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  1. The p53-dependent radioadaptive response

    Science.gov (United States)

    Ohnishi, Takeo

    We already reported that conditioning exposures at low doses, or at low dose-rates, lowered radiation-induced p53-dependent apoptosis in cultured cells in vitro and in the spleens of mice in vivo. In this study, the aim was to characterize the p53-dependent radioadaptive response at the molecular level. We used wild-type (wt) p53 and mutated (m) p53 containing cells derived from the human lung cancer H1299 cell line, which is p53-null. Cellular radiation sensitivities were determined with a colony-forming assay. The accumulation of p53, Hdm2, and iNOS was analyzed with Western blotting. The quantification of chromosomal aberrations was estimated by scoring dicentrics per cell. In wtp53 cells, it was demonstrated that the lack of p53 accumulation was coupled with the activation of Hdm2 after low dose irradiation (0.02 Gy). Although NO radicals were only minimally induced in wtp53 cells irradiated with a challenging irradiation (6 Gy) alone, NO radicals were seen to increase about 2-4 fold after challenging irradiation following a priming irradiation (0.02 Gy). Under similar irradiation conditions with a priming and challenging irradiation in wtp53 cells, induction of radioresistance and a depression of chromosomal aberrations were observed only in the absence of Pifithrin-α (a p53 inhibitor), RITA or Nutlin-3 (p53-Hdm2 interaction inhibitors), aminoguanidine (an iNOS inhibitor) and c-PTIO (an NO radical scavenger). On the other hand, in p53 dysfunctional cells, a radioadaptive response was not observed in the presence or absence of those inhibitors. Moreover, radioresistance developed when wtp53 cells were treated with ISDN (an NO generating agent) alone. These findings suggest that NO radicals are an initiator of the radioadaptive response acting through the activation of Hdm2 and the depression of p53 accumulations.

  2. Cellular inactivation of nitric oxide induces p53-dependent ...

    African Journals Online (AJOL)

    Tropical Journal of Pharmaceutical Research August 2016; 15 (8): 1595-1603 ... Cellular inactivation of nitric oxide induces p53-dependent apoptosis in ... apoptosis induced by a selective iNOS inhibitor, N-[(3-aminomethyl) benzyl] acetamidine (1400W), .... and nitrate. ... Nitrite production was measured in culture media.

  3. Viral single-strand DNA induces p53-dependent apoptosis in human embryonic stem cells.

    Science.gov (United States)

    Hirsch, Matthew L; Fagan, B Matthew; Dumitru, Raluca; Bower, Jacquelyn J; Yadav, Swati; Porteus, Matthew H; Pevny, Larysa H; Samulski, R Jude

    2011-01-01

    Human embryonic stem cells (hESCs) are primed for rapid apoptosis following mild forms of genotoxic stress. A natural form of such cellular stress occurs in response to recombinant adeno-associated virus (rAAV) single-strand DNA genomes, which exploit the host DNA damage response for replication and genome persistence. Herein, we discovered a unique DNA damage response induced by rAAV transduction specific to pluripotent hESCs. Within hours following rAAV transduction, host DNA damage signaling was elicited as measured by increased gamma-H2AX, ser15-p53 phosphorylation, and subsequent p53-dependent transcriptional activation. Nucleotide incorporation assays demonstrated that rAAV transduced cells accumulated in early S-phase followed by the induction of apoptosis. This lethal signaling sequalae required p53 in a manner independent of transcriptional induction of Puma, Bax and Bcl-2 and was not evident in cells differentiated towards a neural lineage. Consistent with a lethal DNA damage response induced upon rAAV transduction of hESCs, empty AAV protein capsids demonstrated no toxicity. In contrast, DNA microinjections demonstrated that the minimal AAV origin of replication and, in particular, a 40 nucleotide G-rich tetrad repeat sequence, was sufficient for hESC apoptosis. Our data support a model in which rAAV transduction of hESCs induces a p53-dependent lethal response that is elicited by a telomeric sequence within the AAV origin of replication.

  4. Viral single-strand DNA induces p53-dependent apoptosis in human embryonic stem cells.

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    Matthew L Hirsch

    Full Text Available Human embryonic stem cells (hESCs are primed for rapid apoptosis following mild forms of genotoxic stress. A natural form of such cellular stress occurs in response to recombinant adeno-associated virus (rAAV single-strand DNA genomes, which exploit the host DNA damage response for replication and genome persistence. Herein, we discovered a unique DNA damage response induced by rAAV transduction specific to pluripotent hESCs. Within hours following rAAV transduction, host DNA damage signaling was elicited as measured by increased gamma-H2AX, ser15-p53 phosphorylation, and subsequent p53-dependent transcriptional activation. Nucleotide incorporation assays demonstrated that rAAV transduced cells accumulated in early S-phase followed by the induction of apoptosis. This lethal signaling sequalae required p53 in a manner independent of transcriptional induction of Puma, Bax and Bcl-2 and was not evident in cells differentiated towards a neural lineage. Consistent with a lethal DNA damage response induced upon rAAV transduction of hESCs, empty AAV protein capsids demonstrated no toxicity. In contrast, DNA microinjections demonstrated that the minimal AAV origin of replication and, in particular, a 40 nucleotide G-rich tetrad repeat sequence, was sufficient for hESC apoptosis. Our data support a model in which rAAV transduction of hESCs induces a p53-dependent lethal response that is elicited by a telomeric sequence within the AAV origin of replication.

  5. Limited role of murine ATM in oncogene-induced senescence and p53-dependent tumor suppression.

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    Alejo Efeyan

    Full Text Available Recent studies in human fibroblasts have provided a new general paradigm of tumor suppression according to which oncogenic signaling produces DNA damage and this, in turn, results in ATM/p53-dependent cellular senescence. Here, we have tested this model in a variety of murine experimental systems. Overexpression of oncogenic Ras in murine fibroblasts efficiently induced senescence but this occurred in the absence of detectable DNA damage signaling, thus suggesting a fundamental difference between human and murine cells. Moreover, lung adenomas initiated by endogenous levels of oncogenic K-Ras presented abundant senescent cells, but undetectable DNA damage signaling. Accordingly, K-Ras-driven adenomas were also senescent in Atm-null mice, and the tumorigenic progression of these lesions was only modestly accelerated by Atm-deficiency. Finally, we have examined chemically-induced fibrosarcomas, which possess a persistently activated DNA damage response and are highly sensitive to the activity of p53. We found that the absence of Atm favored genomic instability in the resulting tumors, but did not affect the persistent DNA damage response and did not impair p53-dependent tumor suppression. All together, we conclude that oncogene-induced senescence in mice may occur in the absence of a detectable DNA damage response. Regarding murine Atm, our data suggest that it plays a minor role in oncogene-induced senescence or in p53-dependent tumor suppression, being its tumor suppressive activity probably limited to the maintenance of genomic stability.

  6. Abnormal mitosis triggers p53-dependent cell cycle arrest in human tetraploid cells.

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    Kuffer, Christian; Kuznetsova, Anastasia Yurievna; Storchová, Zuzana

    2013-08-01

    Erroneously arising tetraploid mammalian cells are chromosomally instable and may facilitate cell transformation. An increasing body of evidence shows that the propagation of mammalian tetraploid cells is limited by a p53-dependent arrest. The trigger of this arrest has not been identified so far. Here we show by live cell imaging of tetraploid cells generated by an induced cytokinesis failure that most tetraploids arrest and die in a p53-dependent manner after the first tetraploid mitosis. Furthermore, we found that the main trigger is a mitotic defect, in particular, chromosome missegregation during bipolar mitosis or spindle multipolarity. Both a transient multipolar spindle followed by efficient clustering in anaphase as well as a multipolar spindle followed by multipolar mitosis inhibited subsequent proliferation to a similar degree. We found that the tetraploid cells did not accumulate double-strand breaks that could cause the cell cycle arrest after tetraploid mitosis. In contrast, tetraploid cells showed increased levels of oxidative DNA damage coinciding with the p53 activation. To further elucidate the pathways involved in the proliferation control of tetraploid cells, we knocked down specific kinases that had been previously linked to the cell cycle arrest and p53 phosphorylation. Our results suggest that the checkpoint kinase ATM phosphorylates p53 in tetraploid cells after abnormal mitosis and thus contributes to proliferation control of human aberrantly arising tetraploids.

  7. p18(Hamlet) mediates different p53-dependent responses to DNA-damage inducing agents.

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    Lafarga, Vanesa; Cuadrado, Ana; Nebreda, Angel R

    2007-10-01

    Cells organize appropriate responses to environmental cues by activating specific signaling networks. Two proteins that play key roles in coordinating stress responses are the kinase p38alpha (MAPK14) and the transcription factor p53 (TP53). Depending on the nature and the extent of the stress-induced damage, cells may respond by arresting the cell cycle or by undergoing cell death, and these responses are usually associated with the phosphorylation of particular substrates by p38alpha as well as the activation of specific target genes by p53. We recently characterized a new p38alpha substrate, named p18(Hamlet) (ZNHIT1), which mediates p53-dependent responses to different genotoxic stresses. Thus, cisplatin or UV light induce stabilization of the p18(Hamlet) protein, which then enhances the ability of p53 to bind to and activate the promoters of pro-apoptotic genes such as NOXA and PUMA leading to apoptosis induction. In a similar way, we report here that p18(Hamlet) can also mediate the cell cycle arrest induced in response to gamma-irradiation, by participating in the p53-dependent upregulation of the cell cycle inhibitor p21(Cip1) (CDKN1A).

  8. CK1α ablation in keratinocytes induces p53-dependent, sunburn-protective skin hyperpigmentation.

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    Chang, Chung-Hsing; Kuo, Che-Jung; Ito, Takamichi; Su, Yu-Ya; Jiang, Si-Tse; Chiu, Min-Hsi; Lin, Yi-Hsiung; Nist, Andrea; Mernberger, Marco; Stiewe, Thorsten; Ito, Shosuke; Wakamatsu, Kazumasa; Hsueh, Yi-An; Shieh, Sheau-Yann; Snir-Alkalay, Irit; Ben-Neriah, Yinon

    2017-09-19

    Casein kinase 1α (CK1α), a component of the β-catenin destruction complex, is a critical regulator of Wnt signaling; its ablation induces both Wnt and p53 activation. To characterize the role of CK1α (encoded by Csnk1a1 ) in skin physiology, we crossed mice harboring floxed Csnk1a1 with mice expressing K14-Cre-ER T2 to generate mice in which tamoxifen induces the deletion of Csnk1a1 exclusively in keratinocytes [single-knockout (SKO) mice]. As expected, CK1α loss was accompanied by β-catenin and p53 stabilization, with the preferential induction of p53 target genes, but phenotypically most striking was hyperpigmentation of the skin, importantly without tumorigenesis, for at least 9 mo after Csnk1a1 ablation. The number of epidermal melanocytes and eumelanin levels were dramatically increased in SKO mice. To clarify the putative role of p53 in epidermal hyperpigmentation, we established K14-Cre-ER T2 CK1α/p53 double-knockout (DKO) mice and found that coablation failed to induce epidermal hyperpigmentation, demonstrating that it was p53-dependent. Transcriptome analysis of the epidermis revealed p53-dependent up-regulation of Kit ligand (KitL). SKO mice treated with ACK2 (a Kit-neutralizing antibody) or imatinib (a Kit inhibitor) abrogated the CK1α ablation-induced hyperpigmentation, demonstrating that it requires the KitL/Kit pathway. Pro-opiomelanocortin (POMC), a precursor of α-melanocyte-stimulating hormone (α-MSH), was not activated in the CK1α ablation-induced hyperpigmentation, which is in contrast to the mechanism of p53-dependent UV tanning. Nevertheless, acute sunburn effects were successfully prevented in the hyperpigmented skin of SKO mice. CK1α inhibition induces skin-protective eumelanin but no carcinogenic pheomelanin and may therefore constitute an effective strategy for safely increasing eumelanin via UV-independent pathways, protecting against acute sunburn.

  9. Implication of p53-dependent cellular senescence related gene, TARSH in tumor suppression

    International Nuclear Information System (INIS)

    Wakoh, Takeshi; Uekawa, Natsuko; Terauchi, Kunihiko; Sugimoto, Masataka; Ishigami, Akihito; Shimada, Jun-ichi; Maruyama, Mitsuo

    2009-01-01

    A novel target of NESH-SH3 (TARSH) was identified as a cellular senescence related gene in mouse embryonic fibroblasts (MEFs) replicative senescence, the expression of which has been suppressed in primary clinical lung cancer specimens. However, the molecular mechanism underlying the regulation of TARSH involved in pulmonary tumorigenesis remains unclear. Here we demonstrate that the reduction of TARSH gene expression by short hairpin RNA (shRNA) system robustly inhibited the MEFs proliferation with increase in senescence-associated β-galactosidase (SA-β-gal) activity. Using p53 -/- MEFs, we further suggest that this growth arrest by loss of TARSH is evoked by p53-dependent p21 Cip1 accumulation. Moreover, we also reveal that TARSH reduction induces multicentrosome in MEFs, which is linked in chromosome instability and tumor development. These results suggest that TARSH plays an important role in proliferation of replicative senescence and may serve as a trigger of tumor development.

  10. p21-LacZ reporter mice reflect p53-dependent toxic insult

    International Nuclear Information System (INIS)

    Vasey, Douglas B.; Wolf, C. Roland; MacArtney, Thomas; Brown, Ken; Whitelaw, C. Bruce A.

    2008-01-01

    There is an urgent need to discover less toxic and more selective drugs to treat disease. The use of transgenic mice that report on toxic insult-induced transcription can provide a valuable tool in this regard. To exemplify this strategy, we have generated transgenic mice carrying a p21-LacZ transgene. Transgene activity reflected endogenous p21 gene activation in various tissues, displayed compound-specific spatial expression signatures in the brain and immune tissues and enabled p53-dependent and p53-independent responses to be identified. We discuss the application of these mice in delineating the molecular events in normal cellular growth and disease and for the evaluation of drug toxicity

  11. HJURP regulates cellular senescence in human fibroblasts and endothelial cells via a p53-dependent pathway.

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    Heo, Jong-Ik; Cho, Jung Hee; Kim, Jae-Ryong

    2013-08-01

    Holliday junction recognition protein (HJURP), a centromere protein-A (CENP-A) histone chaperone, mediates centromere-specific assembly of CENP-A nucleosome, contributing to high-fidelity chromosome segregation during cell division. However, the role of HJURP in cellular senescence of human primary cells remains unclear. We found that the expression levels of HJURP decreased in human dermal fibroblasts and umbilical vein endothelial cells in replicative or premature senescence. Ectopic expression of HJURP in senescent cells partially overcame cell senescence. Conversely, downregulation of HJURP in young cells led to premature senescence. p53 knockdown, but not p16 knockdown, abolished senescence phenotypes caused by HJURP reduction. These data suggest that HJURP plays an important role in the regulation of cellular senescence through a p53-dependent pathway and might contribute to tissue or organismal aging and protection of cellular transformation.

  12. Molecular mechanism of X-ray-induced p53-dependent apoptosis

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    Nakano, Hisako [Tokyo Metropolitan Inst. of Medical Center (Japan)

    1999-03-01

    Radiation-induced cell death has been classified into the interphase- and mitotic-ones, both of which apoptosis involving. This review described the molecular mechanism of the apoptosis, focusing on its p53-dependent process. It is known that there are genes regulating cell death either negatively or positively and the latter is involved in apoptosis. As an important factor in the apoptosis, p53 has become remarkable since it was shown that X-ray-induced apoptosis required RNA and protein syntheses in thymocytes and those cells of p53 gene-depleted mouse were shown to be resistant to gamma-ray-induced apoptosis. Radiation sensitivity of MOLT-4 cells derived from human T cell leukemia, exhibiting the typical X-ray-induced p53-dependent apoptosis, depends on the levels of p53 mRNA and protein. p53 is a gene suppressing tumor and also a transcription factor. Consequently, mutation of p53 conceivably leads to the failure of cell cycle regulation, which allows damaged cells to divide without both repair and exclusion due to loss of the apoptotic mechanism, and finally results in carcinogenesis. The radiation effect occurs in the order of the cell damage, inhibition of p53-Mdm2 binding, accumulation of p53, activation of mdm2 transcription, Mdm2 accumulation, p53-protein degradation and recovery to the steady state level. Here, the cystein protease (caspases) plays an important role as a disposing mechanism for cells scheduled to die. However, many are unknown to be solved in future. (K.H.) 119 refs.

  13. Polycomb Group Protein PHF1 Regulates p53-dependent Cell Growth Arrest and Apoptosis*

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    Yang, Yang; Wang, Chenji; Zhang, Pingzhao; Gao, Kun; Wang, Dejie; Yu, Hongxiu; Zhang, Ting; Jiang, Sirui; Hexige, Saiyin; Hong, Zehui; Yasui, Akira; Liu, Jun O.; Huang, Haojie; Yu, Long

    2013-01-01

    Polycomb group protein PHF1 is well known as a component of a novel EED-EZH2·Polycomb repressive complex 2 complex and plays important roles in H3K27 methylation and Hox gene silencing. PHF1 is also involved in the response to DNA double-strand breaks in human cells, promotes nonhomologous end-joining processes through interaction with Ku70/Ku80. Here, we identified another function of PHF1 as a potential p53 pathway activator in a pathway screen using luminescence reporter assay. Subsequent studies showed PHF1 directly interacts with p53 proteins both in vivo and in vitro and co-localized in nucleus. PHF1 binds to the C-terminal regulatory domain of p53. Overexpression of PHF1 elevated p53 protein level and prolonged its turnover. Knockdown of PHF1 reduced p53 protein level and its target gene expression both in normal state and DNA damage response. Mechanically, PHF1 protects p53 proteins from MDM2-mediated ubiquitination and degradation. Furthermore, we showed that PHF1 regulates cell growth arrest and etoposide-induced apoptosis in a p53-dependent manner. Finally, PHF1 expression was significantly down-regulated in human breast cancer samples. Taken together, we establish PHF1 as a novel positive regulator of the p53 pathway. These data shed light on the potential roles of PHF1 in tumorigenesis and/or tumor progression. PMID:23150668

  14. Novel small molecule induces p53-dependent apoptosis in human colon cancer cells

    International Nuclear Information System (INIS)

    Park, Sang Eun; Min, Yong Ki; Ha, Jae Du; Kim, Bum Tae; Lee, Woo Ghil

    2007-01-01

    Using high-throughput screening with small-molecule libraries, we identified a compound, KCG165 [(2-(3-(2-(pyrrolidin-1-yl)ethoxy)-1,10b-dihydro-[1,2,4]triazolo[1,5-c] quinazolin-5(6H)-one)], which strongly activated p53-mediated transcriptional activity. KCG165-induced phosphorylations of p53 at Ser 6 , Ser 15 , and Ser 20 , which are all key residues involved in the activation and stabilization of p53. Consistent with these findings, KCG165 increased level of p53 protein and led to the accumulation of transcriptionally active p53 in the nucleus with the increased occupancy of p53 in the endogenous promoter region of its downstream target gene, p21 WAF1/CIP . Notably, KCG165-induced p53-dependent apoptosis in cancer cells. Furthermore, we suggested topoisomerase II as the molecular target of KCG165. Together, these results indicate that KCG165 may have potential applications as an antitumor agent

  15. Ribosomal stress induces L11- and p53-dependent apoptosis in mouse pluripotent stem cells.

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    Morgado-Palacin, Lucia; Llanos, Susana; Serrano, Manuel

    2012-02-01

    Ribosome biogenesis is the most demanding energetic process in proliferating cells and it is emerging as a critical sensor of cellular homeostasis. Upon disturbance of ribosome biogenesis, specific free ribosomal proteins, most notably L11, bind and inhibit Mdm2, resulting in activation of the tumor suppressor p53. This pathway has been characterized in somatic and cancer cells, but its function in embryonic pluripotent cells has remained unexplored. Here, we show that treatment with low doses of Actinomycin D or depletion of ribosomal protein L37, two well-established inducers of ribosomal stress, activate p53 in an L11-dependent manner in mouse embryonic stem cells (ESCs) and in induced pluripotent stem cells (iPSCs). Activation of p53 results in transcriptional induction of p53 targets, including p21, Mdm2, Pidd, Puma, Noxa and Bax. Finally, ribosomal stress elicits L11- and p53-dependent apoptosis in ESCs/iPSCs. These results extend to pluripotent cells the functionality of the ribosomal stress pathway and we speculate that this could be a relevant cellular checkpoint during early embryogenesis.

  16. P53-dependent ceramide generation in response ro ionizing irradiation is caspase-dependent

    International Nuclear Information System (INIS)

    Dbaibo, G.; El-Assaad, W.

    2000-01-01

    Full text.We have previously reported that p53-dependent apoptosis is accompanied by ceramide accumulation. Lack of p53 prevents ceramide accumulation in response to induces such as ionizing irradiation. The mechanisms of ceramide accumulation have not been explored. P53 has been reported to function by inducing the death receptors Fas and DR5 both of which function by initiating a caspase cascade that results in apoptosis. We decided to examine the role of caspases in the elevation of cellular ceramide levels. We treated Molt-4 cells with 5Gy of ionizing irradiation and examined the effects of co-treatment with the general caspase inhibitor z-VAD-fmk at concentration of 50 and 100μM. We found that z-VAD blocked apoptosis induced by irradiation without interfering with p53 accumulation indicating that it was not functioning upstream of p53. However, z-VAD treatment resulted in a significant decrease in ceramide accumulation. Additionally, z-VAD partially blocked the loss of glutathione in response to irradiation. This was important since glutathione has been described as an inhibitor of neutral sphindomyelinase, a major source of cellular ceramide via sphingomyelin hydrolysis. These studies indicate that p53 induces ceramide accumulation in a caspase-dependent manner and that the regulation of cellular glutathione by caspases may be a mechanism by which they regulate ceramide accumulation

  17. Nucleolus-derived mediators in oncogenic stress response and activation of p53-dependent pathways.

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    Stępiński, Dariusz

    2016-08-01

    Rapid growth and division of cells, including tumor ones, is correlated with intensive protein biosynthesis. The output of nucleoli, organelles where translational machineries are formed, depends on a rate of particular stages of ribosome production and on accessibility of elements crucial for their effective functioning, including substrates, enzymes as well as energy resources. Different factors that induce cellular stress also often lead to nucleolar dysfunction which results in ribosome biogenesis impairment. Such nucleolar disorders, called nucleolar or ribosomal stress, usually affect cellular functioning which in fact is a result of p53-dependent pathway activation, elicited as a response to stress. These pathways direct cells to new destinations such as cell cycle arrest, damage repair, differentiation, autophagy, programmed cell death or aging. In the case of impaired nucleolar functioning, nucleolar and ribosomal proteins mediate activation of the p53 pathways. They are also triggered as a response to oncogenic factor overexpression to protect tissues and organs against extensive proliferation of abnormal cells. Intentional impairment of any step of ribosome biosynthesis which would direct the cells to these destinations could be a strategy used in anticancer therapy. This review presents current knowledge on a nucleolus, mainly in relation to cancer biology, which is an important and extremely sensitive element of the mechanism participating in cellular stress reaction mediating activation of the p53 pathways in order to counteract stress effects, especially cancer development.

