni

    2017-05-01

    Full Text Available Neural signals are characterized by rich temporal and spatiotemporal dynamics that reflect the organization of cortical networks. Theoretical research has shown how neural networks can operate at different dynamic ranges that correspond to specific types of information processing. Here we present a data analysis framework that uses a linearized model of these dynamic states in order to decompose the measured neural signal into a series of components that capture both rhythmic and non-rhythmic neural activity. The method is based on stochastic differential equations and Gaussian process regression. Through computer simulations and analysis of magnetoencephalographic data, we demonstrate the efficacy of the method in identifying meaningful modulations of oscillatory signals corrupted by structured temporal and spatiotemporal noise. These results suggest that the method is particularly suitable for the analysis and interpretation of complex temporal and spatiotemporal neural signals.

  15. Morphological neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Ritter, G.X.; Sussner, P. [Univ. of Florida, Gainesville, FL (United States)

    1996-12-31

    The theory of artificial neural networks has been successfully applied to a wide variety of pattern recognition problems. In this theory, the first step in computing the next state of a neuron or in performing the next layer neural network computation involves the linear operation of multiplying neural values by their synaptic strengths and adding the results. Thresholding usually follows the linear operation in order to provide for nonlinearity of the network. In this paper we introduce a novel class of neural networks, called morphological neural networks, in which the operations of multiplication and addition are replaced by addition and maximum (or minimum), respectively. By taking the maximum (or minimum) of sums instead of the sum of products, morphological network computation is nonlinear before thresholding. As a consequence, the properties of morphological neural networks are drastically different than those of traditional neural network models. In this paper we consider some of these differences and provide some particular examples of morphological neural network.

  16. Neural Tube Defects

    Science.gov (United States)

    Neural tube defects are birth defects of the brain, spine, or spinal cord. They happen in the ... that she is pregnant. The two most common neural tube defects are spina bifida and anencephaly. In ...

  17. Structural vascular disease in Africans: performance of ethnic-specific waist circumference cut points using logistic regression and neural network analyses: the SABPA study

    OpenAIRE

    Botha, J.; De Ridder, J.H.; Potgieter, J.C.; Steyn, H.S.; Malan, L.

    2013-01-01

    A recently proposed model for waist circumference cut points (RPWC), driven by increased blood pressure, was demonstrated in an African population. We therefore aimed to validate the RPWC by comparing the RPWC and the Joint Statement Consensus (JSC) models via Logistic Regression (LR) and Neural Networks (NN) analyses. Urban African gender groups (N=171) were stratified according to the JSC and RPWC cut point models. Ultrasound carotid intima media thickness (CIMT), blood pressure (BP) and fa...

  18. Neural tissue-spheres

    DEFF Research Database (Denmark)

    Andersen, Rikke K; Johansen, Mathias; Blaabjerg, Morten

    2007-01-01

    By combining new and established protocols we have developed a procedure for isolation and propagation of neural precursor cells from the forebrain subventricular zone (SVZ) of newborn rats. Small tissue blocks of the SVZ were dissected and propagated en bloc as free-floating neural tissue...... content, thus allowing experimental studies of neural precursor cells and their niche...

  19. Neural network recognition of mammographic lesions

    International Nuclear Information System (INIS)

    Oldham, W.J.B.; Downes, P.T.; Hunter, V.

    1987-01-01

    A method for recognition of mammographic lesions through the use of neural networks is presented. Neural networks have exhibited the ability to learn the shape andinternal structure of patterns. Digitized mammograms containing circumscribed and stelate lesions were used to train a feedfoward synchronous neural network that self-organizes to stable attractor states. Encoding of data for submission to the network was accomplished by performing a fractal analysis of the digitized image. This results in scale invariant representation of the lesions. Results are discussed

  20. Neural electrical activity and neural network growth.

    Science.gov (United States)

    Gafarov, F M

    2018-05-01

    The development of central and peripheral neural system depends in part on the emergence of the correct functional connectivity in its input and output pathways. Now it is generally accepted that molecular factors guide neurons to establish a primary scaffold that undergoes activity-dependent refinement for building a fully functional circuit. However, a number of experimental results obtained recently shows that the neuronal electrical activity plays an important role in the establishing of initial interneuronal connections. Nevertheless, these processes are rather difficult to study experimentally, due to the absence of theoretical description and quantitative parameters for estimation of the neuronal activity influence on growth in neural networks. In this work we propose a general framework for a theoretical description of the activity-dependent neural network growth. The theoretical description incorporates a closed-loop growth model in which the neural activity can affect neurite outgrowth, which in turn can affect neural activity. We carried out the detailed quantitative analysis of spatiotemporal activity patterns and studied the relationship between individual cells and the network as a whole to explore the relationship between developing connectivity and activity patterns. The model, developed in this work will allow us to develop new experimental techniques for studying and quantifying the influence of the neuronal activity on growth processes in neural networks and may lead to a novel techniques for constructing large-scale neural networks by self-organization. Copyright © 2018 Elsevier Ltd. All rights reserved.

  1. Methodology of Neural Design: Applications in Microwave Engineering

    Directory of Open Access Journals (Sweden)

    Z. Raida

    2006-06-01

    Full Text Available In the paper, an original methodology for the automatic creation of neural models of microwave structures is proposed and verified. Following the methodology, neural models of the prescribed accuracy are built within the minimum CPU time. Validity of the proposed methodology is verified by developing neural models of selected microwave structures. Functionality of neural models is verified in a design - a neural model is joined with a genetic algorithm to find a global minimum of a formulated objective function. The objective function is minimized using different versions of genetic algorithms, and their mutual combinations. The verified methodology of the automated creation of accurate neural models of microwave structures, and their association with global optimization routines are the most important original features of the paper.

  2. Advanced models of neural networks nonlinear dynamics and stochasticity in biological neurons

    CERN Document Server

    Rigatos, Gerasimos G

    2015-01-01

    This book provides a complete study on neural structures exhibiting nonlinear and stochastic dynamics, elaborating on neural dynamics by introducing advanced models of neural networks. It overviews the main findings in the modelling of neural dynamics in terms of electrical circuits and examines their stability properties with the use of dynamical systems theory. It is suitable for researchers and postgraduate students engaged with neural networks and dynamical systems theory.

  3. Neural substrates of decision-making.

    Science.gov (United States)

    Broche-Pérez, Y; Herrera Jiménez, L F; Omar-Martínez, E

    2016-06-01

    Decision-making is the process of selecting a course of action from among 2 or more alternatives by considering the potential outcomes of selecting each option and estimating its consequences in the short, medium and long term. The prefrontal cortex (PFC) has traditionally been considered the key neural structure in decision-making process. However, new studies support the hypothesis that describes a complex neural network including both cortical and subcortical structures. The aim of this review is to summarise evidence on the anatomical structures underlying the decision-making process, considering new findings that support the existence of a complex neural network that gives rise to this complex neuropsychological process. Current evidence shows that the cortical structures involved in decision-making include the orbitofrontal cortex (OFC), anterior cingulate cortex (ACC), and dorsolateral prefrontal cortex (DLPFC). This process is assisted by subcortical structures including the amygdala, thalamus, and cerebellum. Findings to date show that both cortical and subcortical brain regions contribute to the decision-making process. The neural basis of decision-making is a complex neural network of cortico-cortical and cortico-subcortical connections which includes subareas of the PFC, limbic structures, and the cerebellum. Copyright © 2014 Sociedad Española de Neurología. Published by Elsevier España, S.L.U. All rights reserved.

  4. Intelligent neural network diagnostic system

    International Nuclear Information System (INIS)

    Mohamed, A.H.

    2010-01-01

    Recently, artificial neural network (ANN) has made a significant mark in the domain of diagnostic applications. Neural networks are used to implement complex non-linear mappings (functions) using simple elementary units interrelated through connections with adaptive weights. The performance of the ANN is mainly depending on their topology structure and weights. Some systems have been developed using genetic algorithm (GA) to optimize the topology of the ANN. But, they suffer from some limitations. They are : (1) The computation time requires for training the ANN several time reaching for the average weight required, (2) Slowness of GA for optimization process and (3) Fitness noise appeared in the optimization of ANN. This research suggests new issues to overcome these limitations for finding optimal neural network architectures to learn particular problems. This proposed methodology is used to develop a diagnostic neural network system. It has been applied for a 600 MW turbo-generator as a case of real complex systems. The proposed system has proved its significant performance compared to two common methods used in the diagnostic applications.

  5. Adaptive competitive learning neural networks

    Directory of Open Access Journals (Sweden)

    Ahmed R. Abas

    2013-11-01

    Full Text Available In this paper, the adaptive competitive learning (ACL neural network algorithm is proposed. This neural network not only groups similar input feature vectors together but also determines the appropriate number of groups of these vectors. This algorithm uses a new proposed criterion referred to as the ACL criterion. This criterion evaluates different clustering structures produced by the ACL neural network for an input data set. Then, it selects the best clustering structure and the corresponding network architecture for this data set. The selected structure is composed of the minimum number of clusters that are compact and balanced in their sizes. The selected network architecture is efficient, in terms of its complexity, as it contains the minimum number of neurons. Synaptic weight vectors of these neurons represent well-separated, compact and balanced clusters in the input data set. The performance of the ACL algorithm is evaluated and compared with the performance of a recently proposed algorithm in the literature in clustering an input data set and determining its number of clusters. Results show that the ACL algorithm is more accurate and robust in both determining the number of clusters and allocating input feature vectors into these clusters than the other algorithm especially with data sets that are sparsely distributed.

  6. Artificial neural networks application for horizontal and vertical forecasting radionuclides transport

    International Nuclear Information System (INIS)

    Khil'ko, O.S.; Kovalenko, V.I.; Kundas, S.P.

    2010-01-01

    Artificial neural networks approach for horizontal and vertical radionuclide transport forecasting was proposed. Runoff factors analysis was considered. Additional artificial neural network structures for physical-chemical properties recognition were used. (authors)

  7. Modular representation of layered neural networks.

    Science.gov (United States)

    Watanabe, Chihiro; Hiramatsu, Kaoru; Kashino, Kunio

    2018-01-01

    Layered neural networks have greatly improved the performance of various applications including image processing, speech recognition, natural language processing, and bioinformatics. However, it is still difficult to discover or interpret knowledge from the inference provided by a layered neural network, since its internal representation has many nonlinear and complex parameters embedded in hierarchical layers. Therefore, it becomes important to establish a new methodology by which layered neural networks can be understood. In this paper, we propose a new method for extracting a global and simplified structure from a layered neural network. Based on network analysis, the proposed method detects communities or clusters of units with similar connection patterns. We show its effectiveness by applying it to three use cases. (1) Network decomposition: it can decompose a trained neural network into multiple small independent networks thus dividing the problem and reducing the computation time. (2) Training assessment: the appropriateness of a trained result with a given hyperparameter or randomly chosen initial parameters can be evaluated by using a modularity index. And (3) data analysis: in practical data it reveals the community structure in the input, hidden, and output layers, which serves as a clue for discovering knowledge from a trained neural network. Copyright © 2017 Elsevier Ltd. All rights reserved.

  8. The Laplacian spectrum of neural networks

    Science.gov (United States)

    de Lange, Siemon C.; de Reus, Marcel A.; van den Heuvel, Martijn P.

    2014-01-01

    The brain is a complex network of neural interactions, both at the microscopic and macroscopic level. Graph theory is well suited to examine the global network architecture of these neural networks. Many popular graph metrics, however, encode average properties of individual network elements. Complementing these “conventional” graph metrics, the eigenvalue spectrum of the normalized Laplacian describes a network's structure directly at a systems level, without referring to individual nodes or connections. In this paper, the Laplacian spectra of the macroscopic anatomical neuronal networks of the macaque and cat, and the microscopic network of the Caenorhabditis elegans were examined. Consistent with conventional graph metrics, analysis of the Laplacian spectra revealed an integrative community structure in neural brain networks. Extending previous findings of overlap of network attributes across species, similarity of the Laplacian spectra across the cat, macaque and C. elegans neural networks suggests a certain level of consistency in the overall architecture of the anatomical neural networks of these species. Our results further suggest a specific network class for neural networks, distinct from conceptual small-world and scale-free models as well as several empirical networks. PMID:24454286

  9. A neural flow estimator

    DEFF Research Database (Denmark)

    Jørgensen, Ivan Harald Holger; Bogason, Gudmundur; Bruun, Erik

    1995-01-01

    This paper proposes a new way to estimate the flow in a micromechanical flow channel. A neural network is used to estimate the delay of random temperature fluctuations induced in a fluid. The design and implementation of a hardware efficient neural flow estimator is described. The system...... is implemented using switched-current technique and is capable of estimating flow in the μl/s range. The neural estimator is built around a multiplierless neural network, containing 96 synaptic weights which are updated using the LMS1-algorithm. An experimental chip has been designed that operates at 5 V...

  10. Neural Systems Laboratory

    Data.gov (United States)

    Federal Laboratory Consortium — As part of the Electrical and Computer Engineering Department and The Institute for System Research, the Neural Systems Laboratory studies the functionality of the...

  11. Neural Networks in Mobile Robot Motion

    Directory of Open Access Journals (Sweden)

    Danica Janglová

    2004-03-01

    Full Text Available This paper deals with a path planning and intelligent control of an autonomous robot which should move safely in partially structured environment. This environment may involve any number of obstacles of arbitrary shape and size; some of them are allowed to move. We describe our approach to solving the motion-planning problem in mobile robot control using neural networks-based technique. Our method of the construction of a collision-free path for moving robot among obstacles is based on two neural networks. The first neural network is used to determine the “free” space using ultrasound range finder data. The second neural network “finds” a safe direction for the next robot section of the path in the workspace while avoiding the nearest obstacles. Simulation examples of generated path with proposed techniques will be presented.

  12. Optimal neural computations require analog processors

    Energy Technology Data Exchange (ETDEWEB)

    Beiu, V.

    1998-12-31

    This paper discusses some of the limitations of hardware implementations of neural networks. The authors start by presenting neural structures and their biological inspirations, while mentioning the simplifications leading to artificial neural networks. Further, the focus will be on hardware imposed constraints. They will present recent results for three different alternatives of parallel implementations of neural networks: digital circuits, threshold gate circuits, and analog circuits. The area and the delay will be related to the neurons` fan-in and to the precision of their synaptic weights. The main conclusion is that hardware-efficient solutions require analog computations, and suggests the following two alternatives: (i) cope with the limitations imposed by silicon, by speeding up the computation of the elementary silicon neurons; (2) investigate solutions which would allow the use of the third dimension (e.g. using optical interconnections).

  13. Artificial neural networks a practical course

    CERN Document Server

    da Silva, Ivan Nunes; Andrade Flauzino, Rogerio; Liboni, Luisa Helena Bartocci; dos Reis Alves, Silas Franco

    2017-01-01

    This book provides comprehensive coverage of neural networks, their evolution, their structure, the problems they can solve, and their applications. The first half of the book looks at theoretical investigations on artificial neural networks and addresses the key architectures that are capable of implementation in various application scenarios. The second half is designed specifically for the production of solutions using artificial neural networks to solve practical problems arising from different areas of knowledge. It also describes the various implementation details that were taken into account to achieve the reported results. These aspects contribute to the maturation and improvement of experimental techniques to specify the neural network architecture that is most appropriate for a particular application scope. The book is appropriate for students in graduate and upper undergraduate courses in addition to researchers and professionals.

  14. Dlx proteins position the neural plate border and determine adjacent cell fates.

    Science.gov (United States)

    Woda, Juliana M; Pastagia, Julie; Mercola, Mark; Artinger, Kristin Bruk

    2003-01-01

    The lateral border of the neural plate is a major source of signals that induce primary neurons, neural crest cells and cranial placodes as well as provide patterning cues to mesodermal structures such as somites and heart. Whereas secreted BMP, FGF and Wnt proteins influence the differentiation of neural and non-neural ectoderm, we show here that members of the Dlx family of transcription factors position the border between neural and non-neural ectoderm and are required for the specification of adjacent cell fates. Inhibition of endogenous Dlx activity in Xenopus embryos with an EnR-Dlx homeodomain fusion protein expands the neural plate into non-neural ectoderm tissue whereas ectopic activation of Dlx target genes inhibits neural plate differentiation. Importantly, the stereotypic pattern of border cell fates in the adjacent ectoderm is re-established only under conditions where the expanded neural plate abuts Dlx-positive non-neural ectoderm. Experiments in which presumptive neural plate was grafted to ventral ectoderm reiterate induction of neural crest and placodal lineages and also demonstrate that Dlx activity is required in non-neural ectoderm for the production of signals needed for induction of these cells. We propose that Dlx proteins regulate intercellular signaling across the interface between neural and non-neural ectoderm that is critical for inducing and patterning adjacent cell fates.

  15. Neural Networks: Implementations and Applications

    OpenAIRE

    Vonk, E.; Veelenturf, L.P.J.; Jain, L.C.

    1996-01-01

    Artificial neural networks, also called neural networks, have been used successfully in many fields including engineering, science and business. This paper presents the implementation of several neural network simulators and their applications in character recognition and other engineering areas

  16. Computational modeling of neural plasticity for self-organization of neural networks.

    Science.gov (United States)

    Chrol-Cannon, Joseph; Jin, Yaochu

    2014-11-01

    Self-organization in biological nervous systems during the lifetime is known to largely occur through a process of plasticity that is dependent upon the spike-timing activity in connected neurons. In the field of computational neuroscience, much effort has been dedicated to building up computational models of neural plasticity to replicate experimental data. Most recently, increasing attention has been paid to understanding the role of neural plasticity in functional and structural neural self-organization, as well as its influence on the learning performance of neural networks for accomplishing machine learning tasks such as classification and regression. Although many ideas and hypothesis have been suggested, the relationship between the structure, dynamics and learning performance of neural networks remains elusive. The purpose of this article is to review the most important computational models for neural plasticity and discuss various ideas about neural plasticity's role. Finally, we suggest a few promising research directions, in particular those along the line that combines findings in computational neuroscience and systems biology, and their synergetic roles in understanding learning, memory and cognition, thereby bridging the gap between computational neuroscience, systems biology and computational intelligence. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  17. The effect of the neural activity on topological properties of growing neural networks.

    Science.gov (United States)

    Gafarov, F M; Gafarova, V R

    2016-09-01

    The connectivity structure in cortical networks defines how information is transmitted and processed, and it is a source of the complex spatiotemporal patterns of network's development, and the process of creation and deletion of connections is continuous in the whole life of the organism. In this paper, we study how neural activity influences the growth process in neural networks. By using a two-dimensional activity-dependent growth model we demonstrated the neural network growth process from disconnected neurons to fully connected networks. For making quantitative investigation of the network's activity influence on its topological properties we compared it with the random growth network not depending on network's activity. By using the random graphs theory methods for the analysis of the network's connections structure it is shown that the growth in neural networks results in the formation of a well-known "small-world" network.

  18. Critical Branching Neural Networks

    Science.gov (United States)

    Kello, Christopher T.

    2013-01-01

    It is now well-established that intrinsic variations in human neural and behavioral activity tend to exhibit scaling laws in their fluctuations and distributions. The meaning of these scaling laws is an ongoing matter of debate between isolable causes versus pervasive causes. A spiking neural network model is presented that self-tunes to critical…

  19. A variable structure fuzzy neural network model of squamous dysplasia and esophageal squamous cell carcinoma based on a global chaotic optimization algorithm.

    Science.gov (United States)

    Moghtadaei, Motahareh; Hashemi Golpayegani, Mohammad Reza; Malekzadeh, Reza

    2013-02-07

    Identification of squamous dysplasia and esophageal squamous cell carcinoma (ESCC) is of great importance in prevention of cancer incidence. Computer aided algorithms can be very useful for identification of people with higher risks of squamous dysplasia, and ESCC. Such method can limit the clinical screenings to people with higher risks. Different regression methods have been used to predict ESCC and dysplasia. In this paper, a Fuzzy Neural Network (FNN) model is selected for ESCC and dysplasia prediction. The inputs to the classifier are the risk factors. Since the relation between risk factors in the tumor system has a complex nonlinear behavior, in comparison to most of ordinary data, the cost function of its model can have more local optimums. Thus the need for global optimization methods is more highlighted. The proposed method in this paper is a Chaotic Optimization Algorithm (COA) proceeding by the common Error Back Propagation (EBP) local method. Since the model has many parameters, we use a strategy to reduce the dependency among parameters caused by the chaotic series generator. This dependency was not considered in the previous COA methods. The algorithm is compared with logistic regression model as the latest successful methods of ESCC and dysplasia prediction. The results represent a more precise prediction with less mean and variance of error. Copyright © 2012 Elsevier Ltd. All rights reserved.

  20. Neural networks and their application to nuclear power plant diagnosis

    International Nuclear Information System (INIS)

    Reifman, J.

    1997-01-01

    The authors present a survey of artificial neural network-based computer systems that have been proposed over the last decade for the detection and identification of component faults in thermal-hydraulic systems of nuclear power plants. The capabilities and advantages of applying neural networks as decision support systems for nuclear power plant operators and their inherent characteristics are discussed along with their limitations and drawbacks. The types of neural network structures used and their applications are described and the issues of process diagnosis and neural network-based diagnostic systems are identified. A total of thirty-four publications are reviewed

  1. Optimal Brain Surgeon on Artificial Neural Networks in

    DEFF Research Database (Denmark)

    Christiansen, Niels Hørbye; Job, Jonas Hultmann; Klyver, Katrine

    2012-01-01

    It is shown how the procedure know as optimal brain surgeon can be used to trim and optimize artificial neural networks in nonlinear structural dynamics. Beside optimizing the neural network, and thereby minimizing computational cost in simulation, the surgery procedure can also serve as a quick...

  2. The Neural Correlates of Highly Iconic Structures and Topographic Discourse in French Sign Language as Observed in Six Hearing Native Signers

    Science.gov (United States)

    Courtin, C.; Herve, P. -Y.; Petit, L.; Zago, L.; Vigneau, M.; Beaucousin, V.; Jobard, G.; Mazoyer, B.; Mellet, E.; Tzourio-Mazoyer, N.

    2010-01-01

    "Highly iconic" structures in Sign Language enable a narrator to act, switch characters, describe objects, or report actions in four-dimensions. This group of linguistic structures has no real spoken-language equivalent. Topographical descriptions are also achieved in a sign-language specific manner via the use of signing-space and…

  3. neural control system

    International Nuclear Information System (INIS)

    Elshazly, A.A.E.

    2002-01-01

    Automatic power stabilization control is the desired objective for any reactor operation , especially, nuclear power plants. A major problem in this area is inevitable gap between a real plant ant the theory of conventional analysis and the synthesis of linear time invariant systems. in particular, the trajectory tracking control of a nonlinear plant is a class of problems in which the classical linear transfer function methods break down because no transfer function can represent the system over the entire operating region . there is a considerable amount of research on the model-inverse approach using feedback linearization technique. however, this method requires a prices plant model to implement the exact linearizing feedback, for nuclear reactor systems, this approach is not an easy task because of the uncertainty in the plant parameters and un-measurable state variables . therefore, artificial neural network (ANN) is used either in self-tuning control or in improving the conventional rule-based exper system.the main objective of this thesis is to suggest an ANN, based self-learning controller structure . this method is capable of on-line reinforcement learning and control for a nuclear reactor with a totally unknown dynamics model. previously, researches are based on back- propagation algorithm . back -propagation (BP), fast back -propagation (FBP), and levenberg-marquardt (LM), algorithms are discussed and compared for reinforcement learning. it is found that, LM algorithm is quite superior

  4. Handbook on neural information processing

    CERN Document Server

    Maggini, Marco; Jain, Lakhmi

    2013-01-01

    This handbook presents some of the most recent topics in neural information processing, covering both theoretical concepts and practical applications. The contributions include:                         Deep architectures                         Recurrent, recursive, and graph neural networks                         Cellular neural networks                         Bayesian networks                         Approximation capabilities of neural networks                         Semi-supervised learning                         Statistical relational learning                         Kernel methods for structured data                         Multiple classifier systems                         Self organisation and modal learning                         Applications to ...

  5. Dynamics of neural cryptography.

    Science.gov (United States)

    Ruttor, Andreas; Kinzel, Wolfgang; Kanter, Ido

    2007-05-01

    Synchronization of neural networks has been used for public channel protocols in cryptography. In the case of tree parity machines the dynamics of both bidirectional synchronization and unidirectional learning is driven by attractive and repulsive stochastic forces. Thus it can be described well by a random walk model for the overlap between participating neural networks. For that purpose transition probabilities and scaling laws for the step sizes are derived analytically. Both these calculations as well as numerical simulations show that bidirectional interaction leads to full synchronization on average. In contrast, successful learning is only possible by means of fluctuations. Consequently, synchronization is much faster than learning, which is essential for the security of the neural key-exchange protocol. However, this qualitative difference between bidirectional and unidirectional interaction vanishes if tree parity machines with more than three hidden units are used, so that those neural networks are not suitable for neural cryptography. In addition, the effective number of keys which can be generated by the neural key-exchange protocol is calculated using the entropy of the weight distribution. As this quantity increases exponentially with the system size, brute-force attacks on neural cryptography can easily be made unfeasible.