  18. Loss of ribosomal protein L11 affects zebrafish embryonic development through a p53-dependent apoptotic response.

    Directory of Open Access Journals (Sweden)

    Anirban Chakraborty

    Full Text Available Ribosome is responsible for protein synthesis in all organisms and ribosomal proteins (RPs play important roles in the formation of a functional ribosome. L11 was recently shown to regulate p53 activity through a direct binding with MDM2 and abrogating the MDM2-induced p53 degradation in response to ribosomal stress. However, the studies were performed in cell lines and the significance of this tumor suppressor function of L11 has yet to be explored in animal models. To investigate the effects of the deletion of L11 and its physiological relevance to p53 activity, we knocked down the rpl11 gene in zebrafish and analyzed the p53 response. Contrary to the cell line-based results, our data indicate that an L11 deficiency in a model organism activates the p53 pathway. The L11-deficient embryos (morphants displayed developmental abnormalities primarily in the brain, leading to embryonic lethality within 6-7 days post fertilization. Extensive apoptosis was observed in the head region of the morphants, thus correlating the morphological defects with apparent cell death. A decrease in total abundance of genes involved in neural patterning of the brain was observed in the morphants, suggesting a reduction in neural progenitor cells. Upregulation of the genes involved in the p53 pathway were observed in the morphants. Simultaneous knockdown of the p53 gene rescued the developmental defects and apoptosis in the morphants. These results suggest that ribosomal dysfunction due to the loss of L11 activates a p53-dependent checkpoint response to prevent improper embryonic development.

  19. Imiquimod activates p53-dependent apoptosis in a human basal cell carcinoma cell line.

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    Huang, Shi-Wei; Chang, Shu-Hao; Mu, Szu-Wei; Jiang, Hsin-Yi; Wang, Sin-Ting; Kao, Jun-Kai; Huang, Jau-Ling; Wu, Chun-Ying; Chen, Yi-Ju; Shieh, Jeng-Jer

    2016-03-01

    The tumor suppressor p53 controls DNA repair, cell cycle, apoptosis, autophagy and numerous other cellular processes. Imiquimod (IMQ), a synthetic toll-like receptor (TLR) 7 ligand for the treatment of superficial basal cell carcinoma (BCC), eliminates cancer cells by activating cell-mediated immunity and directly inducing apoptosis and autophagy in cancer cells. To evaluate the role of p53 in IMQ-induced cell death in skin cancer cells. The expression, phosphorylation and subcellular localization of p53 were detected by real-time PCR, luciferase reporter assay, cycloheximide chase analysis, immunoblotting and immunocytochemistry. Using BCC/KMC1 cell line as a model, the upstream signaling of p53 activation was dissected by over-expression of TLR7/8, the addition of ROS scavenger, ATM/ATR inhibitors and pan-caspase inhibitor. The role of p53 in IMQ-induced apoptosis and autophagy was assessed by genetically silencing p53 and evaluated by a DNA content assay, immunoblotting, LC3 puncta detection and acridine orange staining. IMQ induced p53 mRNA expression and protein accumulation, increased Ser15 phosphorylation, promoted nuclear translocation and up-regulated its target genes in skin cancer cells in a TLR7/8-independent manner. In BCC/KMC1 cells, the induction of p53 by IMQ was achieved through increased ROS production to stimulate the ATM/ATR-Chk1/Chk2 axis but was not mediated by inducing DNA damage. The pharmacological inhibition of ATM/ATR significantly suppressed IMQ-induced p53 activation and apoptosis. Silencing of p53 significantly decreased the IMQ-induced caspase cascade activation and apoptosis but enhanced autophagy. Mutant p53 skin cancer cell lines were more resistant to IMQ-induced apoptosis than wildtype p53 skin cancer cell lines. IMQ induced ROS production to stimulate ATM/ATR pathways and contributed to p53-dependent apoptosis in a skin basal cell carcinoma cell line BCC/KMC1. Copyright © 2015 Japanese Society for Investigative Dermatology

  20. REDOX IMAGING OF THE p53-DEPENDENT MITOCHONDRIAL REDOX STATE IN COLON CANCER EX VIVO

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    XU, HE N.; FENG, MIN; MOON, LILY; DOLLOFF, NATHAN; EL-DEIRY, WAFIK; LI, LIN Z.

    2015-01-01

    of the mitochondrial redox state in colon cancer and the first to indicate that at tissue level the mitochondrial redox state is p53 dependent. The findings should assist in our understanding on colon cancer pathology and developing new imaging biomarkers for clinical applications. PMID:26207147

  1. p53-Dependent radiation-induced apoptosis in vivo: relationship to Bcl-2 and Bax expression

    International Nuclear Information System (INIS)

    Hasegawa, Masatoshi; Suzuki, Yoshiyuki; Furuta, Masaya; Yamakawa, Michitaka; Maebayashi, Katsuya; Hayakawa, Kayoko; Saito, Yoshihiro; Mitsuhashi, Norio; Niibe, Hideo

    1997-01-01

    Purpose: A close correlation between p53 protein expression and radiation-induced apoptosis has already been reported, however, Bcl-2 and Bax expression and the ratio of Bcl-2 to Bax have been also suggested to play an important role in the regulation of apoptotic cell death. In this study, we investigated the relationship between p53-dependent radiation-induced apoptosis and expression of Bcl-2 and Bax by using human tumors transplanted into nude mice. Materials and Methods: Three human tumors (an ependymoblastoma, a glioblastoma, and a small cell lung cancer) were subcutaneously transplanted into nude mice and irradiated with single doses of 1, 2, 5, or 10 Gy. The tumors were excised 1, 3, 6, 12, 24, and 48 hours after irradiation, fixed in 10% formalin for 24 hours, and embedded in paraffin. Slides were stained with hematoxylin and eosin for morphologic examination. Immunohistochemical studies were performed with mouse monoclonal antibodies to demonstrate p53, p21 (WAF-1), Bcl-2, and Bax expression. TdT-mediated dUTP-biotin nick-end labeling (TUNEL) and electron microscopic studies were performed to identify apoptosis, and PCR-SSCP analysis was used to evaluate p53 gene mutation. Results: All of the tumors showed only a few cells undergoing apoptosis before irradiation. Beginning several hours after irradiation, only the ependymoblastoma showed a large increase in the number of cells undergoing apoptosis, peaking at 6 hours after irradiation, and there was a clear dose-effect relationship. In contrast, the other tumors showed much less change following irradiation, and the dose-effect relationship was not as clear as in the ependymoblastoma. Immunohistochemically, the non-irradiated ependymoblastoma was negative for p53, p21, Bcl-2, and Bax. Following irradiation, however, many of the tumor cells became positive for p53 and p21, and a few cells became positive for bcl-2. In contrast, the glioblastoma and the small cell lung cancer were positive for p53 and Bcl-2

  2. p53-dependent inhibition of TrxR1 contributes to the tumor-specific induction of apoptosis by RITA.

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    Hedström, Elisabeth; Eriksson, Sofi; Zawacka-Pankau, Joanna; Arnér, Elias S J; Selivanova, Galina

    2009-11-01

    Thioredoxin reductase 1 (TrxR1) is a key regulator in many redox-dependent cellular pathways, and is often overexpressed in cancer. Several studies have identified TrxR1 as a potentially important target for anticancer therapy. The low molecular weight compound RITA (NSC 652287) binds p53 and induces p53-dependent apoptosis. Here we found that RITA also targets TrxR1 by non-covalent binding, followed by inhibition of its activity in vitro and by inhibition of TrxR activity in cancer cells. Interestingly, a novel approximately 130 kDa form of TrxR1, presumably representing a stable covalently linked dimer, and an increased generation of reactive oxygen species (ROS) were induced by RITA in cancer cells in a p53-dependent manner. Similarly, the gold-based TrxR inhibitor auranofin induced apoptosis related to oxidative stress, but independently of p53 and without apparent induction of the approximately 130 kDa form of TrxR1. In contrast to the effects observed in cancer cells, RITA did not inhibit TrxR or ROS formation in normal fibroblasts (NHDF). The inhibition of TrxR1 can sensitize tumor cells to agents that induce oxidative stress and may directly trigger cell death. Thus, our results suggest that a unique p53-dependent effect of RITA on TrxR1 in cancer cells might synergize with p53-dependent induction of pro-apoptotic genes and oxidative stress, thereby leading to a robust induction of cancer cell death, without affecting non-transformed cells.

  3. Astrocytes Can Adopt Endothelial Cell Fates in a p53-Dependent Manner.

    Science.gov (United States)

    Brumm, Andrew J; Nunez, Stefanie; Doroudchi, Mehdi M; Kawaguchi, Riki; Duan, Jinhzu; Pellegrini, Matteo; Lam, Larry; Carmichael, S Thomas; Deb, Arjun; Hinman, Jason D

    2017-08-01

    Astrocytes respond to a variety of CNS injuries by cellular enlargement, process outgrowth, and upregulation of extracellular matrix proteins that function to prevent expansion of the injured region. This astrocytic response, though critical to the acute injury response, results in the formation of a glial scar that inhibits neural repair. Scar-forming cells (fibroblasts) in the heart can undergo mesenchymal-endothelial transition into endothelial cell fates following cardiac injury in a process dependent on p53 that can be modulated to augment cardiac repair. Here, we sought to determine whether astrocytes, as the primary scar-forming cell of the CNS, are able to undergo a similar cellular phenotypic transition and adopt endothelial cell fates. Serum deprivation of differentiated astrocytes resulted in a change in cellular morphology and upregulation of endothelial cell marker genes. In a tube formation assay, serum-deprived astrocytes showed a substantial increase in vessel-like morphology that was comparable to human umbilical vein endothelial cells and dependent on p53. RNA sequencing of serum-deprived astrocytes demonstrated an expression profile that mimicked an endothelial rather than astrocyte transcriptome and identified p53 and angiogenic pathways as specifically upregulated. Inhibition of p53 with genetic or pharmacologic strategies inhibited astrocyte-endothelial transition. Astrocyte-endothelial cell transition could also be modulated by miR-194, a microRNA downstream of p53 that affects expression of genes regulating angiogenesis. Together, these studies demonstrate that differentiated astrocytes retain a stimulus-dependent mechanism for cellular transition into an endothelial phenotype that may modulate formation of the glial scar and promote injury-induced angiogenesis.

  4. Berberine induces p53-dependent cell cycle arrest and apoptosis of human osteosarcoma cells by inflicting DNA damage

    International Nuclear Information System (INIS)

    Liu Zhaojian; Liu Qiao; Xu Bing; Wu Jingjing; Guo Chun; Zhu Faliang; Yang Qiaozi; Gao Guimin; Gong Yaoqin; Shao Changshun

    2009-01-01

    Alkaloid berberine is widely used for the treatment of diarrhea and other diseases. Many laboratory studies showed that it exhibits anti-proliferative activity against a wide spectrum of cancer cells in culture. In this report we studied the mechanisms underlying the inhibitory effects of berberine on human osteosarcoma cells and on normal osteoblasts. The inhibition was largely attributed to cell cycle arrest at G1 and G2/M, and to a less extent, to apoptosis. The G1 arrest was dependent on p53, as G1 arrest was abolished in p53-deficient osteosarcoma cells. The induction of G1 arrest and apoptosis was accompanied by a p53-dependent up-regulation of p21 and pro-apoptotic genes. However, the G2/M arrest could be induced by berberine regardless of the status of p53. Interestingly, DNA double-strand breaks, as measured by the phosphorylation of H2AX, were remarkably accumulated in berberine-treated cells in a dose-dependent manner. Thus, one major mechanism by which berberine exerts its growth-inhibitory effect is to inflict genomic lesions on cells, which in turn trigger the activation of p53 and the p53-dependent cellular responses including cell cycle arrest and apoptosis

  5. Berberine induces p53-dependent cell cycle arrest and apoptosis of human osteosarcoma cells by inflicting DNA damage

    Energy Technology Data Exchange (ETDEWEB)

    Liu Zhaojian; Liu Qiao; Xu Bing; Wu Jingjing [Key Laboratory of Experimental Teratology of Ministry of Education and Institute of Molecular Medicine and Genetics, Shandong University School of Medicine, Jinan, Shandong 250012 (China); Guo Chun; Zhu Faliang [Institute of Immunology, Shandong University School of Medicine, Jinan, Shandong 250012 (China); Yang Qiaozi [Department of Genetics, Rutgers University, Piscataway, NJ 08854 (United States); Gao Guimin [Key Laboratory of Experimental Teratology of Ministry of Education and Institute of Molecular Medicine and Genetics, Shandong University School of Medicine, Jinan, Shandong 250012 (China); Gong Yaoqin [Key Laboratory of Experimental Teratology of Ministry of Education and Institute of Molecular Medicine and Genetics, Shandong University School of Medicine, Jinan, Shandong 250012 (China)], E-mail: yxg8@sdu.edu.cn; Shao Changshun [Key Laboratory of Experimental Teratology of Ministry of Education and Institute of Molecular Medicine and Genetics, Shandong University School of Medicine, Jinan, Shandong 250012 (China); Department of Genetics, Rutgers University, Piscataway, NJ 08854 (United States)], E-mail: shao@biology.rutgers.edu

    2009-03-09

    Alkaloid berberine is widely used for the treatment of diarrhea and other diseases. Many laboratory studies showed that it exhibits anti-proliferative activity against a wide spectrum of cancer cells in culture. In this report we studied the mechanisms underlying the inhibitory effects of berberine on human osteosarcoma cells and on normal osteoblasts. The inhibition was largely attributed to cell cycle arrest at G1 and G2/M, and to a less extent, to apoptosis. The G1 arrest was dependent on p53, as G1 arrest was abolished in p53-deficient osteosarcoma cells. The induction of G1 arrest and apoptosis was accompanied by a p53-dependent up-regulation of p21 and pro-apoptotic genes. However, the G2/M arrest could be induced by berberine regardless of the status of p53. Interestingly, DNA double-strand breaks, as measured by the phosphorylation of H2AX, were remarkably accumulated in berberine-treated cells in a dose-dependent manner. Thus, one major mechanism by which berberine exerts its growth-inhibitory effect is to inflict genomic lesions on cells, which in turn trigger the activation of p53 and the p53-dependent cellular responses including cell cycle arrest and apoptosis.

  6. Oncogenic RAS enables DNA damage- and p53-dependent differentiation of acute myeloid leukemia cells in response to chemotherapy.

    Directory of Open Access Journals (Sweden)

    Mona Meyer

    Full Text Available Acute myeloid leukemia (AML is a clonal disease originating from myeloid progenitor cells with a heterogeneous genetic background. High-dose cytarabine is used as the standard consolidation chemotherapy. Oncogenic RAS mutations are frequently observed in AML, and are associated with beneficial response to cytarabine. Why AML-patients with oncogenic RAS benefit most from high-dose cytarabine post-remission therapy is not well understood. Here we used bone marrow cells expressing a conditional MLL-ENL-ER oncogene to investigate the interaction of oncogenic RAS and chemotherapeutic agents. We show that oncogenic RAS synergizes with cytotoxic agents such as cytarabine in activation of DNA damage checkpoints, resulting in a p53-dependent genetic program that reduces clonogenicity and increases myeloid differentiation. Our data can explain the beneficial effects observed for AML patients with oncogenic RAS treated with higher dosages of cytarabine and suggest that induction of p53-dependent differentiation, e.g. by interfering with Mdm2-mediated degradation, may be a rational approach to increase cure rate in response to chemotherapy. The data also support the notion that the therapeutic success of cytotoxic drugs may depend on their ability to promote the differentiation of tumor-initiating cells.

  7. Genomic alterations during p53-dependent apoptosis induced by γ-irradiation of Molt-4 leukemia cells.

    Directory of Open Access Journals (Sweden)

    Rouba Hage-Sleiman

    Full Text Available Molt-4 leukemia cells undergo p53-dependent apoptosis accompanied by accumulation of de novo ceramide after 14 hours of γ-irradiation. In order to identify the potential mediators involved in ceramide accumulation and the cell death response, differentially expressed genes were identified by Affymetrix Microarray Analysis. Molt-4-LXSN cells, expressing wild type p53, and p53-deficient Molt-4-E6 cells were irradiated and harvested at 3 and 8 hours post-irradiation. Human genome U133 plus 2.0 array containing >47,000 transcripts was used for gene expression profiling. From over 10,000 probes, 281 and 12 probes were differentially expressed in Molt-4-LXSN and Molt-4-E6 cells, respectively. Data analysis revealed 63 (upregulated and 20 (downregulated genes (>2 fold in Molt-4-LXSN at 3 hours and 140 (upregulated and 21 (downregulated at 8 hours post-irradiation. In Molt-4-E6 cells, 5 (upregulated genes each were found at 3 hours and 8 hours, respectively. In Molt-4-LXSN cells, a significant fraction of the genes with altered expression at 3 hours were found to be involved in apoptosis signaling pathway (BCL2L11, p53 pathway (PMAIP1, CDKN1A and FAS and oxidative stress response (FDXR, CROT and JUN. Similarly, at 8 hours the genes with altered expression were involved in the apoptosis signaling pathway (BAX, BIK and JUN, p53 pathway (BAX, CDKN1A and FAS, oxidative stress response (FDXR and CROT and p53 pathway feedback loops 2 (MDM2 and CDKN1A. A global molecular and biological interaction map analysis showed an association of these altered genes with apoptosis, senescence, DNA damage, oxidative stress, cell cycle arrest and caspase activation. In a targeted study, activation of apoptosis correlated with changes in gene expression of some of the above genes and revealed sequential activation of both intrinsic and extrinsic apoptotic pathways that precede ceramide accumulation and subsequent execution of apoptosis. One or more of these altered genes

  8. p53-dependent and p53-independent anticancer activity of a new indole derivative in human osteosarcoma cells

    Energy Technology Data Exchange (ETDEWEB)

    Cappadone, C., E-mail: concettina.cappadone@unibo.it [Department of Pharmacy and Biotechnology, University of Bologna, Bologna (Italy); Stefanelli, C. [Department for Life Quality Studies, University of Bologna, Rimini Campus, Rimini (Italy); Malucelli, E. [Department of Pharmacy and Biotechnology, University of Bologna, Bologna (Italy); Zini, M. [Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna (Italy); Onofrillo, C. [Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Bologna (Italy); Locatelli, A.; Rambaldi, M.; Sargenti, A. [Department of Pharmacy and Biotechnology, University of Bologna, Bologna (Italy); Merolle, L. [ELETTRA–Sincrotrone Trieste S.C.p.A., Trieste (Italy); Farruggia, G. [Department of Pharmacy and Biotechnology, University of Bologna, Bologna (Italy); National Institute of Biostructures and Biosystems, Roma (Italy); Graziadio, A. [Department of Pharmacy and Biotechnology, University of Bologna, Bologna (Italy); Montanaro, L. [Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Bologna (Italy); Iotti, S. [Department of Pharmacy and Biotechnology, University of Bologna, Bologna (Italy); National Institute of Biostructures and Biosystems, Roma (Italy)

    2015-11-13

    Osteosarcoma (OS) is the most common primary malignant tumor of bone, occurring most frequently in children and adolescents. The mechanism of formation and development of OS have been studied for a long time. Tumor suppressor pathway governed by p53 gene are known to be involved in the pathogenesis of osteosarcoma. Moreover, loss of wild-type p53 activity is thought to be a major predictor of failure to respond to chemotherapy in various human cancers. In previous studies, we described the activity of a new indole derivative, NSC743420, belonging to the tubulin inhibitors family, capable to induce apoptosis and arrest of the cell cycle in the G2/M phase of various cancer cell lines. However, this molecule has never been tested on OS cell line. Here we address the activity of NSC743420 by examine whether differences in the p53 status could influence its effects on cell proliferation and death of OS cells. In particular, we compared the effect of the tested molecule on p53-wild type and p53-silenced U2OS cells, and on SaOS2 cell line, which is null for p53. Our results demonstrated that NSC743420 reduces OS cell proliferation by p53-dependent and p53-independent mechanisms. In particular, the molecule induces proliferative arrest that culminate to apoptosis in SaOS2 p53-null cells, while it brings a cytostatic and differentiating effect in U2OS cells, characterized by the cell cycle arrest in G0/G1 phase and increased alkaline phosphatase activity. - Highlights: • The indole derivative NSC743420 induces antitumor effects on osteosarcoma cells. • p53 status could drive the activity of antitumor agents on osteosarcoma cells. • NSC743420 induces cytostatic and differentiating effects on U2OS cells. • NSC743420 causes apoptosis on p53-null SaOS2 cells.

  9. Rapamycin and CHIR99021 Coordinate Robust Cardiomyocyte Differentiation From Human Pluripotent Stem Cells Via Reducing p53-Dependent Apoptosis.

    Science.gov (United States)

    Qiu, Xiao-Xu; Liu, Yang; Zhang, Yi-Fan; Guan, Ya-Na; Jia, Qian-Qian; Wang, Chen; Liang, He; Li, Yong-Qin; Yang, Huang-Tian; Qin, Yong-Wen; Huang, Shuang; Zhao, Xian-Xian; Jing, Qing

    2017-10-02

    Cardiomyocytes differentiated from human pluripotent stem cells can serve as an unexhausted source for a cellular cardiac disease model. Although small molecule-mediated cardiomyocyte differentiation methods have been established, the differentiation efficiency is relatively unsatisfactory in multiple lines due to line-to-line variation. Additionally, hurdles including line-specific low expression of endogenous growth factors and the high apoptotic tendency of human pluripotent stem cells also need to be overcome to establish robust and efficient cardiomyocyte differentiation. We used the H9-human cardiac troponin T-eGFP reporter cell line to screen for small molecules that promote cardiac differentiation in a monolayer-based and growth factor-free differentiation model. We found that collaterally treating human pluripotent stem cells with rapamycin and CHIR99021 during the initial stage was essential for efficient and reliable cardiomyocyte differentiation. Moreover, this method maintained consistency in efficiency across different human embryonic stem cell and human induced pluripotent stem cell lines without specifically optimizing multiple parameters (the efficiency in H7, H9, and UQ1 human induced pluripotent stem cells is 98.3%, 93.3%, and 90.6%, respectively). This combination also increased the yield of cardiomyocytes (1:24) and at the same time reduced medium consumption by about 50% when compared with the previous protocols. Further analysis indicated that inhibition of the mammalian target of rapamycin allows efficient cardiomyocyte differentiation through overcoming p53-dependent apoptosis of human pluripotent stem cells during high-density monolayer culture via blunting p53 translation and mitochondrial reactive oxygen species production. We have demonstrated that mammalian target of rapamycin exerts a stage-specific and multifaceted regulation over cardiac differentiation and provides an optimized approach for generating large numbers of functional

  10. p53-dependent control of cell death by nicastrin: lack of requirement for presenilin-dependent gamma-secretase complex.