  6. Dynamics of neural cryptography

    International Nuclear Information System (INIS)

    Ruttor, Andreas; Kinzel, Wolfgang; Kanter, Ido

    2007-01-01

    Synchronization of neural networks has been used for public channel protocols in cryptography. In the case of tree parity machines the dynamics of both bidirectional synchronization and unidirectional learning is driven by attractive and repulsive stochastic forces. Thus it can be described well by a random walk model for the overlap between participating neural networks. For that purpose transition probabilities and scaling laws for the step sizes are derived analytically. Both these calculations as well as numerical simulations show that bidirectional interaction leads to full synchronization on average. In contrast, successful learning is only possible by means of fluctuations. Consequently, synchronization is much faster than learning, which is essential for the security of the neural key-exchange protocol. However, this qualitative difference between bidirectional and unidirectional interaction vanishes if tree parity machines with more than three hidden units are used, so that those neural networks are not suitable for neural cryptography. In addition, the effective number of keys which can be generated by the neural key-exchange protocol is calculated using the entropy of the weight distribution. As this quantity increases exponentially with the system size, brute-force attacks on neural cryptography can easily be made unfeasible

  7. Dynamics of neural cryptography

    Science.gov (United States)

    Ruttor, Andreas; Kinzel, Wolfgang; Kanter, Ido

    2007-05-01

    Synchronization of neural networks has been used for public channel protocols in cryptography. In the case of tree parity machines the dynamics of both bidirectional synchronization and unidirectional learning is driven by attractive and repulsive stochastic forces. Thus it can be described well by a random walk model for the overlap between participating neural networks. For that purpose transition probabilities and scaling laws for the step sizes are derived analytically. Both these calculations as well as numerical simulations show that bidirectional interaction leads to full synchronization on average. In contrast, successful learning is only possible by means of fluctuations. Consequently, synchronization is much faster than learning, which is essential for the security of the neural key-exchange protocol. However, this qualitative difference between bidirectional and unidirectional interaction vanishes if tree parity machines with more than three hidden units are used, so that those neural networks are not suitable for neural cryptography. In addition, the effective number of keys which can be generated by the neural key-exchange protocol is calculated using the entropy of the weight distribution. As this quantity increases exponentially with the system size, brute-force attacks on neural cryptography can easily be made unfeasible.

  8. Capturing non-local interactions by long short-term memory bidirectional recurrent neural networks for improving prediction of protein secondary structure, backbone angles, contact numbers and solvent accessibility.

    Science.gov (United States)

    Heffernan, Rhys; Yang, Yuedong; Paliwal, Kuldip; Zhou, Yaoqi

    2017-09-15

    The accuracy of predicting protein local and global structural properties such as secondary structure and solvent accessible surface area has been stagnant for many years because of the challenge of accounting for non-local interactions between amino acid residues that are close in three-dimensional structural space but far from each other in their sequence positions. All existing machine-learning techniques relied on a sliding window of 10-20 amino acid residues to capture some 'short to intermediate' non-local interactions. Here, we employed Long Short-Term Memory (LSTM) Bidirectional Recurrent Neural Networks (BRNNs) which are capable of capturing long range interactions without using a window. We showed that the application of LSTM-BRNN to the prediction of protein structural properties makes the most significant improvement for residues with the most long-range contacts (|i-j| >19) over a previous window-based, deep-learning method SPIDER2. Capturing long-range interactions allows the accuracy of three-state secondary structure prediction to reach 84% and the correlation coefficient between predicted and actual solvent accessible surface areas to reach 0.80, plus a reduction of 5%, 10%, 5% and 10% in the mean absolute error for backbone ϕ , ψ , θ and τ angles, respectively, from SPIDER2. More significantly, 27% of 182724 40-residue models directly constructed from predicted C α atom-based θ and τ have similar structures to their corresponding native structures (6Å RMSD or less), which is 3% better than models built by ϕ and ψ angles. We expect the method to be useful for assisting protein structure and function prediction. The method is available as a SPIDER3 server and standalone package at http://sparks-lab.org . yaoqi.zhou@griffith.edu.au or yuedong.yang@griffith.edu.au. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email

  9. Hidden neural networks

    DEFF Research Database (Denmark)

    Krogh, Anders Stærmose; Riis, Søren Kamaric

    1999-01-01

    A general framework for hybrids of hidden Markov models (HMMs) and neural networks (NNs) called hidden neural networks (HNNs) is described. The article begins by reviewing standard HMMs and estimation by conditional maximum likelihood, which is used by the HNN. In the HNN, the usual HMM probability...... parameters are replaced by the outputs of state-specific neural networks. As opposed to many other hybrids, the HNN is normalized globally and therefore has a valid probabilistic interpretation. All parameters in the HNN are estimated simultaneously according to the discriminative conditional maximum...... likelihood criterion. The HNN can be viewed as an undirected probabilistic independence network (a graphical model), where the neural networks provide a compact representation of the clique functions. An evaluation of the HNN on the task of recognizing broad phoneme classes in the TIMIT database shows clear...

  10. Estimating Conditional Distributions by Neural Networks

    DEFF Research Database (Denmark)

    Kulczycki, P.; Schiøler, Henrik

    1998-01-01

    Neural Networks for estimating conditionaldistributions and their associated quantiles are investigated in this paper. A basic network structure is developed on the basis of kernel estimation theory, and consistency property is considered from a mild set of assumptions. A number of applications...

  11. Neural Correlates of Affective Influence on Choice

    Science.gov (United States)

    Piech, Richard M.; Lewis, Jade; Parkinson, Caroline H.; Owen, Adrian M.; Roberts, Angela C.; Downing, Paul E.; Parkinson, John A.

    2010-01-01

    Making the right choice depends crucially on the accurate valuation of the available options in the light of current needs and goals of an individual. Thus, the valuation of identical options can vary considerably with motivational context. The present study investigated the neural structures underlying context dependent evaluation. We instructed…

  12. Neural networks for aircraft control

    Science.gov (United States)

    Linse, Dennis

    1990-01-01

    Current research in Artificial Neural Networks indicates that networks offer some potential advantages in adaptation and fault tolerance. This research is directed at determining the possible applicability of neural networks to aircraft control. The first application will be to aircraft trim. Neural network node characteristics, network topology and operation, neural network learning and example histories using neighboring optimal control with a neural net are discussed.

  13. Theoretical Properties for Neural Networks with Weight Matrices of Low Displacement Rank

    OpenAIRE

    Zhao, Liang; Liao, Siyu; Wang, Yanzhi; Li, Zhe; Tang, Jian; Pan, Victor; Yuan, Bo

    2017-01-01

    Recently low displacement rank (LDR) matrices, or so-called structured matrices, have been proposed to compress large-scale neural networks. Empirical results have shown that neural networks with weight matrices of LDR matrices, referred as LDR neural networks, can achieve significant reduction in space and computational complexity while retaining high accuracy. We formally study LDR matrices in deep learning. First, we prove the universal approximation property of LDR neural networks with a ...

  14. Active Neural Localization

    OpenAIRE

    Chaplot, Devendra Singh; Parisotto, Emilio; Salakhutdinov, Ruslan

    2018-01-01

    Localization is the problem of estimating the location of an autonomous agent from an observation and a map of the environment. Traditional methods of localization, which filter the belief based on the observations, are sub-optimal in the number of steps required, as they do not decide the actions taken by the agent. We propose "Active Neural Localizer", a fully differentiable neural network that learns to localize accurately and efficiently. The proposed model incorporates ideas of tradition...

  15. Neural cryptography with feedback.

    Science.gov (United States)

    Ruttor, Andreas; Kinzel, Wolfgang; Shacham, Lanir; Kanter, Ido

    2004-04-01

    Neural cryptography is based on a competition between attractive and repulsive stochastic forces. A feedback mechanism is added to neural cryptography which increases the repulsive forces. Using numerical simulations and an analytic approach, the probability of a successful attack is calculated for different model parameters. Scaling laws are derived which show that feedback improves the security of the system. In addition, a network with feedback generates a pseudorandom bit sequence which can be used to encrypt and decrypt a secret message.

  16. Novel paths towards neural cellular products for neurological disorders.

    Science.gov (United States)

    Daadi, Marcel M

    2011-11-01

    The prospect of using neural cells derived from stem cells or from reprogrammed adult somatic cells provides a unique opportunity in cell therapy and drug discovery for developing novel strategies for brain repair. Cell-based therapeutic approaches for treating CNS afflictions caused by disease or injury aim to promote structural repair of the injured or diseased neural tissue, an outcome currently not achieved by drug therapy. Preclinical research in animal models of various diseases or injuries report that grafts of neural cells enhance endogenous repair, provide neurotrophic support to neurons undergoing degeneration and replace lost neural cells. In recent years, the sources of neural cells for treating neurological disorders have been rapidly expanding and in addition to offering therapeutic potential, neural cell products hold promise for disease modeling and drug discovery use. Specific neural cell types have been derived from adult or fetal brain, from human embryonic stem cells, from induced pluripotent stem cells and directly transdifferentiated from adult somatic cells, such as skin cells. It is yet to be determined if the latter approach will evolve into a paradigm shift in the fields of stem cell research and regenerative medicine. These multiple sources of neural cells cover a wide spectrum of safety that needs to be balanced with efficacy to determine the viability of the cellular product. In this article, we will review novel sources of neural cells and discuss current obstacles to developing them into viable cellular products for treating neurological disorders.

  17. Identification-based chaos control via backstepping design using self-organizing fuzzy neural networks

    International Nuclear Information System (INIS)

    Peng Yafu; Hsu, C.-F.

    2009-01-01

    This paper proposes an identification-based adaptive backstepping control (IABC) for the chaotic systems. The IABC system is comprised of a neural backstepping controller and a robust compensation controller. The neural backstepping controller containing a self-organizing fuzzy neural network (SOFNN) identifier is the principal controller, and the robust compensation controller is designed to dispel the effect of minimum approximation error introduced by the SOFNN identifier. The SOFNN identifier is used to online estimate the chaotic dynamic function with structure and parameter learning phases of fuzzy neural network. The structure learning phase consists of the growing and pruning of fuzzy rules; thus the SOFNN identifier can avoid the time-consuming trial-and-error tuning procedure for determining the neural structure of fuzzy neural network. The parameter learning phase adjusts the interconnection weights of neural network to achieve favorable approximation performance. Finally, simulation results verify that the proposed IABC can achieve favorable tracking performance.

  18. Google matrix analysis of C.elegans neural network

    Energy Technology Data Exchange (ETDEWEB)

    Kandiah, V., E-mail: kandiah@irsamc.ups-tlse.fr; Shepelyansky, D.L., E-mail: dima@irsamc.ups-tlse.fr

    2014-05-01

    We study the structural properties of the neural network of the C.elegans (worm) from a directed graph point of view. The Google matrix analysis is used to characterize the neuron connectivity structure and node classifications are discussed and compared with physiological properties of the cells. Our results are obtained by a proper definition of neural directed network and subsequent eigenvector analysis which recovers some results of previous studies. Our analysis highlights particular sets of important neurons constituting the core of the neural system. The applications of PageRank, CheiRank and ImpactRank to characterization of interdependency of neurons are discussed.

  19. Google matrix analysis of C.elegans neural network

    International Nuclear Information System (INIS)

    Kandiah, V.; Shepelyansky, D.L.

    2014-01-01

    We study the structural properties of the neural network of the C.elegans (worm) from a directed graph point of view. The Google matrix analysis is used to characterize the neuron connectivity structure and node classifications are discussed and compared with physiological properties of the cells. Our results are obtained by a proper definition of neural directed network and subsequent eigenvector analysis which recovers some results of previous studies. Our analysis highlights particular sets of important neurons constituting the core of the neural system. The applications of PageRank, CheiRank and ImpactRank to characterization of interdependency of neurons are discussed.

  20. Optimized Neural Network for Fault Diagnosis and Classification

    International Nuclear Information System (INIS)

    Elaraby, S.M.

    2005-01-01

    This paper presents a developed and implemented toolbox for optimizing neural network structure of fault diagnosis and classification. Evolutionary algorithm based on hierarchical genetic algorithm structure is used for optimization. The simplest feed-forward neural network architecture is selected. Developed toolbox has friendly user interface. Multiple solutions are generated. The performance and applicability of the proposed toolbox is verified with benchmark data patterns and accident diagnosis of Egyptian Second research reactor (ETRR-2)

  1. Applying Gradient Descent in Convolutional Neural Networks

    Science.gov (United States)

    Cui, Nan

    2018-04-01

    With the development of the integrated circuit and computer science, people become caring more about solving practical issues via information technologies. Along with that, a new subject called Artificial Intelligent (AI) comes up. One popular research interest of AI is about recognition algorithm. In this paper, one of the most common algorithms, Convolutional Neural Networks (CNNs) will be introduced, for image recognition. Understanding its theory and structure is of great significance for every scholar who is interested in this field. Convolution Neural Network is an artificial neural network which combines the mathematical method of convolution and neural network. The hieratical structure of CNN provides it reliable computer speed and reasonable error rate. The most significant characteristics of CNNs are feature extraction, weight sharing and dimension reduction. Meanwhile, combining with the Back Propagation (BP) mechanism and the Gradient Descent (GD) method, CNNs has the ability to self-study and in-depth learning. Basically, BP provides an opportunity for backwardfeedback for enhancing reliability and GD is used for self-training process. This paper mainly discusses the CNN and the related BP and GD algorithms, including the basic structure and function of CNN, details of each layer, the principles and features of BP and GD, and some examples in practice with a summary in the end.

  2. Weather forecasting based on hybrid neural model

    Science.gov (United States)

    Saba, Tanzila; Rehman, Amjad; AlGhamdi, Jarallah S.

    2017-11-01

    Making deductions and expectations about climate has been a challenge all through mankind's history. Challenges with exact meteorological directions assist to foresee and handle problems well in time. Different strategies have been investigated using various machine learning techniques in reported forecasting systems. Current research investigates climate as a major challenge for machine information mining and deduction. Accordingly, this paper presents a hybrid neural model (MLP and RBF) to enhance the accuracy of weather forecasting. Proposed hybrid model ensure precise forecasting due to the specialty of climate anticipating frameworks. The study concentrates on the data representing Saudi Arabia weather forecasting. The main input features employed to train individual and hybrid neural networks that include average dew point, minimum temperature, maximum temperature, mean temperature, average relative moistness, precipitation, normal wind speed, high wind speed and average cloudiness. The output layer composed of two neurons to represent rainy and dry weathers. Moreover, trial and error approach is adopted to select an appropriate number of inputs to the hybrid neural network. Correlation coefficient, RMSE and scatter index are the standard yard sticks adopted for forecast accuracy measurement. On individual standing MLP forecasting results are better than RBF, however, the proposed simplified hybrid neural model comes out with better forecasting accuracy as compared to both individual networks. Additionally, results are better than reported in the state of art, using a simple neural structure that reduces training time and complexity.

  3. Neural codes of seeing architectural styles.

    Science.gov (United States)

    Choo, Heeyoung; Nasar, Jack L; Nikrahei, Bardia; Walther, Dirk B

    2017-01-10

    Images of iconic buildings, such as the CN Tower, instantly transport us to specific places, such as Toronto. Despite the substantial impact of architectural design on people's visual experience of built environments, we know little about its neural representation in the human brain. In the present study, we have found patterns of neural activity associated with specific architectural styles in several high-level visual brain regions, but not in primary visual cortex (V1). This finding suggests that the neural correlates of the visual perception of architectural styles stem from style-specific complex visual structure beyond the simple features computed in V1. Surprisingly, the network of brain regions representing architectural styles included the fusiform face area (FFA) in addition to several scene-selective regions. Hierarchical clustering of error patterns further revealed that the FFA participated to a much larger extent in the neural encoding of architectural styles than entry-level scene categories. We conclude that the FFA is involved in fine-grained neural encoding of scenes at a subordinate-level, in our case, architectural styles of buildings. This study for the first time shows how the human visual system encodes visual aspects of architecture, one of the predominant and longest-lasting artefacts of human culture.

  4. Efficient Cancer Detection Using Multiple Neural Networks.

    Science.gov (United States)

    Shell, John; Gregory, William D

    2017-01-01

    The inspection of live excised tissue specimens to ascertain malignancy is a challenging task in dermatopathology and generally in histopathology. We introduce a portable desktop prototype device that provides highly accurate neural network classification of malignant and benign tissue. The handheld device collects 47 impedance data samples from 1 Hz to 32 MHz via tetrapolar blackened platinum electrodes. The data analysis was implemented with six different backpropagation neural networks (BNN). A data set consisting of 180 malignant and 180 benign breast tissue data files in an approved IRB study at the Aurora Medical Center, Milwaukee, WI, USA, were utilized as a neural network input. The BNN structure consisted of a multi-tiered consensus approach autonomously selecting four of six neural networks to determine a malignant or benign classification. The BNN analysis was then compared with the histology results with consistent sensitivity of 100% and a specificity of 100%. This implementation successfully relied solely on statistical variation between the benign and malignant impedance data and intricate neural network configuration. This device and BNN implementation provides a novel approach that could be a valuable tool to augment current medical practice assessment of the health of breast, squamous, and basal cell carcinoma and other excised tissue without requisite tissue specimen expertise. It has the potential to provide clinical management personnel with a fast non-invasive accurate assessment of biopsied or sectioned excised tissue in various clinical settings.

  5. Parallel consensual neural networks.

    Science.gov (United States)

    Benediktsson, J A; Sveinsson, J R; Ersoy, O K; Swain, P H

    1997-01-01

    A new type of a neural-network architecture, the parallel consensual neural network (PCNN), is introduced and applied in classification/data fusion of multisource remote sensing and geographic data. The PCNN architecture is based on statistical consensus theory and involves using stage neural networks with transformed input data. The input data are transformed several times and the different transformed data are used as if they were independent inputs. The independent inputs are first classified using the stage neural networks. The output responses from the stage networks are then weighted and combined to make a consensual decision. In this paper, optimization methods are used in order to weight the outputs from the stage networks. Two approaches are proposed to compute the data transforms for the PCNN, one for binary data and another for analog data. The analog approach uses wavelet packets. The experimental results obtained with the proposed approach show that the PCNN outperforms both a conjugate-gradient backpropagation neural network and conventional statistical methods in terms of overall classification accuracy of test data.

  6. Development of an artificial neural network model for risk assessment of skin sensitization using human cell line activation test, direct peptide reactivity assay, KeratinoSens™ and in silico structure alert parameter.

    Science.gov (United States)

    Hirota, Morihiko; Ashikaga, Takao; Kouzuki, Hirokazu

    2018-04-01

    It is important to predict the potential of cosmetic ingredients to cause skin sensitization, and in accordance with the European Union cosmetic directive for the replacement of animal tests, several in vitro tests based on the adverse outcome pathway have been developed for hazard identification, such as the direct peptide reactivity assay, KeratinoSens™ and the human cell line activation test. Here, we describe the development of an artificial neural network (ANN) prediction model for skin sensitization risk assessment based on the integrated testing strategy concept, using direct peptide reactivity assay, KeratinoSens™, human cell line activation test and an in silico or structure alert parameter. We first investigated the relationship between published murine local lymph node assay EC3 values, which represent skin sensitization potency, and in vitro test results using a panel of about 134 chemicals for which all the required data were available. Predictions based on ANN analysis using combinations of parameters from all three in vitro tests showed a good correlation with local lymph node assay EC3 values. However, when the ANN model was applied to a testing set of 28 chemicals that had not been included in the training set, predicted EC3s were overestimated for some chemicals. Incorporation of an additional in silico or structure alert descriptor (obtained with TIMES-M or Toxtree software) in the ANN model improved the results. Our findings suggest that the ANN model based on the integrated testing strategy concept could be useful for evaluating the skin sensitization potential. Copyright © 2017 John Wiley & Sons, Ltd.

  7. Identifying Emotions on the Basis of Neural Activation.

    Science.gov (United States)

    Kassam, Karim S; Markey, Amanda R; Cherkassky, Vladimir L; Loewenstein, George; Just, Marcel Adam

    2013-01-01

    We attempt to determine the discriminability and organization of neural activation corresponding to the experience of specific emotions. Method actors were asked to self-induce nine emotional states (anger, disgust, envy, fear, happiness, lust, pride, sadness, and shame) while in an fMRI scanner. Using a Gaussian Naïve Bayes pooled variance classifier, we demonstrate the ability to identify specific emotions experienced by an individual at well over chance accuracy on the basis of: 1) neural activation of the same individual in other trials, 2) neural activation of other individuals who experienced similar trials, and 3) neural activation of the same individual to a qualitatively different type of emotion induction. Factor analysis identified valence, arousal, sociality, and lust as dimensions underlying the activation patterns. These results suggest a structure for neural representations of emotion and inform theories of emotional processing.

  8. Identifying Emotions on the Basis of Neural Activation.

    Directory of Open Access Journals (Sweden)

    Karim S Kassam

    Full Text Available We attempt to determine the discriminability and organization of neural activation corresponding to the experience of specific emotions. Method actors were asked to self-induce nine emotional states (anger, disgust, envy, fear, happiness, lust, pride, sadness, and shame while in an fMRI scanner. Using a Gaussian Naïve Bayes pooled variance classifier, we demonstrate the ability to identify specific emotions experienced by an individual at well over chance accuracy on the basis of: 1 neural activation of the same individual in other trials, 2 neural activation of other individuals who experienced similar trials, and 3 neural activation of the same individual to a qualitatively different type of emotion induction. Factor analysis identified valence, arousal, sociality, and lust as dimensions underlying the activation patterns. These results suggest a structure for neural representations of emotion and inform theories of emotional processing.

  9. Neural networks and their potential application in nuclear power plants

    International Nuclear Information System (INIS)

    Uhrig, R.E.

    1991-01-01

    A neural network is a data processing system consisting of a number of simple, highly interconnected processing elements in an architecture inspired by the structure of the cerebral cortex portion of the brain. Hence, neural networks are often capable of doing things which humans or animals do well but which conventional computers often do poorly. Neural networks have emerged in the past few years as an area of unusual opportunity for research, development and application to a variety of real world problems. Indeed, neural networks exhibit characteristics and capabilities not provided by any other technology. Examples include reading Japanese Kanji characters and human handwriting, reading a typewritten manuscript aloud, compensating for alignment errors in robots, interpreting very noise signals (e.g., electroencephalograms), modeling complex systems that cannot be modeled mathematically, and predicting whether proposed loans will be good or fail. This paper presents a brief tutorial on neural networks and describes research on the potential applications to nuclear power plants

  10. Neural Interfaces for Intracortical Recording: Requirements, Fabrication Methods, and Characteristics.

    Science.gov (United States)

    Szostak, Katarzyna M; Grand, Laszlo; Constandinou, Timothy G

    2017-01-01

    Implantable neural interfaces for central nervous system research have been designed with wire, polymer, or micromachining technologies over the past 70 years. Research on biocompatible materials, ideal probe shapes, and insertion methods has resulted in building more and more capable neural interfaces. Although the trend is promising, the long-term reliability of such devices has not yet met the required criteria for chronic human application. The performance of neural interfaces in chronic settings often degrades due to foreign body response to the implant that is initiated by the surgical procedure, and related to the probe structure, and material properties used in fabricating the neural interface. In this review, we identify the key requirements for neural interfaces for intracortical recording, describe the three different types of probes-microwire, micromachined, and polymer-based probes; their materials, fabrication methods, and discuss their characteristics and related challenges.

  11. Neural Interfaces for Intracortical Recording: Requirements, Fabrication Methods, and Characteristics

    Directory of Open Access Journals (Sweden)

    Katarzyna M. Szostak

    2017-12-01

    Full Text Available Implantable neural interfaces for central nervous system research have been designed with wire, polymer, or micromachining technologies over the past 70 years. Research on biocompatible materials, ideal probe shapes, and insertion methods has resulted in building more and more capable neural interfaces. Although the trend is promising, the long-term reliability of such devices has not yet met the required criteria for chronic human application. The performance of neural interfaces in chronic settings often degrades due to foreign body response to the implant that is initiated by the surgical procedure, and related to the probe structure, and material properties used in fabricating the neural interface. In this review, we identify the key requirements for neural interfaces for intracortical recording, describe the three different types of probes—microwire, micromachined, and polymer-based probes; their materials, fabrication methods, and discuss their characteristics and related challenges.

  12. Neural Architectures for Control

    Science.gov (United States)

    Peterson, James K.

    1991-01-01

    The cerebellar model articulated controller (CMAC) neural architectures are shown to be viable for the purposes of real-time learning and control. Software tools for the exploration of CMAC performance are developed for three hardware platforms, the MacIntosh, the IBM PC, and the SUN workstation. All algorithm development was done using the C programming language. These software tools were then used to implement an adaptive critic neuro-control design that learns in real-time how to back up a trailer truck. The truck backer-upper experiment is a standard performance measure in the neural network literature, but previously the training of the controllers was done off-line. With the CMAC neural architectures, it was possible to train the neuro-controllers on-line in real-time on a MS-DOS PC 386. CMAC neural architectures are also used in conjunction with a hierarchical planning approach to find collision-free paths over 2-D analog valued obstacle fields. The method constructs a coarse resolution version of the original problem and then finds the corresponding coarse optimal path using multipass dynamic programming. CMAC artificial neural architectures are used to estimate the analog transition costs that dynamic programming requires. The CMAC architectures are trained in real-time for each obstacle field presented. The coarse optimal path is then used as a baseline for the construction of a fine scale optimal path through the original obstacle array. These results are a very good indication of the potential power of the neural architectures in control design. In order to reach as wide an audience as possible, we have run a seminar on neuro-control that has met once per week since 20 May 1991. This seminar has thoroughly discussed the CMAC architecture, relevant portions of classical control, back propagation through time, and adaptive critic designs.

  13. Sacred or Neural?

    DEFF Research Database (Denmark)

    Runehov, Anne Leona Cesarine

    Are religious spiritual experiences merely the product of the human nervous system? Anne L.C. Runehov investigates the potential of contemporary neuroscience to explain religious experiences. Following the footsteps of Michael Persinger, Andrew Newberg and Eugene d'Aquili she defines...... the terminological bounderies of "religious experiences" and explores the relevant criteria for the proper evaluation of scientific research, with a particular focus on the validity of reductionist models. Runehov's theis is that the perspectives looked at do not necessarily exclude each other but can be merged....... The question "sacred or neural?" becomes a statement "sacred and neural". The synergies thus produced provide manifold opportunities for interdisciplinary dialogue and research....

  14. Deconvolution using a neural network

    Energy Technology Data Exchange (ETDEWEB)

    Lehman, S.K.

    1990-11-15

    Viewing one dimensional deconvolution as a matrix inversion problem, we compare a neural network backpropagation matrix inverse with LMS, and pseudo-inverse. This is a largely an exercise in understanding how our neural network code works. 1 ref.

  15. Introduction to Artificial Neural Networks

    DEFF Research Database (Denmark)

    Larsen, Jan

    1999-01-01

    The note addresses introduction to signal analysis and classification based on artificial feed-forward neural networks.......The note addresses introduction to signal analysis and classification based on artificial feed-forward neural networks....

  16. Models of neural dynamics in brain information processing - the developments of 'the decade'

    International Nuclear Information System (INIS)

    Borisyuk, G N; Borisyuk, R M; Kazanovich, Yakov B; Ivanitskii, Genrikh R

    2002-01-01

    Neural network models are discussed that have been developed during the last decade with the purpose of reproducing spatio-temporal patterns of neural activity in different brain structures. The main goal of the modeling was to test hypotheses of synchronization, temporal and phase relations in brain information processing. The models being considered are those of temporal structure of spike sequences, of neural activity dynamics, and oscillatory models of attention and feature integration. (reviews of topical problems)

  17. Control of beam halo-chaos using neural network self-adaptation method

    International Nuclear Information System (INIS)

    Fang Jinqing; Huang Guoxian; Luo Xiaoshu

    2004-11-01

    Taking the advantages of neural network control method for nonlinear complex systems, control of beam halo-chaos in the periodic focusing channels (network) of high intensity accelerators is studied by feed-forward back-propagating neural network self-adaptation method. The envelope radius of high-intensity proton beam is reached to the matching beam radius by suitably selecting the control structure of neural network and the linear feedback coefficient, adjusted the right-coefficient of neural network. The beam halo-chaos is obviously suppressed and shaking size is much largely reduced after the neural network self-adaptation control is applied. (authors)

  18. Research on Fault Diagnosis Method Based on Rule Base Neural Network

    Directory of Open Access Journals (Sweden)

    Zheng Ni

    2017-01-01

    Full Text Available The relationship between fault phenomenon and fault cause is always nonlinear, which influences the accuracy of fault location. And neural network is effective in dealing with nonlinear problem. In order to improve the efficiency of uncertain fault diagnosis based on neural network, a neural network fault diagnosis method based on rule base is put forward. At first, the structure of BP neural network is built and the learning rule is given. Then, the rule base is built by fuzzy theory. An improved fuzzy neural construction model is designed, in which the calculated methods of node function and membership function are also given. Simulation results confirm the effectiveness of this method.

  19. Neural growth into a microchannel network: towards a regenerative neural interface

    NARCIS (Netherlands)

    Wieringa, P.A.; Wiertz, Remy; le Feber, Jakob; Rutten, Wim

    2009-01-01

    We propose and validated a design for a highly selective 'endcap' regenerative neural interface towards a neuroprosthesis. In vitro studies using rat cortical neurons determine if a branching microchannel structure can counter fasciculated growth and cause neurites to separte from one another,

  20. Neural Network Ensembles

    DEFF Research Database (Denmark)

    Hansen, Lars Kai; Salamon, Peter

    1990-01-01

    We propose several means for improving the performance an training of neural networks for classification. We use crossvalidation as a tool for optimizing network parameters and architecture. We show further that the remaining generalization error can be reduced by invoking ensembles of similar...... networks....

  1. Neural correlates of consciousness

    African Journals Online (AJOL)

    neural cells.1 Under this approach, consciousness is believed to be a product of the ... possible only when the 40 Hz electrical hum is sustained among the brain circuits, ... expect the brain stem ascending reticular activating system. (ARAS) and the ... related synchrony of cortical neurons.11 Indeed, stimulation of brainstem ...

  2. Neural Networks and Micromechanics

    Science.gov (United States)

    Kussul, Ernst; Baidyk, Tatiana; Wunsch, Donald C.

    The title of the book, "Neural Networks and Micromechanics," seems artificial. However, the scientific and technological developments in recent decades demonstrate a very close connection between the two different areas of neural networks and micromechanics. The purpose of this book is to demonstrate this connection. Some artificial intelligence (AI) methods, including neural networks, could be used to improve automation system performance in manufacturing processes. However, the implementation of these AI methods within industry is rather slow because of the high cost of conducting experiments using conventional manufacturing and AI systems. To lower the cost, we have developed special micromechanical equipment that is similar to conventional mechanical equipment but of much smaller size and therefore of lower cost. This equipment could be used to evaluate different AI methods in an easy and inexpensive way. The proved methods could be transferred to industry through appropriate scaling. In this book, we describe the prototypes of low cost microequipment for manufacturing processes and the implementation of some AI methods to increase precision, such as computer vision systems based on neural networks for microdevice assembly and genetic algorithms for microequipment characterization and the increase of microequipment precision.

  3. Introduction to neural networks

    International Nuclear Information System (INIS)

    Pavlopoulos, P.

    1996-01-01

    This lecture is a presentation of today's research in neural computation. Neural computation is inspired by knowledge from neuro-science. It draws its methods in large degree from statistical physics and its potential applications lie mainly in computer science and engineering. Neural networks models are algorithms for cognitive tasks, such as learning and optimization, which are based on concepts derived from research into the nature of the brain. The lecture first gives an historical presentation of neural networks development and interest in performing complex tasks. Then, an exhaustive overview of data management and networks computation methods is given: the supervised learning and the associative memory problem, the capacity of networks, the Perceptron networks, the functional link networks, the Madaline (Multiple Adalines) networks, the back-propagation networks, the reduced coulomb energy (RCE) networks, the unsupervised learning and the competitive learning and vector quantization. An example of application in high energy physics is given with the trigger systems and track recognition system (track parametrization, event selection and particle identification) developed for the CPLEAR experiment detectors from the LEAR at CERN. (J.S.). 56 refs., 20 figs., 1 tab., 1 appendix

  4. Learning from neural control.

    Science.gov (United States)

    Wang, Cong; Hill, David J

    2006-01-01

    One of the amazing successes of biological systems is their ability to "learn by doing" and so adapt to their environment. In this paper, first, a deterministic learning mechanism is presented, by which an appropriately designed adaptive neural controller is capable of learning closed-loop system dynamics during tracking control to a periodic reference orbit. Among various neural network (NN) architectures, the localized radial basis function (RBF) network is employed. A property of persistence of excitation (PE) for RBF networks is established, and a partial PE condition of closed-loop signals, i.e., the PE condition of a regression subvector constructed out of the RBFs along a periodic state trajectory, is proven to be satisfied. Accurate NN approximation for closed-loop system dynamics is achieved in a local region along the periodic state trajectory, and a learning ability is implemented during a closed-loop feedback control process. Second, based on the deterministic learning mechanism, a neural learning control scheme is proposed which can effectively recall and reuse the learned knowledge to achieve closed-loop stability and improved control performance. The significance of this paper is that the presented deterministic learning mechanism and the neural learning control scheme provide elementary components toward the development of a biologically-plausible learning and control methodology. Simulation studies are included to demonstrate the effectiveness of the approach.

  5. Neural systems for control

    National Research Council Canada - National Science Library

    Omidvar, Omid; Elliott, David L

    1997-01-01

    ... is reprinted with permission from A. Barto, "Reinforcement Learning," Handbook of Brain Theory and Neural Networks, M.A. Arbib, ed.. The MIT Press, Cambridge, MA, pp. 804-809, 1995. Chapter 4, Figures 4-5 and 7-9 and Tables 2-5, are reprinted with permission, from S. Cho, "Map Formation in Proprioceptive Cortex," International Jour...

  6. Neural underpinnings of music

    DEFF Research Database (Denmark)

    Vuust, Peter; Gebauer, Line K; Witek, Maria A G

    2014-01-01

    . According to this theory, perception and learning is manifested through the brain’s Bayesian minimization of the error between the input to the brain and the brain’s prior expectations. Fourth, empirical studies of neural and behavioral effects of syncopation, polyrhythm and groove will be reported, and we...

  7. Neural correlates of affective influence on choice.

    Science.gov (United States)

    Piech, Richard M; Lewis, Jade; Parkinson, Caroline H; Owen, Adrian M; Roberts, Angela C; Downing, Paul E; Parkinson, John A

    2010-03-01

    Making the right choice depends crucially on the accurate valuation of the available options in the light of current needs and goals of an individual. Thus, the valuation of identical options can vary considerably with motivational context. The present study investigated the neural structures underlying context dependent evaluation. We instructed participants to choose from food menu items based on different criteria: on their anticipated taste or on ease of preparation. The aim of the manipulation was to assess which neural sites were activated during choice guided by incentive value, and which during choice based on a value-irrelevant criterion. To assess the impact of increased motivation, affect-guided choice and cognition-guided choice was compared during the sated and hungry states. During affective choice, we identified increased activity in structures representing primarily valuation and taste (medial prefrontal cortex, insula). During cognitive choice, structures showing increased activity included those implicated in suppression and conflict monitoring (lateral orbitofrontal cortex, anterior cingulate). Hunger influenced choice-related activity in the ventrolateral prefrontal cortex. Our results show that choice is associated with the use of distinct neural structures for the pursuit of different goals. Published by Elsevier Inc.

  8. Robust neural network with applications to credit portfolio data analysis.

    Science.gov (United States)

    Feng, Yijia; Li, Runze; Sudjianto, Agus; Zhang, Yiyun

    2010-01-01

    In this article, we study nonparametric conditional quantile estimation via neural network structure. We proposed an estimation method that combines quantile regression and neural network (robust neural network, RNN). It provides good smoothing performance in the presence of outliers and can be used to construct prediction bands. A Majorization-Minimization (MM) algorithm was developed for optimization. Monte Carlo simulation study is conducted to assess the performance of RNN. Comparison with other nonparametric regression methods (e.g., local linear regression and regression splines) in real data application demonstrate the advantage of the newly proposed procedure.

  9. Bioprinting for Neural Tissue Engineering.

    Science.gov (United States)

    Knowlton, Stephanie; Anand, Shivesh; Shah, Twisha; Tasoglu, Savas

    2018-01-01

    Bioprinting is a method by which a cell-encapsulating bioink is patterned to create complex tissue architectures. Given the potential impact of this technology on neural research, we review the current state-of-the-art approaches for bioprinting neural tissues. While 2D neural cultures are ubiquitous for studying neural cells, 3D cultures can more accurately replicate the microenvironment of neural tissues. By bioprinting neuronal constructs, one can precisely control the microenvironment by specifically formulating the bioink for neural tissues, and by spatially patterning cell types and scaffold properties in three dimensions. We review a range of bioprinted neural tissue models and discuss how they can be used to observe how neurons behave, understand disease processes, develop new therapies and, ultimately, design replacement tissues. Copyright © 2017 Elsevier Ltd. All rights reserved.

  10. Structuralism.

    Science.gov (United States)

    Piaget, Jean

    Provided is an overview of the analytical method known as structuralism. The first chapter discusses the three key components of the concept of a structure: the view of a system as a whole instead of so many parts; the study of the transformations in the system; and the fact that these transformations never lead beyond the system but always…

  11. Neural nets for job-shop scheduling, will they do the job?

    NARCIS (Netherlands)

    Rooda, J.E.; Willems, T.M.; Goodwin, G.C.; Evans, R.J.

    1993-01-01

    A neural net structure has been developed which is capable of solving deterministic jobshop scheduling problems, part of the large class of np-complete problems. The problem was translated in an integer linear-programming format which facilitated translation in an adequate neural net structure. Use

  12. Neural Networks as a Tool for Georadar Data Processing

    Directory of Open Access Journals (Sweden)

    Szymczyk Piotr

    2015-12-01

    Full Text Available In this article a new neural network based method for automatic classification of ground penetrating radar (GPR traces is proposed. The presented approach is based on a new representation of GPR signals by polynomials approximation. The coefficients of the polynomial (the feature vector are neural network inputs for automatic classification of a special kind of geologic structure—a sinkhole. The analysis and results show that the classifier can effectively distinguish sinkholes from other geologic structures.

  13. Prediction of Shanghai Index based on Additive Legendre Neural Network

    Directory of Open Access Journals (Sweden)

    Yang Bin

    2017-01-01

    Full Text Available In this paper, a novel Legendre neural network model is proposed, namely additive Legendre neural network (ALNN. A new hybrid evolutionary method besed on binary particle swarm optimization (BPSO algorithm and firefly algorithm is proposed to optimize the structure and parameters of ALNN model. Shanghai stock exchange composite index is used to evaluate the performance of ALNN. Results reveal that ALNN performs better than LNN model.

  14. Cellular neural networks for the stereo matching problem

    International Nuclear Information System (INIS)

    Taraglio, S.; Zanela, A.

    1997-03-01

    The applicability of the Cellular Neural Network (CNN) paradigm to the problem of recovering information on the tridimensional structure of the environment is investigated. The approach proposed is the stereo matching of video images. The starting point of this work is the Zhou-Chellappa neural network implementation for the same problem. The CNN based system we present here yields the same results as the previous approach, but without the many existing drawbacks

  15. Distributed Recurrent Neural Forward Models with Neural Control for Complex Locomotion in Walking Robots

    DEFF Research Database (Denmark)

    Dasgupta, Sakyasingha; Goldschmidt, Dennis; Wörgötter, Florentin

    2015-01-01

    here, an artificial bio-inspired walking system which effectively combines biomechanics (in terms of the body and leg structures) with the underlying neural mechanisms. The neural mechanisms consist of (1) central pattern generator based control for generating basic rhythmic patterns and coordinated......Walking animals, like stick insects, cockroaches or ants, demonstrate a fascinating range of locomotive abilities and complex behaviors. The locomotive behaviors can consist of a variety of walking patterns along with adaptation that allow the animals to deal with changes in environmental...... conditions, like uneven terrains, gaps, obstacles etc. Biological study has revealed that such complex behaviors are a result of a combination of biomechanics and neural mechanism thus representing the true nature of embodied interactions. While the biomechanics helps maintain flexibility and sustain...

  16. Relation Classification via Recurrent Neural Network

    OpenAIRE

    Zhang, Dongxu; Wang, Dong

    2015-01-01

    Deep learning has gained much success in sentence-level relation classification. For example, convolutional neural networks (CNN) have delivered competitive performance without much effort on feature engineering as the conventional pattern-based methods. Thus a lot of works have been produced based on CNN structures. However, a key issue that has not been well addressed by the CNN-based method is the lack of capability to learn temporal features, especially long-distance dependency between no...

  17. Localizing Tortoise Nests by Neural Networks.

    Directory of Open Access Journals (Sweden)

    Roberto Barbuti

    Full Text Available The goal of this research is to recognize the nest digging activity of tortoises using a device mounted atop the tortoise carapace. The device classifies tortoise movements in order to discriminate between nest digging, and non-digging activity (specifically walking and eating. Accelerometer data was collected from devices attached to the carapace of a number of tortoises during their two-month nesting period. Our system uses an accelerometer and an activity recognition system (ARS which is modularly structured using an artificial neural network and an output filter. For the purpose of experiment and comparison, and with the aim of minimizing the computational cost, the artificial neural network has been modelled according to three different architectures based on the input delay neural network (IDNN. We show that the ARS can achieve very high accuracy on segments of data sequences, with an extremely small neural network that can be embedded in programmable low power devices. Given that digging is typically a long activity (up to two hours, the application of ARS on data segments can be repeated over time to set up a reliable and efficient system, called Tortoise@, for digging activity recognition.

  18. Embedding responses in spontaneous neural activity shaped through sequential learning.

    Directory of Open Access Journals (Sweden)

    Tomoki Kurikawa

    Full Text Available Recent experimental measurements have demonstrated that spontaneous neural activity in the absence of explicit external stimuli has remarkable spatiotemporal structure. This spontaneous activity has also been shown to play a key role in the response to external stimuli. To better understand this role, we proposed a viewpoint, "memories-as-bifurcations," that differs from the traditional "memories-as-attractors" viewpoint. Memory recall from the memories-as-bifurcations viewpoint occurs when the spontaneous neural activity is changed to an appropriate output activity upon application of an input, known as a bifurcation in dynamical systems theory, wherein the input modifies the flow structure of the neural dynamics. Learning, then, is a process that helps create neural dynamical systems such that a target output pattern is generated as an attractor upon a given input. Based on this novel viewpoint, we introduce in this paper an associative memory model with a sequential learning process. Using a simple hebbian-type learning, the model is able to memorize a large number of input/output mappings. The neural dynamics shaped through the learning exhibit different bifurcations to make the requested targets stable upon an increase in the input, and the neural activity in the absence of input shows chaotic dynamics with occasional approaches to the memorized target patterns. These results suggest that these dynamics facilitate the bifurcations to each target attractor upon application of the corresponding input, which thus increases the capacity for learning. This theoretical finding about the behavior of the spontaneous neural activity is consistent with recent experimental observations in which the neural activity without stimuli wanders among patterns evoked by previously applied signals. In addition, the neural networks shaped by learning properly reflect the correlations of input and target-output patterns in a similar manner to those designed in

  19. Controlling the dynamics of multi-state neural networks

    International Nuclear Information System (INIS)

    Jin, Tao; Zhao, Hong

    2008-01-01

    In this paper, we first analyze the distribution of local fields (DLF) which is induced by the memory patterns in the Q-Ising model. It is found that the structure of the DLF is closely correlated with the network dynamics and the system performance. However, the design rule adopted in the Q-Ising model, like the other rules adopted for multi-state neural networks with associative memories, cannot be applied to directly control the DLF for a given set of memory patterns, and thus cannot be applied to further study the relationships between the structure of the DLF and the dynamics of the network. We then extend a design rule, which was presented recently for designing binary-state neural networks, to make it suitable for designing general multi-state neural networks. This rule is able to control the structure of the DLF as expected. We show that controlling the DLF not only can affect the dynamic behaviors of the multi-state neural networks for a given set of memory patterns, but also can improve the storage capacity. With the change of the DLF, the network shows very rich dynamic behaviors, such as the 'chaos phase', the 'memory phase', and the 'mixture phase'. These dynamic behaviors are also observed in the binary-state neural networks; therefore, our results imply that they may be the universal behaviors of feedback neural networks

  20. Neural correlates of prosody and information structure

    NARCIS (Netherlands)

    Dimitrova, Diana Valentinova

    2012-01-01

    Dit proefschrift onderzoekt welke neurocognitieve processen geactiveerd worden in de hersenen wanneer luisteraars gesproken taal verwerken, met name de melodie en ritme van gesproken zinnen die bekend is als prosodie. De resultaten van de uitgevoerde elektrofysiologische studies laten zien dat

  1. Analysis of neural data

    CERN Document Server

    Kass, Robert E; Brown, Emery N

    2014-01-01

    Continual improvements in data collection and processing have had a huge impact on brain research, producing data sets that are often large and complicated. By emphasizing a few fundamental principles, and a handful of ubiquitous techniques, Analysis of Neural Data provides a unified treatment of analytical methods that have become essential for contemporary researchers. Throughout the book ideas are illustrated with more than 100 examples drawn from the literature, ranging from electrophysiology, to neuroimaging, to behavior. By demonstrating the commonality among various statistical approaches the authors provide the crucial tools for gaining knowledge from diverse types of data. Aimed at experimentalists with only high-school level mathematics, as well as computationally-oriented neuroscientists who have limited familiarity with statistics, Analysis of Neural Data serves as both a self-contained introduction and a reference work.

  2. CRIM1 Complexes with ß-catenin and Cadherins, Stabilizes Cell-Cell Junctions and Is Critical for Neural Morphogenesis

    OpenAIRE

    Ponferrada, Virgilio G.; Fan, Jieqing; Vallance, Jefferson E.; Hu, Shengyong; Mamedova, Aygun; Rankin, Scott A.; Kofron, Matthew; Zorn, Aaron M.; Hegde, Rashmi S.; Lang, Richard A.

    2012-01-01

    In multicellular organisms, morphogenesis is a highly coordinated process that requires dynamically regulated adhesion between cells. An excellent example of cellular morphogenesis is the formation of the neural tube from the flattened epithelium of the neural plate. Cysteine-rich motor neuron protein 1 (CRIM1) is a single-pass (type 1) transmembrane protein that is expressed in neural structures beginning at the neural plate stage. In the frog Xenopus laevis, loss of function studies using C...

  3. Deep Neural Yodelling

    OpenAIRE

    Pfäffli, Daniel (Autor/in)

    2018-01-01

    Yodel music differs from most other genres by exercising the transition from chest voice to falsetto with an audible glottal stop which is recognised even by laymen. Yodel often consists of a yodeller with a choir accompaniment. In Switzerland, it is differentiated between the natural yodel and yodel songs. Today's approaches to music generation with machine learning algorithms are based on neural networks, which are best described by stacked layers of neurons which are connected with neurons...

  4. Neural networks for triggering

    International Nuclear Information System (INIS)

    Denby, B.; Campbell, M.; Bedeschi, F.; Chriss, N.; Bowers, C.; Nesti, F.

    1990-01-01

    Two types of neural network beauty trigger architectures, based on identification of electrons in jets and recognition of secondary vertices, have been simulated in the environment of the Fermilab CDF experiment. The efficiencies for B's and rejection of background obtained are encouraging. If hardware tests are successful, the electron identification architecture will be tested in the 1991 run of CDF. 10 refs., 5 figs., 1 tab

  5. Rotation Invariance Neural Network

    OpenAIRE

    Li, Shiyuan

    2017-01-01

    Rotation invariance and translation invariance have great values in image recognition tasks. In this paper, we bring a new architecture in convolutional neural network (CNN) named cyclic convolutional layer to achieve rotation invariance in 2-D symbol recognition. We can also get the position and orientation of the 2-D symbol by the network to achieve detection purpose for multiple non-overlap target. Last but not least, this architecture can achieve one-shot learning in some cases using thos...

  6. Neural Mechanisms of Foraging

    OpenAIRE

    Kolling, Nils; Behrens, Timothy EJ; Mars, Rogier B; Rushworth, Matthew FS

    2012-01-01

    Behavioural economic studies, involving limited numbers of choices, have provided key insights into neural decision-making mechanisms. By contrast, animals’ foraging choices arise in the context of sequences of encounters with prey/food. On each encounter the animal chooses to engage or whether the environment is sufficiently rich that searching elsewhere is merited. The cost of foraging is also critical. We demonstrate humans can alternate between two modes of choice, comparative decision-ma...

  7. Stability analysis of fractional-order Hopfield neural networks with time delays.

    Science.gov (United States)

    Wang, Hu; Yu, Yongguang; Wen, Guoguang

    2014-07-01

    This paper investigates the stability for fractional-order Hopfield neural networks with time delays. Firstly, the fractional-order Hopfield neural networks with hub structure and time delays are studied. Some sufficient conditions for stability of the systems are obtained. Next, two fractional-order Hopfield neural networks with different ring structures and time delays are developed. By studying the developed neural networks, the corresponding sufficient conditions for stability of the systems are also derived. It is shown that the stability conditions are independent of time delays. Finally, numerical simulations are given to illustrate the effectiveness of the theoretical results obtained in this paper. Copyright © 2014 Elsevier Ltd. All rights reserved.

  8. Enteric nervous system specific deletion of Foxd3 disrupts glial cell differentiation and activates compensatory enteric progenitors.