    Science.gov (United States)

    Pardossi-Piquard, Raphaëlle; Dunys, Julie; Giaime, Emilie; Guillot-Sestier, Marie-Victoire; St George-Hyslop, Peter; Checler, Frédéric; Alves da Costa, Cristine

    2009-04-01

    Nicastrin (NCT) is a component of the presenilin (PS)-dependent gamma-secretase complexes that liberate amyloid beta-peptides from the beta-Amyloid Precursor Protein. Several lines of evidence indicate that the members of these complexes could also contribute to the control of cell death. Here we show that over-expression of NCT increases the viability of human embryonic kidney (HEK293) cells and decreases staurosporine (STS)- and thapsigargin (TPS)-induced caspase-3 activation in various cell lines from human and neuronal origins by Akt-dependent pathway. NCT lowers p53 expression, transcriptional activity and promoter transactivation and reduces p53 phosphorylation. NCT-associated protection against STS-stimulated cell death was completely abolished by p53 deficiency. Conversely, the depletion of NCT drastically enhances STS-induced caspase-3 activation and p53 pathway and favored p53 nuclear translocation. We examined whether NCT protective function depends on PS-dependent gamma-secretase activity. First, a 29-amino acid deletion known to reduce NCT-dependent amyloid beta-peptide production did not affect NCT-associated protective phenotype. Second, NCT still reduces STS-induced caspase-3 activation in fibroblasts lacking PS1 and PS2. Third, the gamma-secretase inhibitor DFK167 did not affect NCT-mediated reduction of p53 activity. Altogether, our study indicates that NCT controls cell death via phosphoinositide 3-kinase/Akt and p53-dependent pathways and that this function remains independent of the activity and molecular integrity of the gamma-secretase complexes.

  11. Porcine epidemic diarrhea virus through p53-dependent pathway causes cell cycle arrest in the G0/G1 phase.

    Science.gov (United States)

    Sun, Pei; Wu, Haoyang; Huang, Jiali; Xu, Ying; Yang, Feng; Zhang, Qi; Xu, Xingang

    2018-05-22

    Porcine epidemic diarrhea virus (PEDV), an enteropathogenic Alphacoronavirus, has caused enormous economic losses in the swine industry. p53 protein exists in a wide variety of animal cells, which is involved in cell cycle regulation, apoptosis, cell differentiation and other biological functions. In this study, we investigated the effects of PEDV infection on the cell cycle of Vero cells and p53 activation. The results demonstrated that PEDV infection induces cell cycle arrest at G0/G1 phase in Vero cells, while UV-inactivated PEDV does not cause cell cycle arrest. PEDV infection up-regulates the levels of p21, cdc2, cdk2, cdk4, Cyclin A protein and down-regulates Cyclin E protein. Further research results showed that inhibition of p53 signaling pathway can reverse the cell cycle arrest in G0/G1 phase induced by PEDV infection and cancel out the up-regulation of p21 and corresponding Cyclin/cdk mentioned above. In addition, PEDV infection of the cells synchronized in various stages of cell cycle showed that viral subgenomic RNA and virus titer were higher in the cells released from G0/G1 phase synchronized cells than that in the cells released from the G1/S phase and G2/M phase synchronized or asynchronous cells after 18 h p.i.. This is the first report to demonstrate that the p53-dependent pathway plays an important role in PEDV induced cell cycle arrest and beneficially contributes to viral infection. Copyright © 2018 Elsevier B.V. All rights reserved.

  12. p53-dependent and p53-independent anticancer activity of a new indole derivative in human osteosarcoma cells

    International Nuclear Information System (INIS)

    Cappadone, C.; Stefanelli, C.; Malucelli, E.; Zini, M.; Onofrillo, C.; Locatelli, A.; Rambaldi, M.; Sargenti, A.; Merolle, L.; Farruggia, G.; Graziadio, A.; Montanaro, L.; Iotti, S.

    2015-01-01

    Osteosarcoma (OS) is the most common primary malignant tumor of bone, occurring most frequently in children and adolescents. The mechanism of formation and development of OS have been studied for a long time. Tumor suppressor pathway governed by p53 gene are known to be involved in the pathogenesis of osteosarcoma. Moreover, loss of wild-type p53 activity is thought to be a major predictor of failure to respond to chemotherapy in various human cancers. In previous studies, we described the activity of a new indole derivative, NSC743420, belonging to the tubulin inhibitors family, capable to induce apoptosis and arrest of the cell cycle in the G2/M phase of various cancer cell lines. However, this molecule has never been tested on OS cell line. Here we address the activity of NSC743420 by examine whether differences in the p53 status could influence its effects on cell proliferation and death of OS cells. In particular, we compared the effect of the tested molecule on p53-wild type and p53-silenced U2OS cells, and on SaOS2 cell line, which is null for p53. Our results demonstrated that NSC743420 reduces OS cell proliferation by p53-dependent and p53-independent mechanisms. In particular, the molecule induces proliferative arrest that culminate to apoptosis in SaOS2 p53-null cells, while it brings a cytostatic and differentiating effect in U2OS cells, characterized by the cell cycle arrest in G0/G1 phase and increased alkaline phosphatase activity. - Highlights: • The indole derivative NSC743420 induces antitumor effects on osteosarcoma cells. • p53 status could drive the activity of antitumor agents on osteosarcoma cells. • NSC743420 induces cytostatic and differentiating effects on U2OS cells. • NSC743420 causes apoptosis on p53-null SaOS2 cells.

  13. FGF1 protects neuroblastoma SH-SY5Y cells from p53-dependent apoptosis through an intracrine pathway regulated by FGF1 phosphorylation

    Science.gov (United States)

    Pirou, Caroline; Montazer-Torbati, Fatemeh; Jah, Nadège; Delmas, Elisabeth; Lasbleiz, Christelle; Mignotte, Bernard; Renaud, Flore

    2017-01-01

    Neuroblastoma, a sympathetic nervous system tumor, accounts for 15% of cancer deaths in children. In contrast to most human tumors, p53 is rarely mutated in human primary neuroblastoma, suggesting impaired p53 activation in neuroblastoma. Various studies have shown correlations between fgf1 expression levels and both prognosis severity and tumor chemoresistance. As we previously showed that fibroblast growth factor 1 (FGF1) inhibited p53-dependent apoptosis in neuron-like PC12 cells, we initiated the study of the interaction between the FGF1 and p53 pathways in neuroblastoma. We focused on the activity of either extracellular FGF1 by adding recombinant rFGF1 in media, or of intracellular FGF1 by overexpression in human SH-SY5Y and mouse N2a neuroblastoma cell lines. In both cell lines, the genotoxic drug etoposide induced a classical mitochondrial p53-dependent apoptosis. FGF1 was able to inhibit p53-dependent apoptosis upstream of mitochondrial events in SH-SY5Y cells by both extracellular and intracellular pathways. Both rFGF1 addition and etoposide treatment increased fgf1 expression in SH-SY5Y cells. Conversely, rFGF1 or overexpressed FGF1 had no effect on p53-dependent apoptosis and fgf1 expression in neuroblastoma N2a cells. Using different FGF1 mutants (that is, FGF1K132E, FGF1S130A and FGF1S130D), we further showed that the C-terminal domain and phosphorylation of FGF1 regulate its intracrine anti-apoptotic activity in neuroblastoma SH-SY5Y cells. This study provides the first evidence for a role of an intracrine growth factor pathway on p53-dependent apoptosis in neuroblastoma, and could lead to the identification of key regulators involved in neuroblastoma tumor progression and chemoresistance. PMID:29048426

  14. The monofunctional alkylating agent N-methyl-N'-nitro-N-nitrosoguanidine triggers apoptosis through p53-dependent and -independent pathways

    International Nuclear Information System (INIS)

    Kim, W.-J.; Beardsley, Dillon I.; Adamson, Aaron W.; Brown, Kevin D.

    2005-01-01

    One of the cellular responses to DNA damaging events is the activation of programmed cell death, also known as apoptosis. Apoptosis is an important process in limiting tumorigenesis by eliminating cells with damaged DNA. This view is reinforced by the finding that many genes with pro-apoptotic function are absent or altered in cancer cells. The tumor suppressor p53 performs a significant role in apoptotic signaling by controlling expression of a host of genes that have pro-apoptotic or pro-survival function. The S N 1 DNA alkylating agent N-methyl-N'-nitro-N-nitrosoguanidine (MNNG) triggers apoptosis and the upregulation/phosphorylation of p53; however, the mechanism(s) governing MNNG-induced cell death remain unresolved. We observed that the human lymphoblastoid cell line WTK-1, which expresses mutant p53, shows far less sensitivity to the cytotoxic effects of MNNG than the closely related, p53-normal line TK-6. Exposure to 15 μM MNNG (LD50 at 24 h in TK-6) leads to a kinetically slower rate of apoptotic onset in WTK-1 cells compared to TK-6 as judged by viability assays and approaches that directly examine apoptotic onset. Similar results were obtained using an unrelated human lymphoblastoid line B310 expressing reduced levels of p53 due to E6 oncoprotein expression, indicating that MNNG activates both p53-dependent and -independent apoptotic mechanisms and that these two mechanisms are discernable by the rates which they trigger apoptotic onset. We document, during time points corresponding to peak apoptotic response in TK6, WTK-1, B310, and B310-E6, that these cell lines show marked decreases in mitochondrial transmembrane potential and increases in cytochrome c within the cytosolic fraction of MNNG-treated cells. Consistent with these events, we observed that both caspase-9 and -3 are activated in our panel of lymphoblastoid cells after MNNG exposure. We also found, using both broad spectrum and specific inhibitors, that blocking caspase activity in TK-6 and

  15. Iron oxide magnetic nanoparticles combined with actein suppress non-small-cell lung cancer growth in a p53-dependent manner

    Directory of Open Access Journals (Sweden)

    Wang MS

    2017-10-01

    Full Text Available Ming-Shan Wang,1 Liang Chen,2 Ya-Qiong Xiong,2 Jing Xu,2 Ji-Peng Wang,2 Zi-Li Meng2 1Department of Oncology, Huaiyin Hospital of Huai’an City, Huai’an, China; 2Department of Respiration, Huai’an First People’s Hospital, Nanjing Medical University, Huai’an, China Abstract: Actein (AT is a triterpene glycoside isolated from the rhizomes of Cimicifuga foetida that has been investigated for its antitumor effects. AT treatment leads to apoptosis in various cell types, including breast cancer cells, by regulating different signaling pathways. Iron oxide (Fe3O4 magnetic nanoparticles (MNPs are nanomaterials with biocompatible activity and low toxicity. In the present study, the possible benefits of AT in combination with MNPs on non-small-cell lung cancer (NSCLC were explored in in vitro and in vivo studies. AT-MNP treatment contributed to apoptosis in NSCLC cells, as evidenced by activation of the caspase 3-signaling pathway, which was accompanied by downregulation of the antiapoptotic proteins Bcl2 and BclXL, and upregulation of the proapoptotic signals Bax and Bad. The death receptors of TRAIL were also elevated following AT-MNP treatment in a p53-dependent manner. Furthermore, a mouse xenograft model in vivo revealed that AT-MNP treatment exhibited no toxicity and suppressed NSCLC growth compared to either AT or MNP monotherapies. In conclusion, this study suggests a novel therapy to induce apoptosis in suppressing NSCLC growth in a p53-dependent manner by combining AT with Fe3O4 MNPs. Keywords: actein, Fe3O4 magnetic nanoparticles, NSCLC, apoptosis, p53

  16. Transcriptome analysis of the zebrafish model of Diamond-Blackfan anemia from RPS19 deficiency via p53-dependent and -independent pathways.

    Directory of Open Access Journals (Sweden)

    Qiong Jia

    Full Text Available Diamond-Blackfan anemia (DBA is a rare inherited bone marrow failure syndrome that is characterized by pure red-cell aplasia and associated physical deformities. It has been proven that defects of ribosomal proteins can lead to this disease and that RPS19 is the most frequently mutated gene in DBA patients. Previous studies suggest that p53-dependent genes and pathways play important roles in RPS19-deficient embryos. However, whether there are other vital factors linked to DBA has not been fully clarified. In this study, we compared the whole genome RNA-Seq data of zebrafish embryos injected with RPS19 morpholino (RPS19 MO, RPS19 and p53 morpholino simultaneously (RPS19+p53 MO and control morpholino (control. We found that genes enriched in the functions of hematological systems, nervous system development and skeletal and muscular disorders had significant differential expression in RPS19 MO embryos compared with controls. Co-inhibition of p53 partially alleviates the abnormalities for RPS19-deficient embryos. However, the hematopoietic genes, which were down-regulated significantly in RPS19 MO embryos, were not completely recovered by the co-inhibition of p53. Furthermore, we identified the genome-wide p53-dependent and -independent genes and pathways. These results indicate that not only p53 family members but also other factors have important impacts on RPS19-deficient embryos. The detection of potential pathogenic genes and pathways provides us a new paradigm for future research on DBA, which is a systematic and complex hereditary disease.

  17. The MDM2-inhibitor Nutlin-3 synergizes with cisplatin to induce p53 dependent tumor cell apoptosis in non-small cell lung cancer

    Science.gov (United States)

    Deben, Christophe; Wouters, An; de Beeck, Ken Op; van Den Bossche, Jolien; Jacobs, Julie; Zwaenepoel, Karen; Peeters, Marc; Van Meerbeeck, Jan; Lardon, Filip; Rolfo, Christian; Deschoolmeester, Vanessa; Pauwels, Patrick

    2015-01-01

    The p53/MDM2 interaction has been a well-studied target for new drug design leading to the development of the small molecule inhibitor Nutlin-3. Our objectives were to combine Nutlin-3 with cisplatin (CDDP), a well-known activator of the p53 pathway, in a series of non-small cell lung cancer cell lines in order to increase the cytotoxic response to CDDP. We report that sequential treatment (CDDP followed by Nutlin-3), but not simultaneous treatment, resulted in strong synergism. Combination treatment induced p53's transcriptional activity, resulting in increased mRNA and protein levels of MDM2, p21, PUMA and BAX. In addition we report the induction of a strong p53 dependent apoptotic response and induction of G2/M cell cycle arrest. The strongest synergistic effect was observed at low doses of both CDDP and Nutlin-3, which could result in fewer (off-target) side effects while maintaining a strong cytotoxic effect. Our results indicate a promising preclinical potential, emphasizing the importance of the applied treatment scheme and the presence of wild type p53 for the combination of CDDP and Nutlin-3. PMID:26125230

  18. Radioadaptive response and radiation-induced teratogenesis in the late period of organogenesis in mice. Involvement of p53-dependent apoptosis

    International Nuclear Information System (INIS)

    Wang, Bing; Ohyama, Harumi; Nose, Masako; Yukawa, Osami; Yamada, Takeshi; Hayata, Isamu

    2003-01-01

    In the past 5 years, a series of study was done at our institute to investigate radiation effects on the embryogenesis in mice with an emphasis on mechanisms involved in the radiation-induced adaptive response and the role of radiation-induced apoptosis played in teratogenesis in the late period of organogenesis. Using the limb bud system, we first found that radiation-induced apoptosis is involved in malformations, namely, radiation-induced apoptosis in the predigital regions of embryonic limb buds is responsible for digital defects in ICR mice. Examination of embryonic C57BL/6J mice with different p53 status led to further finding that susceptibility to the radiation-induced apoptosis and digital defects depends on both the p53 status and the radiation dose. p53 wild-type mice appeared to be the most sensitive, while p53 knockout mice were the most resistant. These results indicate that p53-dependent apoptosis mediates radiation-induced digital defects. The existence of a radioadaptive response in fetuses, i.e., the priming dose significantly decreases the apoptosis induction, prenatal death, and digital defects in the living fetuses induced by the challenging dose, was found first in ICR strain mice and later confirmed again in C57BL/6J mice. p53 heterozygous embryos did not show the radioadaptive response, indicating the involvement of p53 in the radioadaptive response. (author)

  19. 53BP1 and USP28 mediate p53-dependent cell cycle arrest in response to centrosome loss and prolonged mitosis.

    Science.gov (United States)

    Fong, Chii Shyang; Mazo, Gregory; Das, Tuhin; Goodman, Joshua; Kim, Minhee; O'Rourke, Brian P; Izquierdo, Denisse; Tsou, Meng-Fu Bryan

    2016-07-02

    Mitosis occurs efficiently, but when it is disturbed or delayed, p53-dependent cell death or senescence is often triggered after mitotic exit. To characterize this process, we conducted CRISPR-mediated loss-of-function screens using a cell-based assay in which mitosis is consistently disturbed by centrosome loss. We identified 53BP1 and USP28 as essential components acting upstream of p53, evoking p21-dependent cell cycle arrest in response not only to centrosome loss, but also to other distinct defects causing prolonged mitosis. Intriguingly, 53BP1 mediates p53 activation independently of its DNA repair activity, but requiring its interacting protein USP28 that can directly deubiquitinate p53 in vitro and ectopically stabilize p53 in vivo. Moreover, 53BP1 can transduce prolonged mitosis to cell cycle arrest independently of the spindle assembly checkpoint (SAC), suggesting that while SAC protects mitotic accuracy by slowing down mitosis, 53BP1 and USP28 function in parallel to select against disturbed or delayed mitosis, promoting mitotic efficiency.

  20. Epothilones Suppress Neointimal Thickening in the Rat Carotid Balloon-Injury Model by Inducing Vascular Smooth Muscle Cell Apoptosis through p53-Dependent Signaling Pathway.

    Science.gov (United States)

    Son, Dong Ju; Jung, Jae Chul; Hong, Jin Tae

    2016-01-01

    Microtubule stabilizing agents (MTSA) are known to inhibit vascular smooth muscle cell (VSMC) proliferation and migration, and effectively reduce neointimal hyperplasia and restenosis. Epothilones (EPOs), non-taxane MTSA, have been found to be effective in the inhibition of VSMC proliferation and neointimal formation by cell cycle arrest. However, effect of EPOs on apoptosis in hyper-proliferated VSMCs as a possible way to reduce neointimal formation and its action mechanism related to VSMC viability has not been suited yet. Thus, the purposes of the present study was to investigate whether EPOs are able to inhibit neointimal formation by inducing apoptosis within the region of neointimal hyperplasia in balloon-injured rat carotid artery, as well as underlying action mechanism. Treatment of EPO-B and EPO-D significantly induced apoptotic cell death and mitotic catastrophe in hyper-proliferated VSMCs, resulting in cell growth inhibition. Further, EPOs significantly suppressed VSMC proliferation and induced apoptosis by activation of p53-dependent apoptotic signaling pathway, Bax/cytochrome c/caspase-3. We further demonstrated that the local treatment of carotid arteries with EPOs potently inhibited neointimal lesion formation by induction of apoptosis in rat carotid injury model. Our findings demonstrate a potent anti-neointimal hyperplasia property of EPOs by inducing p53-depedent apoptosis in hyper-proliferated VSMCs.

  1. The Fanconi anemia pathway sensitizes to DNA alkylating agents by inducing JNK-p53-dependent mitochondrial apoptosis in breast cancer cells.

    Science.gov (United States)

    Zhao, Lin; Li, Yanlin; He, Miao; Song, Zhiguo; Lin, Shu; Yu, Zhaojin; Bai, Xuefeng; Wang, Enhua; Wei, Minjie

    2014-07-01

    The Fanconi anemia/BRCA (FA/BRCA) DNA damage repair pathway plays a pivotal role in the cellular response to DNA alkylating agents and greatly influences drug response in cancer treatment. However, the molecular mechanisms underlying the FA/BRCA pathway reversed resistance have received limited attention. In the present study, we investigated the effect of Fanconi anemia complementation group F protein (FANCF), a critical factor of the FA/BRCA pathway, on cancer cell apoptosis induced by DNA alkylating agents such as mitomycin c (MMC). We found that FANCF shRNA potentiated MMC-induced cytotoxicity and apoptosis in MCF-7 and MDA-MB-231 breast cancer cells. At a mechanistic level, FANCF shRNA downregulated the anti-apoptotic protein Bcl-2 and upregulated the pro-apoptotic protein Bax, accompanied by release of cyt-c and smac into the cytosol in MMC-treated cells. Furthermore, activation of caspase-3 and -9, other than caspase-8, cleavage of poly(ADP ribose) polymerase (PARP), and a decrease of mitochondrial membrane potential (MMP) indicated that involvement of the mitochondrial apoptotic pathway in FANCF silencing of MMC-treated breast cancer cells. A decrease in IAP family proteins XIAP and survivin were also observed following FANCF silencing in MMC-treated breast cancer cells. Notably, FANCF shRNA was able to increase p53 levels through activation of the JNK pathway in MMC-treated breast cancer cells. Furthermore, p53 inhibition using pifithrin-α abolished the induction of caspase-3 and PARP by FANCF shRNA and MMC, indicating that MMC-induced apoptosis is substantially enhanced by FANCF shRNA via p53-dependent mechanisms. To our knowledge, we provide new evidence for the potential application of FANCF as a chemosensitizer in breast cancer therapy.

  2. Quercetin suppresses DNA double-strand break repair and enhances the radiosensitivity of human ovarian cancer cells via p53-dependent endoplasmic reticulum stress pathway

    Directory of Open Access Journals (Sweden)

    Gong C

    2017-12-01

    Full Text Available Cheng Gong,1 Zongyuan Yang,1 Lingyun Zhang,2 Yuehua Wang,2 Wei Gong,2 Yi Liu3 1Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 2Department of Oncology, XiangYang Central Hospital, Hubei University of Arts and Science, XiangYang, 3Department of Medicinal Chemistry, School of Pharmacy, Hubei University of Chinese Medicine, Wuhan, China Abstract: Quercetin is proven to have anticancer effects for many cancers. However, the role of tumor suppressor p53 on quercetin’s radiosensitization and regulation of endoplasmic reticulum (ER stress response in this process remains obscure. Here, quercetin exposure resulted in ER stress, prolonged DNA repair, and the expression of p53 protein; phosphorylation on serine 15 and 20 increased in combination with X-irradiation. Quercetin pretreatment could potentiate radiation-induced cell death. The combination of irradiation and quercetin treatment aggravated DNA damages and caused typical apoptotic cell death; as well the expression of Bax and p21 elevated and the expression of Bcl-2 decreased. Knocking down of p53 could reverse all the above effects under quercetin in combination with radiation. In addition, quercetin-induced radiosensitization was through stimulation of ATM phosphorylation. In human ovarian cancer xenograft model, combined treatment of quercetin and radiation significantly restrained the growth of tumors, accompanied with the activation of p53, CCAAT/enhancer-binding protein homologous protein, and γ-H2AX. Overall, these results indicated that quercetin acted as a promising radiosensitizer through p53-dependent ER stress signals. Keywords: quercetin, p53, endoplasmic reticulum stress, DNA double-strand breaks, eIF-2α (eukaryotic initiation factor 2α, ATM kinase

  3. Genistein induces G2/M cell cycle arrest and apoptosis via ATM/p53-dependent pathway in human colon cancer cells.

    Science.gov (United States)

    Zhang, Zhiyu; Wang, Chong-Zhi; Du, Guang-Jian; Qi, Lian-Wen; Calway, Tyler; He, Tong-Chuan; Du, Wei; Yuan, Chun-Su

    2013-07-01

    Soybean isoflavones have been used as a potential preventive agent in anticancer research for many years. Genistein is one of the most active flavonoids in soybeans. Accumulating evidence suggests that genistein alters a variety of biological processes in estrogen-related malignancies, such as breast and prostate cancers. However, the molecular mechanism of genistein in the prevention of human colon cancer remains unclear. Here we attempted to elucidate the anticarcinogenic mechanism of genistein in human colon cancer cells. First we evaluated the growth inhibitory effect of genistein and two other isoflavones, daidzein and biochanin A, on HCT-116 and SW-480 human colon cancer cells. In addition, flow cyto-metry was performed to observe the morphological changes in HCT-116/SW-480 cells undergoing apoptosis or cell cycle arrest, which had been visualized using Annexin V-FITC and/or propidium iodide staining. Real-time PCR and western blot analyses were also employed to study the changes in expression of several important genes associated with cell cycle regulation. Our data showed that genistein, daidzein and biochanin A exhibited growth inhibitory effects on HCT-116/SW-480 colon cancer cells and promoted apoptosis. Genistein showed a significantly greater effect than the other two compounds, in a time- and dose-dependent manner. In addition, genistein caused cell cycle arrest in the G2/M phase, which was accompanied by activation of ATM/p53, p21waf1/cip1 and GADD45α as well as downregulation of cdc2 and cdc25A demonstrated by q-PCR and immunoblotting assay. Interestingly, genistein induced G2/M cell cycle arrest in a p53-dependent manner. These findings exemplify that isoflavones, especially genistein, could promote colon cancer cell growth inhibition and facilitate apoptosis and cell cycle arrest in the G2/M phase. The ATM/p53-p21 cross-regulatory network may play a crucial role in mediating the anticarcinogenic activities of genistein in colon cancer.