    Science.gov (United States)

    Mundell, Nathan A; Plank, Jennifer L; LeGrone, Alison W; Frist, Audrey Y; Zhu, Lei; Shin, Myung K; Southard-Smith, E Michelle; Labosky, Patricia A

    2012-03-15

    The enteric nervous system (ENS) arises from the coordinated migration, expansion and differentiation of vagal and sacral neural crest progenitor cells. During development, vagal neural crest cells enter the foregut and migrate in a rostro-to-caudal direction, colonizing the entire gastrointestinal tract and generating the majority of the ENS. Sacral neural crest contributes to a subset of enteric ganglia in the hindgut, colonizing the colon in a caudal-to-rostral wave. During this process, enteric neural crest-derived progenitors (ENPs) self-renew and begin expressing markers of neural and glial lineages as they populate the intestine. Our earlier work demonstrated that the transcription factor Foxd3 is required early in neural crest-derived progenitors for self-renewal, multipotency and establishment of multiple neural crest-derived cells and structures including the ENS. Here, we describe Foxd3 expression within the fetal and postnatal intestine: Foxd3 was strongly expressed in ENPs as they colonize the gastrointestinal tract and was progressively restricted to enteric glial cells. Using a novel Ednrb-iCre transgene to delete Foxd3 after vagal neural crest cells migrate into the midgut, we demonstrated a late temporal requirement for Foxd3 during ENS development. Lineage labeling of Ednrb-iCre expressing cells in Foxd3 mutant embryos revealed a reduction of ENPs throughout the gut and loss of Ednrb-iCre lineage cells in the distal colon. Although mutant mice were viable, defects in patterning and distribution of ENPs were associated with reduced proliferation and severe reduction of glial cells derived from the Ednrb-iCre lineage. Analyses of ENS-lineage and differentiation in mutant embryos suggested activation of a compensatory population of Foxd3-positive ENPs that did not express the Ednrb-iCre transgene. Our findings highlight the crucial roles played by Foxd3 during ENS development including progenitor proliferation, neural patterning, and glial

  9. Bio-inspired spiking neural network for nonlinear systems control.

    Science.gov (United States)

    Pérez, Javier; Cabrera, Juan A; Castillo, Juan J; Velasco, Juan M

    2018-08-01

    Spiking neural networks (SNN) are the third generation of artificial neural networks. SNN are the closest approximation to biological neural networks. SNNs make use of temporal spike trains to command inputs and outputs, allowing a faster and more complex computation. As demonstrated by biological organisms, they are a potentially good approach to designing controllers for highly nonlinear dynamic systems in which the performance of controllers developed by conventional techniques is not satisfactory or difficult to implement. SNN-based controllers exploit their ability for online learning and self-adaptation to evolve when transferred from simulations to the real world. SNN's inherent binary and temporary way of information codification facilitates their hardware implementation compared to analog neurons. Biological neural networks often require a lower number of neurons compared to other controllers based on artificial neural networks. In this work, these neuronal systems are imitated to perform the control of non-linear dynamic systems. For this purpose, a control structure based on spiking neural networks has been designed. Particular attention has been paid to optimizing the structure and size of the neural network. The proposed structure is able to control dynamic systems with a reduced number of neurons and connections. A supervised learning process using evolutionary algorithms has been carried out to perform controller training. The efficiency of the proposed network has been verified in two examples of dynamic systems control. Simulations show that the proposed control based on SNN exhibits superior performance compared to other approaches based on Neural Networks and SNNs. Copyright © 2018 Elsevier Ltd. All rights reserved.

  10. Neural plasticity and its initiating conditions in tinnitus.

    Science.gov (United States)

    Roberts, L E

    2018-03-01

    Deafferentation caused by cochlear pathology (which can be hidden from the audiogram) activates forms of neural plasticity in auditory pathways, generating tinnitus and its associated conditions including hyperacusis. This article discusses tinnitus mechanisms and suggests how these mechanisms may relate to those involved in normal auditory information processing. Research findings from animal models of tinnitus and from electromagnetic imaging of tinnitus patients are reviewed which pertain to the role of deafferentation and neural plasticity in tinnitus and hyperacusis. Auditory neurons compensate for deafferentation by increasing their input/output functions (gain) at multiple levels of the auditory system. Forms of homeostatic plasticity are believed to be responsible for this neural change, which increases the spontaneous and driven activity of neurons in central auditory structures in animals expressing behavioral evidence of tinnitus. Another tinnitus correlate, increased neural synchrony among the affected neurons, is forged by spike-timing-dependent neural plasticity in auditory pathways. Slow oscillations generated by bursting thalamic neurons verified in tinnitus animals appear to modulate neural plasticity in the cortex, integrating tinnitus neural activity with information in brain regions supporting memory, emotion, and consciousness which exhibit increased metabolic activity in tinnitus patients. The latter process may be induced by transient auditory events in normal processing but it persists in tinnitus, driven by phantom signals from the auditory pathway. Several tinnitus therapies attempt to suppress tinnitus through plasticity, but repeated sessions will likely be needed to prevent tinnitus activity from returning owing to deafferentation as its initiating condition.

  11. Neural Based Orthogonal Data Fitting The EXIN Neural Networks

    CERN Document Server

    Cirrincione, Giansalvo

    2008-01-01

    Written by three leaders in the field of neural based algorithms, Neural Based Orthogonal Data Fitting proposes several neural networks, all endowed with a complete theory which not only explains their behavior, but also compares them with the existing neural and traditional algorithms. The algorithms are studied from different points of view, including: as a differential geometry problem, as a dynamic problem, as a stochastic problem, and as a numerical problem. All algorithms have also been analyzed on real time problems (large dimensional data matrices) and have shown accurate solutions. Wh

  12. Construction of a Piezoresistive Neural Sensor Array

    Science.gov (United States)

    Carlson, W. B.; Schulze, W. A.; Pilgrim, P. M.

    1996-01-01

    The construction of a piezoresistive - piezoelectric sensor (or actuator) array is proposed using 'neural' connectivity for signal recognition and possible actuation functions. A closer integration of the sensor and decision functions is necessary in order to achieve intrinsic identification within the sensor. A neural sensor is the next logical step in development of truly 'intelligent' arrays. This proposal will integrate 1-3 polymer piezoresistors and MLC electroceramic devices for applications involving acoustic identification. The 'intelligent' piezoresistor -piezoelectric system incorporates printed resistors, composite resistors, and a feedback for the resetting of resistances. A model of a design is proposed in order to simulate electromechanical resistor interactions. The goal of optimizing a sensor geometry for improving device reliability, training, & signal identification capabilities is the goal of this work. At present, studies predict performance of a 'smart' device with a significant control of 'effective' compliance over a narrow pressure range due to a piezoresistor percolation threshold. An interesting possibility may be to use an array of control elements to shift the threshold function in order to change the level of resistance in a neural sensor array for identification, or, actuation applications. The proposed design employs elements of: (1) conductor loaded polymers for a 'fast' RC time constant response; and (2) multilayer ceramics for actuation or sensing and shifting of resistance in the polymer. Other material possibilities also exist using magnetoresistive layered systems for shifting the resistance. It is proposed to use a neural net configuration to test and to help study the possible changes required in the materials design of these devices. Numerical design models utilize electromechanical elements, in conjunction with structural elements in order to simulate piezoresistively controlled actuators and changes in resistance of sensors

  13. Dynamic Adaptive Neural Network Arrays: A Neuromorphic Architecture

    Energy Technology Data Exchange (ETDEWEB)

    Disney, Adam [University of Tennessee (UT); Reynolds, John [University of Tennessee (UT)

    2015-01-01

    Dynamic Adaptive Neural Network Array (DANNA) is a neuromorphic hardware implementation. It differs from most other neuromorphic projects in that it allows for programmability of structure, and it is trained or designed using evolutionary optimization. This paper describes the DANNA structure, how DANNA is trained using evolutionary optimization, and an application of DANNA to a very simple classification task.

  14. Trimaran Resistance Artificial Neural Network

    Science.gov (United States)

    2011-01-01

    11th International Conference on Fast Sea Transportation FAST 2011, Honolulu, Hawaii, USA, September 2011 Trimaran Resistance Artificial Neural Network Richard...Trimaran Resistance Artificial Neural Network 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER 5e... Artificial Neural Network and is restricted to the center and side-hull configurations tested. The value in the parametric model is that it is able to

  15. Calcium signaling mediates five types of cell morphological changes to form neural rosettes.

    Science.gov (United States)

    Hříbková, Hana; Grabiec, Marta; Klemová, Dobromila; Slaninová, Iva; Sun, Yuh-Man

    2018-02-12

    Neural rosette formation is a critical morphogenetic process during neural development, whereby neural stem cells are enclosed in rosette niches to equipoise proliferation and differentiation. How neural rosettes form and provide a regulatory micro-environment remains to be elucidated. We employed the human embryonic stem cell-based neural rosette system to investigate the structural development and function of neural rosettes. Our study shows that neural rosette formation consists of five types of morphological change: intercalation, constriction, polarization, elongation and lumen formation. Ca 2+ signaling plays a pivotal role in the five steps by regulating the actions of the cytoskeletal complexes, actin, myosin II and tubulin during intercalation, constriction and elongation. These, in turn, control the polarizing elements, ZO-1, PARD3 and β-catenin during polarization and lumen production for neural rosette formation. We further demonstrate that the dismantlement of neural rosettes, mediated by the destruction of cytoskeletal elements, promotes neurogenesis and astrogenesis prematurely, indicating that an intact rosette structure is essential for orderly neural development. © 2018. Published by The Company of Biologists Ltd.

  16. Topology influences performance in the associative memory neural networks

    International Nuclear Information System (INIS)

    Lu Jianquan; He Juan; Cao Jinde; Gao Zhiqiang

    2006-01-01

    To explore how topology affects performance within Hopfield-type associative memory neural networks (AMNNs), we studied the computational performance of the neural networks with regular lattice, random, small-world, and scale-free structures. In this Letter, we found that the memory performance of neural networks obtained through asynchronous updating from 'larger' nodes to 'smaller' nodes are better than asynchronous updating in random order, especially for the scale-free topology. The computational performance of associative memory neural networks linked by the above-mentioned network topologies with the same amounts of nodes (neurons) and edges (synapses) were studied respectively. Along with topologies becoming more random and less locally disordered, we will see that the performance of associative memory neural network is quite improved. By comparing, we show that the regular lattice and random network form two extremes in terms of patterns stability and retrievability. For a network, its patterns stability and retrievability can be largely enhanced by adding a random component or some shortcuts to its structured component. According to the conclusions of this Letter, we can design the associative memory neural networks with high performance and minimal interconnect requirements

  17. Representation of neutron noise data using neural networks

    International Nuclear Information System (INIS)

    Korsah, K.; Damiano, B.; Wood, R.T.

    1992-01-01

    This paper describes a neural network-based method of representing neutron noise spectra using a model developed at the Oak Ridge National Laboratory (ORNL). The backpropagation neural network learned to represent neutron noise data in terms of four descriptors, and the network response matched calculated values to within 3.5 percent. These preliminary results are encouraging, and further research is directed towards the application of neural networks in a diagnostics system for the identification of the causes of changes in structural spectral resonances. This work is part of our current investigation of advanced technologies such as expert systems and neural networks for neutron noise data reduction, analysis, and interpretation. The objective is to improve the state-of-the-art of noise analysis as a diagnostic tool for nuclear power plants and other mechanical systems

  18. System Identification, Prediction, Simulation and Control with Neural Networks

    DEFF Research Database (Denmark)

    Sørensen, O.

    1997-01-01

    a Gauss-Newton search direction is applied. 3) Amongst numerous model types, often met in control applications, only the Non-linear ARMAX (NARMAX) model, representing input/output description, is examined. A simulated example confirms that a neural network has the potential to perform excellent System......The intention of this paper is to make a systematic examination of the possibilities of applying neural networks in those technical areas, which are familiar to a control engineer. In other words, the potential of neural networks in control applications is given higher priority than a detailed...... study of the networks themselves. With this end in view the following restrictions have been made: 1) Amongst numerous neural network structures, only the Multi Layer Perceptron (a feed-forward network) is applied. 2) Amongst numerous training algorithms, only the Recursive Prediction Error Method using...

  19. Neural network for solving convex quadratic bilevel programming problems.

    Science.gov (United States)

    He, Xing; Li, Chuandong; Huang, Tingwen; Li, Chaojie

    2014-03-01

    In this paper, using the idea of successive approximation, we propose a neural network to solve convex quadratic bilevel programming problems (CQBPPs), which is modeled by a nonautonomous differential inclusion. Different from the existing neural network for CQBPP, the model has the least number of state variables and simple structure. Based on the theory of nonsmooth analysis, differential inclusions and Lyapunov-like method, the limit equilibrium points sequence of the proposed neural networks can approximately converge to an optimal solution of CQBPP under certain conditions. Finally, simulation results on two numerical examples and the portfolio selection problem show the effectiveness and performance of the proposed neural network. Copyright © 2013 Elsevier Ltd. All rights reserved.

  20. Neural networks and their potential application to nuclear power plants

    International Nuclear Information System (INIS)

    Uhrig, R.E.

    1991-01-01

    A network of artificial neurons, usually called an artificial neural network is a data processing system consisting of a number of highly interconnected processing elements in an architecture inspired by the structure of the cerebral cortex portion of the brain. Hence, neural networks are often capable of doing things which humans or animals do well but which conventional computers often do poorly. Neural networks exhibit characteristics and capabilities not provided by any other technology. Neural networks may be designed so as to classify an input pattern as one of several predefined types or to create, as needed, categories or classes of system states which can be interpreted by a human operator. Neural networks have the ability to recognize patterns, even when the information comprising these patterns is noisy, sparse, or incomplete. Thus, systems of artificial neural networks show great promise for use in environments in which robust, fault-tolerant pattern recognition is necessary in a real-time mode, and in which the incoming data may be distorted or noisy. The application of neural networks, a rapidly evolving technology used extensively in defense applications, alone or in conjunction with other advanced technologies, to some of the problems of operating nuclear power plants has the potential to enhance the safety, reliability and operability of nuclear power plants. The potential applications of neural networking include, but are not limited to diagnosing specific abnormal conditions, identification of nonlinear dynamics and transients, detection of the change of mode of operation, control of temperature and pressure during start-up, signal validation, plant-wide monitoring using autoassociative neural networks, monitoring of check valves, modeling of the plant thermodynamics, emulation of core reload calculations, analysis of temporal sequences in NRC's ''licensee event reports,'' and monitoring of plant parameters

  1. Advances in Artificial Neural Networks – Methodological Development and Application

    Directory of Open Access Journals (Sweden)

    Yanbo Huang

    2009-08-01

    Full Text Available Artificial neural networks as a major soft-computing technology have been extensively studied and applied during the last three decades. Research on backpropagation training algorithms for multilayer perceptron networks has spurred development of other neural network training algorithms for other networks such as radial basis function, recurrent network, feedback network, and unsupervised Kohonen self-organizing network. These networks, especially the multilayer perceptron network with a backpropagation training algorithm, have gained recognition in research and applications in various scientific and engineering areas. In order to accelerate the training process and overcome data over-fitting, research has been conducted to improve the backpropagation algorithm. Further, artificial neural networks have been integrated with other advanced methods such as fuzzy logic and wavelet analysis, to enhance the ability of data interpretation and modeling and to avoid subjectivity in the operation of the training algorithm. In recent years, support vector machines have emerged as a set of high-performance supervised generalized linear classifiers in parallel with artificial neural networks. A review on development history of artificial neural networks is presented and the standard architectures and algorithms of artificial neural networks are described. Furthermore, advanced artificial neural networks will be introduced with support vector machines, and limitations of ANNs will be identified. The future of artificial neural network development in tandem with support vector machines will be discussed in conjunction with further applications to food science and engineering, soil and water relationship for crop management, and decision support for precision agriculture. Along with the network structures and training algorithms, the applications of artificial neural networks will be reviewed as well, especially in the fields of agricultural and biological

  2. Project of neural network for steel grade selection with the assumed CCT diagram

    OpenAIRE

    S. Malara; L.A. Dobrzański; J. Trzaska

    2008-01-01

    Purpose: The aim of this paper was developing a project of neural network for selection of steel grade with the specified CCT diagram – structure and of harness after heat treatment.Design/methodology/approach: The goal has been achieved in the following stages: at the first stage characteristic points of CCT diagram have been determined. At the second stage neural network has been developed and optimized.Findings: The neural network was developed in this paper, that allowed selection of stee...

  3. Convergent dynamics for multistable delayed neural networks

    International Nuclear Information System (INIS)

    Shih, Chih-Wen; Tseng, Jui-Pin

    2008-01-01

    This investigation aims at developing a methodology to establish convergence of dynamics for delayed neural network systems with multiple stable equilibria. The present approach is general and can be applied to several network models. We take the Hopfield-type neural networks with both instantaneous and delayed feedbacks to illustrate the idea. We shall construct the complete dynamical scenario which comprises exactly 2 n stable equilibria and exactly (3 n − 2 n ) unstable equilibria for the n-neuron network. In addition, it is shown that every solution of the system converges to one of the equilibria as time tends to infinity. The approach is based on employing the geometrical structure of the network system. Positively invariant sets and componentwise dynamical properties are derived under the geometrical configuration. An iteration scheme is subsequently designed to confirm the convergence of dynamics for the system. Two examples with numerical simulations are arranged to illustrate the present theory

  4. Noise Analysis studies with neural networks

    International Nuclear Information System (INIS)

    Seker, S.; Ciftcioglu, O.

    1996-01-01

    Noise analysis studies with neural network are aimed. Stochastic signals at the input of the network are used to obtain an algorithmic multivariate stochastic signal modeling. To this end, lattice modeling of a stochastic signal is performed to obtain backward residual noise sources which are uncorrelated among themselves. There are applied together with an additional input to the network to obtain an algorithmic model which is used for signal detection for early failure in plant monitoring. The additional input provides the information to the network to minimize the difference between the signal and the network's one-step-ahead prediction. A stochastic algorithm is used for training where the errors reflecting the measurement error during the training are also modelled so that fast and consistent convergence of network's weights is obtained. The lattice structure coupled to neural network investigated with measured signals from an actual power plant. (authors)

  5. Supervised Sequence Labelling with Recurrent Neural Networks

    CERN Document Server

    Graves, Alex

    2012-01-01

    Supervised sequence labelling is a vital area of machine learning, encompassing tasks such as speech, handwriting and gesture recognition, protein secondary structure prediction and part-of-speech tagging. Recurrent neural networks are powerful sequence learning tools—robust to input noise and distortion, able to exploit long-range contextual information—that would seem ideally suited to such problems. However their role in large-scale sequence labelling systems has so far been auxiliary.    The goal of this book is a complete framework for classifying and transcribing sequential data with recurrent neural networks only. Three main innovations are introduced in order to realise this goal. Firstly, the connectionist temporal classification output layer allows the framework to be trained with unsegmented target sequences, such as phoneme-level speech transcriptions; this is in contrast to previous connectionist approaches, which were dependent on error-prone prior segmentation. Secondly, multidimensional...

  6. A class of convergent neural network dynamics

    Science.gov (United States)

    Fiedler, Bernold; Gedeon, Tomáš

    1998-01-01

    We consider a class of systems of differential equations in Rn which exhibits convergent dynamics. We find a Lyapunov function and show that every bounded trajectory converges to the set of equilibria. Our result generalizes the results of Cohen and Grossberg (1983) for convergent neural networks. It replaces the symmetry assumption on the matrix of weights by the assumption on the structure of the connections in the neural network. We prove the convergence result also for a large class of Lotka-Volterra systems. These are naturally defined on the closed positive orthant. We show that there are no heteroclinic cycles on the boundary of the positive orthant for the systems in this class.

  7. Aeroelasticity of morphing wings using neural networks

    Science.gov (United States)

    Natarajan, Anand

    In this dissertation, neural networks are designed to effectively model static non-linear aeroelastic problems in adaptive structures and linear dynamic aeroelastic systems with time varying stiffness. The use of adaptive materials in aircraft wings allows for the change of the contour or the configuration of a wing (morphing) in flight. The use of smart materials, to accomplish these deformations, can imply that the stiffness of the wing with a morphing contour changes as the contour changes. For a rapidly oscillating body in a fluid field, continuously adapting structural parameters may render the wing to behave as a time variant system. Even the internal spars/ribs of the aircraft wing which define the wing stiffness can be made adaptive, that is, their stiffness can be made to vary with time. The immediate effect on the structural dynamics of the wing, is that, the wing motion is governed by a differential equation with time varying coefficients. The study of this concept of a time varying torsional stiffness, made possible by the use of active materials and adaptive spars, in the dynamic aeroelastic behavior of an adaptable airfoil is performed here. Another type of aeroelastic problem of an adaptive structure that is investigated here, is the shape control of an adaptive bump situated on the leading edge of an airfoil. Such a bump is useful in achieving flow separation control for lateral directional maneuverability of the aircraft. Since actuators are being used to create this bump on the wing surface, the energy required to do so needs to be minimized. The adverse pressure drag as a result of this bump needs to be controlled so that the loss in lift over the wing is made minimal. The design of such a "spoiler bump" on the surface of the airfoil is an optimization problem of maximizing pressure drag due to flow separation while minimizing the loss in lift and energy required to deform the bump. One neural network is trained using the CFD code FLUENT to

  8. Neutron spectrometry and dosimetry by means of evolutive neural networks

    International Nuclear Information System (INIS)

    Ortiz R, J.M.; Martinez B, M.R.; Vega C, H.R.

    2008-01-01

    The artificial neural networks and the genetic algorithms are two relatively new areas of research, which have been subject to a growing interest during the last years. Both models are inspired by the nature, however, the neural networks are interested in the learning of a single individual, which is defined as fenotypic learning, while the evolutionary algorithms are interested in the adaptation of a population to a changing environment, that which is defined as genotypic learning. Recently, the use of the technology of neural networks has been applied with success in the area of the nuclear sciences, mainly in the areas of neutron spectrometry and dosimetry. The structure (network topology), as well as the learning parameters of a neural network, are factors that contribute in a significant way with the acting of the same one, however, it has been observed that the investigators in this area, carry out the selection of the network parameters through the essay and error technique, that which produces neural networks of poor performance and low generalization capacity. From the revised sources, it has been observed that the use of the evolutionary algorithms, seen as search techniques, it has allowed him to be possible to evolve and to optimize different properties of the neural networks, just as the initialization of the synaptic weights, the network architecture or the training algorithms without the human intervention. The objective of the present work is focused in analyzing the intersection of the neural networks and the evolutionary algorithms, analyzing like it is that the same ones can be used to help in the design processes and training of a neural network, this is, in the good selection of the structural parameters and of network learning, improving its generalization capacity, in such way that the same one is able to reconstruct in an efficient way neutron spectra and to calculate equivalent doses starting from the counting rates of a Bonner sphere

  9. Optics in neural computation

    Science.gov (United States)

    Levene, Michael John

    In all attempts to emulate the considerable powers of the brain, one is struck by both its immense size, parallelism, and complexity. While the fields of neural networks, artificial intelligence, and neuromorphic engineering have all attempted oversimplifications on the considerable complexity, all three can benefit from the inherent scalability and parallelism of optics. This thesis looks at specific aspects of three modes in which optics, and particularly volume holography, can play a part in neural computation. First, holography serves as the basis of highly-parallel correlators, which are the foundation of optical neural networks. The huge input capability of optical neural networks make them most useful for image processing and image recognition and tracking. These tasks benefit from the shift invariance of optical correlators. In this thesis, I analyze the capacity of correlators, and then present several techniques for controlling the amount of shift invariance. Of particular interest is the Fresnel correlator, in which the hologram is displaced from the Fourier plane. In this case, the amount of shift invariance is limited not just by the thickness of the hologram, but by the distance of the hologram from the Fourier plane. Second, volume holography can provide the huge storage capacity and high speed, parallel read-out necessary to support large artificial intelligence systems. However, previous methods for storing data in volume holograms have relied on awkward beam-steering or on as-yet non- existent cheap, wide-bandwidth, tunable laser sources. This thesis presents a new technique, shift multiplexing, which is capable of very high densities, but which has the advantage of a very simple implementation. In shift multiplexing, the reference wave consists of a focused spot a few millimeters in front of the hologram. Multiplexing is achieved by simply translating the hologram a few tens of microns or less. This thesis describes the theory for how shift

  10. Analysis of neural networks

    CERN Document Server

    Heiden, Uwe

    1980-01-01

    The purpose of this work is a unified and general treatment of activity in neural networks from a mathematical pOint of view. Possible applications of the theory presented are indica­ ted throughout the text. However, they are not explored in de­ tail for two reasons : first, the universal character of n- ral activity in nearly all animals requires some type of a general approach~ secondly, the mathematical perspicuity would suffer if too many experimental details and empirical peculiarities were interspersed among the mathematical investigation. A guide to many applications is supplied by the references concerning a variety of specific issues. Of course the theory does not aim at covering all individual problems. Moreover there are other approaches to neural network theory (see e.g. Poggio-Torre, 1978) based on the different lev­ els at which the nervous system may be viewed. The theory is a deterministic one reflecting the average be­ havior of neurons or neuron pools. In this respect the essay is writt...

  11. Neural Synchronization and Cryptography

    Science.gov (United States)

    Ruttor, Andreas

    2007-11-01

    Neural networks can synchronize by learning from each other. In the case of discrete weights full synchronization is achieved in a finite number of steps. Additional networks can be trained by using the inputs and outputs generated during this process as examples. Several learning rules for both tasks are presented and analyzed. In the case of Tree Parity Machines synchronization is much faster than learning. Scaling laws for the number of steps needed for full synchronization and successful learning are derived using analytical models. They indicate that the difference between both processes can be controlled by changing the synaptic depth. In the case of bidirectional interaction the synchronization time increases proportional to the square of this parameter, but it grows exponentially, if information is transmitted in one direction only. Because of this effect neural synchronization can be used to construct a cryptographic key-exchange protocol. Here the partners benefit from mutual interaction, so that a passive attacker is usually unable to learn the generated key in time. The success probabilities of different attack methods are determined by numerical simulations and scaling laws are derived from the data. They show that the partners can reach any desired level of security by just increasing the synaptic depth. Then the complexity of a successful attack grows exponentially, but there is only a polynomial increase of the effort needed to generate a key. Further improvements of security are possible by replacing the random inputs with queries generated by the partners.

  12. Neural Networks for Optimal Control

    DEFF Research Database (Denmark)

    Sørensen, O.

    1995-01-01

    Two neural networks are trained to act as an observer and a controller, respectively, to control a non-linear, multi-variable process.......Two neural networks are trained to act as an observer and a controller, respectively, to control a non-linear, multi-variable process....

  13. Neural networks at the Tevatron

    International Nuclear Information System (INIS)

    Badgett, W.; Burkett, K.; Campbell, M.K.; Wu, D.Y.; Bianchin, S.; DeNardi, M.; Pauletta, G.; Santi, L.; Caner, A.; Denby, B.; Haggerty, H.; Lindsey, C.S.; Wainer, N.; Dall'Agata, M.; Johns, K.; Dickson, M.; Stanco, L.; Wyss, J.L.

    1992-10-01

    This paper summarizes neural network applications at the Fermilab Tevatron, including the first online hardware application in high energy physics (muon tracking): the CDF and DO neural network triggers; offline quark/gluon discrimination at CDF; ND a new tool for top to multijets recognition at CDF

  14. Neural Networks for the Beginner.

    Science.gov (United States)

    Snyder, Robin M.

    Motivated by the brain, neural networks are a right-brained approach to artificial intelligence that is used to recognize patterns based on previous training. In practice, one would not program an expert system to recognize a pattern and one would not train a neural network to make decisions from rules; but one could combine the best features of…

  15. Research progress on neural mechanisms of primary insomnia by MRI

    Directory of Open Access Journals (Sweden)

    Man WANG

    2018-04-01

    Full Text Available In recent years, more and more researches focused on the neural mechanism of primary insomnia (PI, especially with the development and application of MRI, and researches of brain structure and function related with primary insomnia were more and more in-depth. According to the hyperarousal hypothesis, there are abnormal structure, function and metabolism under certain brain regions of the cortex and subcortex of primary insomnia patients, including amygdala, hippocampus, cingulate gyrus, insular lobe, frontal lobe and parietal lobe. This paper reviewed the research progress of neural mechanisms of primary insomnia by using MRI. DOI: 10.3969/j.issn.1672-6731.2018.03.003

  16. Neural fields theory and applications

    CERN Document Server

    Graben, Peter; Potthast, Roland; Wright, James

    2014-01-01

    With this book, the editors present the first comprehensive collection in neural field studies, authored by leading scientists in the field - among them are two of the founding-fathers of neural field theory. Up to now, research results in the field have been disseminated across a number of distinct journals from mathematics, computational neuroscience, biophysics, cognitive science and others. Starting with a tutorial for novices in neural field studies, the book comprises chapters on emergent patterns, their phase transitions and evolution, on stochastic approaches, cortical development, cognition, robotics and computation, large-scale numerical simulations, the coupling of neural fields to the electroencephalogram and phase transitions in anesthesia. The intended readership are students and scientists in applied mathematics, theoretical physics, theoretical biology, and computational neuroscience. Neural field theory and its applications have a long-standing tradition in the mathematical and computational ...

  17. Artificial neural networks in NDT

    International Nuclear Information System (INIS)

    Abdul Aziz Mohamed

    2001-01-01

    Artificial neural networks, simply known as neural networks, have attracted considerable interest in recent years largely because of a growing recognition of the potential of these computational paradigms as powerful alternative models to conventional pattern recognition or function approximation techniques. The neural networks approach is having a profound effect on almost all fields, and has been utilised in fields Where experimental inter-disciplinary work is being carried out. Being a multidisciplinary subject with a broad knowledge base, Nondestructive Testing (NDT) or Nondestructive Evaluation (NDE) is no exception. This paper explains typical applications of neural networks in NDT/NDE. Three promising types of neural networks are highlighted, namely, back-propagation, binary Hopfield and Kohonen's self-organising maps. (Author)

  18. Identification of generalized state transfer matrix using neural networks

    International Nuclear Information System (INIS)

    Zhu Changchun

    2001-01-01

    The research is introduced on identification of generalized state transfer matrix of linear time-invariant (LTI) system by use of neural networks based on LM (Levenberg-Marquart) algorithm. Firstly, the generalized state transfer matrix is defined. The relationship between the identification of state transfer matrix of structural dynamics and the identification of the weight matrix of neural networks has been established in theory. A singular layer neural network is adopted to obtain the structural parameters as a powerful tool that has parallel distributed processing ability and the property of adaptation or learning. The constraint condition of weight matrix of the neural network is deduced so that the learning and training of the designed network can be more effective. The identified neural network can be used to simulate the structural response excited by any other signals. In order to cope with its further application in practical problems, some noise (5% and 10%) is expected to be present in the response measurements. Results from computer simulation studies show that this method is valid and feasible

  19. Hybrid computing using a neural network with dynamic external memory.

    Science.gov (United States)

    Graves, Alex; Wayne, Greg; Reynolds, Malcolm; Harley, Tim; Danihelka, Ivo; Grabska-Barwińska, Agnieszka; Colmenarejo, Sergio Gómez; Grefenstette, Edward; Ramalho, Tiago; Agapiou, John; Badia, Adrià Puigdomènech; Hermann, Karl Moritz; Zwols, Yori; Ostrovski, Georg; Cain, Adam; King, Helen; Summerfield, Christopher; Blunsom, Phil; Kavukcuoglu, Koray; Hassabis, Demis

    2016-10-27

    Artificial neural networks are remarkably adept at sensory processing, sequence learning and reinforcement learning, but are limited in their ability to represent variables and data structures and to store data over long timescales, owing to the lack of an external memory. Here we introduce a machine learning model called a differentiable neural computer (DNC), which consists of a neural network that can read from and write to an external memory matrix, analogous to the random-access memory in a conventional computer. Like a conventional computer, it can use its memory to represent and manipulate complex data structures, but, like a neural network, it can learn to do so from data. When trained with supervised learning, we demonstrate that a DNC can successfully answer synthetic questions designed to emulate reasoning and inference problems in natural language. We show that it can learn tasks such as finding the shortest path between specified points and inferring the missing links in randomly generated graphs, and then generalize these tasks to specific graphs such as transport networks and family trees. When trained with reinforcement learning, a DNC can complete a moving blocks puzzle in which changing goals are specified by sequences of symbols. Taken together, our results demonstrate that DNCs have the capacity to solve complex, structured tasks that are inaccessible to neural networks without external read-write memory.

  20. Storage capacity and retrieval time of small-world neural networks

    International Nuclear Information System (INIS)

    Oshima, Hiraku; Odagaki, Takashi

    2007-01-01

    To understand the influence of structure on the function of neural networks, we study the storage capacity and the retrieval time of Hopfield-type neural networks for four network structures: regular, small world, random networks generated by the Watts-Strogatz (WS) model, and the same network as the neural network of the nematode Caenorhabditis elegans. Using computer simulations, we find that (1) as the randomness of network is increased, its storage capacity is enhanced; (2) the retrieval time of WS networks does not depend on the network structure, but the retrieval time of C. elegans's neural network is longer than that of WS networks; (3) the storage capacity of the C. elegans network is smaller than that of networks generated by the WS model, though the neural network of C. elegans is considered to be a small-world network

  1. Natural language acquisition in large scale neural semantic networks

    Science.gov (United States)

    Ealey, Douglas

    This thesis puts forward the view that a purely signal- based approach to natural language processing is both plausible and desirable. By questioning the veracity of symbolic representations of meaning, it argues for a unified, non-symbolic model of knowledge representation that is both biologically plausible and, potentially, highly efficient. Processes to generate a grounded, neural form of this model-dubbed the semantic filter-are discussed. The combined effects of local neural organisation, coincident with perceptual maturation, are used to hypothesise its nature. This theoretical model is then validated in light of a number of fundamental neurological constraints and milestones. The mechanisms of semantic and episodic development that the model predicts are then used to explain linguistic properties, such as propositions and verbs, syntax and scripting. To mimic the growth of locally densely connected structures upon an unbounded neural substrate, a system is developed that can grow arbitrarily large, data- dependant structures composed of individual self- organising neural networks. The maturational nature of the data used results in a structure in which the perception of concepts is refined by the networks, but demarcated by subsequent structure. As a consequence, the overall structure shows significant memory and computational benefits, as predicted by the cognitive and neural models. Furthermore, the localised nature of the neural architecture also avoids the increasing error sensitivity and redundancy of traditional systems as the training domain grows. The semantic and episodic filters have been demonstrated to perform as well, or better, than more specialist networks, whilst using significantly larger vocabularies, more complex sentence forms and more natural corpora.

  2. Models of neural dynamics in brain information processing - the developments of 'the decade'

    Energy Technology Data Exchange (ETDEWEB)

    Borisyuk, G N; Borisyuk, R M; Kazanovich, Yakov B [Institute of Mathematical Problems of Biology, Russian Academy of Sciences, Pushchino, Moscow region (Russian Federation); Ivanitskii, Genrikh R [Institute for Theoretical and Experimental Biophysics, Russian Academy of Sciences, Pushchino, Moscow region (Russian Federation)

    2002-10-31

    Neural network models are discussed that have been developed during the last decade with the purpose of reproducing spatio-temporal patterns of neural activity in different brain structures. The main goal of the modeling was to test hypotheses of synchronization, temporal and phase relations in brain information processing. The models being considered are those of temporal structure of spike sequences, of neural activity dynamics, and oscillatory models of attention and feature integration. (reviews of topical problems)

  3. Improvement of the Hopfield Neural Network by MC-Adaptation Rule

    Science.gov (United States)

    Zhou, Zhen; Zhao, Hong

    2006-06-01

    We show that the performance of the Hopfield neural networks, especially the quality of the recall and the capacity of the effective storing, can be greatly improved by making use of a recently presented neural network designing method without altering the whole structure of the network. In the improved neural network, a memory pattern is recalled exactly from initial states having a given degree of similarity with the memory pattern, and thus one can avoids to apply the overlap criterion as carried out in the Hopfield neural networks.

  4. Sensory Entrainment Mechanisms in Auditory Perception: Neural Synchronization Cortico-Striatal Activation.

    Science.gov (United States)

    Sameiro-Barbosa, Catia M; Geiser, Eveline

    2016-01-01

    The auditory system displays modulations in sensitivity that can align with the temporal structure of the acoustic environment. This sensory entrainment can facilitate sensory perception and is particularly relevant for audition. Systems neuroscience is slowly uncovering the neural mechanisms underlying the behaviorally observed sensory entrainment effects in the human sensory system. The present article summarizes the prominent behavioral effects of sensory entrainment and reviews our current understanding of the neural basis of sensory entrainment, such as synchronized neural oscillations, and potentially, neural activation in the cortico-striatal system.

  5. A simple miniature device for wireless stimulation of neural circuits in small behaving animals.

    Science.gov (United States)

    Zhang, Yisi; Langford, Bruce; Kozhevnikov, Alexay

    2011-10-30

    The use of wireless neural stimulation devices offers significant advantages for neural stimulation experiments in behaving animals. We demonstrate a simple, low-cost and extremely lightweight wireless neural stimulation device which is made from off-the-shelf components. The device has low power consumption and does not require a high-power RF preamplifier. Neural stimulation can be carried out in either a voltage source mode or a current source mode. Using the device, we carry out wireless stimulation in the premotor brain area HVC of a songbird and demonstrate that such stimulation causes rapid perturbations of the acoustic structure of the song. Published by Elsevier B.V.

  6. Sensory Entrainment Mechanisms in Auditory Perception: Neural Synchronization Cortico-Striatal Activation

    Science.gov (United States)

    Sameiro-Barbosa, Catia M.; Geiser, Eveline

    2016-01-01

    The auditory system displays modulations in sensitivity that can align with the temporal structure of the acoustic environment. This sensory entrainment can facilitate sensory perception and is particularly relevant for audition. Systems neuroscience is slowly uncovering the neural mechanisms underlying the behaviorally observed sensory entrainment effects in the human sensory system. The present article summarizes the prominent behavioral effects of sensory entrainment and reviews our current understanding of the neural basis of sensory entrainment, such as synchronized neural oscillations, and potentially, neural activation in the cortico-striatal system. PMID:27559306

  7. Forecasting macroeconomic variables using neural network models and three automated model selection techniques

    DEFF Research Database (Denmark)

    Kock, Anders Bredahl; Teräsvirta, Timo

    2016-01-01

    When forecasting with neural network models one faces several problems, all of which influence the accuracy of the forecasts. First, neural networks are often hard to estimate due to their highly nonlinear structure. To alleviate the problem, White (2006) presented a solution (QuickNet) that conv...

  8. Sejarah, Penerapan, dan Analisis Resiko dari Neural Network: Sebuah Tinjauan Pustaka

    Directory of Open Access Journals (Sweden)

    Cristina Cristina

    2018-05-01

    Full Text Available A neural network is a form of artificial intelligence that has the ability to learn, grow, and adapt in a dynamic environment. Neural network began since 1890 because a great American psychologist named William James created the book "Principles of Psycology". James was the first one publish a number of facts related to the structure and function of the brain. The history of neural network development is divided into 4 epochs, the Camelot era, the Depression, the Renaissance, and the Neoconnectiosm era. Neural networks used today are not 100 percent accurate. However, neural networks are still used because of better performance than alternative computing models. The use of neural network consists of pattern recognition, signal analysis, robotics, and expert systems. For risk analysis of the neural network, it is first performed using hazards and operability studies (HAZOPS. Determining the neural network requirements in a good way will help in determining its contribution to system hazards and validating the control or mitigation of any hazards. After completion of the first stage at HAZOPS and the second stage determines the requirements, the next stage is designing. Neural network underwent repeated design-train-test development. At the design stage, the hazard analysis should consider the design aspects of the development, which include neural network architecture, size, intended use, and so on. It will be continued at the implementation stage, test phase, installation and inspection phase, operation phase, and ends at the maintenance stage.

  9. Neural network classifier of attacks in IP telephony

    Science.gov (United States)

    Safarik, Jakub; Voznak, Miroslav; Mehic, Miralem; Partila, Pavol; Mikulec, Martin

    2014-05-01

    Various types of monitoring mechanism allow us to detect and monitor behavior of attackers in VoIP networks. Analysis of detected malicious traffic is crucial for further investigation and hardening the network. This analysis is typically based on statistical methods and the article brings a solution based on neural network. The proposed algorithm is used as a classifier of attacks in a distributed monitoring network of independent honeypot probes. Information about attacks on these honeypots is collected on a centralized server and then classified. This classification is based on different mechanisms. One of them is based on the multilayer perceptron neural network. The article describes inner structure of used neural network and also information about implementation of this network. The learning set for this neural network is based on real attack data collected from IP telephony honeypot called Dionaea. We prepare the learning set from real attack data after collecting, cleaning and aggregation of this information. After proper learning is the neural network capable to classify 6 types of most commonly used VoIP attacks. Using neural network classifier brings more accurate attack classification in a distributed system of honeypots. With this approach is possible to detect malicious behavior in a different part of networks, which are logically or geographically divided and use the information from one network to harden security in other networks. Centralized server for distributed set of nodes serves not only as a collector and classifier of attack data, but also as a mechanism for generating a precaution steps against attacks.

  10. Neural network-based model reference adaptive control system.

    Science.gov (United States)

    Patino, H D; Liu, D

    2000-01-01

    In this paper, an approach to model reference adaptive control based on neural networks is proposed and analyzed for a class of first-order continuous-time nonlinear dynamical systems. The controller structure can employ either a radial basis function network or a feedforward neural network to compensate adaptively the nonlinearities in the plant. A stable controller-parameter adjustment mechanism, which is determined using the Lyapunov theory, is constructed using a sigma-modification-type updating law. The evaluation of control error in terms of the neural network learning error is performed. That is, the control error converges asymptotically to a neighborhood of zero, whose size is evaluated and depends on the approximation error of the neural network. In the design and analysis of neural network-based control systems, it is important to take into account the neural network learning error and its influence on the control error of the plant. Simulation results showing the feasibility and performance of the proposed approach are given.

  11. Transient analysis for PWR reactor core using neural networks predictors

    International Nuclear Information System (INIS)

    Gueray, B.S.

    2001-01-01

    In this study, transient analysis for a Pressurized Water Reactor core has been performed. A lumped parameter approximation is preferred for that purpose, to describe the reactor core together with mechanism which play an important role in dynamic analysis. The dynamic behavior of the reactor core during transients is analyzed considering the transient initiating events, wich are an essential part of Safety Analysis Reports. several transients are simulated based on the employed core model. Simulation results are in accord the physical expectations. A neural network is developed to predict the future response of the reactor core, in advance. The neural network is trained using the simulation results of a number of representative transients. Structure of the neural network is optimized by proper selection of transfer functions for the neurons. Trained neural network is used to predict the future responses following an early observation of the changes in system variables. Estimated behaviour using the neural network is in good agreement with the simulation results for various for types of transients. Results of this study indicate that the designed neural network can be used as an estimator of the time dependent behavior of the reactor core under transient conditions

  12. Linear programming based on neural networks for radiotherapy treatment planning

    International Nuclear Information System (INIS)

    Xingen Wu; Limin Luo

    2000-01-01

    In this paper, we propose a neural network model for linear programming that is designed to optimize radiotherapy treatment planning (RTP). This kind of neural network can be easily implemented by using a kind of 'neural' electronic system in order to obtain an optimization solution in real time. We first give an introduction to the RTP problem and construct a non-constraint objective function for the neural network model. We adopt a gradient algorithm to minimize the objective function and design the structure of the neural network for RTP. Compared to traditional linear programming methods, this neural network model can reduce the time needed for convergence, the size of problems (i.e., the number of variables to be searched) and the number of extra slack and surplus variables needed. We obtained a set of optimized beam weights that result in a better dose distribution as compared to that obtained using the simplex algorithm under the same initial condition. The example presented in this paper shows that this model is feasible in three-dimensional RTP. (author)

  13. SOLAR PHOTOVOLTAIC OUTPUT POWER FORECASTING USING BACK PROPAGATION NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    B. Jency Paulin

    2016-01-01

    Full Text Available Solar Energy is an important renewable and unlimited source of energy. Solar photovoltaic power forecasting, is an estimation of the expected power production, that help the grid operators to better manage the electric balance between power demand and supply. Neural network is a computational model that can predict new outcomes from past trends. The artificial neural network is used for photovoltaic plant energy forecasting. The output power for solar photovoltaic cell is predicted on hourly basis. In historical dataset collection process, two dataset was collected and used for analysis. The dataset was provided with three independent attributes and one dependent attributes. The implementation of Artificial Neural Network structure is done by Multilayer Perceptron (MLP and training procedure for neural network is done by error Back Propagation (BP. In order to train and test the neural network, the datasets are divided in the ratio 70:30. The accuracy of prediction can be done by using various error measurement criteria and the performance of neural network is to be noted.

  14. Identifying Broadband Rotational Spectra with Neural Networks

    Science.gov (United States)

    Zaleski, Daniel P.; Prozument, Kirill

    2017-06-01

    A typical broadband rotational spectrum may contain several thousand observable transitions, spanning many species. Identifying the individual spectra, particularly when the dynamic range reaches 1,000:1 or even 10,000:1, can be challenging. One approach is to apply automated fitting routines. In this approach, combinations of 3 transitions can be created to form a "triple", which allows fitting of the A, B, and C rotational constants in a Watson-type Hamiltonian. On a standard desktop computer, with a target molecule of interest, a typical AUTOFIT routine takes 2-12 hours depending on the spectral density. A new approach is to utilize machine learning to train a computer to recognize the patterns (frequency spacing and relative intensities) inherit in rotational spectra and to identify the individual spectra in a raw broadband rotational spectrum. Here, recurrent neural networks have been trained to identify different types of rotational spectra and classify them accordingly. Furthermore, early results in applying convolutional neural networks for spectral object recognition in broadband rotational spectra appear promising. Perez et al. "Broadband Fourier transform rotational spectroscopy for structure determination: The water heptamer." Chem. Phys. Lett., 2013, 571, 1-15. Seifert et al. "AUTOFIT, an Automated Fitting Tool for Broadband Rotational Spectra, and Applications to 1-Hexanal." J. Mol. Spectrosc., 2015, 312, 13-21. Bishop. "Neural networks for pattern recognition." Oxford university press, 1995.

  15. Memory Consolidation and Neural Substrate of Reward

    Directory of Open Access Journals (Sweden)

    Redolar-Ripoll, Diego

    2012-08-01

    Full Text Available The aim of this report is to analyze the relationships between reward and learning and memory processes. Different studies have described how information about rewards influences behavior and how the brain uses this reward information to control learning and memory processes. Reward nature seems to be processed in different ways by neurons in different brain structures, ranging from the detection and perception of rewards to the use of information about predicted rewards for the control of goal-directed behavior. The neural substrate underling this processing of reward information is a reliable way of improving learning and memory processes. Evidence from several studies indicates that this neural system can facilitate memory consolidation in a wide variety of learning tasks. From a molecular perspective, certain cardinal features of reward have been described as forms of memory. Studies of human addicts and studies in animal models of addiction show that chronic drug exposure produces stable changes in the brain at the cellular and molecular levels that underlie the long-lasting behavioral plasticity associated with addiction. These molecular and cellular adaptations involved in addiction are also implicated in learning and memory processes. Dopamine seems to be a critical common signal to activate different genetic mechanisms that ultimately remodel synapses and circuits. Despite memory is an active and complex process mediated by different brain areas, the neural substrate of reward is able to improve memory consolidation in a several paradigms. We believe that there are many equivalent traits between reward and learning and memory processes.

  16. Neural System Prediction and Identification Challenge

    Directory of Open Access Journals (Sweden)

    Ioannis eVlachos

    2013-12-01

    Full Text Available Can we infer the function of a biological neural network (BNN if we know the connectivity and activity of all its constituent neurons? This question is at the core of neuroscience and, accordingly, various methods have been developed to record the activity and connectivity of as many neurons as possible. Surprisingly, there is no theoretical or computational demonstration that neuronal activity and connectivity are indeed sufficient to infer the function of a BNN. Therefore, we pose the Neural Systems Identification and Prediction Challenge (nuSPIC. We provide the connectivity and activity of all neurons and invite participants (i to infer the functions implemented (hard-wired in spiking neural networks (SNNs by stimulating and recording the activity of neurons and, (ii to implement predefined mathematical/biological functions using SNNs. The nuSPICs can be accessed via a web-interface to the NEST simulator and the user is not required to know any specific programming language. Furthermore, the nuSPICs can be used as a teaching tool. Finally, nuSPICs use the crowd-sourcing model to address scientific issues. With this computational approach we aim to identify which functions can be inferred by systematic recordings of neuronal activity and connectivity. In addition, nuSPICs will help the design and application of new experimental paradigms based on the structure of the SNN and the presumed function which is to be discovered.

  17. Neural system prediction and identification challenge.

    Science.gov (United States)

    Vlachos, Ioannis; Zaytsev, Yury V; Spreizer, Sebastian; Aertsen, Ad; Kumar, Arvind

    2013-01-01

    Can we infer the function of a biological neural network (BNN) if we know the connectivity and activity of all its constituent neurons?This question is at the core of neuroscience and, accordingly, various methods have been developed to record the activity and connectivity of as many neurons as possible. Surprisingly, there is no theoretical or computational demonstration that neuronal activity and connectivity are indeed sufficient to infer the function of a BNN. Therefore, we pose the Neural Systems Identification and Prediction Challenge (nuSPIC). We provide the connectivity and activity of all neurons and invite participants (1) to infer the functions implemented (hard-wired) in spiking neural networks (SNNs) by stimulating and recording the activity of neurons and, (2) to implement predefined mathematical/biological functions using SNNs. The nuSPICs can be accessed via a web-interface to the NEST simulator and the user is not required to know any specific programming language. Furthermore, the nuSPICs can be used as a teaching tool. Finally, nuSPICs use the crowd-sourcing model to address scientific issues. With this computational approach we aim to identify which functions can be inferred by systematic recordings of neuronal activity and connectivity. In addition, nuSPICs will help the design and application of new experimental paradigms based on the structure of the SNN and the presumed function which is to be discovered.

  18. Genetic optimization of neural network architecture

    International Nuclear Information System (INIS)

    Harp, S.A.; Samad, T.

    1994-03-01

    Neural networks are now a popular technology for a broad variety of application domains, including the electric utility industry. Yet, as the technology continues to gain increasing acceptance, it is also increasingly apparent that the power that neural networks provide is not an unconditional blessing. Considerable care must be exercised during application development if the full benefit of the technology is to be realized. At present, no fully general theory or methodology for neural network design is available, and application development is a trial-and-error process that is time-consuming and expertise-intensive. Each application demands appropriate selections of the network input space, the network structure, and values of learning algorithm parameters-design choices that are closely coupled in ways that largely remain a mystery. This EPRI-funded exploratory research project was initiated to take the key next step in this research program: the validation of the approach on a realistic problem. We focused on the problem of modeling the thermal performance of the TVA Sequoyah nuclear power plant (units 1 and 2)

  19. 3D silicon neural probe with integrated optical fibers for optogenetic modulation.