  4. Cyclin G2 is a centrosome-associated nucleocytoplasmic shuttling protein that influences microtubule stability and induces a p53-dependent cell cycle arrest

    International Nuclear Information System (INIS)

    Arachchige Don, Aruni S.; Dallapiazza, Robert F.; Bennin, David A.; Brake, Tiffany; Cowan, Colleen E.; Horne, Mary C.

    2006-01-01

    Cyclin G2 is an atypical cyclin that associates with active protein phosphatase 2A. Cyclin G2 gene expression correlates with cell cycle inhibition; it is significantly upregulated in response to DNA damage and diverse growth inhibitory stimuli, but repressed by mitogenic signals. Ectopic expression of cyclin G2 promotes cell cycle arrest, cyclin dependent kinase 2 inhibition and the formation of aberrant nuclei [Bennin, D. A., Don, A. S., Brake, T., McKenzie, J. L., Rosenbaum, H., Ortiz, L., DePaoli-Roach, A. A., and Horne, M. C. (2002). Cyclin G2 associates with protein phosphatase 2A catalytic and regulatory B' subunits in active complexes and induces nuclear aberrations and a G 1 /S-phase cell cycle arrest. J Biol Chem 277, 27449-67]. Here we report that endogenous cyclin G2 copurifies with centrosomes and microtubules (MT) and that ectopic G2 expression alters microtubule stability. We find exogenous and endogenous cyclin G2 present at microtubule organizing centers (MTOCs) where it colocalizes with centrosomal markers in a variety of cell lines. We previously reported that cyclin G2 forms complexes with active protein phosphatase 2A (PP2A) and colocalizes with PP2A in a detergent-resistant compartment. We now show that cyclin G2 and PP2A colocalize at MTOCs in transfected cells and that the endogenous proteins copurify with isolated centrosomes. Displacement of the endogenous centrosomal scaffolding protein AKAP450 that anchors PP2A at the centrosome resulted in the depletion of centrosomal cyclin G2. We find that ectopic expression of cyclin G2 induces microtubule bundling and resistance to depolymerization, inhibition of polymer regrowth from MTOCs and a p53-dependent cell cycle arrest. Furthermore, we determined that a 100 amino acid carboxy-terminal region of cyclin G2 is sufficient to both direct GFP localization to centrosomes and induce cell cycle inhibition. Colocalization of endogenous cyclin G2 with only one of two GFP-centrin-tagged centrioles, the

  5. The effect of caffeine on p53-dependent radioresponses in undifferentiated mouse embryonal carcinoma cells after X-ray and UV-irradiations

    International Nuclear Information System (INIS)

    Taga, Masataka; Shiraishi, Kazunori; Shimura, Tsutomu; Uematsu, Norio; Kato, Tomohisa; Niwa, Ohtsura; Nishimune, Yoshitake; Aizawa, Shinichi; Oshimura, Mitsuo

    2000-01-01

    The effect of caffeine was studied on the radioresponses of undifferentiated mouse embryonal carcinoma cells (EC cells) with or without the functional p53. The radioresponses studied included radiosensitivity, the activation of p53, apoptosis with characteristic DNA ladder formation and cell cycle progression. An undifferentiated mouse EC cell line, ECA2, and a newly established p53-deficient EC cell line, p53δ, were used in the present study. The status of the p53 gene did not significantly affect the colony survivals of undifferentiated EC cells to X-rays and UV. Although a post-irradiation treatment with caffeine sensitized both lines to X-rays marginally, the sensitization was prominent for UV regardless of the p53 status of the cells. The activation of a p53 responsible lacZ reporter construct was observed in stably transfected ECA2 cells after X-ray and UV irradiations. Caffeine suppressed the X-ray induced activation of the lacZ reporter, while it drastically enhanced the activation after UV irradiation. X-rays and UV readily triggered the apoptosis of ECA2 cells with the characteristic DNA ladder. Although UV-induced DNA ladder formation was enhanced by caffeine, that induced by X-rays was unaffected. Therefore, the effects of caffeine on the p53-dependent radioresponses were found to be agent specific: suppression for the X-ray induced and augmentation for the UV induced. In contrast to p53-proficient ECA2 cells, smear-like DNA degradation was observed for irradiated p53δ cells, suggesting the presence of a mode of cell death without DNA ladder formation. UV induction of the smear-like DNA degradation was enhanced in the presence of caffeine. Regardless of the state of the p53 gene, G1/S arrest was not observed in X-ray and UV irradiated EC cells. X-rays induced G2/M arrest in both lines, which was abrogated by caffeine, while G2/M arrest after UV was unaffected by a caffeine treatment. These results indicate that the radioresponses of undifferentiated

  6. The effect of caffeine on p53-dependent radioresponses in undifferentiated mouse embryonal carcinoma cells after X-ray and UV-irradiations

    Energy Technology Data Exchange (ETDEWEB)

    Taga, Masataka; Shiraishi, Kazunori; Shimura, Tsutomu; Uematsu, Norio; Kato, Tomohisa; Niwa, Ohtsura [Kyoto Univ. (Japan). Radiation Biology Center; Nishimune, Yoshitake; Aizawa, Shinichi; Oshimura, Mitsuo

    2000-09-01

    The effect of caffeine was studied on the radioresponses of undifferentiated mouse embryonal carcinoma cells (EC cells) with or without the functional p53. The radioresponses studied included radiosensitivity, the activation of p53, apoptosis with characteristic DNA ladder formation and cell cycle progression. An undifferentiated mouse EC cell line, ECA2, and a newly established p53-deficient EC cell line, p53{delta}, were used in the present study. The status of the p53 gene did not significantly affect the colony survivals of undifferentiated EC cells to X-rays and UV. Although a post-irradiation treatment with caffeine sensitized both lines to X-rays marginally, the sensitization was prominent for UV regardless of the p53 status of the cells. The activation of a p53 responsible lacZ reporter construct was observed in stably transfected ECA2 cells after X-ray and UV irradiations. Caffeine suppressed the X-ray induced activation of the lacZ reporter, while it drastically enhanced the activation after UV irradiation. X-rays and UV readily triggered the apoptosis of ECA2 cells with the characteristic DNA ladder. Although UV-induced DNA ladder formation was enhanced by caffeine, that induced by X-rays was unaffected. Therefore, the effects of caffeine on the p53-dependent radioresponses were found to be agent specific: suppression for the X-ray induced and augmentation for the UV induced. In contrast to p53-proficient ECA2 cells, smear-like DNA degradation was observed for irradiated p53{delta} cells, suggesting the presence of a mode of cell death without DNA ladder formation. UV induction of the smear-like DNA degradation was enhanced in the presence of caffeine. Regardless of the state of the p53 gene, G1/S arrest was not observed in X-ray and UV irradiated EC cells. X-rays induced G2/M arrest in both lines, which was abrogated by caffeine, while G2/M arrest after UV was unaffected by a caffeine treatment. These results indicate that the radioresponses of

  7. Down-regulation of Wild-type p53-induced Phosphatase 1 (Wip1) Plays a Critical Role in Regulating Several p53-dependent Functions in Premature Senescent Tumor Cells*

    Science.gov (United States)

    Crescenzi, Elvira; Raia, Zelinda; Pacifico, Francesco; Mellone, Stefano; Moscato, Fortunato; Palumbo, Giuseppe; Leonardi, Antonio

    2013-01-01

    Premature or drug-induced senescence is a major cellular response to chemotherapy in solid tumors. The senescent phenotype develops slowly and is associated with chronic DNA damage response. We found that expression of wild-type p53-induced phosphatase 1 (Wip1) is markedly down-regulated during persistent DNA damage and after drug release during the acquisition of the senescent phenotype in carcinoma cells. We demonstrate that down-regulation of Wip1 is required for maintenance of permanent G2 arrest. In fact, we show that forced expression of Wip1 in premature senescent tumor cells induces inappropriate re-initiation of mitosis, uncontrolled polyploid progression, and cell death by mitotic failure. Most of the effects of Wip1 may be attributed to its ability to dephosphorylate p53 at Ser15 and to inhibit DNA damage response. However, we also uncover a regulatory pathway whereby suppression of p53 Ser15 phosphorylation is associated with enhanced phosphorylation at Ser46, increased p53 protein levels, and induction of Noxa expression. On the whole, our data indicate that down-regulation of Wip1 expression during premature senescence plays a pivotal role in regulating several p53-dependent aspects of the senescent phenotype. PMID:23612976

  8. Chk2 regulates transcription-independent p53-mediated apoptosis in response to DNA damage

    International Nuclear Information System (INIS)

    Chen Chen; Shimizu, Shigeomi; Tsujimoto, Yoshihide; Motoyama, Noboru

    2005-01-01

    The tumor suppressor protein p53 plays a central role in the induction of apoptosis in response to genotoxic stress. The protein kinase Chk2 is an important regulator of p53 function in mammalian cells exposed to ionizing radiation (IR). Cells derived from Chk2-deficient mice are resistant to the induction of apoptosis by IR, and this resistance has been thought to be a result of the defective transcriptional activation of p53 target genes. It was recently shown, however, that p53 itself and histone H1.2 translocate to mitochondria and thereby induces apoptosis in a transcription-independent manner in response to IR. We have now examined whether Chk2 also regulates the transcription-independent induction of apoptosis by p53 and histone H1.2. The reduced ability of IR to induce p53 stabilization in Chk2-deficient thymocytes was associated with a marked impairment of p53 and histone H1 translocation to mitochondria. These results suggest that Chk2 regulates the transcription-independent mechanism of p53-mediated apoptosis by inducing stabilization of p53 in response to IR

  9. p53-Dependent suppression of genome instability in germ cells

    Energy Technology Data Exchange (ETDEWEB)

    Otozai, Shinji [Department of Otorhinolaryngology and Head and Neck Surgery, Osaka University School of Medicine, Osaka 565-0871 (Japan); Ishikawa-Fujiwara, Tomoko [Department of Radiation Biology and Medical Genetics, Graduate School of Medicine, Osaka University, B4, 2-2 Yamadaoka, Suita, Osaka 565-0871 (Japan); Oda, Shoji [Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba 277-8562 (Japan); Kamei, Yasuhiro [Department of Radiation Biology and Medical Genetics, Graduate School of Medicine, Osaka University, B4, 2-2 Yamadaoka, Suita, Osaka 565-0871 (Japan); Ryo, Haruko [Nomura Project, National Institute of Biomedical Innovation, Osaka 565-0085 (Japan); Sato, Ayuko [Department of Pathology, Hyogo College of Medicine, Hyogo 663-8501 (Japan); Nomura, Taisei [Nomura Project, National Institute of Biomedical Innovation, Osaka 565-0085 (Japan); Mitani, Hiroshi [Department of Integrated Biosciences, Graduate School of Frontier Sciences, The University of Tokyo, Chiba 277-8562 (Japan); Tsujimura, Tohru [Department of Pathology, Hyogo College of Medicine, Hyogo 663-8501 (Japan); Inohara, Hidenori [Department of Otorhinolaryngology and Head and Neck Surgery, Osaka University School of Medicine, Osaka 565-0871 (Japan); Todo, Takeshi, E-mail: todo@radbio.med.osaka-u.ac.jp [Department of Radiation Biology and Medical Genetics, Graduate School of Medicine, Osaka University, B4, 2-2 Yamadaoka, Suita, Osaka 565-0871 (Japan)

    2014-02-15

    Highlights: • Radiation-induced microsatellite instability (MSI) was investigated in medaka fish. • msh2{sup −/−} fish had a high frequency of spontaneous MSI. • p53{sup −/−} fish had a high frequency of radiation-induced MSI. • p53 and msh2 suppress MSI by different pathways: mismatch removal and apoptosis. - Abstract: Radiation increases mutation frequencies at tandem repeat loci. Germline mutations in γ-ray-irradiated medaka fish (Oryzias latipes) were studied, focusing on the microsatellite loci. Mismatch-repair genes suppress microsatellite mutation by directly removing altered sequences at the nucleotide level, whereas the p53 gene suppresses genetic alterations by eliminating damaged cells. The contribution of these two defense mechanisms to radiation-induced microsatellite instability was addressed. The spontaneous mutation frequency was significantly higher in msh2{sup −/−} males than in wild-type fish, whereas there was no difference in the frequency of radiation-induced mutations between msh2{sup −/−} and wild-type fish. By contrast, irradiated p53{sup −/−} fish exhibited markedly increased mutation frequencies, whereas their spontaneous mutation frequency was the same as that of wild-type fish. In the spermatogonia of the testis, radiation induced a high level of apoptosis both in wild-type and msh2{sup −/−} fish, but negligible levels in p53{sup −/−} fish. The results demonstrate that the msh2 and p53 genes protect genome integrity against spontaneous and radiation-induced mutation by two different pathways: direct removal of mismatches and elimination of damaged cells.

  10. p53-Dependent suppression of genome instability in germ cells

    International Nuclear Information System (INIS)

    Otozai, Shinji; Ishikawa-Fujiwara, Tomoko; Oda, Shoji; Kamei, Yasuhiro; Ryo, Haruko; Sato, Ayuko; Nomura, Taisei; Mitani, Hiroshi; Tsujimura, Tohru; Inohara, Hidenori; Todo, Takeshi

    2014-01-01

    Highlights: • Radiation-induced microsatellite instability (MSI) was investigated in medaka fish. • msh2 −/− fish had a high frequency of spontaneous MSI. • p53 −/− fish had a high frequency of radiation-induced MSI. • p53 and msh2 suppress MSI by different pathways: mismatch removal and apoptosis. - Abstract: Radiation increases mutation frequencies at tandem repeat loci. Germline mutations in γ-ray-irradiated medaka fish (Oryzias latipes) were studied, focusing on the microsatellite loci. Mismatch-repair genes suppress microsatellite mutation by directly removing altered sequences at the nucleotide level, whereas the p53 gene suppresses genetic alterations by eliminating damaged cells. The contribution of these two defense mechanisms to radiation-induced microsatellite instability was addressed. The spontaneous mutation frequency was significantly higher in msh2 −/− males than in wild-type fish, whereas there was no difference in the frequency of radiation-induced mutations between msh2 −/− and wild-type fish. By contrast, irradiated p53 −/− fish exhibited markedly increased mutation frequencies, whereas their spontaneous mutation frequency was the same as that of wild-type fish. In the spermatogonia of the testis, radiation induced a high level of apoptosis both in wild-type and msh2 −/− fish, but negligible levels in p53 −/− fish. The results demonstrate that the msh2 and p53 genes protect genome integrity against spontaneous and radiation-induced mutation by two different pathways: direct removal of mismatches and elimination of damaged cells

  11. Thymocyte apoptosis induced by p53-dependent and independent pathways

    International Nuclear Information System (INIS)

    Clarke, A.R.; Purdie, C.A.; Harrison, D.J.; Morris, R.G.; Bird, C.C.; Hooper, M.L.; Wyllie, A.H.

    1993-01-01

    The authors studied the dependence of apoptosis on p53 expression in cells from the thymus cortex. Short-term thymocyte cultures were prepared from mice constitutively heterozygous or homozygous for a deletion in the p53 gene introduced into the germ line after gene targeting. Wild-type thymocytes readily undergo apoptosis after treatment with ionizing radiation, the glucocorticoid methylprednisolone, or etoposide (an inhibitor of topoisomerase II), or after Ca 2+ -dependent activation by phorbol ester and a calcium ionophore. In contrast, homozygous null p53 thymocytes are resistant to induction of apoptosis by radiation or etoposide, but retain normal sensitivity to glucocorticoid and calcium. The time-dependent apoptosis that occurs in untreated cultures is unaffected by p53 status. Cells heterozygous for p53 deletion are partially resistant to radiation and etoposide. Results show that p53 exerts a significant and dose-dependent effect in the initiation of apoptosis, but only when it is induced by agents that cause DNA-strand breakage. (Author)

  12. Fermentative metabolism impedes p53-dependent apoptosis in a ...

    Indian Academy of Sciences (India)

    Abhay Kumar

    2017-10-31

    Oct 31, 2017 ... inhibit respiration of mitochondria isolated from normal rat liver cells ... yeast as a model, it was reported that Warburg effect in addition to inducing aerobic ...... of adenine nucleotides carrier and cytochrome c. FEBS Lett. 456.

  13. DNA-binding protects p53 from interactions with cofactors involved in transcription-independent functions.

    Science.gov (United States)

    Lambrughi, Matteo; De Gioia, Luca; Gervasio, Francesco Luigi; Lindorff-Larsen, Kresten; Nussinov, Ruth; Urani, Chiara; Bruschi, Maurizio; Papaleo, Elena

    2016-11-02

    Binding-induced conformational changes of a protein at regions distant from the binding site may play crucial roles in protein function and regulation. The p53 tumour suppressor is an example of such an allosterically regulated protein. Little is known, however, about how DNA binding can affect distal sites for transcription factors. Furthermore, the molecular details of how a local perturbation is transmitted through a protein structure are generally elusive and occur on timescales hard to explore by simulations. Thus, we employed state-of-the-art enhanced sampling atomistic simulations to unveil DNA-induced effects on p53 structure and dynamics that modulate the recruitment of cofactors and the impact of phosphorylation at Ser215. We show that DNA interaction promotes a conformational change in a region 3 nm away from the DNA binding site. Specifically, binding to DNA increases the population of an occluded minor state at this distal site by more than 4-fold, whereas phosphorylation traps the protein in its major state. In the minor conformation, the interface of p53 that binds biological partners related to p53 transcription-independent functions is not accessible. Significantly, our study reveals a mechanism of DNA-mediated protection of p53 from interactions with partners involved in the p53 transcription-independent signalling. This also suggests that conformational dynamics is tightly related to p53 signalling. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

  14. Curcumin significantly enhances dual PI3K/Akt and mTOR inhibitor NVP-BEZ235-induced apoptosis in human renal carcinoma Caki cells through down-regulation of p53-dependent Bcl-2 expression and inhibition of Mcl-1 protein stability.

    Directory of Open Access Journals (Sweden)

    Bo Ram Seo

    Full Text Available The PI3K/Akt and mTOR signaling pathways are important for cell survival and growth, and they are highly activated in cancer cells compared with normal cells. Therefore, these signaling pathways are targets for inducing cancer cell death. The dual PI3K/Akt and mTOR inhibitor NVP-BEZ235 completely inhibited both signaling pathways. However, NVP-BEZ235 had no effect on cell death in human renal carcinoma Caki cells. We tested whether combined treatment with natural compounds and NVP-BEZ235 could induce cell death. Among several chemopreventive agents, curcumin, a natural biologically active compound that is extracted from the rhizomes of Curcuma species, markedly induced apoptosis in NVP-BEZ235-treated cells. Co-treatment with curcumin and NVP-BEZ235 led to the down-regulation of Mcl-1 protein expression but not mRNA expression. Ectopic expression of Mcl-1 completely inhibited curcumin plus NVP-NEZ235-induced apoptosis. Furthermore, the down-regulation of Bcl-2 was involved in curcumin plus NVP-BEZ235-induced apoptosis. Curcumin or NVP-BEZ235 alone did not change Bcl-2 mRNA or protein expression, but co-treatment reduced Bcl-2 mRNA and protein expression. Combined treatment with NVP-BEZ235 and curcumin reduced Bcl-2 expression in wild-type p53 HCT116 human colon carcinoma cells but not p53-null HCT116 cells. Moreover, Bcl-2 expression was completely reversed by treatment with pifithrin-α, a p53-specific inhibitor. Ectopic expression of Bcl-2 also inhibited apoptosis in NVP-BE235 plus curcumin-treated cells. In contrast, NVP-BEZ235 combined with curcumin did not have a synergistic effect on normal human skin fibroblasts and normal human mesangial cells. Taken together, combined treatment with NVP-BEZ235 and curcumin induces apoptosis through p53-dependent Bcl-2 mRNA down-regulation at the transcriptional level and Mcl-1 protein down-regulation at the post-transcriptional level.

  15. Low grade inflammation inhibits VEGF induced HUVECs migration in p53 dependent manner

    International Nuclear Information System (INIS)

    Panta, Sushil; Yamakuchi, Munekazu; Shimizu, Toshiaki; Takenouchi, Kazunori; Oyama, Yoko; Koriyama, Toyoyasu; Kojo, Tsuyoshi; Hashiguchi, Teruto

    2017-01-01

    In the course of studying crosstalk between inflammation and angiogenesis, high doses of pro-inflammatory factors have been reported to induce apoptosis in cells. Under normal circumstances also the pro-inflammatory cytokines are being released in low doses and are actively involved in cell signaling pathways. We studied the effects of low grade inflammation in growth factor induced angiogenesis using tumor necrosis factor alfa (TNFα) and vascular endothelial growth factor A (VEGF) respectively. We found that low dose of TNFα can inhibit VEGF induced angiogenesis in human umbilical vein endothelial cells (HUVECs). Low dose of TNFα induces mild upregulation and moreover nuclear localization of tumor suppressor protein 53 (P53) which causes decrease in inhibitor of DNA binding-1 (Id1) expression and shuttling to the cytoplasm. In absence of Id1, HUVECs fail to upregulate β 3 -integrin and cell migration is decreased. Connecting low dose of TNFα induced p53 to β 3 -integrin through Id1, we present additional link in cross talk between inflammation and angiogenesis. - Highlights: • Low grade inflammation (low dose of TNF alfa) inhibits VEGF induced endothelial cells migration. • The low grade inflammation with VEGF treatment upregulates P53 to a nonlethal level. • P53 activation inhibits Id1 shuttling to the cytoplasm in endothelial cells. • Inhibition of Id1 resulted in downregulation of β 3 -integrin which cause decrease in cell migration. • Inflammation and angiogenesis might cross-talk by P53 – Id1 – β 3 -integrin pathway in endothelial cells.

  16. p53-Dependent and -Independent Epithelial Integrity: Beyond miRNAs and Metabolic Fluctuations

    Directory of Open Access Journals (Sweden)

    Tsukasa Oikawa

    2018-05-01

    Full Text Available In addition to its classical roles as a tumor suppressor, p53 has also been shown to act as a guardian of epithelial integrity by inducing the microRNAs that target transcriptional factors driving epithelial–mesenchymal transition. On the other hand, the ENCODE project demonstrated an enrichment of putative motifs for the binding of p53 in epithelial-specific enhancers, such as CDH1 (encoding E-cadherin enhancers although its biological significance remained unknown. Recently, we identified two novel modes of epithelial integrity (i.e., maintenance of CDH1 expression: one involves the binding of p53 to a CDH1 enhancer region and the other does not. In the former, the binding of p53 is necessary to maintain permissive histone modifications around the CDH1 transcription start site, whereas in the latter, p53 does not bind to this region nor affect histone modifications. Furthermore, these mechanisms likely coexisted within the same tissue. Thus, the mechanisms involved in epithelial integrity appear to be much more complex than previously thought. In this review, we describe our findings, which may instigate further experimental scrutiny towards understanding the whole picture of epithelial integrity as well as the related complex asymmetrical functions of p53. Such understanding will be important not only for cancer biology but also for the safety of regenerative medicine.

  17. P53-dependent upregulation of neutral sphingomyelinase-2: role in doxorubicin-induced growth arrest.

    Science.gov (United States)

    Shamseddine, A A; Clarke, C J; Carroll, B; Airola, M V; Mohammed, S; Rella, A; Obeid, L M; Hannun, Y A

    2015-10-29

    Neutral sphingomyelinase-2 (nSMase2) is a ceramide-generating enzyme that has been implicated in growth arrest, apoptosis and exosome secretion. Although previous studies have reported transcriptional upregulation of nSMase2 in response to daunorubicin, through Sp1 and Sp3 transcription factors, the role of the DNA damage pathway in regulating nSMase2 remains unclear. In this study, we show that doxorubicin induces a dose-dependent induction of nSMase2 mRNA and protein with concomitant increases in nSMase activity and ceramide levels. Upregulation of nSMase2 was dependent on ATR, Chk1 and p53, thus placing it downstream of the DNA damage pathway. Moreover, overexpression of p53 was sufficient to transcriptionally induce nSMase2, without the need for DNA damage. DNA-binding mutants as well as acetylation mutants of p53 were unable to induce nSMase2, suggesting a role of nSMase2 in growth arrest. Moreover, knockdown of nSMase2 prevented doxorubicin-induced growth arrest. Finally, p53-induced nSMase2 upregulation appears to occur via a novel transcription start site upstream of exon 3. These results identify nSMase2 as a novel p53 target gene, regulated by the DNA damage pathway to induce cell growth arrest.

  18. Chronic lymphocytic leukemia cells display p53-dependent drug-induced Puma upregulation

    NARCIS (Netherlands)

    Mackus, W. J. M.; Kater, A. P.; Grummels, A.; Evers, L. M.; Hooijbrink, B.; Kramer, M. H. H.; Castro, J. E.; Kipps, T. J.; van Lier, R. A. W.; van Oers, M. H. J.; Eldering, E.

    2005-01-01

    We investigated the apoptosis gene expression profile of chronic lymphocytic leukemia (CLL) cells in relation to (1) normal peripheral and tonsillar B-cell subsets, (2) IgV(H) mutation status, and (3) effects of cytotoxic drugs. In accord with their noncycling, antiapoptotic status in vivo, CLL

  19. Suberoyl bis-hydroxamic acid induces p53-dependent apoptosis of MCF-7 breast cancer cells

    Institute of Scientific and Technical Information of China (English)

    Zhi-gang ZHUANG; Fei FEI; Ying CHEN; Wei JIN

    2008-01-01

    Aim: To study the effects of suberoyl bis-hydroxamic acid (SBHA), an inhibitor of histone deacetylases, on the apoptosis of MCF-7 breast cancer cells. Meth-ods: Apoptosis in MCF-7 cells induced by SBHA was demonstrated by flow cytometric analysis, morphological observation, and DNA ladder. Mitochondrial membrane potential (△ψm) was measured using the fluorescent probe JC-1. The expressions of p53, p21, Bax, and PUMA were determined using RT-PCR or Western blotting analysis after the MCF-7 cells were treated with SBHA or p53 siRNA. Results: SBHA induced apoptosis in MCF-7 cells. The expressions of p53, p21, Bax, and PUMA were induced, and △ψm collapsed after treatment with SBHA. p53 siRNA abrogated the SBHA-induced apoptosis and the expressions of p53, p21, Bax, and PUMA. Conclusion: The activation of the p53 pathway is involved in SBHA-induced apoptosis in MCF-7 cells.

  20. p53 Dependent Centrosome Clustering Prevents Multipolar Mitosis in Tetraploid Cells

    Science.gov (United States)

    Yi, Qiyi; Zhao, Xiaoyu; Huang, Yun; Ma, Tieliang; Zhang, Yingyin; Hou, Heli; Cooke, Howard J.; Yang, Da-Qing; Wu, Mian; Shi, Qinghua

    2011-01-01

    Background p53 abnormality and aneuploidy often coexist in human tumors, and tetraploidy is considered as an intermediate between normal diploidy and aneuploidy. The purpose of this study was to investigate whether and how p53 influences the transformation from tetraploidy to aneuploidy. Principal Findings Live cell imaging was performed to determine the fates and mitotic behaviors of several human and mouse tetraploid cells with different p53 status, and centrosome and spindle immunostaining was used to investigate centrosome behaviors. We found that p53 dominant-negative mutation, point mutation, or knockout led to a 2∼ 33-fold increase of multipolar mitosis in N/TERT1, 3T3 and mouse embryonic fibroblasts (MEFs), while mitotic entry and cell death were not significantly affected. In p53-/- tetraploid MEFs, the ability of centrosome clustering was compromised, while centrosome inactivation was not affected. Suppression of RhoA/ROCK activity by specific inhibitors in p53-/- tetraploid MEFs enhanced centrosome clustering, decreased multipolar mitosis from 38% to 20% and 16% for RhoA and ROCK, respectively, while expression of constitutively active RhoA in p53+/+ tetraploid 3T3 cells increased the frequency of multipolar mitosis from 15% to 35%. Conclusions p53 could not prevent tetraploid cells entering mitosis or induce tetraploid cell death. However, p53 abnormality impaired centrosome clustering and lead to multipolar mitosis in tetraploid cells by modulating the RhoA/ROCK signaling pathway. PMID:22076149

  1. P53-dependent antiproliferative and pro-apoptotic effects of trichostatin A (TSA) in glioblastoma cells.

    Science.gov (United States)

    Bajbouj, K; Mawrin, C; Hartig, R; Schulze-Luehrmann, J; Wilisch-Neumann, A; Roessner, A; Schneider-Stock, R

    2012-05-01

    Glioblastomas are known to be highly chemoresistant, but HDAC inhibitors (HDACi) have been shown to be of therapeutic relevance for this aggressive tumor type. We treated U87 glioblastoma cells with trichostatin A (TSA) to define potential epigenetic targets for HDACi-mediated antitumor effects. Using a cDNA array analysis covering 96 cell cycle genes, cyclin-dependent kinase inhibitor p21(WAF1) was identified as the major player in TSA-induced cell cycle arrest. TSA slightly inhibited proliferation and viability of U87 cells, cumulating in a G1/S cell cycle arrest. This effect was accompanied by a significant up-regulation of p53 and its transcriptional target p21(WAF1) and by down-regulation of key G1/S regulators, such as cdk4, cdk6, and cyclin D1. Nevertheless, TSA did not induce apoptosis in U87 cells. As expected, TSA promoted the accumulation of total acetylated histones H3 and H4 and a decrease in endogenous HDAC activity. Characterizing the chromatin modulation around the p21(WAF1) promoter after TSA treatment using chromatin immunoprecipitation, we found (1) a release of HDAC1, (2) an increase of acetylated H4 binding, and (3) enhanced recruitment of p53. p53-depleted U87 cells showed an abrogation of the G1/S arrest and re-entered the cell cycle. Immunofluorescence staining revealed that TSA induced the nuclear translocation of p21(WAF1) verifying a cell cycle arrest. On the other hand, a significant portion of p21(WAF1) was present in the cytoplasmic compartment causing apoptosis resistance. Furthermore, TSA-treated p53-mutant cell line U138 failed to show an induction in p21(WAF1), showed a deficient G2/M checkpoint, and underwent mitotic catastrophe. We suggest that HDAC inhibition in combination with other clinically used drugs may be considered an effective strategy to overcome chemoresistance in glioblastoma cells.

  2. p53-dependent non-coding RNA networks in chronic lymphocytic leukemia

    NARCIS (Netherlands)

    Blume, C. J.; Hotz-Wagenblatt, A.; Hüllein, J.; Sellner, L.; Jethwa, A.; Stolz, T.; Slabicki, M.; Lee, K.; Sharathchandra, A.; Benner, A.; Dietrich, S.; Oakes, C. C.; Dreger, P.; te Raa, D.; Kater, A. P.; Jauch, A.; Merkel, O.; Oren, M.; Hielscher, T.; Zenz, T.

    2015-01-01

    Mutations of the tumor suppressor p53 lead to chemotherapy resistance and a dismal prognosis in chronic lymphocytic leukemia (CLL). Whereas p53 targets are used to identify patient subgroups with impaired p53 function, a comprehensive assessment of non-coding RNA targets of p53 in CLL is missing. We

  3. RUNX Family Participates in the Regulation of p53-Dependent DNA Damage Response

    Directory of Open Access Journals (Sweden)

    Toshinori Ozaki

    2013-01-01

    Full Text Available A proper DNA damage response (DDR, which monitors and maintains the genomic integrity, has been considered to be a critical barrier against genetic alterations to prevent tumor initiation and progression. The representative tumor suppressor p53 plays an important role in the regulation of DNA damage response. When cells receive DNA damage, p53 is quickly activated and induces cell cycle arrest and/or apoptotic cell death through transactivating its target genes implicated in the promotion of cell cycle arrest and/or apoptotic cell death such as p21WAF1, BAX, and PUMA. Accumulating evidence strongly suggests that DNA damage-mediated activation as well as induction of p53 is regulated by posttranslational modifications and also by protein-protein interaction. Loss of p53 activity confers growth advantage and ensures survival in cancer cells by inhibiting apoptotic response required for tumor suppression. RUNX family, which is composed of RUNX1, RUNX2, and RUNX3, is a sequence-specific transcription factor and is closely involved in a variety of cellular processes including development, differentiation, and/or tumorigenesis. In this review, we describe a background of p53 and a functional collaboration between p53 and RUNX family in response to DNA damage.

  4. Cyclophilin B Supports Myc and Mutant p53 Dependent Survival of Glioblastoma Multiforme Cells

    Science.gov (United States)

    Choi, Jae Won; Schroeder, Mark A.; Sarkaria, Jann N.; Bram, Richard J.

    2014-01-01

    Glioblastoma multiforme (GBM) is an aggressive, treatment-refractory type of brain tumor for which effective therapeutic targets remain important to identify. Here we report that cyclophilin B (CypB), a prolyl isomerase residing in the endoplasmic reticulum (ER), provides an essential survival signal in GBM cells. Analysis of gene expression databases revealed that CypB is upregulated in many cases of malignant glioma. We found that suppression of CypB reduced cell proliferation and survival in human GBM cells in vitro and in vivo. We also found that treatment with small molecule inhibitors of cyclophilins, including the approved drug cyclosporine, greatly reduced the viability of GBM cells. Mechanistically, depletion or pharmacologic inhibition of CypB caused hyperactivation of the oncogenic RAS-MAPK pathway, induction of cellular senescence signals, and death resulting from loss of MYC, mutant p53, Chk1 and JAK/STAT3 signaling. Elevated reactive oxygen species, ER expansion and abnormal unfolded protein responses in CypB-depleted GBM cells indicated that CypB alleviates oxidative and ER stresses and coordinates stress adaptation responses. Enhanced cell survival and sustained expression of multiple oncogenic proteins downstream of CypB may thus contribute to the poor outcome of GBM tumors. Our findings link chaperone-mediated protein folding in the ER to mechanisms underlying oncogenic transformation, and they make CypB an attractive and immediately targetable molecule for GBM therapy. PMID:24272483

  5. Cyclophilin B supports Myc and mutant p53-dependent survival of glioblastoma multiforme cells.

    Science.gov (United States)

    Choi, Jae Won; Schroeder, Mark A; Sarkaria, Jann N; Bram, Richard J

    2014-01-15

    Glioblastoma multiforme is an aggressive, treatment-refractory type of brain tumor for which effective therapeutic targets remain important to identify. Here, we report that cyclophilin B (CypB), a prolyl isomerase residing in the endoplasmic reticulum (ER), provides an essential survival signal in glioblastoma multiforme cells. Analysis of gene expression databases revealed that CypB is upregulated in many cases of malignant glioma. We found that suppression of CypB reduced cell proliferation and survival in human glioblastoma multiforme cells in vitro and in vivo. We also found that treatment with small molecule inhibitors of cyclophilins, including the approved drug cyclosporine, greatly reduced the viability of glioblastoma multiforme cells. Mechanistically, depletion or pharmacologic inhibition of CypB caused hyperactivation of the oncogenic RAS-mitogen-activated protein kinase pathway, induction of cellular senescence signals, and death resulting from loss of MYC, mutant p53, Chk1, and Janus-activated kinase/STAT3 signaling. Elevated reactive oxygen species, ER expansion, and abnormal unfolded protein responses in CypB-depleted glioblastoma multiforme cells indicated that CypB alleviates oxidative and ER stresses and coordinates stress adaptation responses. Enhanced cell survival and sustained expression of multiple oncogenic proteins downstream of CypB may thus contribute to the poor outcome of glioblastoma multiforme tumors. Our findings link chaperone-mediated protein folding in the ER to mechanisms underlying oncogenic transformation, and they make CypB an attractive and immediately targetable molecule for glioblastoma multiforme therapy.

  6. Nardilysin controls intestinal tumorigenesis through HDAC1/p53-dependent transcriptional regulation.

    Science.gov (United States)

    Kanda, Keitaro; Sakamoto, Jiro; Matsumoto, Yoshihide; Ikuta, Kozo; Goto, Norihiro; Morita, Yusuke; Ohno, Mikiko; Nishi, Kiyoto; Eto, Koji; Kimura, Yuto; Nakanishi, Yuki; Ikegami, Kanako; Yoshikawa, Takaaki; Fukuda, Akihisa; Kawada, Kenji; Sakai, Yoshiharu; Ito, Akihiro; Yoshida, Minoru; Kimura, Takeshi; Chiba, Tsutomu; Nishi, Eiichiro; Seno, Hiroshi

    2018-04-19

    Colon cancer is a complex disease affected by a combination of genetic and epigenetic factors. Here we demonstrate that nardilysin (N-arginine dibasic convertase; NRDC), a metalloendopeptidase of the M16 family, regulates intestinal tumorigenesis via its nuclear functions. NRDC is highly expressed in human colorectal cancers. Deletion of the Nrdc gene in ApcMin mice crucially suppressed intestinal tumor development. In ApcMin mice, epithelial cell-specific deletion of Nrdc recapitulated the tumor suppression observed in Nrdc-null mice. Moreover, epithelial cell-specific overexpression of Nrdc significantly enhanced tumor formation in ApcMin mice. Notably, epithelial NRDC controlled cell apoptosis in a gene dosage-dependent manner. In human colon cancer cells, nuclear NRDC directly associated with HDAC1, and controlled both acetylation and stabilization of p53, with alterations of p53 target apoptotic factors. These findings demonstrate that NRDC is critically involved in intestinal tumorigenesis through its epigenetic regulatory function, and targeting NRDC may lead to a novel prevention or therapeutic strategy against colon cancer.

  7. p53-Dependent Nestin Regulation Links Tumor Suppression to Cellular Plasticity in Liver Cancer

    DEFF Research Database (Denmark)

    Tschaharganeh, Darjus F; Xue, Wen; Calvisi, Diego F

    2014-01-01

    The p53 tumor suppressor coordinates a series of antiproliferative responses that restrict the expansion of malignant cells, and as a consequence, p53 is lost or mutated in the majority of human cancers. Here, we show that p53 restricts expression of the stem and progenitor-cell-associated protei...... by p53 restricts cellular plasticity and tumorigenesis in liver cancer....

  8. Ubiquitin-specific protease 2 decreases p53-dependent apoptosis in cutaneous T-cell lymphoma

    DEFF Research Database (Denmark)

    Wei, Tianling; Biskup, Edyta; Gjerdrum, Lise Mette Rahbek

    2016-01-01

    expression is 50% lower in CTCL cell lines (MyLa2000, SeAx and Hut-78) than in normal T-cells. USP2 is expressed in neoplastic cells in early, plaque-stage mycosis fungoides (MF) and is downregulated in advanced tumor stages. Upon treatment with psoralen with UVA (PUVA) or a p53 activator, nutlin3a, USP2...

  9. MYC activates stem-like cell potential in hepatocarcinoma by a p53-dependent mechanism

    DEFF Research Database (Denmark)

    Akita, Hirofumi; Marquardt, Jens U; Durkin, Marian E

    2014-01-01

    Activation of c-MYC is an oncogenic hallmark of many cancers including liver cancer, and is associated with a variety of adverse prognostic characteristics. Despite a causative role during malignant transformation and progression in hepatocarcinogenesis, consequences of c-MYC activation for the b...

  10. p53 dependent apoptotic cell death induces embryonic malformation in Carassius auratus under chronic hypoxia.

    Directory of Open Access Journals (Sweden)

    Paramita Banerjee Sawant

    Full Text Available Hypoxia is a global phenomenon affecting recruitment as well as the embryonic development of aquatic fauna. The present study depicts hypoxia induced disruption of the intrinsic pathway of programmed cell death (PCD, leading to embryonic malformation in the goldfish, Carrasius auratus. Constant hypoxia induced the early expression of pro-apoptotic/tumor suppressor p53 and concomitant expression of the cell death molecule, caspase-3, leading to high level of DNA damage and cell death in hypoxic embryos, as compared to normoxic ones. As a result, the former showed delayed 4 and 64 celled stages and a delay in appearance of epiboly stage. Expression of p53 efficiently switched off expression of the anti-apoptotic Bcl-2 during the initial 12 hours post fertilization (hpf and caused embryonic cell death. However, after 12 hours, simultaneous downregulation of p53 and Caspase-3 and exponential increase of Bcl-2, caused uncontrolled cell proliferation and prevented essential programmed cell death (PCD, ultimately resulting in significant (p<0.05 embryonic malformation up to 144 hpf. Evidences suggest that uncontrolled cell proliferation after 12 hpf may have been due to downregulation of p53 abundance, which in turn has an influence on upregulation of anti-apoptotic Bcl-2. Therefore, we have been able to show for the first time and propose that hypoxia induced downregulation of p53 beyond 12 hpf, disrupts PCD and leads to failure in normal differentiation, causing malformation in gold fish embryos.

  11. PRAP1 is a novel executor of p53-dependent mechanisms in cell survival after DNA damage.

    Science.gov (United States)

    Huang, B H; Zhuo, J L; Leung, C H W; Lu, G D; Liu, J J; Yap, C T; Hooi, S C

    2012-12-13

    p53 has a crucial role in governing cellular mechanisms in response to a broad range of genotoxic stresses. During DNA damage, p53 can either promote cell survival by activating senescence or cell-cycle arrest and DNA repair to maintain genomic integrity for cell survival or direct cells to undergo apoptosis to eliminate extensively damaged cells. The ability of p53 to execute these two opposing cell fates depends on distinct signaling pathways downstream of p53. In this study, we showed that under DNA damage conditions induced by chemotherapeutic drugs, gamma irradiation and hydrogen peroxide, p53 upregulates a novel protein, proline-rich acidic protein 1 (PRAP1). We identified functional p53-response elements within intron 1 of PRAP1 gene and showed that these regions interact directly with p53 using ChIP assays, indicating that PRAP1 is a novel p53 target gene. The induction of PRAP1 expression by p53 may promote resistance of cancer cells to chemotherapeutic drugs such as 5-fluorouracil (5-FU), as knockdown of PRAP1 increases apoptosis in cancer cells after 5-FU treatment. PRAP1 appears to protect cells from apoptosis by inducing cell-cycle arrest, suggesting that the induction of PRAP1 expression by p53 in response to DNA-damaging agents contributes to cancer cell survival. Our findings provide a greater insight into the mechanisms underlying the pro-survival role of p53 in response to cytotoxic treatments.

  12. Eriocalyxin B induces apoptosis and cell cycle arrest in pancreatic adenocarcinoma cells through caspase- and p53-dependent pathways

    International Nuclear Information System (INIS)

    Li, Lin; Yue, Grace G.L.; Lau, Clara B.S.; Sun, Handong; Fung, Kwok Pui; Leung, Ping Chung; Han, Quanbin; Leung, Po Sing

    2012-01-01

    Pancreatic cancer is difficult to detect early and responds poorly to chemotherapy. A breakthrough in the development of new therapeutic agents is urgently needed. Eriocalyxin B (EriB), isolated from the Isodon eriocalyx plant, is an ent-kaurane diterpenoid with promise as a broad-spectrum anti-cancer agent. The anti-leukemic activity of EriB, including the underlying mechanisms involved, has been particularly well documented. In this study, we demonstrated for the first time EriB's potent cytotoxicity against four pancreatic adenocarcinoma cell lines, namely PANC-1, SW1990, CAPAN-1, and CAPAN-2. The effects were comparable to that of the chemotherapeutic camptothecin (CAM), but with much lower toxicity against normal human liver WRL68 cells. EriB's cytoxicity against CAPAN-2 cells was found to involve caspase-dependent apoptosis and cell cycle arrest at the G2/M phase. Moreover, the p53 pathway was found to be activated by EriB in these cells. Furthermore, in vivo studies showed that EriB inhibited the growth of human pancreatic tumor xenografts in BALB/c nude mice without significant secondary adverse effects. These results suggest that EriB should be considered a candidate for pancreatic cancer treatment. -- Highlights: ► We study Eriocalyxin B (EriB)'s cytotoxic effects on pancreatic cancer cell lines. ► EriB inhibits cell proliferation via mediation of apoptosis and cell cycle arrest. ► The effects are involved in caspase-dependent apoptosis and p53 pathway. ► In vivo study also shows EriB inhibits the growth of human pancreatic tumor. ► EriB can be a good candidate for chemotherapy in pancreatic cancer.