    Science.gov (United States)

    Kim, Eric G R; Tu, Hongen; Luo, Hao; Liu, Bin; Bao, Shaowen; Zhang, Jinsheng; Xu, Yong

    2015-07-21

    Optogenetics is a powerful modality for neural modulation that can be useful for a wide array of biomedical studies. Penetrating microelectrode arrays provide a means of recording neural signals with high spatial resolution. It is highly desirable to integrate optics with neural probes to allow for functional study of neural tissue by optogenetics. In this paper, we report the development of a novel 3D neural probe coupled simply and robustly to optical fibers using a hollow parylene tube structure. The device shanks are hollow tubes with rigid silicon tips, allowing the insertion and encasement of optical fibers within the shanks. The position of the fiber tip can be precisely controlled relative to the electrodes on the shank by inherent design features. Preliminary in vivo rat studies indicate that these devices are capable of optogenetic modulation simultaneously with 3D neural signal recording.

  20. Interacting neural networks

    Science.gov (United States)

    Metzler, R.; Kinzel, W.; Kanter, I.

    2000-08-01

    Several scenarios of interacting neural networks which are trained either in an identical or in a competitive way are solved analytically. In the case of identical training each perceptron receives the output of its neighbor. The symmetry of the stationary state as well as the sensitivity to the used training algorithm are investigated. Two competitive perceptrons trained on mutually exclusive learning aims and a perceptron which is trained on the opposite of its own output are examined analytically. An ensemble of competitive perceptrons is used as decision-making algorithms in a model of a closed market (El Farol Bar problem or the Minority Game. In this game, a set of agents who have to make a binary decision is considered.); each network is trained on the history of minority decisions. This ensemble of perceptrons relaxes to a stationary state whose performance can be better than random.

  1. Neural circuitry and immunity

    Science.gov (United States)

    Pavlov, Valentin A.; Tracey, Kevin J.

    2015-01-01

    Research during the last decade has significantly advanced our understanding of the molecular mechanisms at the interface between the nervous system and the immune system. Insight into bidirectional neuroimmune communication has characterized the nervous system as an important partner of the immune system in the regulation of inflammation. Neuronal pathways, including the vagus nerve-based inflammatory reflex are physiological regulators of immune function and inflammation. In parallel, neuronal function is altered in conditions characterized by immune dysregulation and inflammation. Here, we review these regulatory mechanisms and describe the neural circuitry modulating immunity. Understanding these mechanisms reveals possibilities to use targeted neuromodulation as a therapeutic approach for inflammatory and autoimmune disorders. These findings and current clinical exploration of neuromodulation in the treatment of inflammatory diseases defines the emerging field of Bioelectronic Medicine. PMID:26512000

  2. Neural Darwinism and consciousness.

    Science.gov (United States)

    Seth, Anil K; Baars, Bernard J

    2005-03-01

    Neural Darwinism (ND) is a large scale selectionist theory of brain development and function that has been hypothesized to relate to consciousness. According to ND, consciousness is entailed by reentrant interactions among neuronal populations in the thalamocortical system (the 'dynamic core'). These interactions, which permit high-order discriminations among possible core states, confer selective advantages on organisms possessing them by linking current perceptual events to a past history of value-dependent learning. Here, we assess the consistency of ND with 16 widely recognized properties of consciousness, both physiological (for example, consciousness is associated with widespread, relatively fast, low amplitude interactions in the thalamocortical system), and phenomenal (for example, consciousness involves the existence of a private flow of events available only to the experiencing subject). While no theory accounts fully for all of these properties at present, we find that ND and its recent extensions fare well.

  3. Neural correlates of rhythmic expectancy

    Directory of Open Access Journals (Sweden)

    Theodore P. Zanto

    2006-01-01

    Full Text Available Temporal expectancy is thought to play a fundamental role in the perception of rhythm. This review summarizes recent studies that investigated rhythmic expectancy by recording neuroelectric activity with high temporal resolution during the presentation of rhythmic patterns. Prior event-related brain potential (ERP studies have uncovered auditory evoked responses that reflect detection of onsets, offsets, sustains,and abrupt changes in acoustic properties such as frequency, intensity, and spectrum, in addition to indexing higher-order processes such as auditory sensory memory and the violation of expectancy. In our studies of rhythmic expectancy, we measured emitted responses - a type of ERP that occurs when an expected event is omitted from a regular series of stimulus events - in simple rhythms with temporal structures typical of music. Our observations suggest that middle-latency gamma band (20-60 Hz activity (GBA plays an essential role in auditory rhythm processing. Evoked (phase-locked GBA occurs in the presence of physically presented auditory events and reflects the degree of accent. Induced (non-phase-locked GBA reflects temporally precise expectancies for strongly and weakly accented events in sound patterns. Thus far, these findings support theories of rhythm perception that posit temporal expectancies generated by active neural processes.

  4. Efficient Neural Network Modeling for Flight and Space Dynamics Simulation

    Directory of Open Access Journals (Sweden)

    Ayman Hamdy Kassem

    2011-01-01

    Full Text Available This paper represents an efficient technique for neural network modeling of flight and space dynamics simulation. The technique will free the neural network designer from guessing the size and structure for the required neural network model and will help to minimize the number of neurons. For linear flight/space dynamics systems, the technique can find the network weights and biases directly by solving a system of linear equations without the need for training. Nonlinear flight dynamic systems can be easily modeled by training its linearized models keeping the same network structure. The training is fast, as it uses the linear system knowledge to speed up the training process. The technique is tested on different flight/space dynamic models and showed promising results.

  5. Compensatory recruitment of neural resources in chronic alcoholism.

    Science.gov (United States)

    Chanraud, Sandra; Sullivan, Edith V

    2014-01-01

    Functional recovery occurs with sustained sobriety, but the neural mechanisms enabling recovery are only now emerging. Theories about promising mechanisms involve concepts of neuroadaptation, where excessive alcohol consumption results in untoward structural and functional brain changes which are subsequently candidates for reversal with sobriety. Views on functional adaptation in chronic alcoholism have expanded with results from neuroimaging studies. Here, we first describe and define the concept of neuroadaptation according to emerging theories based on the growing literature in aging-related cognitive functioning. Then we describe findings as they apply to chronic alcoholism and factors that could influence compensation, such as functional brain reserve and the integrity of brain structure. Finally, we review brain plasticity based on physiologic mechanisms that could underlie mechanisms of neural compensation. Where possible, we provide operational criteria to define functional and neural compensation. © 2014 Elsevier B.V. All rights reserved.

  6. Neural mechanisms of hypnosis and meditation.

    Science.gov (United States)

    De Benedittis, Giuseppe

    2015-12-01

    Hypnosis has been an elusive concept for science for a long time. However, the explosive advances in neuroscience in the last few decades have provided a "bridge of understanding" between classical neurophysiological studies and psychophysiological studies. These studies have shed new light on the neural basis of the hypnotic experience. Furthermore, an ambitious new area of research is focusing on mapping the core processes of psychotherapy and the neurobiology/underlying them. Hypnosis research offers powerful techniques to isolate psychological processes in ways that allow their neural bases to be mapped. The Hypnotic Brain can serve as a way to tap neurocognitive questions and our cognitive assays can in turn shed new light on the neural bases of hypnosis. This cross-talk should enhance research and clinical applications. An increasing body of evidence provides insight in the neural mechanisms of the Meditative Brain. Discrete meditative styles are likely to target different neurodynamic patterns. Recent findings emphasize increased attentional resources activating the attentional and salience networks with coherent perception. Cognitive and emotional equanimity gives rise to an eudaimonic state, made of calm, resilience and stability, readiness to express compassion and empathy, a main goal of Buddhist practices. Structural changes in gray matter of key areas of the brain involved in learning processes suggest that these skills can be learned through practice. Hypnosis and Meditation represent two important, historical and influential landmarks of Western and Eastern civilization and culture respectively. Neuroscience has beginning to provide a better understanding of the mechanisms of both Hypnotic and Meditative Brain, outlining similarities but also differences between the two states and processes. It is important not to view either the Eastern or the Western system as superior to the other. Cross-fertilization of the ancient Eastern meditation techniques

  7. Character Recognition Using Genetically Trained Neural Networks

    Energy Technology Data Exchange (ETDEWEB)

    Diniz, C.; Stantz, K.M.; Trahan, M.W.; Wagner, J.S.

    1998-10-01

    Computationally intelligent recognition of characters and symbols addresses a wide range of applications including foreign language translation and chemical formula identification. The combination of intelligent learning and optimization algorithms with layered neural structures offers powerful techniques for character recognition. These techniques were originally developed by Sandia National Laboratories for pattern and spectral analysis; however, their ability to optimize vast amounts of data make them ideal for character recognition. An adaptation of the Neural Network Designer soflsvare allows the user to create a neural network (NN_) trained by a genetic algorithm (GA) that correctly identifies multiple distinct characters. The initial successfid recognition of standard capital letters can be expanded to include chemical and mathematical symbols and alphabets of foreign languages, especially Arabic and Chinese. The FIN model constructed for this project uses a three layer feed-forward architecture. To facilitate the input of characters and symbols, a graphic user interface (GUI) has been developed to convert the traditional representation of each character or symbol to a bitmap. The 8 x 8 bitmap representations used for these tests are mapped onto the input nodes of the feed-forward neural network (FFNN) in a one-to-one correspondence. The input nodes feed forward into a hidden layer, and the hidden layer feeds into five output nodes correlated to possible character outcomes. During the training period the GA optimizes the weights of the NN until it can successfully recognize distinct characters. Systematic deviations from the base design test the network's range of applicability. Increasing capacity, the number of letters to be recognized, requires a nonlinear increase in the number of hidden layer neurodes. Optimal character recognition performance necessitates a minimum threshold for the number of cases when genetically training the net. And, the

  8. Neural correlates of continuous causal word generation.

    Science.gov (United States)

    Wende, Kim C; Straube, Benjamin; Stratmann, Mirjam; Sommer, Jens; Kircher, Tilo; Nagels, Arne

    2012-09-01

    Causality provides a natural structure for organizing our experience and language. Causal reasoning during speech production is a distinct aspect of verbal communication, whose related brain processes are yet unknown. The aim of the current study was to investigate the neural mechanisms underlying the continuous generation of cause-and-effect coherences during overt word production. During fMRI data acquisition participants performed three verbal fluency tasks on identical cue words: A novel causal verbal fluency task (CVF), requiring the production of multiple reasons to a given cue word (e.g. reasons for heat are fire, sun etc.), a semantic (free association, FA, e.g. associations with heat are sweat, shower etc.) and a phonological control task (phonological verbal fluency, PVF, e.g. rhymes with heat are meat, wheat etc.). We found that, in contrast to PVF, both CVF and FA activated a left lateralized network encompassing inferior frontal, inferior parietal and angular regions, with further bilateral activation in middle and inferior as well as superior temporal gyri and the cerebellum. For CVF contrasted against FA, we found greater bold responses only in the left middle frontal cortex. Large overlaps in the neural activations during free association and causal verbal fluency indicate that the access to causal relationships between verbal concepts is at least partly based on the semantic neural network. The selective activation in the left middle frontal cortex for causal verbal fluency suggests that distinct neural processes related to cause-and-effect-relations are associated with the recruitment of middle frontal brain areas. Copyright © 2012 Elsevier Inc. All rights reserved.

  9. Generating Seismograms with Deep Neural Networks

    Science.gov (United States)

    Krischer, L.; Fichtner, A.

    2017-12-01

    The recent surge of successful uses of deep neural networks in computer vision, speech recognition, and natural language processing, mainly enabled by the availability of fast GPUs and extremely large data sets, is starting to see many applications across all natural sciences. In seismology these are largely confined to classification and discrimination tasks. In this contribution we explore the use of deep neural networks for another class of problems: so called generative models.Generative modelling is a branch of statistics concerned with generating new observed data samples, usually by drawing from some underlying probability distribution. Samples with specific attributes can be generated by conditioning on input variables. In this work we condition on seismic source (mechanism and location) and receiver (location) parameters to generate multi-component seismograms.The deep neural networks are trained on synthetic data calculated with Instaseis (http://instaseis.net, van Driel et al. (2015)) and waveforms from the global ShakeMovie project (http://global.shakemovie.princeton.edu, Tromp et al. (2010)). The underlying radially symmetric or smoothly three dimensional Earth structures result in comparatively small waveform differences from similar events or at close receivers and the networks learn to interpolate between training data samples.Of particular importance is the chosen misfit functional. Generative adversarial networks (Goodfellow et al. (2014)) implement a system in which two networks compete: the generator network creates samples and the discriminator network distinguishes these from the true training examples. Both are trained in an adversarial fashion until the discriminator can no longer distinguish between generated and real samples. We show how this can be applied to seismograms and in particular how it compares to networks trained with more conventional misfit metrics. Last but not least we attempt to shed some light on the black-box nature of

  10. Event- and Time-Driven Techniques Using Parallel CPU-GPU Co-processing for Spiking Neural Networks.

    Science.gov (United States)

    Naveros, Francisco; Garrido, Jesus A; Carrillo, Richard R; Ros, Eduardo; Luque, Niceto R

    2017-01-01

    Modeling and simulating the neural structures which make up our central neural system is instrumental for deciphering the computational neural cues beneath. Higher levels of biological plausibility usually impose higher levels of complexity in mathematical modeling, from neural to behavioral levels. This paper focuses on overcoming the simulation problems (accuracy and performance) derived from using higher levels of mathematical complexity at a neural level. This study proposes different techniques for simulating neural models that hold incremental levels of mathematical complexity: leaky integrate-and-fire (LIF), adaptive exponential integrate-and-fire (AdEx), and Hodgkin-Huxley (HH) neural models (ranged from low to high neural complexity). The studied techniques are classified into two main families depending on how the neural-model dynamic evaluation is computed: the event-driven or the time-driven families. Whilst event-driven techniques pre-compile and store the neural dynamics within look-up tables, time-driven techniques compute the neural dynamics iteratively during the simulation time. We propose two modifications for the event-driven family: a look-up table recombination to better cope with the incremental neural complexity together with a better handling of the synchronous input activity. Regarding the time-driven family, we propose a modification in computing the neural dynamics: the bi-fixed-step integration method. This method automatically adjusts the simulation step size to better cope with the stiffness of the neural model dynamics running in CPU platforms. One version of this method is also implemented for hybrid CPU-GPU platforms. Finally, we analyze how the performance and accuracy of these modifications evolve with increasing levels of neural complexity. We also demonstrate how the proposed modifications which constitute the main contribution of this study systematically outperform the traditional event- and time-driven techniques under

  11. Program Helps Simulate Neural Networks

    Science.gov (United States)

    Villarreal, James; Mcintire, Gary

    1993-01-01

    Neural Network Environment on Transputer System (NNETS) computer program provides users high degree of flexibility in creating and manipulating wide variety of neural-network topologies at processing speeds not found in conventional computing environments. Supports back-propagation and back-propagation-related algorithms. Back-propagation algorithm used is implementation of Rumelhart's generalized delta rule. NNETS developed on INMOS Transputer(R). Predefines back-propagation network, Jordan network, and reinforcement network to assist users in learning and defining own networks. Also enables users to configure other neural-network paradigms from NNETS basic architecture. Small portion of software written in OCCAM(R) language.

  12. Artificial Neural Network Analysis System

    Science.gov (United States)

    2001-02-27

    Contract No. DASG60-00-M-0201 Purchase request no.: Foot in the Door-01 Title Name: Artificial Neural Network Analysis System Company: Atlantic... Artificial Neural Network Analysis System 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Powell, Bruce C 5d. PROJECT NUMBER 5e. TASK NUMBER...34) 27-02-2001 Report Type N/A Dates Covered (from... to) ("DD MON YYYY") 28-10-2000 27-02-2001 Title and Subtitle Artificial Neural Network Analysis

  13. Cooperating attackers in neural cryptography.

    Science.gov (United States)

    Shacham, Lanir N; Klein, Einat; Mislovaty, Rachel; Kanter, Ido; Kinzel, Wolfgang

    2004-06-01

    A successful attack strategy in neural cryptography is presented. The neural cryptosystem, based on synchronization of neural networks by mutual learning, has been recently shown to be secure under different attack strategies. The success of the advanced attacker presented here, called the "majority-flipping attacker," does not decay with the parameters of the model. This attacker's outstanding success is due to its using a group of attackers which cooperate throughout the synchronization process, unlike any other attack strategy known. An analytical description of this attack is also presented, and fits the results of simulations.

  14. Creative-Dynamics Approach To Neural Intelligence

    Science.gov (United States)

    Zak, Michail A.

    1992-01-01

    Paper discusses approach to mathematical modeling of artificial neural networks exhibiting complicated behaviors reminiscent of creativity and intelligence of biological neural networks. Neural network treated as non-Lipschitzian dynamical system - as described in "Non-Lipschitzian Dynamics For Modeling Neural Networks" (NPO-17814). System serves as tool for modeling of temporal-pattern memories and recognition of complicated spatial patterns.

  15. NNSYSID and NNCTRL Tools for system identification and control with neural networks

    DEFF Research Database (Denmark)

    Nørgaard, Magnus; Ravn, Ole; Poulsen, Niels Kjølstad

    2001-01-01

    choose among several designs such as direct inverse control, internal model control, nonlinear feedforward, feedback linearisation, optimal control, gain scheduling based on instantaneous linearisation of neural network models and nonlinear model predictive control. This article gives an overview......Two toolsets for use with MATLAB have been developed: the neural network based system identification toolbox (NNSYSID) and the neural network based control system design toolkit (NNCTRL). The NNSYSID toolbox has been designed to assist identification of nonlinear dynamic systems. It contains...... a number of nonlinear model structures based on neural networks, effective training algorithms and tools for model validation and model structure selection. The NNCTRL toolkit is an add-on to NNSYSID and provides tools for design and simulation of control systems based on neural networks. The user can...

  16. NNSYSID and NNCTRL Tools for system identification and control with neural networks

    DEFF Research Database (Denmark)

    Nørgaard, Magnus; Ravn, Ole; Poulsen, Niels Kjølstad

    2001-01-01

    a number of nonlinear model structures based on neural networks, effective training algorithms and tools for model validation and model structure selection. The NNCTRL toolkit is an add-on to NNSYSID and provides tools for design and simulation of control systems based on neural networks. The user can...... choose among several designs such as direct inverse control, internal model control, nonlinear feedforward, feedback linearisation, optimal control, gain scheduling based on instantaneous linearisation of neural network models and nonlinear model predictive control. This article gives an overview......Two toolsets for use with MATLAB have been developed: the neural network based system identification toolbox (NNSYSID) and the neural network based control system design toolkit (NNCTRL). The NNSYSID toolbox has been designed to assist identification of nonlinear dynamic systems. It contains...

  17. Neural components of altruistic punishment

    Directory of Open Access Journals (Sweden)

    Emily eDu

    2015-02-01

    Full Text Available Altruistic punishment, which occurs when an individual incurs a cost to punish in response to unfairness or a norm violation, may play a role in perpetuating cooperation. The neural correlates underlying costly punishment have only recently begun to be explored. Here we review the current state of research on the neural basis of altruism from the perspectives of costly punishment, emphasizing the importance of characterizing elementary neural processes underlying a decision to punish. In particular, we emphasize three cognitive processes that contribute to the decision to altruistically punish in most scenarios: inequity aversion, cost-benefit calculation, and social reference frame to distinguish self from others. Overall, we argue for the importance of understanding the neural correlates of altruistic punishment with respect to the core computations necessary to achieve a decision to punish.

  18. Neural complexity, dissociation, and schizophrenia

    Czech Academy of Sciences Publication Activity Database

    Bob, P.; Šusta, M.; Chládek, Jan; Glaslová, K.; Fedor-Ferybergh, P.

    2007-01-01

    Roč. 13, č. 10 (2007), HY1-5 ISSN 1234-1010 Institutional research plan: CEZ:AV0Z20650511 Keywords : neural complexity * dissociation * schizophrenia Subject RIV: FH - Neurology Impact factor: 1.607, year: 2007

  19. Complex-Valued Neural Networks

    CERN Document Server

    Hirose, Akira

    2012-01-01

    This book is the second enlarged and revised edition of the first successful monograph on complex-valued neural networks (CVNNs) published in 2006, which lends itself to graduate and undergraduate courses in electrical engineering, informatics, control engineering, mechanics, robotics, bioengineering, and other relevant fields. In the second edition the recent trends in CVNNs research are included, resulting in e.g. almost a doubled number of references. The parametron invented in 1954 is also referred to with discussion on analogy and disparity. Also various additional arguments on the advantages of the complex-valued neural networks enhancing the difference to real-valued neural networks are given in various sections. The book is useful for those beginning their studies, for instance, in adaptive signal processing for highly functional sensing and imaging, control in unknown and changing environment, robotics inspired by human neural systems, and brain-like information processing, as well as interdisciplina...

  20. Artificial intelligence: Deep neural reasoning

    Science.gov (United States)

    Jaeger, Herbert

    2016-10-01

    The human brain can solve highly abstract reasoning problems using a neural network that is entirely physical. The underlying mechanisms are only partially understood, but an artificial network provides valuable insight. See Article p.471

  1. Optical Neural Network Classifier Architectures

    National Research Council Canada - National Science Library

    Getbehead, Mark

    1998-01-01

    We present an adaptive opto-electronic neural network hardware architecture capable of exploiting parallel optics to realize real-time processing and classification of high-dimensional data for Air...

  2. Memristor-based neural networks

    International Nuclear Information System (INIS)

    Thomas, Andy

    2013-01-01

    The synapse is a crucial element in biological neural networks, but a simple electronic equivalent has been absent. This complicates the development of hardware that imitates biological architectures in the nervous system. Now, the recent progress in the experimental realization of memristive devices has renewed interest in artificial neural networks. The resistance of a memristive system depends on its past states and exactly this functionality can be used to mimic the synaptic connections in a (human) brain. After a short introduction to memristors, we present and explain the relevant mechanisms in a biological neural network, such as long-term potentiation and spike time-dependent plasticity, and determine the minimal requirements for an artificial neural network. We review the implementations of these processes using basic electric circuits and more complex mechanisms that either imitate biological systems or could act as a model system for them. (topical review)

  3. Recognition of sign language gestures using neural networks

    Directory of Open Access Journals (Sweden)

    Simon Vamplew

    2007-04-01

    Full Text Available This paper describes the structure and performance of the SLARTI sign language recognition system developed at the University of Tasmania. SLARTI uses a modular architecture consisting of multiple feature-recognition neural networks and a nearest-neighbour classifier to recognise Australian sign language (Auslan hand gestures.

  4. Recognition of sign language gestures using neural networks

    OpenAIRE

    Simon Vamplew

    2007-01-01

    This paper describes the structure and performance of the SLARTI sign language recognition system developed at the University of Tasmania. SLARTI uses a modular architecture consisting of multiple feature-recognition neural networks and a nearest-neighbour classifier to recognise Australian sign language (Auslan) hand gestures.

  5. Bach in 2014: Music Composition with Recurrent Neural Network

    OpenAIRE

    Liu, I-Ting; Ramakrishnan, Bhiksha

    2014-01-01

    We propose a framework for computer music composition that uses resilient propagation (RProp) and long short term memory (LSTM) recurrent neural network. In this paper, we show that LSTM network learns the structure and characteristics of music pieces properly by demonstrating its ability to recreate music. We also show that predicting existing music using RProp outperforms Back propagation through time (BPTT).

  6. Classical Conditioning with Pulsed Integrated Neural Networks: Circuits and System

    DEFF Research Database (Denmark)

    Lehmann, Torsten

    1998-01-01

    In this paper we investigate on-chip learning for pulsed, integrated neural networks. We discuss the implementational problems the technology imposes on learning systems and we find that abiologically inspired approach using simple circuit structures is most likely to bring success. We develop a ...... chip to solve simple classical conditioning tasks, thus verifying the design methodologies put forward in the paper....

  7. Representation of linguistic form and function in recurrent neural networks

    NARCIS (Netherlands)

    Kadar, Akos; Chrupala, Grzegorz; Alishahi, Afra

    2017-01-01

    We present novel methods for analyzing the activation patterns of recurrent neural networks from a linguistic point of view and explore the types of linguistic structure they learn. As a case study, we use a standard standalone language model, and a multi-task gated recurrent network architecture

  8. Active Control of Sound based on Diagonal Recurrent Neural Network

    NARCIS (Netherlands)

    Jayawardhana, Bayu; Xie, Lihua; Yuan, Shuqing

    2002-01-01

    Recurrent neural network has been known for its dynamic mapping and better suited for nonlinear dynamical system. Nonlinear controller may be needed in cases where the actuators exhibit the nonlinear characteristics, or in cases when the structure to be controlled exhibits nonlinear behavior. The

  9. Bilingual Lexical Interactions in an Unsupervised Neural Network Model

    Science.gov (United States)

    Zhao, Xiaowei; Li, Ping

    2010-01-01

    In this paper we present an unsupervised neural network model of bilingual lexical development and interaction. We focus on how the representational structures of the bilingual lexicons can emerge, develop, and interact with each other as a function of the learning history. The results show that: (1) distinct representations for the two lexicons…

  10. Commentary: Elucidating the Neural Correlates of Early Childhood Memory

    Science.gov (United States)

    Mullally, Sinead L.