  13. Deoxyinosine triphosphate induces MLH1/PMS2- and p53-dependent cell growth arrest and DNA instability in mammalian cells

    Science.gov (United States)

    Yoneshima, Yasuto; Abolhassani, Nona; Iyama, Teruaki; Sakumi, Kunihiko; Shiomi, Naoko; Mori, Masahiko; Shiomi, Tadahiro; Noda, Tetsuo; Tsuchimoto, Daisuke; Nakabeppu, Yusaku

    2016-01-01

    Deoxyinosine (dI) occurs in DNA either by oxidative deamination of a previously incorporated deoxyadenosine residue or by misincorporation of deoxyinosine triphosphate (dITP) from the nucleotide pool during replication. To exclude dITP from the pool, mammals possess specific hydrolysing enzymes, such as inosine triphosphatase (ITPA). Previous studies have shown that deficiency in ITPA results in cell growth suppression and DNA instability. To explore the mechanisms of these phenotypes, we analysed ITPA-deficient human and mouse cells. We found that both growth suppression and accumulation of single-strand breaks in nuclear DNA of ITPA-deficient cells depended on MLH1/PMS2. The cell growth suppression of ITPA-deficient cells also depended on p53, but not on MPG, ENDOV or MSH2. ITPA deficiency significantly increased the levels of p53 protein and p21 mRNA/protein, a well-known target of p53, in an MLH1-dependent manner. Furthermore, MLH1 may also contribute to cell growth arrest by increasing the basal level of p53 activity. PMID:27618981

  14. Eriocalyxin B induces apoptosis and cell cycle arrest in pancreatic adenocarcinoma cells through caspase- and p53-dependent pathways

    Energy Technology Data Exchange (ETDEWEB)

    Li, Lin [School of Biomedical Sciences, The Chinese University of Hong Kong, Hong Kong (China); Institute of Chinese Medicine, The Chinese University of Hong Kong, Hong Kong (China); State Key Laboratory of Phytochemistry and Plant Resources in West China, The Chinese University of Hong Kong, Hong Kong (China); Yue, Grace G.L. [Institute of Chinese Medicine, The Chinese University of Hong Kong, Hong Kong (China); State Key Laboratory of Phytochemistry and Plant Resources in West China, The Chinese University of Hong Kong, Hong Kong (China); Lau, Clara B.S. [Institute of Chinese Medicine, The Chinese University of Hong Kong, Hong Kong (China); Institute of Chinese Medicine, The Chinese University of Hong Kong, Hong Kong (China); Sun, Handong [State Key Laboratory of Phytochemistry and Plant Resources in West China, Kunming Institute of Botany, CAS, Yunnan (China); Fung, Kwok Pui [School of Biomedical Sciences, The Chinese University of Hong Kong, Hong Kong (China); Institute of Chinese Medicine, The Chinese University of Hong Kong, Hong Kong (China); State Key Laboratory of Phytochemistry and Plant Resources in West China, The Chinese University of Hong Kong, Hong Kong (China); Leung, Ping Chung [Institute of Chinese Medicine, The Chinese University of Hong Kong, Hong Kong (China); State Key Laboratory of Phytochemistry and Plant Resources in West China, The Chinese University of Hong Kong, Hong Kong (China); Han, Quanbin, E-mail: simonhan@hkbu.edu.hk [Institute of Chinese Medicine, The Chinese University of Hong Kong, Hong Kong (China); State Key Laboratory of Phytochemistry and Plant Resources in West China, The Chinese University of Hong Kong, Hong Kong (China); School of Chinese Medicine, The Hong Kong Baptist University, Hong Kong (China); Leung, Po Sing, E-mail: psleung@cuhk.edu.hk [School of Biomedical Sciences, The Chinese University of Hong Kong, Hong Kong (China)

    2012-07-01

    Pancreatic cancer is difficult to detect early and responds poorly to chemotherapy. A breakthrough in the development of new therapeutic agents is urgently needed. Eriocalyxin B (EriB), isolated from the Isodon eriocalyx plant, is an ent-kaurane diterpenoid with promise as a broad-spectrum anti-cancer agent. The anti-leukemic activity of EriB, including the underlying mechanisms involved, has been particularly well documented. In this study, we demonstrated for the first time EriB's potent cytotoxicity against four pancreatic adenocarcinoma cell lines, namely PANC-1, SW1990, CAPAN-1, and CAPAN-2. The effects were comparable to that of the chemotherapeutic camptothecin (CAM), but with much lower toxicity against normal human liver WRL68 cells. EriB's cytoxicity against CAPAN-2 cells was found to involve caspase-dependent apoptosis and cell cycle arrest at the G2/M phase. Moreover, the p53 pathway was found to be activated by EriB in these cells. Furthermore, in vivo studies showed that EriB inhibited the growth of human pancreatic tumor xenografts in BALB/c nude mice without significant secondary adverse effects. These results suggest that EriB should be considered a candidate for pancreatic cancer treatment. -- Highlights: ► We study Eriocalyxin B (EriB)'s cytotoxic effects on pancreatic cancer cell lines. ► EriB inhibits cell proliferation via mediation of apoptosis and cell cycle arrest. ► The effects are involved in caspase-dependent apoptosis and p53 pathway. ► In vivo study also shows EriB inhibits the growth of human pancreatic tumor. ► EriB can be a good candidate for chemotherapy in pancreatic cancer.

  15. Restoration of mp53 to wtp53 by chemical chaperones restores p53-dependent apoptosis after radiotherapy

    International Nuclear Information System (INIS)

    Ohnishi, T.; Asakawa, I.; Tamamoto, T.; Takahashi, A.; Ohnishi, K.

    2003-01-01

    The mutations of many kinds of cancer related genes have been investigated for the predictive assay against cancer therapy by the application of molecular biology. A tumor suppressor gene product of wtp53 plays important roles in cancer suppression through the induction of cell growth arrest, DNA repair or apoptosis. The p53 exerts its function by induction of downstream genes and/or interaction to various proteins. Mutations in the p53 gene (mp53) cause conformational alterations in the p53 protein, the majority of which can no longer induce expression of the downstream genes. The genetic status of p53 gene has been focused as the most important candidate among them for cancer therapy. The gene therapy of p53 has been already applied. We reported that the transfection of mp53 gene increased the radio-, thermo- and chemo-resistance, and depressed apoptosis introduced with them through bax-induction and proteolysis of PARP and caspase-3. From these results, we propose that the gene therapy of wtp53 to p53-deleted cancer cells may be very useful for cancer therapy by the combination with radiotherapy. Even in the case of mp53 cancer cells, we succeeded the restoration of mp53 to wtp53 by glycerol or C-terminal peptide of p53 as chemical chaperones. These experimental progresses might support effective cancer therapy against individual patients bearing with different p53 gene status by the use of the most suitable treatment to them in the near future

  16. The Quiescent Cellular State is Arf/p53-Dependent and Associated with H2AX Downregulation and Genome Stability

    Directory of Open Access Journals (Sweden)

    Mitsuko Masutani

    2012-05-01

    Full Text Available Cancer is a disease associated with genomic instability and mutations. Excluding some tumors with specific chromosomal translocations, most cancers that develop at an advanced age are characterized by either chromosomal or microsatellite instability. However, it is still unclear how genomic instability and mutations are generated during the process of cellular transformation and how the development of genomic instability contributes to cellular transformation. Recent studies of cellular regulation and tetraploidy development have provided insights into the factors triggering cellular transformation and the regulatory mechanisms that protect chromosomes from genomic instability.

  17. RITA enhances chemosensivity of pre-B ALL cells to doxorubicin by inducing p53-dependent apoptosis.

    Science.gov (United States)

    Kazemi, Ahmad; Safa, Majid; Shahbazi, Atefeh

    2011-07-01

    The use of low-molecular-weight, non-peptidic molecules that disrupt the interaction between the p53 tumor suppressor and its negative regulator MDM2 has provided a promising alternative for the treatment of different types of cancer. Here, we used small-molecule reactivation of p53 and induction of tumor cell apoptosis (RITA) to sensitize leukemic NALM-6 cells to doxorubicin by upregulating p53 protein. RITA alone effectively inhibited NALM-6 cells viability in dose-dependent manner as measured by 3-(4,5-dimethylthiazolyl-2)-2,5-diphenyltetrazolium bromide assay and induced apoptosis as evaluated by flow cytometry, whereas RITA in combination with doxorubicin enhanced NALM-6 cells to doxorubicin-sensitivity and promoted doxorubicin induced apoptosis. Levels of p53 protein and its proapoptotic target genes, quantified by western blot and real-time PCR respectively, showed that expression of p53 was significantly increased after RITA treatment. Using p53 inhibitors PFT-alpha and PFT-mu it was shown that p53-mediated apoptosis induced by RITA can be regulated by both p53-transcription-dependent and -independent pathways. Moreover, RITA-induced apoptosis was accompanied by the activation of caspase-3 and PARP cleavage. Therefore, exploiting synergistic effects between RITA and chemotherapeutics might be an effective clinical strategy for leukemia chemotherapy.

  18. PEG-b-PCL polymeric nano-micelle inhibits vascular angiogenesis by activating p53-dependent apoptosis in zebrafish.

    Science.gov (United States)

    Zhou, Tian; Dong, Qinglei; Shen, Yang; Wu, Wei; Wu, Haide; Luo, Xianglin; Liao, Xiaoling; Wang, Guixue

    Micro/nanoparticles could cause adverse effects on cardiovascular system and increase the risk for cardiovascular disease-related events. Nanoparticles prepared from poly(ethylene glycol) (PEG)- b -poly( ε -caprolactone) (PCL), namely PEG- b -PCL, a widely studied biodegradable copolymer, are promising carriers for the drug delivery systems. However, it is unknown whether polymeric PEG- b -PCL nano-micelles give rise to potential complications of the cardiovascular system. Zebrafish were used as an in vivo model to evaluate the effects of PEG- b -PCL nano-micelle on cardiovascular development. The results showed that PEG- b -PCL nano-micelle caused embryo mortality as well as embryonic and larval malformations in a dose-dependent manner. To determine PEG- b -PCL nano-micelle effects on embryonic angiogenesis, a critical process in zebrafish cardiovascular development, growth of intersegmental vessels (ISVs) and caudal vessels (CVs) in flk1-GFP transgenic zebrafish embryos using fluorescent stereomicroscopy were examined. The expression of fetal liver kinase 1 (flk1), an angiogenic factor, by real-time quantitative polymerase chain reaction (qPCR) and in situ whole-mount hybridization were also analyzed. PEG- b -PCL nano-micelle decreased growth of ISVs and CVs, as well as reduced flk1 expression in a concentration-dependent manner. Parallel to the inhibitory effects on angiogenesis, PEG- b -PCL nano-micelle exposure upregulated p53 pro-apoptotic pathway and induced cellular apoptosis in angiogenic regions by qPCR and terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) apoptosis assay. This study further showed that inhibiting p53 activity, either by pharmacological inhibitor or RNA interference, could abrogate the apoptosis and angiogenic defects caused by PEG- b -PCL nano-micelles, indicating that PEG- b -PCL nano-micelle inhibits angiogenesis by activating p53-mediated apoptosis. This study indicates that polymeric PEG- b -PCL nano-micelle could pose potential hazards to cardiovascular development.

  19. Pomegranate protects against arsenic-induced p53-dependent ROS-mediated inflammation and apoptosis in liver cells.

    Science.gov (United States)

    Choudhury, Sreetama; Ghosh, Sayan; Mukherjee, Sudeshna; Gupta, Payal; Bhattacharya, Saurav; Adhikary, Arghya; Chattopadhyay, Sreya

    2016-12-01

    Molecular mechanisms involved in arsenic-induced toxicity are complex and elusive. Liver is one of the most favored organs for arsenic toxicity as methylation of arsenic occurs mostly in the liver. In this study, we have selected a range of environmentally relevant doses of arsenic to examine the basis of arsenic toxicity and the role of pomegranate fruit extract (PFE) in combating it. Male Swiss albino mice exposed to different doses of arsenic presented marked hepatic injury as evident from histological and electron microscopic studies. Increased activities of enzymes alanine aminotransferase, aspartate aminotransferase, lactate dehydrogenase and alkaline phosphatase corroborated extensive liver damage. It was further noted that arsenic exposure initiated reactive oxygen species (ROS)-dependent apoptosis in the hepatocytes involving loss of mitochondrial membrane potential. Arsenic significantly increased nuclear translocation of nuclear factor erythroid 2-related factor 2 (Nrf2) and nuclear factor-κB (NF-κB), coupled with increase in phosphorylated Iκ-B, possibly as adaptive cellular survival strategies. Arsenic-induced oxidative DNA damage to liver cells culminated in p53 activation and increased expression of p53 targets like miR-34a and Bax. Pomegranate polyphenols are known to possess remarkable antioxidant properties and are capable of protecting normal cells from various stimuli-induced oxidative stress and toxicities. We explored the protective role of PFE in ameliorating arsenic-induced hepatic damage. PFE was shown to reduce ROS generation in hepatocytes, thereby reducing arsenic-induced Nrf2 activation. PFE also inhibited arsenic-induced NF-κB-inflammatory pathway. Data revealed that PFE reversed arsenic-induced hepatotoxicity and apoptosis by modulating the ROS/Nrf2/p53-miR-34a axis. For the first time, we have mapped the possible signaling pathways associated with arsenic-induced hepatotoxicity and its rescue by pomegranate polyphenols. Copyright © 2016 Elsevier Inc. All rights reserved.

  20. Gain-of-function mutations of PPM1D/Wip1 impair the p53-dependent G1 checkpoint

    Czech Academy of Sciences Publication Activity Database

    Kleiblová, P.; Shaltiel, I.A.; Benada, Jan; Ševčík, J.; Pecháčková, Soňa; Pohlreich, P.; Voest, E.E.; Dundr, P.; Bartek, Jiří; Kleibl, Z.; Medema, R.H.; Macůrek, Libor

    2013-01-01

    Roč. 201, č. 4 (2013), s. 511-521 ISSN 0021-9525 R&D Projects: GA ČR GAP301/10/1525; GA ČR GAP305/12/2485; GA ČR GA13-18392S Grant - others:GA MZd(CZ) NT13343; EK(XE) 223575; EK(XE) ED0030/01/01 Institutional support: RVO:68378050 Keywords : DNA damage * cell cycle * cancer Subject RIV: EB - Genetics ; Molecular Biology Impact factor: 9.688, year: 2013

  1. Radiosensitization of NSCLC cells by EGFR inhibition is the result of an enhanced p53-dependent G1 arrest

    International Nuclear Information System (INIS)

    Kriegs, Malte; Gurtner, Kristin; Can, Yildiz; Brammer, Ingo; Rieckmann, Thorsten; Oertel, Reinhard; Wysocki, Marek; Dorniok, Franziska; Gal, Andreas; Grob, Tobias J.; Laban, Simon; Kasten-Pisula, Ulla; Petersen, Cordula; Baumann, Michael; Krause, Mechthild; Dikomey, Ekkehard

    2015-01-01

    Purpose: How EGF receptor (EGFR) inhibition induces cellular radiosensitization and with that increase in tumor control is still a matter of discussion. Since EGFR predominantly regulates cell cycle and proliferation, we studied whether a G1-arrest caused by EGFR inhibition may contribute to these effects. Materials and methods: We analyzed human non-small cell lung cancer (NSCLC) cell lines either wild type (wt) or mutated in p53 (A549, H460, vs. H1299, H3122) and HCT116 cells (p21 wt and negative). EGFR was inhibited by BIBX1382BS, erlotinib or cetuximab; p21 was knocked down by siRNA. Functional endpoints analyzed were cell signaling, proliferation, G1-arrest, cell survival as well as tumor control using an A549 tumor model. Results: When combined with IR, EGFR inhibition enhances the radiation-induced permanent G1 arrest, though solely in cells with intact p53/p21 signaling. This increase in G1-arrest was always associated with enhanced cellular radiosensitivity. Strikingly, this effect was abrogated when cells were re-stimulated, suggesting the initiation of dormancy. In line with this, only a small non-significant increase in tumor control was observed for A549 tumors treated with fractionated RT and EGFR inhibition. Conclusion: For NSCLC cells increase in radiosensitivity by EGFR inhibition results from enhanced G1-arrest. However, this effect does not lead to improved tumor control because cells can be released from this arrest by re-stimulation

  2. Minnelide/Triptolide Impairs Mitochondrial Function by Regulating SIRT3 in P53-Dependent Manner in Non-Small Cell Lung Cancer.

    Directory of Open Access Journals (Sweden)

    Ajay Kumar

    Full Text Available Minnelide/Triptolide (TL has recently emerged as a potent anticancer drug in non-small cell lung cancer (NSCLC. However, the precise mechanism of its action remains ambiguous. In this study, we elucidated the molecular basis for TL-induced cell death in context to p53 status. Cell death was attributed to dysfunction of mitochondrial bioenergetics in p53-deficient cells, which was characterized by decreased mitochondrial respiration, steady-state ATP level and membrane potential, but augmented reactive oxygen species (ROS. Increased ROS production resulted in oxidative stress in TL-treated cells. This was exhibited by elevated nuclear levels of a redox-sensitive transcriptional factor, NF-E2-related factor-2 (NRF2, along with diminished cellular glutathione (GSH content. We further demonstrated that in the absence of p53, TL blunted the expression of mitochondrial SIRT3 triggering increased acetylation of NDUAF9 and succinate dehydrogenase, components of complexes I and II of the electron transport chain (ETC. TL-mediated hyperacetylation of complexes I and II proteins and these complexes displayed decreased enzymatic activities. We also provide the evidence that P53 regulate steady-state level of SIRT3 through Proteasome-Pathway. Finally, forced overexpression of Sirt3, but not deacetylase-deficient mutant of Sirt3 (H243Y, restored the deleterious effect of TL on p53-deficient cells by rescuing mitochondrial bioenergetics. On contrary, Sirt3 deficiency in the background of wild-type p53 triggered TL-induced mitochondrial impairment that echoed TL effect in p53-deficeint cells. These findings illustrate a novel mechanism by which TL exerts its potent effects on mitochondrial function and ultimately the viability of NSCLC tumor.

  3. Metformin downregulates the insulin/IGF-I signaling pathway and inhibits different uterine serous carcinoma (USC) cells proliferation and migration in p53-dependent or -independent manners.

    Science.gov (United States)

    Sarfstein, Rive; Friedman, Yael; Attias-Geva, Zohar; Fishman, Ami; Bruchim, Ilan; Werner, Haim

    2013-01-01

    Accumulating epidemiological evidence shows that obesity is associated with an increased risk of several types of adult cancers, including endometrial cancer. Chronic hyperinsulinemia, a typical hallmark of diabetes, is one of the leading factors responsible for the obesity-cancer connection. Numerous cellular and circulating factors are involved in the biochemical chain of events leading from hyperinsulinemia and insulin resistance to increased cancer risk and, eventually, tumor development. Metformin is an oral anti-diabetic drug of the biguanide family used for treatment of type 2 diabetes. Recently, metformin was shown to exhibit anti-proliferative effects in ovarian and Type I endometrial cancer, although the mechanisms responsible for this non-classical metformin action remain unclear. The insulin-like growth factors (IGFs) play a prominent role in cancer biology and their mechanisms of action are tightly interconnected with the insulin signaling pathways. Given the cross-talk between the insulin and IGF signaling pathways, the aim of this study was to examine the hypothesis that the anti-proliferative actions of metformin in uterine serous carcinoma (USC) are potentially mediated via suppression of the IGF-I receptor (IGF-IR) pathway. Our results show that metformin interacts with the IGF pathway, and induces apoptosis and inhibition of proliferation and migration of USC cell lines with both wild type and mutant p53. Taken together, our results suggest that metformin therapy could be a novel and attractive therapeutic approach for human USC, a highly aggressive variant of endometrial cancer.

  4. The natural triterpene maslinic acid induces apoptosis in HT29 colon cancer cells by a JNK-p53-dependent mechanism

    International Nuclear Information System (INIS)

    Reyes-Zurita, Fernando J; Pachón-Peña, Gisela; Lizárraga, Daneida; Rufino-Palomares, Eva E; Cascante, Marta; Lupiáñez, José A

    2011-01-01

    Maslinic acid, a pentacyclic triterpene found in the protective wax-like coating of the leaves and fruit of Olea europaea L., is a promising agent for the prevention of colon cancer. We have shown elsewhere that maslinic acid inhibits cell proliferation to a significant extent and activates mitochondrial apoptosis in colon cancer cells. In our latest work we have investigated further this compound's apoptotic molecular mechanism. We used HT29 adenocarcinoma cells. Changes genotoxicity were analyzed by single-cell gel electrophoresis (comet assay). The cell cycle was determined by flow cytometry. Finally, changes in protein expression were examined by western blotting. Student's t-test was used for statistical comparison. HT29 cells treated with maslinic acid showed significant increases in genotoxicity and cell-cycle arrest during the G0/G1 phase after 72 hours' treatment and an apoptotic sub-G0/G1 peak after 96 hours. Nevertheless, the molecular mechanism for this cytotoxic effect of maslinic acid has never been properly explored. We show here that the anti-tumoral activity of maslinic acid might proceed via p53-mediated apoptosis by acting upon the main signaling components that lead to an increase in p53 activity and the induction of the rest of the factors that participate in the apoptotic pathway. We found that in HT29 cells maslinic acid activated the expression of c-Jun NH2-terminal kinase (JNK), thus inducing p53. Treatment of tumor cells with maslinic acid also resulted in an increase in the expression of Bid and Bax, repression of Bcl-2, release of cytochrome-c and an increase in the expression of caspases -9, -3, and -7. Moreover, maslinic acid produced belated caspase-8 activity, thus amplifying the initial mitochondrial apoptotic signaling. All these results suggest that maslinic acid induces apoptosis in human HT29 colon-cancer cells through the JNK-Bid-mediated mitochondrial apoptotic pathway via the activation of p53. Thus we propose a plausible sequential molecular mechanism for the expression of the different proteins responsible for the intrinsic mitochondrial apoptotic pathway. Further studies with other cell lines will be needed to confirm the general nature of these findings

  5. Genotoxic polycyclic aromatic hydrocarbons fail to induce the p53-dependent DNA damage response, apoptosis or cell-cycle arrest in human prostate carcinoma LNCaP cells

    Czech Academy of Sciences Publication Activity Database

    Hrubá, E.; Trilecová, L.; Marvanová, S.; Krčmář, P.; Vykopalová, L.; Milcová, Alena; Líbalová, Helena; Topinka, Jan; Staršíchová, Andrea; Souček, Karel; Vondráček, Jan; Machala, M.

    2010-01-01

    Roč. 198, č. 3 (2010), s. 227-235 ISSN 0378-4274 Grant - others:GA ČR(CZ) GA310/07/0961 Program:GA Institutional research plan: CEZ:AV0Z50040507; CEZ:AV0Z50040702; CEZ:AV0Z50390512; CEZ:AV0Z50390703 Keywords : prostate epithelial cells * DNA adducts * carcinogenesis Subject RIV: BO - Biophysics Impact factor: 3.581, year: 2010

  6. 1800 MHz mobile phone irradiation induced oxidative and nitrosative stress leads to p53 dependent Bax mediated testicular apoptosis in mice, Mus musculus.