    2015-01-01

    Both episodic memory and the key neural structure believed to support it, namely the hippocampus, are believed to undergo protracted periods of postnatal developmental. Critically however, the hippocampus is comprised of distinct subfields and circuits, and these circuits appear to mature at different rates (Lavenex and Banta Lavenex, 2013).…

  11. Implantable Neural Interfaces for Sharks

    Science.gov (United States)

    2007-05-01

    technology for recording and stimulating from the auditory and olfactory sensory nervous systems of the awake, swimming nurse shark , G. cirratum (Figures...overlay of the central nervous system of the nurse shark on a horizontal MR image. Implantable Neural Interfaces for Sharks ...Neural Interfaces for Characterizing Population Responses to Odorants and Electrical Stimuli in the Nurse Shark , Ginglymostoma cirratum.” AChemS Abs

  12. Regulated expression of the neural cell adhesion molecule L1 by specific patterns of neural impulses.

    Science.gov (United States)

    Itoh, K; Stevens, B; Schachner, M; Fields, R D

    1995-11-24

    Development of the mammalian nervous system is regulated by neural impulse activity, but the molecular mechanisms are not well understood. If cell recognition molecules [for example, L1 and the neural cell adhesion molecule (NCAM)] were influenced by specific patterns of impulse activity, cell-cell interactions controlling nervous system structure could be regulated by nervous system function at critical stages of development. Low-frequency electrical pulses delivered to mouse sensory neurons in culture (0.1 hertz for 5 days) down-regulated expression of L1 messenger RNA and protein (but not NCAM). Fasciculation of neurites, adhesion of neuroblastoma cells, and the number of Schwann cells on neurites was reduced after 0.1-hertz stimulation, but higher frequencies or stimulation after synaptogenesis were without effect.

  13. Neural activation toward erotic stimuli in homosexual and heterosexual males.

    Science.gov (United States)

    Kagerer, Sabine; Klucken, Tim; Wehrum, Sina; Zimmermann, Mark; Schienle, Anne; Walter, Bertram; Vaitl, Dieter; Stark, Rudolf

    2011-11-01

    Studies investigating sexual arousal exist, yet there are diverging findings on the underlying neural mechanisms with regard to sexual orientation. Moreover, sexual arousal effects have often been confounded with general arousal effects. Hence, it is still unclear which structures underlie the sexual arousal response in homosexual and heterosexual men. Neural activity and subjective responses were investigated in order to disentangle sexual from general arousal. Considering sexual orientation, differential and conjoint neural activations were of interest. The functional magnetic resonance imaging (fMRI) study focused on the neural networks involved in the processing of sexual stimuli in 21 male participants (11 homosexual, 10 heterosexual). Both groups viewed pictures with erotic content as well as aversive and neutral stimuli. The erotic pictures were subdivided into three categories (most sexually arousing, least sexually arousing, and rest) based on the individual subjective ratings of each participant. Blood oxygen level-dependent responses measured by fMRI and subjective ratings. A conjunction analysis revealed conjoint neural activation related to sexual arousal in thalamus, hypothalamus, occipital cortex, and nucleus accumbens. Increased insula, amygdala, and anterior cingulate gyrus activation could be linked to general arousal. Group differences emerged neither when viewing the most sexually arousing pictures compared with highly arousing aversive pictures nor compared with neutral pictures. Results suggest that a widespread neural network is activated by highly sexually arousing visual stimuli. A partly distinct network of structures underlies sexual and general arousal effects. The processing of preferred, highly sexually arousing stimuli recruited similar structures in homosexual and heterosexual males. © 2011 International Society for Sexual Medicine.

  14. Effect of neural connectivity on autocovariance and cross covariance estimates

    Directory of Open Access Journals (Sweden)

    Stecker Mark M

    2007-01-01

    Full Text Available Abstract Background Measurements of auto and cross covariance functions are frequently used to investigate neural systems. In interpreting this data, it is commonly assumed that the largest contribution to the recordings comes from sources near the electrode. However, the potential recorded at an electrode represents the superimposition of the potentials generated by large numbers of active neural structures. This creates situations under which the measured auto and cross covariance functions are dominated by the activity in structures far from the electrode and in which the distance dependence of the cross-covariance function differs significantly from that describing the activity in the actual neural structures. Methods Direct application of electrostatics to calculate the theoretical auto and cross covariance functions that would be recorded from electrodes immersed in a large volume filled with active neural structures with specific statistical properties. Results It is demonstrated that the potentials recorded from a monopolar electrode surrounded by dipole sources in a uniform medium are predominantly due to activity in neural structures far from the electrode when neuronal correlations drop more slowly than 1/r3 or when the size of the neural system is much smaller than a known correlation distance. Recordings from quadrupolar sources are strongly dependent on distant neurons when correlations drop more slowly than 1/r or the size of the system is much smaller than the correlation distance. Differences between bipolar and monopolar recordings are discussed. It is also demonstrated that the cross covariance of the recorded in two spatially separated electrodes declines as a power-law function of the distance between them even when the electrical activity from different neuronal structures is uncorrelated. Conclusion When extracellular electrophysiologic recordings are made from systems containing large numbers of neural structures, it is

  15. Neural correlates of hate.

    Directory of Open Access Journals (Sweden)

    Semir Zeki

    Full Text Available In this work, we address an important but unexplored topic, namely the neural correlates of hate. In a block-design fMRI study, we scanned 17 normal human subjects while they viewed the face of a person they hated and also faces of acquaintances for whom they had neutral feelings. A hate score was obtained for the object of hate for each subject and this was used as a covariate in a between-subject random effects analysis. Viewing a hated face resulted in increased activity in the medial frontal gyrus, right putamen, bilaterally in premotor cortex, in the frontal pole and bilaterally in the medial insula. We also found three areas where activation correlated linearly with the declared level of hatred, the right insula, right premotor cortex and the right fronto-medial gyrus. One area of deactivation was found in the right superior frontal gyrus. The study thus shows that there is a unique pattern of activity in the brain in the context of hate. Though distinct from the pattern of activity that correlates with romantic love, this pattern nevertheless shares two areas with the latter, namely the putamen and the insula.

  16. Trade-offs in the distribution of neural networks in a wireless sensor network

    NARCIS (Netherlands)

    Holenderski, M.J.; Lukkien, J.J.; Tham, C.K.

    2005-01-01

    This article investigates the tradeoff between communication and memory usage in different methods of distributing neural networks in a Wireless Sensor Network. A structural approach is presented, categorized in two dimensions: horizontal and vertical decomposition. Horizontal decomposition turns

  17. Nondestructive pavement evaluation using ILLI-PAVE based artificial neural network models.

    Science.gov (United States)

    2008-09-01

    The overall objective in this research project is to develop advanced pavement structural analysis models for more accurate solutions with fast computation schemes. Soft computing and modeling approaches, specifically the Artificial Neural Network (A...

  18. artificial neural network model for low strength rc beam shear capacity

    African Journals Online (AJOL)

    User

    RESEARCH PAPER. Keywords: Shear strength, reinforced concrete, Artificial Neural Network, design equations ... searchers using artificial intelligence to im- prove on theoretical ...... benefit to humanity or a waste of time?” The. Structural ...

  19. Influence of neural adaptation on dynamics and equilibrium state of neural activities in a ring neural network

    Science.gov (United States)

    Takiyama, Ken

    2017-12-01

    How neural adaptation affects neural information processing (i.e. the dynamics and equilibrium state of neural activities) is a central question in computational neuroscience. In my previous works, I analytically clarified the dynamics and equilibrium state of neural activities in a ring-type neural network model that is widely used to model the visual cortex, motor cortex, and several other brain regions. The neural dynamics and the equilibrium state in the neural network model corresponded to a Bayesian computation and statistically optimal multiple information integration, respectively, under a biologically inspired condition. These results were revealed in an analytically tractable manner; however, adaptation effects were not considered. Here, I analytically reveal how the dynamics and equilibrium state of neural activities in a ring neural network are influenced by spike-frequency adaptation (SFA). SFA is an adaptation that causes gradual inhibition of neural activity when a sustained stimulus is applied, and the strength of this inhibition depends on neural activities. I reveal that SFA plays three roles: (1) SFA amplifies the influence of external input in neural dynamics; (2) SFA allows the history of the external input to affect neural dynamics; and (3) the equilibrium state corresponds to the statistically optimal multiple information integration independent of the existence of SFA. In addition, the equilibrium state in a ring neural network model corresponds to the statistically optimal integration of multiple information sources under biologically inspired conditions, independent of the existence of SFA.

  20. Direct Genesis of Functional Rodent and Human Schwann Cells from Skin Mesenchymal Precursors

    Directory of Open Access Journals (Sweden)

    Matthew P. Krause

    2014-07-01

    Full Text Available Recent reports of directed reprogramming have raised questions about the stability of cell lineages. Here, we have addressed this issue, focusing upon skin-derived precursors (SKPs, a dermally derived precursor cell. We show by lineage tracing that murine SKPs from dorsal skin originate from mesenchymal and not neural crest-derived cells. These mesenchymally derived SKPs can, without genetic manipulation, generate functional Schwann cells, a neural crest cell type, and are highly similar at the transcriptional level to Schwann cells isolated from the peripheral nerve. This is not a mouse-specific phenomenon, since human SKPs that are highly similar at the transcriptome level can be made from neural crest-derived facial and mesodermally derived foreskin dermis and the foreskin SKPs can make myelinating Schwann cells. Thus, nonneural crest-derived mesenchymal precursors can differentiate into bona fide peripheral glia in the absence of genetic manipulation, suggesting that developmentally defined lineage boundaries are more flexible than widely thought.

  1. A new neural observer for an anaerobic bioreactor.

    Science.gov (United States)

    Belmonte-Izquierdo, R; Carlos-Hernandez, S; Sanchez, E N

    2010-02-01

    In this paper, a recurrent high order neural observer (RHONO) for anaerobic processes is proposed. The main objective is to estimate variables of methanogenesis: biomass, substrate and inorganic carbon in a completely stirred tank reactor (CSTR). The recurrent high order neural network (RHONN) structure is based on the hyperbolic tangent as activation function. The learning algorithm is based on an extended Kalman filter (EKF). The applicability of the proposed scheme is illustrated via simulation. A validation using real data from a lab scale process is included. Thus, this observer can be successfully implemented for control purposes.

  2. Simulation of nonlinear random vibrations using artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Paez, T.L.; Tucker, S.; O`Gorman, C.

    1997-02-01

    The simulation of mechanical system random vibrations is important in structural dynamics, but it is particularly difficult when the system under consideration is nonlinear. Artificial neural networks provide a useful tool for the modeling of nonlinear systems, however, such modeling may be inefficient or insufficiently accurate when the system under consideration is complex. This paper shows that there are several transformations that can be used to uncouple and simplify the components of motion of a complex nonlinear system, thereby making its modeling and random vibration simulation, via component modeling with artificial neural networks, a much simpler problem. A numerical example is presented.

  3. Using Neural Networks to Predict the Hardness of Aluminum Alloys

    Directory of Open Access Journals (Sweden)

    B. Zahran

    2015-02-01

    Full Text Available Aluminum alloys have gained significant industrial importance being involved in many of the light and heavy industries and especially in aerospace engineering. The mechanical properties of aluminum alloys are defined by a number of principal microstructural features. Conventional mathematical models of these properties are sometimes very complex to be analytically calculated. In this paper, a neural network model is used to predict the correlations between the hardness of aluminum alloys in relation to certain alloying elements. A backpropagation neural network is trained using a thorough dataset. The impact of certain elements is documented and an optimum structure is proposed.

  4. Adrenergic innervation of the developing chick heart: neural crest ablations to produce sympathetically aneural hearts

    International Nuclear Information System (INIS)

    Kirby, M.; Stewart, D.

    1984-01-01

    Ablation of various regions of premigratory trunk neural crest which gives rise to the sympathetic trunks was used to remove sympathetic cardiac innervation. Neuronal uptake of [ 3 H]-norepinephrine was used as an index of neuronal development in the chick atrium. Following ablation of neural crest over somites 10-15 or 15-20, uptake was significantly decreased in the atrium at 16 and 17 days of development. Ablation of neural crest over somites 5-10 and 20-25 caused no decrease in [ 3 H]-norepinephrine uptake. Removal of neural crest over somites 5-25 or 10-20 caused approximately equal depletions of [ 3 H]-norepinephrine uptake in the atrium. Cardiac norepinephrine concentration was significantly depressed following ablation of neural crest over somites 5-25 but not over somites 10-20. Light-microscopic and histofluorescent preparations confirmed the absence of sympathetic trunks in the region of the normal origin of the sympathetic cardiac nerves following neural crest ablation over somites 10-20. The neural tube and dorsal root ganglia were damaged in the area of the neural-crest ablation; however, all of these structures were normal cranial and caudal to the lesioned area. Development of most of the embryos as well as the morphology of all of the hearts was normal following the lesion. These results indicate that it is possible to produce sympathetically aneural hearts by neural-crest ablation; however, sympathetic cardiac nerves account for an insignificant amount of cardiac norepinephrine

  5. Neural Net Safety Monitor Design

    Science.gov (United States)

    Larson, Richard R.

    2007-01-01

    The National Aeronautics and Space Administration (NASA) at the Dryden Flight Research Center (DFRC) has been conducting flight-test research using an F-15 aircraft (figure 1). This aircraft has been specially modified to interface a neural net (NN) controller as part of a single-string Airborne Research Test System (ARTS) computer with the existing quad-redundant flight control system (FCC) shown in figure 2. The NN commands are passed to FCC channels 2 and 4 and are cross channel data linked (CCDL) to the other computers as shown. Numerous types of fault-detection monitors exist in the FCC when the NN mode is engaged; these monitors would cause an automatic disengagement of the NN in the event of a triggering fault. Unfortunately, these monitors still may not prevent a possible NN hard-over command from coming through to the control laws. Therefore, an additional and unique safety monitor was designed for a single-string source that allows authority at maximum actuator rates but protects the pilot and structural loads against excessive g-limits in the case of a NN hard-over command input. This additional monitor resides in the FCCs and is executed before the control laws are computed. This presentation describes a floating limiter (FL) concept1 that was developed and successfully test-flown for this program (figure 3). The FL computes the rate of change of the NN commands that are input to the FCC from the ARTS. A window is created with upper and lower boundaries, which is constantly floating and trying to stay centered as the NN command rates are changing. The limiter works by only allowing the window to move at a much slower rate than those of the NN commands. Anywhere within the window, however, full rates are allowed. If a rate persists in one direction, it will eventually hit the boundary and be rate-limited to the floating limiter rate. When this happens, a persistent counter begins and after a limit is reached, a NN disengage command is generated. The

  6. The neural basis of unconditional love.

    Science.gov (United States)

    Beauregard, Mario; Courtemanche, Jérôme; Paquette, Vincent; St-Pierre, Evelyne Landry

    2009-05-15

    Functional neuroimaging studies have shown that romantic love and maternal love are mediated by regions specific to each, as well as overlapping regions in the brain's reward system. Nothing is known yet regarding the neural underpinnings of unconditional love. The main goal of this functional magnetic resonance imaging study was to identify the brain regions supporting this form of love. Participants were scanned during a control condition and an experimental condition. In the control condition, participants were instructed to simply look at a series of pictures depicting individuals with intellectual disabilities. In the experimental condition, participants were instructed to feel unconditional love towards the individuals depicted in a series of similar pictures. Significant loci of activation were found, in the experimental condition compared with the control condition, in the middle insula, superior parietal lobule, right periaqueductal gray, right globus pallidus (medial), right caudate nucleus (dorsal head), left ventral tegmental area and left rostro-dorsal anterior cingulate cortex. These results suggest that unconditional love is mediated by a distinct neural network relative to that mediating other emotions. This network contains cerebral structures known to be involved in romantic love or maternal love. Some of these structures represent key components of the brain's reward system.

  7. Online neural monitoring of statistical learning.

    Science.gov (United States)

    Batterink, Laura J; Paller, Ken A

    2017-05-01

    The extraction of patterns in the environment plays a critical role in many types of human learning, from motor skills to language acquisition. This process is known as statistical learning. Here we propose that statistical learning has two dissociable components: (1) perceptual binding of individual stimulus units into integrated composites and (2) storing those integrated representations for later use. Statistical learning is typically assessed using post-learning tasks, such that the two components are conflated. Our goal was to characterize the online perceptual component of statistical learning. Participants were exposed to a structured stream of repeating trisyllabic nonsense words and a random syllable stream. Online learning was indexed by an EEG-based measure that quantified neural entrainment at the frequency of the repeating words relative to that of individual syllables. Statistical learning was subsequently assessed using conventional measures in an explicit rating task and a reaction-time task. In the structured stream, neural entrainment to trisyllabic words was higher than in the random stream, increased as a function of exposure to track the progression of learning, and predicted performance on the reaction time (RT) task. These results demonstrate that monitoring this critical component of learning via rhythmic EEG entrainment reveals a gradual acquisition of knowledge whereby novel stimulus sequences are transformed into familiar composites. This online perceptual transformation is a critical component of learning. Copyright © 2017 Elsevier Ltd. All rights reserved.

  8. Fractional Hopfield Neural Networks: Fractional Dynamic Associative Recurrent Neural Networks.

    Science.gov (United States)

    Pu, Yi-Fei; Yi, Zhang; Zhou, Ji-Liu

    2017-10-01

    This paper mainly discusses a novel conceptual framework: fractional Hopfield neural networks (FHNN). As is commonly known, fractional calculus has been incorporated into artificial neural networks, mainly because of its long-term memory and nonlocality. Some researchers have made interesting attempts at fractional neural networks and gained competitive advantages over integer-order neural networks. Therefore, it is naturally makes one ponder how to generalize the first-order Hopfield neural networks to the fractional-order ones, and how to implement FHNN by means of fractional calculus. We propose to introduce a novel mathematical method: fractional calculus to implement FHNN. First, we implement fractor in the form of an analog circuit. Second, we implement FHNN by utilizing fractor and the fractional steepest descent approach, construct its Lyapunov function, and further analyze its attractors. Third, we perform experiments to analyze the stability and convergence of FHNN, and further discuss its applications to the defense against chip cloning attacks for anticounterfeiting. The main contribution of our work is to propose FHNN in the form of an analog circuit by utilizing a fractor and the fractional steepest descent approach, construct its Lyapunov function, prove its Lyapunov stability, analyze its attractors, and apply FHNN to the defense against chip cloning attacks for anticounterfeiting. A significant advantage of FHNN is that its attractors essentially relate to the neuron's fractional order. FHNN possesses the fractional-order-stability and fractional-order-sensitivity characteristics.

  9. Comparing Models GRM, Refraction Tomography and Neural Network to Analyze Shallow Landslide

    Directory of Open Access Journals (Sweden)

    Armstrong F. Sompotan

    2011-11-01

    Full Text Available Detailed investigations of landslides are essential to understand fundamental landslide mechanisms. Seismic refraction method has been proven as a useful geophysical tool for investigating shallow landslides. The objective of this study is to introduce a new workflow using neural network in analyzing seismic refraction data and to compare the result with some methods; that are general reciprocal method (GRM and refraction tomography. The GRM is effective when the velocity structure is relatively simple and refractors are gently dipping. Refraction tomography is capable of modeling the complex velocity structures of landslides. Neural network is found to be more potential in application especially in time consuming and complicated numerical methods. Neural network seem to have the ability to establish a relationship between an input and output space for mapping seismic velocity. Therefore, we made a preliminary attempt to evaluate the applicability of neural network to determine velocity and elevation of subsurface synthetic models corresponding to arrival times. The training and testing process of the neural network is successfully accomplished using the synthetic data. Furthermore, we evaluated the neural network using observed data. The result of the evaluation indicates that the neural network can compute velocity and elevation corresponding to arrival times. The similarity of those models shows the success of neural network as a new alternative in seismic refraction data interpretation.

  10. Neural Behavior Chain Learning of Mobile Robot Actions

    Directory of Open Access Journals (Sweden)

    Lejla Banjanovic-Mehmedovic

    2012-01-01

    Full Text Available This paper presents a visual/motor behavior learning approach, based on neural networks. We propose Behavior Chain Model (BCM in order to create a way of behavior learning. Our behavior-based system evolution task is a mobile robot detecting a target and driving/acting towards it. First, the mapping relations between the image feature domain of the object and the robot action domain are derived. Second, a multilayer neural network for offline learning of the mapping relations is used. This learning structure through neural network training process represents a connection between the visual perceptions and motor sequence of actions in order to grip a target. Last, using behavior learning through a noticed action chain, we can predict mobile robot behavior for a variety of similar tasks in similar environment. Prediction results suggest that the methodology is adequate and could be recognized as an idea for designing different mobile robot behaviour assistance.

  11. Forecasting Flare Activity Using Deep Convolutional Neural Networks

    Science.gov (United States)

    Hernandez, T.

    2017-12-01

    Current operational flare forecasting relies on human morphological analysis of active regions and the persistence of solar flare activity through time (i.e. that the Sun will continue to do what it is doing right now: flaring or remaining calm). In this talk we present the results of applying deep Convolutional Neural Networks (CNNs) to the problem of solar flare forecasting. CNNs operate by training a set of tunable spatial filters that, in combination with neural layer interconnectivity, allow CNNs to automatically identify significant spatial structures predictive for classification and regression problems. We will start by discussing the applicability and success rate of the approach, the advantages it has over non-automated forecasts, and how mining our trained neural network provides a fresh look into the mechanisms behind magnetic energy storage and release.

  12. The specialization of function: cognitive and neural perspectives.

    Science.gov (United States)

    Mahon, Bradford Z; Cantlon, Jessica F

    2011-05-01

    A unifying theme that cuts across all research areas and techniques in the cognitive and brain sciences is whether there is specialization of function at levels of processing that are "abstracted away" from sensory inputs and motor outputs. Any theory that articulates claims about specialization of function in the mind/brain confronts the following types of interrelated questions, each of which carries with it certain theoretical commitments. What methods are appropriate for decomposing complex cognitive and neural processes into their constituent parts? How do cognitive processes map onto neural processes, and at what resolution are they related? What types of conclusions can be drawn about the structure of mind from dissociations observed at the neural level, and vice versa? The contributions that form this Special Issue of Cognitive Neuropsychology represent recent reflections on these and other issues from leading researchers in different areas of the cognitive and brain sciences.

  13. Application of neural networks to seismic active control

    International Nuclear Information System (INIS)

    Tang, Yu.

    1995-01-01

    An exploratory study on seismic active control using an artificial neural network (ANN) is presented in which a singledegree-of-freedom (SDF) structural system is controlled by a trained neural network. A feed-forward neural network and the backpropagation training method are used in the study. In backpropagation training, the learning rate is determined by ensuring the decrease of the error function at each training cycle. The training patterns for the neural net are generated randomly. Then, the trained ANN is used to compute the control force according to the control algorithm. The control strategy proposed herein is to apply the control force at every time step to destroy the build-up of the system response. The ground motions considered in the simulations are the N21E and N69W components of the Lake Hughes No. 12 record that occurred in the San Fernando Valley in California on February 9, 1971. Significant reduction of the structural response by one order of magnitude is observed. Also, it is shown that the proposed control strategy has the ability to reduce the peak that occurs during the first few cycles of the time history. These promising results assert the potential of applying ANNs to active structural control under seismic loads

  14. Temporal neural networks and transient analysis of complex engineering systems

    Science.gov (United States)

    Uluyol, Onder

    A theory is introduced for a multi-layered Local Output Gamma Feedback (LOGF) neural network within the paradigm of Locally-Recurrent Globally-Feedforward neural networks. It is developed for the identification, prediction, and control tasks of spatio-temporal systems and allows for the presentation of different time scales through incorporation of a gamma memory. It is initially applied to the tasks of sunspot and Mackey-Glass series prediction as benchmarks, then it is extended to the task of power level control of a nuclear reactor at different fuel cycle conditions. The developed LOGF neuron model can also be viewed as a Transformed Input and State (TIS) Gamma memory for neural network architectures for temporal processing. The novel LOGF neuron model extends the static neuron model by incorporating into it a short-term memory structure in the form of a digital gamma filter. A feedforward neural network made up of LOGF neurons can thus be used to model dynamic systems. A learning algorithm based upon the Backpropagation-Through-Time (BTT) approach is derived. It is applicable for training a general L-layer LOGF neural network. The spatial and temporal weights and parameters of the network are iteratively optimized for a given problem using the derived learning algorithm.

  15. 2D neural hardware versus 3D biological ones

    Energy Technology Data Exchange (ETDEWEB)

    Beiu, V.

    1998-12-31

    This paper will present important limitations of hardware neural nets as opposed to biological neural nets (i.e. the real ones). The author starts by discussing neural structures and their biological inspirations, while mentioning the simplifications leading to artificial neural nets. Going further, the focus will be on hardware constraints. The author will present recent results for three different alternatives of implementing neural networks: digital, threshold gate, and analog, while the area and the delay will be related to neurons' fan-in and weights' precision. Based on all of these, it will be shown why hardware implementations cannot cope with their biological inspiration with respect to their power of computation: the mapping onto silicon lacking the third dimension of biological nets. This translates into reduced fan-in, and leads to reduced precision. The main conclusion is that one is faced with the following alternatives: (1) try to cope with the limitations imposed by silicon, by speeding up the computation of the elementary silicon neurons; (2) investigate solutions which would allow one to use the third dimension, e.g. using optical interconnections.