    Science.gov (United States)

    Shahin, Saba; Singh, Surya P; Chaturvedi, Chandra M

    2018-09-01

    Present study was carried out to investigate the effect of long-term mobile phone radiation exposure in different operative modes (Dialing, Receiving, and Stand-by) on immature male mice. Three-week old male mice were exposed to mobile phone (1800 MHz) radiation for 3 hr/day for 120 days in different operative modes. To check the changes/alteration in testicular histoarchitecture and serum testosterone level, HE staining and ELISA was performed respectively. Further, we have checked the redox status (ROS, NO, MDA level, and antioxidant enzymes: SOD, CAT, and GPx) by biochemical estimation, alteration in the expression of pro-apoptotic proteins (p53 and Bax), active executioner caspase-3, full length/uncleaved PARP-1 (DNA repair enzyme), anti-apoptotic proteins (Bcl-2 and Bcl-x L ) in testes by immunofluorescence and cytosolic cytochrome-c by Western blot. Decreased seminiferous tubule diameter, sperm count, and viability along with increased germ cells apoptosis and decreased serum testosterone level, was observed in the testes of all the mobile phone exposed mice compared with control. We also observed that, mobile phone radiation exposure in all the three different operative modes alters the testicular redox status via increasing ROS, NO, and MDA level, and decreasing antioxidant enzymes levels leading to enhanced apoptosis of testicular cells by increasing the expression of pro-apoptotic and apoptotic proteins along with decreasing the expression of anti-apoptotic protein. On the basis of results, it is conclude that long-term mobile phone radiation exposure induced oxidative stress leads to apoptosis of testicular cells and thus impairs testicular function. © 2018 Wiley Periodicals, Inc.

  7. Loss of p19(Arf facilitates the angiogenic switch and tumor initiation in a multi-stage cancer model via p53-dependent and independent mechanisms.

    Directory of Open Access Journals (Sweden)

    Danielle B Ulanet

    2010-08-01

    Full Text Available The Arf tumor suppressor acts as a sensor of oncogenic signals, countering aberrant proliferation in large part via activation of the p53 transcriptional program, though a number of p53-independent functions have been described. Mounting evidence suggests that, in addition to promoting tumorigenesis via disruptions in the homeostatic balance between cell proliferation and apoptosis of overt cancer cells, genetic alterations leading to tumor suppressor loss of function or oncogene gain of function can also incite tumor development via effects on the tumor microenvironment. In a transgenic mouse model of multi-stage pancreatic neuroendocrine carcinogenesis (PNET driven by inhibition of the canonical p53 and Rb tumor suppressors with SV40 large T-antigen (Tag, stochastic progression to tumors is limited in part by a requirement for initiation of an angiogenic switch. Despite inhibition of p53 by Tag in this mouse PNET model, concomitant disruption of Arf via genetic knockout resulted in a significantly accelerated pathway to tumor formation that was surprisingly not driven by alterations in tumor cell proliferation or apoptosis, but rather via earlier activation of the angiogenic switch. In the setting of a constitutional p53 gene knockout, loss of Arf also accelerated tumor development, albeit to a lesser degree. These findings demonstrate that Arf loss of function can promote tumorigenesis via facilitating angiogenesis, at least in part, through p53-independent mechanisms.

  8. Ser46 phosphorylation and prolyl-isomerase Pin1-mediated isomerization of p53 are key events in p53-dependent apoptosis induced by mutant huntingtin.

    Science.gov (United States)

    Grison, Alice; Mantovani, Fiamma; Comel, Anna; Agostoni, Elena; Gustincich, Stefano; Persichetti, Francesca; Del Sal, Giannino

    2011-11-01

    Huntington disease (HD) is a neurodegenerative disorder caused by a CAG repeat expansion in the gene coding for huntingtin protein. Several mechanisms have been proposed by which mutant huntingtin (mHtt) may trigger striatal neurodegeneration, including mitochondrial dysfunction, oxidative stress, and apoptosis. Furthermore, mHtt induces DNA damage and activates a stress response. In this context, p53 plays a crucial role in mediating mHtt toxic effects. Here we have dissected the pathway of p53 activation by mHtt in human neuronal cells and in HD mice, with the aim of highlighting critical nodes that may be pharmacologically manipulated for therapeutic intervention. We demonstrate that expression of mHtt causes increased phosphorylation of p53 on Ser46, leading to its interaction with phosphorylation-dependent prolyl isomerase Pin1 and consequent dissociation from the apoptosis inhibitor iASPP, thereby inducing the expression of apoptotic target genes. Inhibition of Ser46 phosphorylation by targeting homeodomain-interacting protein kinase 2 (HIPK2), PKCδ, or ataxia telangiectasia mutated kinase, as well as inhibition of the prolyl isomerase Pin1, prevents mHtt-dependent apoptosis of neuronal cells. These results provide a rationale for the use of small-molecule inhibitors of stress-responsive protein kinases and Pin1 as a potential therapeutic strategy for HD treatment.

  9. The Histone Lysine Demethylase JMJD3/KDM6B Is Recruited to p53 Bound Promoters and Enhancer Elements in a p53 Dependent Manner

    DEFF Research Database (Denmark)

    Williams, Kristine; Christensen, Jesper; Rappsilber, Juri

    2014-01-01

    linked to the regulation of different biological processes such as differentiation of embryonic stem cells, inflammatory responses in macrophages, and induction of cellular senescence via regulation of the INK4A-ARF locus. Here we show here that JMJD3 interacts with the tumour suppressor protein p53. We...... find that the interaction is dependent on the p53 tetramerization domain. Following DNA damage, JMJD3 is transcriptionally upregulated and by performing genome-wide mapping of JMJD3, we demonstrate that it binds genes involved in basic cellular processes, as well as genes regulating cell cycle......, response to stress and apoptosis. Moreover, we find that JMJD3 binding sites show significant overlap with p53 bound promoters and enhancer elements. The binding of JMJD3 to p53 target sites is increased in response to DNA damage, and we demonstrate that the recruitment of JMJD3 to these sites is dependent...

  10. p53-dependent manner of persistent activation of the radiation-induced reversion in the pink-eyed unstable mouse embryo

    International Nuclear Information System (INIS)

    Shiraishi, K.; Yonezawa, M.; Niwa, O.

    2003-01-01

    Full text: We previously reported that radiation has an ability to induce genomic instability which causes delayed and untargeted mutation. These mutations aren't accounted for by the usual relationship between DNA damages and repair. However, the mechanisms of a long-term memory of DNA damage and the persistence of up-regulated recombination activity have yet to be elucidated. The mouse pink-eyed unstable (pun) mutation is due to an intragenic duplication of the pink-eyed dilution locus and frequently reverts black to the wild type in germ cells as well as somatic cells. The frequency of reversion was estimated by counting cluster of pigment cells in retinal pigment epithelium. Twice increase of the reversion was observed in F1 mice born to 6Gy irradiated male at spermatozoa stage, but not at other spermatogenesis stages( -tid, -cyte, -gonia ). Trans-genarational effect in F2 mice also didn't observe. Therefore, this phenomenon only occurs under the restricted germ cell stage. Additionally, the reversion frequency of p53 deficient F1 mouse born to irradiated sperm was less than irradiated wild mouse. 5aza-dc chemical agent, which is DNA methylation emzyme inhibitor, also suppressed pun allele recombination in mouse embryo. These data indicate that p53 contributes delayed and untargeted mutation, perhaps, by regulation of DNA metylation status

  11. Alpha-tocopheryl succinate induces DR4 and DR5 expression by a p53-dependent route: implication for sensitisation of resistant cancer cells to TRAIL apoptosis

    Czech Academy of Sciences Publication Activity Database

    Tomasetti, M.; Anděra, Ladislav; Alleva, R.; Borghi, B.; Neužil, Jiří; Procopio, A.

    2006-01-01

    Roč. 580, č. 8 (2006), s. 1925-1931 ISSN 0014-5793 R&D Projects: GA AV ČR(CZ) IAA5052001; GA MŠk(CZ) LN00A026 Institutional research plan: CEZ:AV0Z50520514 Keywords : malignant mesothelioma * TRAIL * alpha-TOS Subject RIV: EB - Genetics ; Molecular Biology Impact factor: 3.372, year: 2006

  12. ETME, a novel β-elemene derivative, synergizes with arsenic trioxide in inducing apoptosis and cell cycle arrest in hepatocarcinoma cells via a p53-dependent pathway

    Directory of Open Access Journals (Sweden)

    Zhiying Yu

    2014-12-01

    Full Text Available Arsenic trioxide (ATO has been identified as an effective treatment for acute promyelocytic leukemia (APL but is much less effective against solid tumors such as hepatocellular carcinoma (HCC. In the search for ways to enhance its therapeutic efficacy against solid tumors, we have examined its use in combination with a novel derivative of β-elemene, N-(β-elemene-13-yltryptophan methyl ester (ETME. Here we report the effects of the combination on cell viability, apoptosis, the cell cycle and mitochondria membrane potential (MMP in HCC SMMC-7721 cells. We found that the two compounds acted synergistically to enhance antiproliferative activity and apoptosis. The combination also decreased the MMP, down-regulated Bcl-2 and pro-proteins of the caspase family, and up-regulated Bax and BID, all of which were reversed by the p53 inhibitor, pifithrin-α. In addition, the combination induced cell cycle arrest at the G2/M phase and reduced tumor volume and weight in an xenograft model of nude mice. Overall, the results suggest that ETME in combination with ATO may be useful in the treatment of HCC patients particularly those unresponsive to ATO alone.

  13. Characterization of tumorigenicity and radio-sensitivity markers by an ex vivo approach. In vivo identification of p53 dependent radio-sensitivity markers

    International Nuclear Information System (INIS)

    Alvarez, S.

    2003-12-01

    After a detailed discussion of the relationship between cancer and genetic instability, of the structure, activation mechanisms, activity and biological functions of the p53 protein, a presentation of p53 mutants, and a recall of the effects of ionizing radiations, the author reports a biology research during which he investigated a cell model established from rat embryo lungs treated with Benzo[a]pyrene and made of tumoral lines muted by the p53 gene. He tried to identify markers which could report differences of tumorigenicity and radio-sensitivity observed in these different lines. He also tried to characterize radio-sensitivity molecular markers dependent on the p53 gene in a context of normal cells

  14. Activation of postnatal neural stem cells requires nuclear receptor TLX.

    Science.gov (United States)

    Niu, Wenze; Zou, Yuhua; Shen, Chengcheng; Zhang, Chun-Li

    2011-09-28

    Neural stem cells (NSCs) continually produce new neurons in postnatal brains. However, the majority of these cells stay in a nondividing, inactive state. The molecular mechanism that is required for these cells to enter proliferation still remains largely unknown. Here, we show that nuclear receptor TLX (NR2E1) controls the activation status of postnatal NSCs in mice. Lineage tracing indicates that TLX-expressing cells give rise to both activated and inactive postnatal NSCs. Surprisingly, loss of TLX function does not result in spontaneous glial differentiation, but rather leads to a precipitous age-dependent increase of inactive cells with marker expression and radial morphology for NSCs. These inactive cells are mispositioned throughout the granular cell layer of the dentate gyrus during development and can proliferate again after reintroduction of ectopic TLX. RNA-seq analysis of sorted NSCs revealed a TLX-dependent global expression signature, which includes the p53 signaling pathway. TLX regulates p21 expression in a p53-dependent manner, and acute removal of p53 can rescue the proliferation defect of TLX-null NSCs in culture. Together, these findings suggest that TLX acts as an essential regulator that ensures the proliferative ability of postnatal NSCs by controlling their activation through genetic interaction with p53 and other signaling pathways.

  15. cables1 Is Required for Embryonic Neural Development: Molecular, Cellular, and Behavioral Evidence From the Zebrafish

    Science.gov (United States)

    GROENEWEG, JOLIJN W.; WHITE, YVONNE A.R.; KOKEL, DAVID; PETERSON, RANDALL T.; ZUKERBERG, LAWRENCE R.; BERIN, INNA; RUEDA, BO R.; WOOD, ANTONY W.

    2014-01-01

    SUMMARY In vitro studies have suggested that the Cables1 gene regulates epithelial cell proliferation, whereas other studies suggest a role in promoting neural differentiation. In efforts to clarify the functions of Cables1 in vivo, we conducted gain- and loss-of-function studies targeting its ortholog (cables1) in the zebrafish embryo. Similar to rodents, zebrafish cables1 mRNA expression is detected most robustly in embryonic neural tissues. Antisense knockdown of cables1 leads to increased numbers of apoptotic cells, particularly in brain tissue, in addition to a distinct behavioral phenotype, characterized by hyperactivity in response to stimulation. Apoptosis and the behavioral abnormality could be rescued by co-expression of a morpholino-resistant cables1 construct. Suppression of p53 expression in cables1 morphants partially rescued both apoptosis and the behavioral phenotype, suggesting that the phenotype of cables1 morphants is due in part to p53-dependent apoptosis. Alterations in the expression patterns of several neural transcription factors were observed in cables1 morphants during early neurulation, suggesting that cables1 is required for early neural differentiation. Ectopic overexpression of cables1 strongly disrupted embryonic morphogenesis, while overexpression of a cables1 mutant lacking the C-terminal cyclin box had little effect, suggesting functional importance of the cyclin box. Lastly, marked reductions in p35, but not Cdk5, were observed in cables1 morphants. Collectively, these data suggest that cables1 is important for neural differentiation during embryogenesis, in a mechanism that likely involves interactions with the Cdk5/p35 kinase pathway. PMID:21268180

  16. 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

  17. 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

  18. 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.

  19. Repair kinetics of DNA double-strand breaks and incidence of apoptosis in mouse neural stem/progenitor cells and their differentiated neurons exposed to ionizing radiation.

    Science.gov (United States)

    Kashiwagi, Hiroki; Shiraishi, Kazunori; Sakaguchi, Kenta; Nakahama, Tomoya; Kodama, Seiji

    2018-05-01

    Neuronal loss leads to neurodegenerative disorders, including Alzheimer's disease, Parkinson's disease and Huntington's disease. Because of their long lifespans, neurons are assumed to possess highly efficient DNA repair ability and to be able to protect themselves from deleterious DNA damage such as DNA double-strand breaks (DSBs) produced by intrinsic and extrinsic sources. However, it remains largely unknown whether the DSB repair ability of neurons is more efficient compared with that of other cells. Here, we investigated the repair kinetics of X-ray-induced DSBs in mouse neural cells by scoring the number of phosphorylated 53BP1 foci post irradiation. We found that p53-independent apoptosis was induced time dependently during differentiation from neural stem/progenitor cells (NSPCs) into neurons in culture for 48 h. DSB repair in neurons differentiated from NSPCs in culture was faster than that in mouse embryonic fibroblasts (MEFs), possibly due to the higher DNA-dependent protein kinase activity, but it was similar to that in NSPCs. Further, the incidence of p53-dependent apoptosis induced by X-irradiation in neurons was significantly higher than that in NSPCs. This difference in response of X-ray-induced apoptosis between neurons and NSPCs may reflect a difference in the fidelity of non-homologous end joining or a differential sensitivity to DNA damage other than DSBs.

  20. 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.

  1. MG132 plus apoptosis antigen-1 (APO-1) antibody cooperate to restore p53 activity inducing autophagy and p53-dependent apoptosis in HPV16 E6-expressing keratinocytes.

    Science.gov (United States)

    Lagunas-Martínez, Alfredo; García-Villa, Enrique; Arellano-Gaytán, Magaly; Contreras-Ochoa, Carla O; Dimas-González, Jisela; López-Arellano, María E; Madrid-Marina, Vicente; Gariglio, Patricio

    2017-01-01

    The E6 oncoprotein can interfere with the ability of infected cells to undergo programmed cell death through the proteolytic degradation of proapoptotic proteins such as p53, employing the proteasome pathway. Therefore, inactivation of the proteasome through MG132 should restore the activity of several proapoptotic proteins. We investigated whether in HPV16 E6-expressing keratinocytes (KE6 cells), the restoration of p53 levels mediated by MG132 and/or activation of the CD95 pathway through apoptosis antigen-1 (APO-1) antibody are responsible for the induction of apoptosis. We found that KE6 cells underwent apoptosis mainly after incubation for 24 h with MG132 alone or APO-1 plus MG132. Both treatments activated the extrinsic and intrinsic apoptosis pathways. Autophagy was also activated, principally by APO-1 plus MG132. Inhibition of E6-mediated p53 proteasomal degradation by MG132 resulted in the elevation of p53 protein levels and its phosphorylation in Ser46 and Ser20; the p53 protein was localized mainly at nucleus after treatment with MG132 or APO-1 plus MG132. In addition, induction of its transcriptional target genes such as p21, Bax and TP53INP was observed 3 and 6 h after treatment. Also, LC3 mRNA was induced after 3 and 6 h, which correlates with lipidation of LC3B protein and induction of autophagy. Finally, using pifithrin alpha we observed a decrease in apoptosis induced by MG132, and by APO-1 plus MG132, suggesting that restoration of APO-1 sensitivity occurs in part through an increase in both the levels and the activity of p53. The use of small molecules to inhibit the proteasome pathway might permit the activation of cell death, providing new opportunities for CC treatment.

  2. Disruption of the MDM2-p53 interaction strongly potentiates p53-dependent apoptosis in cisplatin-resistant human testicular carcinoma cells via the Fas/FasL pathway

    NARCIS (Netherlands)

    Koster, R.; Timmer-Bosscha, H.; Bischoff, R.; Gietema, J. A.; de Jong, S.

    Wild-type p53 has a major role in the response and execution of apoptosis after chemotherapy in many cancers. Although high levels of wild-type p53 and hardly any TP53 mutations are found in testicular cancer (TC), chemotherapy resistance is still observed in a significant subgroup of TC patients.

  3. The absence of Ser389 phosphorylation in p53 affects the basal gene expression level of many p53-dependent genes and alters the biphasic response to UV exposure in mouse embryonic fibroblasts

    NARCIS (Netherlands)

    Bruins, Wendy; Bruning, Oskar; Jonker, Martijs J.; Zwart, Edwin; van der Hoeven, Tessa V.; Pennings, Jeroen L. A.; Rauwerda, Han; de Vries, Annemieke; Breit, Timo M.

    2008-01-01

    Phosphorylation is important in p53-mediated DNA damage responses. After UV irradiation, p53 is phosphorylated specifically at murine residue Ser389. Phosphorylation mutant p53.S389A cells and mice show reduced apoptosis and compromised tumor suppression after UV irradiation. We investigated the

  4. 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.

  5. 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 ...

  6. 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...

  7. 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.

  8. 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)

  9. 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.

  10. 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...

  11. 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...

  12. 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

  13. 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…

  14. 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...

  15. 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.

  16. 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

  17. 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.

  18. 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.

  19. 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

  20. 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...

  1. 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.

  2. 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...

  3. 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.

  4. 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.

  5. 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.

  6. 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....

  7. 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.

  8. 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....

  9. 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....

  10. 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 ...

  11. 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.

  12. 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

  13. 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.

  14. 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...

  15. 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...

  16. 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.

  17. 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.

  18. 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...

  19. 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

  20. 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. .

  1. 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...

  2. 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...

  3. 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

  4. 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

  5. Acetylation-mediated suppression of transcription-independent memory: bidirectional modulation of memory by acetylation.

    Directory of Open Access Journals (Sweden)

    Katja Merschbaecher

    Full Text Available Learning induced changes in protein acetylation, mediated by histone acetyl transferases (HATs, and the antagonistic histone deacetylases (HDACs play a critical role in memory formation. The status of histone acetylation affects the interaction between the transcription-complex and DNA and thus regulates transcription-dependent processes required for long-term memory (LTM. While the majority of studies report on the role of elevated acetylation in memory facilitation, we address the impact of both, increased and decreased acetylation on formation of appetitive olfactory memory in honeybees. We show that learning-induced changes in the acetylation of histone H3 at aminoacid-positions H3K9 and H3K18 exhibit distinct and different dynamics depending on the training strength. A strong training that induces LTM leads to an immediate increase in acetylation at H3K18 that stays elevated for hours. A weak training, not sufficient to trigger LTM, causes an initial increase in acetylation at H3K18, followed by a strong reduction in acetylation at H3K18 below the control group level. Acetylation at position H3K9 is not affected by associative conditioning, indicating specific learning-induced actions on the acetylation machinery. Elevating acetylation levels by blocking HDACs after conditioning leads to an improved memory. While memory after strong training is enhanced for at least 2 days, the enhancement after weak training is restricted to 1 day. Reducing acetylation levels by blocking HAT activity after strong training leads to a suppression of transcription-dependent LTM. The memory suppression is also observed in case of weak training, which does not require transcription processes. Thus, our findings demonstrate that acetylation-mediated processes act as bidirectional regulators of memory formation that facilitate or suppress memory independent of its transcription-requirement.

  6. MLL2 conveys transcription-independent H3K4 trimethylation in oocytes

    Czech Academy of Sciences Publication Activity Database

    Hanna, C. W.; Taudt, A.; Huang, J.; Gahurová, Lenka; Kranz, A.; Andrews, S.; Dean, W.; Francis Stewart, A.; Colomé-Tatché, M.; Kelsey, G.

    2018-01-01

    Roč. 25, č. 1 (2018), s. 73-82 ISSN 1545-9993 Institutional support: RVO:67985904 Keywords : H3K4 trimethylation Subject RIV: EB - Genetics ; Molecular Biology OBOR OECD: Biochemistry and molecular biology Impact factor: 12.595, year: 2016

  7. 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

  8. 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...

  9. 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.

  10. 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....

  11. 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

  12. 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…

  13. 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 ...

  14. 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)

  15. 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.

  16. 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

  17. 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.

  18. 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.

  19. 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

  20. 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.

  1. 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.

  2. 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.

  3. 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

  4. 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...

  5. 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...

  6. 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

  7. 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...

  8. 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)

  9. 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...

  10. 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

  11. 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...

  12. 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.

  13. 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

  14. 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.

  15. 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.

  16. 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.

  17. 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...

  18. 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.)

  19. 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...

  20. 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.

  1. 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.

  2. 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...

  3. 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

  4. 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 ...

  5. 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.

  6. 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...

  7. 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...

  8. 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

  9. 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.

  10. 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.

  11. Simplified LQG Control with Neural Networks

    DEFF Research Database (Denmark)

    Sørensen, O.

    1997-01-01

    A new neural network application for non-linear state control is described. One neural network is modelled to form a Kalmann predictor and trained to act as an optimal state observer for a non-linear process. Another neural network is modelled to form a state controller and trained to produce...

  12. Analysis of neural networks through base functions

    NARCIS (Netherlands)

    van der Zwaag, B.J.; Slump, Cornelis H.; Spaanenburg, L.