  16. CRIM1 complexes with ß-catenin and cadherins, stabilizes cell-cell junctions and is critical for neural morphogenesis.

    Directory of Open Access Journals (Sweden)

    Virgilio G Ponferrada

    Full Text Available In multicellular organisms, morphogenesis is a highly coordinated process that requires dynamically regulated adhesion between cells. An excellent example of cellular morphogenesis is the formation of the neural tube from the flattened epithelium of the neural plate. Cysteine-rich motor neuron protein 1 (CRIM1 is a single-pass (type 1 transmembrane protein that is expressed in neural structures beginning at the neural plate stage. In the frog Xenopus laevis, loss of function studies using CRIM1 antisense morpholino oligonucleotides resulted in a failure of neural development. The CRIM1 knockdown phenotype was, in some cases, mild and resulted in perturbed neural fold morphogenesis. In severely affected embryos there was a dramatic failure of cell adhesion in the neural plate and complete absence of neural structures subsequently. Investigation of the mechanism of CRIM1 function revealed that it can form complexes with ß-catenin and cadherins, albeit indirectly, via the cytosolic domain. Consistent with this, CRIM1 knockdown resulted in diminished levels of cadherins and ß-catenin in junctional complexes in the neural plate. We conclude that CRIM1 is critical for cell-cell adhesion during neural development because it is required for the function of cadherin-dependent junctions.

  17. Neural correlates of executive functions in patients with obesity.

    Science.gov (United States)

    Ho, Ming-Chou; Chen, Vincent Chin-Hung; Chao, Seh-Huang; Fang, Ching-Tzu; Liu, Yi-Chun; Weng, Jun-Cheng

    2018-01-01

    Obesity is one of the most challenging problems in human health and is recognized as an important risk factor for many chronic diseases. It remains unclear how the neural systems (e.g., the mesolimbic "reward" and the prefrontal "control" neural systems) are correlated with patients' executive function (EF), conceptualized as the integration of "cool" EF and "hot" EF. "Cool" EF refers to relatively abstract, non-affective operations such as inhibitory control and mental flexibility. "Hot" EF refers to motivationally significant affective operations such as affective decision-making. We tried to find the correlation between structural and functional neuroimaging indices and EF in obese patients. The study population comprised seventeen patients with obesity (seven males and 10 females, BMI = 37.99 ± 5.40, age = 31.82 ± 8.75 year-old) preparing to undergo bariatric surgery. We used noninvasive diffusion tensor imaging, generalized q-sampling imaging, and resting-state functional magnetic resonance imaging to examine the neural correlations between structural and functional neuroimaging indices and EF performances in patients with obesity. We reported that many brain areas are correlated to the patients' EF performances. More interestingly, some correlations may implicate the possible associations of EF and the incentive motivational effects of food. The neural correlation between the left precuneus and middle occipital gyrus and inhibitory control may suggest that patients with a better ability to detect appetitive food may have worse inhibitory control. Also, the neural correlation between the superior frontal blade and affective decision-making may suggest that patients' affective decision-making may be associated with the incentive motivational effects of food. Our results provide evidence suggesting neural correlates of EF in patients with obesity.

  18. Neural network regulation driven by autonomous neural firings

    Science.gov (United States)

    Cho, Myoung Won

    2016-07-01

    Biological neurons naturally fire spontaneously due to the existence of a noisy current. Such autonomous firings may provide a driving force for network formation because synaptic connections can be modified due to neural firings. Here, we study the effect of autonomous firings on network formation. For the temporally asymmetric Hebbian learning, bidirectional connections lose their balance easily and become unidirectional ones. Defining the difference between reciprocal connections as new variables, we could express the learning dynamics as if Ising model spins interact with each other in magnetism. We present a theoretical method to estimate the interaction between the new variables in a neural system. We apply the method to some network systems and find some tendencies of autonomous neural network regulation.

  19. Knowledge synthesis with maps of neural connectivity.

    Science.gov (United States)

    Tallis, Marcelo; Thompson, Richard; Russ, Thomas A; Burns, Gully A P C

    2011-01-01

    This paper describes software for neuroanatomical knowledge synthesis based on neural connectivity data. This software supports a mature methodology developed since the early 1990s. Over this time, the Swanson laboratory at USC has generated an account of the neural connectivity of the sub-structures of the hypothalamus, amygdala, septum, hippocampus, and bed nucleus of the stria terminalis. This is based on neuroanatomical data maps drawn into a standard brain atlas by experts. In earlier work, we presented an application for visualizing and comparing anatomical macro connections using the Swanson third edition atlas as a framework for accurate registration. Here we describe major improvements to the NeuARt application based on the incorporation of a knowledge representation of experimental design. We also present improvements in the interface and features of the data mapping components within a unified web-application. As a step toward developing an accurate sub-regional account of neural connectivity, we provide navigational access between the data maps and a semantic representation of area-to-area connections that they support. We do so based on an approach called "Knowledge Engineering from Experimental Design" (KEfED) model that is based on experimental variables. We have extended the underlying KEfED representation of tract-tracing experiments by incorporating the definition of a neuronanatomical data map as a measurement variable in the study design. This paper describes the software design of a web-application that allows anatomical data sets to be described within a standard experimental context and thus indexed by non-spatial experimental design features.

  20. Knowledge synthesis with maps of neural connectivity

    Directory of Open Access Journals (Sweden)

    Marcelo eTallis

    2011-11-01

    Full Text Available This paper describes software for neuroanatomical knowledge synthesis based on high-quality neural connectivity data. This software supports a mature neuroanatomical methodology developed since the early 1990s. Over this time, the Swanson laboratory at USC has generated an account of the neural connectivity of the sub-structures of the hypothalamus, amygdala, septum, hippocampus and bed nucleus of the stria terminalis. This is based on neuroanatomical data maps drawn into a standard brain atlas by experts. In earlier work, we presented an application for visualizing and comparing anatomical macroconnections using the Swanson 3rd edition atlas as a framework for accurate registration. Here we describe major improvements to the NeuARt application based on the incorporation of a knowledge representation of experimental design. We also present improvements in the interface and features of the neuroanatomical data mapping components within a unified web-application. As a step towards developing an accurate sub-regional account of neural connectivity, we provide navigational access between the neuroanatomical data maps and a semantic representation of area-to-area connections that they support. We do so based on an approach called ’Knowledge Engineering from Experimental Design’ (KEfED model that is based on experimental variables. We have extended the underlying KEfED representation of tract-tracing experiments by incorporating the definition of a neuronanatomical data map as a measurement variable in the study design. This paper describes the software design of a web application that allows anatomical data sets to be described within a standard experimental context and thus incorporated with non-spatial data sets.

  1. Neural networks and statistical learning

    CERN Document Server

    Du, Ke-Lin

    2014-01-01

    Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content. Each of the twenty-five chapters includes state-of-the-art descriptions and important research results on the respective topics. The broad coverage includes the multilayer perceptron, the Hopfield network, associative memory models, clustering models and algorithms, the radial basis function network, recurrent neural networks, principal component analysis, nonnegative matrix factorization, independent component analysis, discriminant analysis, support vector machines, kernel methods, reinforcement learning, probabilistic and Bayesian networks, data fusion and ensemble learning, fuzzy sets and logic, neurofuzzy models, hardw...

  2. Principles of neural information processing

    CERN Document Server

    Seelen, Werner v

    2016-01-01

    In this fundamental book the authors devise a framework that describes the working of the brain as a whole. It presents a comprehensive introduction to the principles of Neural Information Processing as well as recent and authoritative research. The books´ guiding principles are the main purpose of neural activity, namely, to organize behavior to ensure survival, as well as the understanding of the evolutionary genesis of the brain. Among the developed principles and strategies belong self-organization of neural systems, flexibility, the active interpretation of the world by means of construction and prediction as well as their embedding into the world, all of which form the framework of the presented description. Since, in brains, their partial self-organization, the lifelong adaptation and their use of various methods of processing incoming information are all interconnected, the authors have chosen not only neurobiology and evolution theory as a basis for the elaboration of such a framework, but also syst...

  3. Modelling and Prediction of Photovoltaic Power Output Using Artificial Neural Networks

    Directory of Open Access Journals (Sweden)

    Aminmohammad Saberian

    2014-01-01

    Full Text Available This paper presents a solar power modelling method using artificial neural networks (ANNs. Two neural network structures, namely, general regression neural network (GRNN feedforward back propagation (FFBP, have been used to model a photovoltaic panel output power and approximate the generated power. Both neural networks have four inputs and one output. The inputs are maximum temperature, minimum temperature, mean temperature, and irradiance; the output is the power. The data used in this paper started from January 1, 2006, until December 31, 2010. The five years of data were split into two parts: 2006–2008 and 2009-2010; the first part was used for training and the second part was used for testing the neural networks. A mathematical equation is used to estimate the generated power. At the end, both of these networks have shown good modelling performance; however, FFBP has shown a better performance comparing with GRNN.

  4. PID Neural Network Based Speed Control of Asynchronous Motor Using Programmable Logic Controller

    Directory of Open Access Journals (Sweden)

    MARABA, V. A.

    2011-11-01

    Full Text Available This paper deals with the structure and characteristics of PID Neural Network controller for single input and single output systems. PID Neural Network is a new kind of controller that includes the advantages of artificial neural networks and classic PID controller. Functioning of this controller is based on the update of controller parameters according to the value extracted from system output pursuant to the rules of back propagation algorithm used in artificial neural networks. Parameters obtained from the application of PID Neural Network training algorithm on the speed model of the asynchronous motor exhibiting second order linear behavior were used in the real time speed control of the motor. Programmable logic controller (PLC was used as real time controller. The real time control results show that reference speed successfully maintained under various load conditions.

  5. Neural Decoder for Topological Codes

    Science.gov (United States)

    Torlai, Giacomo; Melko, Roger G.

    2017-07-01

    We present an algorithm for error correction in topological codes that exploits modern machine learning techniques. Our decoder is constructed from a stochastic neural network called a Boltzmann machine, of the type extensively used in deep learning. We provide a general prescription for the training of the network and a decoding strategy that is applicable to a wide variety of stabilizer codes with very little specialization. We demonstrate the neural decoder numerically on the well-known two-dimensional toric code with phase-flip errors.

  6. Entropy Learning in Neural Network

    Directory of Open Access Journals (Sweden)

    Geok See Ng

    2017-12-01

    Full Text Available In this paper, entropy term is used in the learning phase of a neural network.  As learning progresses, more hidden nodes get into saturation.  The early creation of such hidden nodes may impair generalisation.  Hence entropy approach is proposed to dampen the early creation of such nodes.  The entropy learning also helps to increase the importance of relevant nodes while dampening the less important nodes.  At the end of learning, the less important nodes can then be eliminated to reduce the memory requirements of the neural network.

  7. Antenna analysis using neural networks

    Science.gov (United States)

    Smith, William T.

    1992-01-01

    Conventional computing schemes have long been used to analyze problems in electromagnetics (EM). The vast majority of EM applications require computationally intensive algorithms involving numerical integration and solutions to large systems of equations. The feasibility of using neural network computing algorithms for antenna analysis is investigated. The ultimate goal is to use a trained neural network algorithm to reduce the computational demands of existing reflector surface error compensation techniques. Neural networks are computational algorithms based on neurobiological systems. Neural nets consist of massively parallel interconnected nonlinear computational elements. They are often employed in pattern recognition and image processing problems. Recently, neural network analysis has been applied in the electromagnetics area for the design of frequency selective surfaces and beam forming networks. The backpropagation training algorithm was employed to simulate classical antenna array synthesis techniques. The Woodward-Lawson (W-L) and Dolph-Chebyshev (D-C) array pattern synthesis techniques were used to train the neural network. The inputs to the network were samples of the desired synthesis pattern. The outputs are the array element excitations required to synthesize the desired pattern. Once trained, the network is used to simulate the W-L or D-C techniques. Various sector patterns and cosecant-type patterns (27 total) generated using W-L synthesis were used to train the network. Desired pattern samples were then fed to the neural network. The outputs of the network were the simulated W-L excitations. A 20 element linear array was used. There were 41 input pattern samples with 40 output excitations (20 real parts, 20 imaginary). A comparison between the simulated and actual W-L techniques is shown for a triangular-shaped pattern. Dolph-Chebyshev is a different class of synthesis technique in that D-C is used for side lobe control as opposed to pattern

  8. Computational modeling of spiking neural network with learning rules from STDP and intrinsic plasticity

    Science.gov (United States)

    Li, Xiumin; Wang, Wei; Xue, Fangzheng; Song, Yongduan

    2018-02-01

    Recently there has been continuously increasing interest in building up computational models of spiking neural networks (SNN), such as the Liquid State Machine (LSM). The biologically inspired self-organized neural networks with neural plasticity can enhance the capability of computational performance, with the characteristic features of dynamical memory and recurrent connection cycles which distinguish them from the more widely used feedforward neural networks. Despite a variety of computational models for brain-like learning and information processing have been proposed, the modeling of self-organized neural networks with multi-neural plasticity is still an important open challenge. The main difficulties lie in the interplay among different forms of neural plasticity rules and understanding how structures and dynamics of neural networks shape the computational performance. In this paper, we propose a novel approach to develop the models of LSM with a biologically inspired self-organizing network based on two neural plasticity learning rules. The connectivity among excitatory neurons is adapted by spike-timing-dependent plasticity (STDP) learning; meanwhile, the degrees of neuronal excitability are regulated to maintain a moderate average activity level by another learning rule: intrinsic plasticity (IP). Our study shows that LSM with STDP+IP performs better than LSM with a random SNN or SNN obtained by STDP alone. The noticeable improvement with the proposed method is due to the better reflected competition among different neurons in the developed SNN model, as well as the more effectively encoded and processed relevant dynamic information with its learning and self-organizing mechanism. This result gives insights to the optimization of computational models of spiking neural networks with neural plasticity.

  9. Arabic Handwriting Recognition Using Neural Network Classifier

    African Journals Online (AJOL)

    pc

    2018-03-05

    Mar 5, 2018 ... an OCR using Neural Network classifier preceded by a set of preprocessing .... Artificial Neural Networks (ANNs), which we adopt in this research, consist of ... advantage and disadvantages of each technique. In [9],. Khemiri ...

  10. Neural overlap in processing music and speech.

    Science.gov (United States)

    Peretz, Isabelle; Vuvan, Dominique; Lagrois, Marie-Élaine; Armony, Jorge L

    2015-03-19

    Neural overlap in processing music and speech, as measured by the co-activation of brain regions in neuroimaging studies, may suggest that parts of the neural circuitries established for language may have been recycled during evolution for musicality, or vice versa that musicality served as a springboard for language emergence. Such a perspective has important implications for several topics of general interest besides evolutionary origins. For instance, neural overlap is an important premise for the possibility of music training to influence language acquisition and literacy. However, neural overlap in processing music and speech does not entail sharing neural circuitries. Neural separability between music and speech may occur in overlapping brain regions. In this paper, we review the evidence and outline the issues faced in interpreting such neural data, and argue that converging evidence from several methodologies is needed before neural overlap is taken as evidence of sharing. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

  11. Ocean wave forecasting using recurrent neural networks

    Digital Repository Service at National Institute of Oceanography (India)

    Mandal, S.; Prabaharan, N.

    , merchant vessel routing, nearshore construction, etc. more efficiently and safely. This paper describes an artificial neural network, namely recurrent neural network with rprop update algorithm and is applied for wave forecasting. Measured ocean waves off...

  12. Neural overlap in processing music and speech

    Science.gov (United States)

    Peretz, Isabelle; Vuvan, Dominique; Lagrois, Marie-Élaine; Armony, Jorge L.

    2015-01-01

    Neural overlap in processing music and speech, as measured by the co-activation of brain regions in neuroimaging studies, may suggest that parts of the neural circuitries established for language may have been recycled during evolution for musicality, or vice versa that musicality served as a springboard for language emergence. Such a perspective has important implications for several topics of general interest besides evolutionary origins. For instance, neural overlap is an important premise for the possibility of music training to influence language acquisition and literacy. However, neural overlap in processing music and speech does not entail sharing neural circuitries. Neural separability between music and speech may occur in overlapping brain regions. In this paper, we review the evidence and outline the issues faced in interpreting such neural data, and argue that converging evidence from several methodologies is needed before neural overlap is taken as evidence of sharing. PMID:25646513

  13. Neural Alterations in Acquired Age-Related Hearing Loss

    Directory of Open Access Journals (Sweden)

    Raksha Anand Mudar

    2016-06-01

    Full Text Available Hearing loss is one of the most prevalent chronic health conditions in older adults. Growing evidence suggests that hearing loss is associated with reduced cognitive functioning and incident dementia. In this mini-review, we briefly examine literature on anatomical and functional alterations in the brains of adults with acquired age-associated hearing loss, which may underlie the cognitive consequences observed in this population, focusing on studies that have used structural and functional magnetic resonance imaging, diffusion tensor imaging, and event-related electroencephalography. We discuss structural and functional alterations observed in the temporal and frontal cortices and the limbic system. These neural alterations are discussed in the context of common cause, information-degradation, and sensory-deprivation hypotheses, and we suggest possible rehabilitation strategies. Although we are beginning to learn more about changes in neural architecture and functionality related to age-associated hearing loss, much work remains to be done. Understanding the neural alterations will provide objective markers for early identification of neural consequences of age-associated hearing loss and for evaluating benefits of intervention approaches.

  14. Media Multitasking and Cognitive, Psychological, Neural, and Learning Differences.

    Science.gov (United States)

    Uncapher, Melina R; Lin, Lin; Rosen, Larry D; Kirkorian, Heather L; Baron, Naomi S; Bailey, Kira; Cantor, Joanne; Strayer, David L; Parsons, Thomas D; Wagner, Anthony D

    2017-11-01

    American youth spend more time with media than any other waking activity: an average of 7.5 hours per day, every day. On average, 29% of that time is spent juggling multiple media streams simultaneously (ie, media multitasking). This phenomenon is not limited to American youth but is paralleled across the globe. Given that a large number of media multitaskers (MMTs) are children and young adults whose brains are still developing, there is great urgency to understand the neurocognitive profiles of MMTs. It is critical to understand the relation between the relevant cognitive domains and underlying neural structure and function. Of equal importance is understanding the types of information processing that are necessary in 21st century learning environments. The present review surveys the growing body of evidence demonstrating that heavy MMTs show differences in cognition (eg, poorer memory), psychosocial behavior (eg, increased impulsivity), and neural structure (eg, reduced volume in anterior cingulate cortex). Furthermore, research indicates that multitasking with media during learning (in class or at home) can negatively affect academic outcomes. Until the direction of causality is understood (whether media multitasking causes such behavioral and neural differences or whether individuals with such differences tend to multitask with media more often), the data suggest that engagement with concurrent media streams should be thoughtfully considered. Findings from such research promise to inform policy and practice on an increasingly urgent societal issue while significantly advancing our understanding of the intersections between cognitive, psychosocial, neural, and academic factors. Copyright © 2017 by the American Academy of Pediatrics.

  15. Sex differences in the neural basis of emotional memories.

    Science.gov (United States)

    Canli, Turhan; Desmond, John E; Zhao, Zuo; Gabrieli, John D E

    2002-08-06

    Psychological studies have found better memory in women than men for emotional events, but the neural basis for this difference is unknown. We used event-related functional MRI to assess whether sex differences in memory for emotional stimuli is associated with activation of different neural systems in men and women. Brain activation in 12 men and 12 women was recorded while they rated their experience of emotional arousal in response to neutral and emotionally negative pictures. In a recognition memory test 3 weeks after scanning, highly emotional pictures were remembered best, and remembered better by women than by men. Men and women activated different neural circuits to encode stimuli effectively into memory even when the analysis was restricted to pictures rated equally arousing by both groups. Men activated significantly more structures than women in a network that included the right amygdala, whereas women activated significantly fewer structures in a network that included the left amygdala. Women had significantly more brain regions where activation correlated with both ongoing evaluation of emotional experience and with subsequent memory for the most emotionally arousing pictures. Greater overlap in brain regions sensitive to current emotion and contributing to subsequent memory may be a neural mechanism for emotions to enhance memory more powerfully in women than in men.

  16. Neural and psychological underpinnings of gambling disorder

    DEFF Research Database (Denmark)

    Grant, Jon E; Odlaug, Brian L; Chamberlain, Samuel R

    2016-01-01

    Gambling disorder affects 0.4 to 1.6% of adults worldwide, and is highly comorbid with other mental health disorders. This article provides a concise primer on the neural and psychological underpinnings of gambling disorder based on a selective review of the literature. Gambling disorder is assoc......Gambling disorder affects 0.4 to 1.6% of adults worldwide, and is highly comorbid with other mental health disorders. This article provides a concise primer on the neural and psychological underpinnings of gambling disorder based on a selective review of the literature. Gambling disorder...... is associated with dysfunction across multiple cognitive domains which can be considered in terms of impulsivity and compulsivity. Neuroimaging data suggest structural and functional abnormalities of networks involved in reward processing and top-down control. Gambling disorder shows 50-60% heritability...... is required to evaluate whether cognitive dysfunction and personality aspects influence the longitudinal course and treatment outcome for gambling disorder. It is hoped that improved understanding of the biological and psychological components of gambling disorder, and their interactions, may lead to improved...

  17. The neural basis of monitoring goal progress

    Directory of Open Access Journals (Sweden)

    Yael eBenn

    2014-09-01

    Full Text Available The neural basis of progress monitoring has received relatively little attention compared to other sub-processes that are involved in goal directed behavior such as motor control and response inhibition. Studies of error-monitoring have identified the dorsal anterior cingulate cortex (dACC as a structure that is sensitive to conflict detection, and triggers corrective action. However, monitoring goal progress involves monitoring correct as well as erroneous events over a period of time. In the present research, 20 healthy participants underwent fMRI while playing a game that involved monitoring progress towards either a numerical or a visuo-spatial target. The findings confirmed the role of the dACC in detecting situations in which the current state may conflict with the desired state, but also revealed activations in the frontal and parietal regions, pointing to the involvement of processes such as attention and working memory in monitoring progress over time. In addition, activation of the cuneus was associated with monitoring progress towards a specific target presented in the visual modality. This is the first time that activation in this region has been linked to higher-order processing of goal-relevant information, rather than low-level anticipation of visual stimuli. Taken together, these findings identify the neural substrates involved in monitoring progress over time, and how these extend beyond activations observed in conflict and error monitoring.

  18. A gentle introduction to artificial neural networks.

    Science.gov (United States)

    Zhang, Zhongheng

    2016-10-01

    Artificial neural network (ANN) is a flexible and powerful machine learning technique. However, it is under utilized in clinical medicine because of its technical challenges. The article introduces some basic ideas behind ANN and shows how to build ANN using R in a step-by-step framework. In topology and function, ANN is in analogue to the human brain. There are input and output signals transmitting from input to output nodes. Input signals are weighted before reaching output nodes according to their respective importance. Then the combined signal is processed by activation function. I simulated a simple example to illustrate how to build a simple ANN model using nnet() function. This function allows for one hidden layer with varying number of units in that layer. The basic structure of ANN can be visualized with plug-in plot.nnet() function. The plot function is powerful that it allows for varieties of adjustment to the appearance of the neural networks. Prediction with ANN can be performed with predict() function, similar to that of conventional generalized linear models. Finally, the prediction power of ANN is examined using confusion matrix and average accuracy. It appears that ANN is slightly better than conventional linear model.

  19. Bilingualism, dementia, cognitive and neural reserve.

    Science.gov (United States)

    Perani, Daniela; Abutalebi, Jubin

    2015-12-01

    We discuss the role of bilingualism as a source of cognitive reserve and we propose the putative neural mechanisms through which lifelong bilingualism leads to a neural reserve that delays the onset of dementia. Recent findings highlight that the use of more than one language affects the human brain in terms of anatomo-structural changes. It is noteworthy that recent evidence from different places and cultures throughout the world points to a significant delay of dementia onset in bilingual/multilingual individuals. This delay has been reported not only for Alzheimer's dementia and its prodromal mild cognitive impairment phase, but also for other dementias such as vascular and fronto-temporal dementia, and was found to be independent of literacy, education and immigrant status. Lifelong bilingualism represents a powerful cognitive reserve delaying the onset of dementia by approximately 4 years. As to the causal mechanism, because speaking more than one language heavily relies upon executive control and attention, brain systems handling these functions are more developed in bilinguals resulting in increases of gray and white matter densities that may help protect from dementia onset. These neurocognitive benefits are even more prominent when second language proficiency and exposure are kept high throughout life.

  20. MEMBRAIN NEURAL NETWORK FOR VISUAL PATTERN RECOGNITION

    Directory of Open Access Journals (Sweden)

    Artur Popko

    2013-06-01

    Full Text Available Recognition of visual patterns is one of significant applications of Artificial Neural Networks, which partially emulate human thinking in the domain of artificial intelligence. In the paper, a simplified neural approach to recognition of visual patterns is portrayed and discussed. This paper is dedicated for investigators in visual patterns recognition, Artificial Neural Networking and related disciplines. The document describes also MemBrain application environment as a powerful and easy to use neural networks’ editor and simulator supporting ANN.