    Problem statement. Despite their success-story, neural networks have one major disadvantage compared to other techniques: the inability to explain comprehensively how a trained neural network reaches its output; neural networks are not only (incorrectly) seen as a "magic tool" but possibly even more

  13. Genetic Algorithm Optimized Neural Networks Ensemble as ...

    African Journals Online (AJOL)

    NJD

    Improvements in neural network calibration models by a novel approach using neural network ensemble (NNE) for the simultaneous ... process by training a number of neural networks. .... Matlab® version 6.1 was employed for building principal component ... provide a fair simulation of calibration data set with some degree.

  14. 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.

  15. 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.

  16. Neural chips, neural computers and application in high and superhigh energy physics experiments

    International Nuclear Information System (INIS)

    Nikityuk, N.M.; )

    2001-01-01

    Architecture peculiarity and characteristics of series of neural chips and neural computes used in scientific instruments are considered. Tendency of development and use of them in high energy and superhigh energy physics experiments are described. Comparative data which characterize the efficient use of neural chips for useful event selection, classification elementary particles, reconstruction of tracks of charged particles and for search of hypothesis Higgs particles are given. The characteristics of native neural chips and accelerated neural boards are considered [ru

  17. Medical Imaging with Neural Networks

    International Nuclear Information System (INIS)

    Pattichis, C.; Cnstantinides, A.

    1994-01-01

    The objective of this paper is to provide an overview of the recent developments in the use of artificial neural networks in medical imaging. The areas of medical imaging that are covered include : ultrasound, magnetic resonance, nuclear medicine and radiological (including computerized tomography). (authors)

  18. Optoelectronic Implementation of Neural Networks

    Indian Academy of Sciences (India)

    neural networks, such as learning, adapting and copying by means of parallel ... to provide robust recognition of hand-printed English text. Engine idle and misfiring .... and s represents the bounded activation function of a neuron. It is typically ...

  19. Aphasia Classification Using Neural Networks

    DEFF Research Database (Denmark)

    Axer, H.; Jantzen, Jan; Berks, G.

    2000-01-01

    A web-based software model (http://fuzzy.iau.dtu.dk/aphasia.nsf) was developed as an example for classification of aphasia using neural networks. Two multilayer perceptrons were used to classify the type of aphasia (Broca, Wernicke, anomic, global) according to the results in some subtests...

  20. 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.

  1. Medical Imaging with Neural Networks

    Energy Technology Data Exchange (ETDEWEB)

    Pattichis, C [Department of Computer Science, University of Cyprus, Kallipoleos 75, P.O.Box 537, Nicosia (Cyprus); Cnstantinides, A [Department of Electrical Engineering, Imperial College of Science, Technology and Medicine, London SW7 2BT (United Kingdom)

    1994-12-31

    The objective of this paper is to provide an overview of the recent developments in the use of artificial neural networks in medical imaging. The areas of medical imaging that are covered include : ultrasound, magnetic resonance, nuclear medicine and radiological (including computerized tomography). (authors). 61 refs, 4 tabs.

  2. Numerical experiments with neural networks

    International Nuclear Information System (INIS)

    Miranda, Enrique.

    1990-01-01

    Neural networks are highly idealized models which, in spite of their simplicity, reproduce some key features of the real brain. In this paper, they are introduced at a level adequate for an undergraduate computational physics course. Some relevant magnitudes are defined and evaluated numerically for the Hopfield model and a short term memory model. (Author)

  3. Serotonin, neural markers and memory

    Directory of Open Access Journals (Sweden)

    Alfredo eMeneses

    2015-07-01

    Full Text Available Diverse neuropsychiatric disorders present dysfunctional memory and no effective treatment exits for them; likely as result of the absence of neural markers associated to memory. Neurotransmitter systems and signaling pathways have been implicated in memory and dysfunctional memory; however, their role is poorly understood. Hence, neural markers and cerebral functions and dysfunctions are revised. To our knowledge no previous systematic works have been published addressing these issues. The interactions among behavioral tasks, control groups and molecular changes and/or pharmacological effects are mentioned. Neurotransmitter receptors and signaling pathways, during normal and abnormally functioning memory with an emphasis on the behavioral aspects of memory are revised. With focus on serotonin, since as it is a well characterized neurotransmitter, with multiple pharmacological tools, and well characterized downstream signaling in mammals’ species. 5-HT1A, 5-HT4, 5-HT5, 5-HT6 and 5-HT7 receptors as well as SERT (serotonin transporter seem to be useful neural markers and/or therapeutic targets. Certainly, if the mentioned evidence is replicated, then the translatability from preclinical and clinical studies to neural changes might be confirmed. Hypothesis and theories might provide appropriate limits and perspectives of evidence

  4. Neural correlates of viewing paintings

    DEFF Research Database (Denmark)

    Vartanian, Oshin; Skov, Martin

    2014-01-01

    Many studies involving functional magnetic resonance imaging (fMRI) have exposed participants to paintings under varying task demands. To isolate neural systems that are activated reliably across fMRI studies in response to viewing paintings regardless of variation in task demands, a quantitative...

  5. Neural Basis of Visual Distraction

    Science.gov (United States)

    Kim, So-Yeon; Hopfinger, Joseph B.

    2010-01-01

    The ability to maintain focus and avoid distraction by goal-irrelevant stimuli is critical for performing many tasks and may be a key deficit in attention-related problems. Recent studies have demonstrated that irrelevant stimuli that are consciously perceived may be filtered out on a neural level and not cause the distraction triggered by…

  6. Vestibular hearing and neural synchronization.

    Science.gov (United States)

    Emami, Seyede Faranak; Daneshi, Ahmad

    2012-01-01

    Objectives. Vestibular hearing as an auditory sensitivity of the saccule in the human ear is revealed by cervical vestibular evoked myogenic potentials (cVEMPs). The range of the vestibular hearing lies in the low frequency. Also, the amplitude of an auditory brainstem response component depends on the amount of synchronized neural activity, and the auditory nerve fibers' responses have the best synchronization with the low frequency. Thus, the aim of this study was to investigate correlation between vestibular hearing using cVEMPs and neural synchronization via slow wave Auditory Brainstem Responses (sABR). Study Design. This case-control survey was consisted of twenty-two dizzy patients, compared to twenty healthy controls. Methods. Intervention comprised of Pure Tone Audiometry (PTA), Impedance acoustic metry (IA), Videonystagmography (VNG), fast wave ABR (fABR), sABR, and cVEMPs. Results. The affected ears of the dizzy patients had the abnormal findings of cVEMPs (insecure vestibular hearing) and the abnormal findings of sABR (decreased neural synchronization). Comparison of the cVEMPs at affected ears versus unaffected ears and the normal persons revealed significant differences (P < 0.05). Conclusion. Safe vestibular hearing was effective in the improvement of the neural synchronization.

  7. Spin glasses and neural networks

    International Nuclear Information System (INIS)

    Parga, N.; Universidad Nacional de Cuyo, San Carlos de Bariloche

    1989-01-01

    The mean-field theory of spin glass models has been used as a prototype of systems with frustration and disorder. One of the most interesting related systems are models of associative memories. In these lectures we review the main concepts developed to solve the Sherrington-Kirkpatrick model and its application to neural networks. (orig.)

  8. Non-invasive neural stimulation

    Science.gov (United States)

    Tyler, William J.; Sanguinetti, Joseph L.; Fini, Maria; Hool, Nicholas

    2017-05-01

    Neurotechnologies for non-invasively interfacing with neural circuits have been evolving from those capable of sensing neural activity to those capable of restoring and enhancing human brain function. Generally referred to as non-invasive neural stimulation (NINS) methods, these neuromodulation approaches rely on electrical, magnetic, photonic, and acoustic or ultrasonic energy to influence nervous system activity, brain function, and behavior. Evidence that has been surmounting for decades shows that advanced neural engineering of NINS technologies will indeed transform the way humans treat diseases, interact with information, communicate, and learn. The physics underlying the ability of various NINS methods to modulate nervous system activity can be quite different from one another depending on the energy modality used as we briefly discuss. For members of commercial and defense industry sectors that have not traditionally engaged in neuroscience research and development, the science, engineering and technology required to advance NINS methods beyond the state-of-the-art presents tremendous opportunities. Within the past few years alone there have been large increases in global investments made by federal agencies, foundations, private investors and multinational corporations to develop advanced applications of NINS technologies. Driven by these efforts NINS methods and devices have recently been introduced to mass markets via the consumer electronics industry. Further, NINS continues to be explored in a growing number of defense applications focused on enhancing human dimensions. The present paper provides a brief introduction to the field of non-invasive neural stimulation by highlighting some of the more common methods in use or under current development today.

  9. Neural networks and applications tutorial

    Science.gov (United States)

    Guyon, I.

    1991-09-01

    The importance of neural networks has grown dramatically during this decade. While only a few years ago they were primarily of academic interest, now dozens of companies and many universities are investigating the potential use of these systems and products are beginning to appear. The idea of building a machine whose architecture is inspired by that of the brain has roots which go far back in history. Nowadays, technological advances of computers and the availability of custom integrated circuits, permit simulations of hundreds or even thousands of neurons. In conjunction, the growing interest in learning machines, non-linear dynamics and parallel computation spurred renewed attention in artificial neural networks. Many tentative applications have been proposed, including decision systems (associative memories, classifiers, data compressors and optimizers), or parametric models for signal processing purposes (system identification, automatic control, noise canceling, etc.). While they do not always outperform standard methods, neural network approaches are already used in some real world applications for pattern recognition and signal processing tasks. The tutorial is divided into six lectures, that where presented at the Third Graduate Summer Course on Computational Physics (September 3-7, 1990) on Parallel Architectures and Applications, organized by the European Physical Society: (1) Introduction: machine learning and biological computation. (2) Adaptive artificial neurons (perceptron, ADALINE, sigmoid units, etc.): learning rules and implementations. (3) Neural network systems: architectures, learning algorithms. (4) Applications: pattern recognition, signal processing, etc. (5) Elements of learning theory: how to build networks which generalize. (6) A case study: a neural network for on-line recognition of handwritten alphanumeric characters.

  10. Parameterization Of Solar Radiation Using Neural Network

    International Nuclear Information System (INIS)

    Jiya, J. D.; Alfa, B.

    2002-01-01

    This paper presents a neural network technique for parameterization of global solar radiation. The available data from twenty-one stations is used for training the neural network and the data from other ten stations is used to validate the neural model. The neural network utilizes latitude, longitude, altitude, sunshine duration and period number to parameterize solar radiation values. The testing data was not used in the training to demonstrate the performance of the neural network in unknown stations to parameterize solar radiation. The results indicate a good agreement between the parameterized solar radiation values and actual measured values

  11. Spike Neural Models Part II: Abstract Neural Models

    Directory of Open Access Journals (Sweden)

    Johnson, Melissa G.

    2018-02-01

    Full Text Available Neurons are complex cells that require a lot of time and resources to model completely. In spiking neural networks (SNN though, not all that complexity is required. Therefore simple, abstract models are often used. These models save time, use less computer resources, and are easier to understand. This tutorial presents two such models: Izhikevich's model, which is biologically realistic in the resulting spike trains but not in the parameters, and the Leaky Integrate and Fire (LIF model which is not biologically realistic but does quickly and easily integrate input to produce spikes. Izhikevich's model is based on Hodgkin-Huxley's model but simplified such that it uses only two differentiation equations and four parameters to produce various realistic spike patterns. LIF is based on a standard electrical circuit and contains one equation. Either of these two models, or any of the many other models in literature can be used in a SNN. Choosing a neural model is an important task that depends on the goal of the research and the resources available. Once a model is chosen, network decisions such as connectivity, delay, and sparseness, need to be made. Understanding neural models and how they are incorporated into the network is the first step in creating a SNN.

  12. Optical resonators and neural networks

    Science.gov (United States)

    Anderson, Dana Z.

    1986-08-01

    It may be possible to implement neural network models using continuous field optical architectures. These devices offer the inherent parallelism of propagating waves and an information density in principle dictated by the wavelength of light and the quality of the bulk optical elements. Few components are needed to construct a relatively large equivalent network. Various associative memories based on optical resonators have been demonstrated in the literature, a ring resonator design is discussed in detail here. Information is stored in a holographic medium and recalled through a competitive processes in the gain medium supplying energy to the ring rsonator. The resonator memory is the first realized example of a neural network function implemented with this kind of architecture.

  13. Neural Network for Sparse Reconstruction

    Directory of Open Access Journals (Sweden)

    Qingfa Li

    2014-01-01

    Full Text Available We construct a neural network based on smoothing approximation techniques and projected gradient method to solve a kind of sparse reconstruction problems. Neural network can be implemented by circuits and can be seen as an important method for solving optimization problems, especially large scale problems. Smoothing approximation is an efficient technique for solving nonsmooth optimization problems. We combine these two techniques to overcome the difficulties of the choices of the step size in discrete algorithms and the item in the set-valued map of differential inclusion. In theory, the proposed network can converge to the optimal solution set of the given problem. Furthermore, some numerical experiments show the effectiveness of the proposed network in this paper.

  14. IMNN: Information Maximizing Neural Networks

    Science.gov (United States)

    Charnock, Tom; Lavaux, Guilhem; Wandelt, Benjamin D.

    2018-04-01

    This software trains artificial neural networks to find non-linear functionals of data that maximize Fisher information: information maximizing neural networks (IMNNs). As compressing large data sets vastly simplifies both frequentist and Bayesian inference, important information may be inadvertently missed. Likelihood-free inference based on automatically derived IMNN summaries produces summaries that are good approximations to sufficient statistics. IMNNs are robustly capable of automatically finding optimal, non-linear summaries of the data even in cases where linear compression fails: inferring the variance of Gaussian signal in the presence of noise, inferring cosmological parameters from mock simulations of the Lyman-α forest in quasar spectra, and inferring frequency-domain parameters from LISA-like detections of gravitational waveforms. In this final case, the IMNN summary outperforms linear data compression by avoiding the introduction of spurious likelihood maxima.

  15. Genetic attack on neural cryptography.

    Science.gov (United States)

    Ruttor, Andreas; Kinzel, Wolfgang; Naeh, Rivka; Kanter, Ido

    2006-03-01

    Different scaling properties for the complexity of bidirectional synchronization and unidirectional learning are essential for the security of neural cryptography. Incrementing the synaptic depth of the networks increases the synchronization time only polynomially, but the success of the geometric attack is reduced exponentially and it clearly fails in the limit of infinite synaptic depth. This method is improved by adding a genetic algorithm, which selects the fittest neural networks. The probability of a successful genetic attack is calculated for different model parameters using numerical simulations. The results show that scaling laws observed in the case of other attacks hold for the improved algorithm, too. The number of networks needed for an effective attack grows exponentially with increasing synaptic depth. In addition, finite-size effects caused by Hebbian and anti-Hebbian learning are analyzed. These learning rules converge to the random walk rule if the synaptic depth is small compared to the square root of the system size.

  16. Neural Networks Methodology and Applications

    CERN Document Server

    Dreyfus, Gérard

    2005-01-01

    Neural networks represent a powerful data processing technique that has reached maturity and broad application. When clearly understood and appropriately used, they are a mandatory component in the toolbox of any engineer who wants make the best use of the available data, in order to build models, make predictions, mine data, recognize shapes or signals, etc. Ranging from theoretical foundations to real-life applications, this book is intended to provide engineers and researchers with clear methodologies for taking advantage of neural networks in industrial, financial or banking applications, many instances of which are presented in the book. For the benefit of readers wishing to gain deeper knowledge of the topics, the book features appendices that provide theoretical details for greater insight, and algorithmic details for efficient programming and implementation. The chapters have been written by experts ands seemlessly edited to present a coherent and comprehensive, yet not redundant, practically-oriented...

  17. Genetic attack on neural cryptography

    International Nuclear Information System (INIS)

    Ruttor, Andreas; Kinzel, Wolfgang; Naeh, Rivka; Kanter, Ido

    2006-01-01

    Different scaling properties for the complexity of bidirectional synchronization and unidirectional learning are essential for the security of neural cryptography. Incrementing the synaptic depth of the networks increases the synchronization time only polynomially, but the success of the geometric attack is reduced exponentially and it clearly fails in the limit of infinite synaptic depth. This method is improved by adding a genetic algorithm, which selects the fittest neural networks. The probability of a successful genetic attack is calculated for different model parameters using numerical simulations. The results show that scaling laws observed in the case of other attacks hold for the improved algorithm, too. The number of networks needed for an effective attack grows exponentially with increasing synaptic depth. In addition, finite-size effects caused by Hebbian and anti-Hebbian learning are analyzed. These learning rules converge to the random walk rule if the synaptic depth is small compared to the square root of the system size

  18. Genetic attack on neural cryptography

    Science.gov (United States)

    Ruttor, Andreas; Kinzel, Wolfgang; Naeh, Rivka; Kanter, Ido

    2006-03-01

    Different scaling properties for the complexity of bidirectional synchronization and unidirectional learning are essential for the security of neural cryptography. Incrementing the synaptic depth of the networks increases the synchronization time only polynomially, but the success of the geometric attack is reduced exponentially and it clearly fails in the limit of infinite synaptic depth. This method is improved by adding a genetic algorithm, which selects the fittest neural networks. The probability of a successful genetic attack is calculated for different model parameters using numerical simulations. The results show that scaling laws observed in the case of other attacks hold for the improved algorithm, too. The number of networks needed for an effective attack grows exponentially with increasing synaptic depth. In addition, finite-size effects caused by Hebbian and anti-Hebbian learning are analyzed. These learning rules converge to the random walk rule if the synaptic depth is small compared to the square root of the system size.

  19. Scheduling with artificial neural networks

    OpenAIRE

    Gürgün, Burçkaan

    1993-01-01

    Ankara : Department of Industrial Engineering and The Institute of Engineering and Sciences of Bilkent Univ., 1993. Thesis (Master's) -- Bilkent University, 1993. Includes bibliographical references leaves 59-65. Artificial Neural Networks (ANNs) attempt to emulate the massively parallel and distributed processing of the human brain. They are being examined for a variety of problems that have been very difficult to solve. The objective of this thesis is to review the curren...

  20. 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 ...

  1. 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

  2. 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...

  3. Adaptive Regularization of Neural Classifiers

    DEFF Research Database (Denmark)

    Andersen, Lars Nonboe; Larsen, Jan; Hansen, Lars Kai

    1997-01-01

    We present a regularization scheme which iteratively adapts the regularization parameters by minimizing the validation error. It is suggested to use the adaptive regularization scheme in conjunction with optimal brain damage pruning to optimize the architecture and to avoid overfitting. Furthermo......, we propose an improved neural classification architecture eliminating an inherent redundancy in the widely used SoftMax classification network. Numerical results demonstrate the viability of the method...

  4. Central neural pathways for thermoregulation

    Science.gov (United States)

    Morrison, Shaun F.; Nakamura, Kazuhiro

    2010-01-01

    Central neural circuits orchestrate a homeostatic repertoire to maintain body temperature during environmental temperature challenges and to alter body temperature during the inflammatory response. This review summarizes the functional organization of the neural pathways through which cutaneous thermal receptors alter thermoregulatory effectors: the cutaneous circulation for heat loss, the brown adipose tissue, skeletal muscle and heart for thermogenesis and species-dependent mechanisms (sweating, panting and saliva spreading) for evaporative heat loss. These effectors are regulated by parallel but distinct, effector-specific neural pathways that share a common peripheral thermal sensory input. The thermal afferent circuits include cutaneous thermal receptors, spinal dorsal horn neurons and lateral parabrachial nucleus neurons projecting to the preoptic area to influence warm-sensitive, inhibitory output neurons which control thermogenesis-promoting neurons in the dorsomedial hypothalamus that project to premotor neurons in the rostral ventromedial medulla, including the raphe pallidus, that descend to provide the excitation necessary to drive thermogenic thermal effectors. A distinct population of warm-sensitive preoptic neurons controls heat loss through an inhibitory input to raphe pallidus neurons controlling cutaneous vasoconstriction. PMID:21196160

  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. Neural mechanisms of social dominance

    Science.gov (United States)

    Watanabe, Noriya; Yamamoto, Miyuki

    2015-01-01

    In a group setting, individuals' perceptions of their own level of dominance or of the dominance level of others, and the ability to adequately control their behavior based on these perceptions are crucial for living within a social environment. Recent advances in neural imaging and molecular technology have enabled researchers to investigate the neural substrates that support the perception of social dominance and the formation of a social hierarchy in humans. At the systems' level, recent studies showed that dominance perception is represented in broad brain regions which include the amygdala, hippocampus, striatum, and various cortical networks such as the prefrontal, and parietal cortices. Additionally, neurotransmitter systems such as the dopaminergic and serotonergic systems, modulate and are modulated by the formation of the social hierarchy in a group. While these monoamine systems have a wide distribution and multiple functions, it was recently found that the Neuropeptide B/W contributes to the perception of dominance and is present in neurons that have a limited projection primarily to the amygdala. The present review discusses the specific roles of these neural regions and neurotransmitter systems in the perception of dominance and in hierarchy formation. PMID:26136644

  7. Neural mechanisms of social dominance

    Directory of Open Access Journals (Sweden)

    Noriya eWatanabe

    2015-06-01

    Full Text Available In a group setting, individuals’ perceptions of their own level of dominance or of the dominance level of others, and the ability to adequately control their behavior based on these perceptions are crucial for living within a social environment. Recent advances in neural imaging and molecular technology have enabled researchers to investigate the neural substrates that support the perception of social dominance and the formation of a social hierarchy in humans. At the systems’ level, recent studies showed that dominance perception is represented in broad brain regions which include the amygdala, hippocampus, striatum, and various cortical networks such as the prefrontal, and parietal cortices. Additionally, neurotransmitter systems such as the dopaminergic and serotonergic systems, modulate and are modulated by the formation of the social hierarchy in a group. While these monoamine systems have a wide distribution and multiple functions, it was recently found that the Neuropeptide B/W contributes to the perception of dominance and is present in neurons that have a limited projection primarily to the amygdala. The present review discusses the specific roles of these neural regions and neurotransmitter systems in the perception of dominance and in hierarchy formation.

  8. Neural Representations of Physics Concepts.

    Science.gov (United States)

    Mason, Robert A; Just, Marcel Adam

    2016-06-01

    We used functional MRI (fMRI) to assess neural representations of physics concepts (momentum, energy, etc.) in juniors, seniors, and graduate students majoring in physics or engineering. Our goal was to identify the underlying neural dimensions of these representations. Using factor analysis to reduce the number of dimensions of activation, we obtained four physics-related factors that were mapped to sets of voxels. The four factors were interpretable as causal motion visualization, periodicity, algebraic form, and energy flow. The individual concepts were identifiable from their fMRI signatures with a mean rank accuracy of .75 using a machine-learning (multivoxel) classifier. Furthermore, there was commonality in participants' neural representation of physics; a classifier trained on data from all but one participant identified the concepts in the left-out participant (mean accuracy = .71 across all nine participant samples). The findings indicate that abstract scientific concepts acquired in an educational setting evoke activation patterns that are identifiable and common, indicating that science education builds abstract knowledge using inherent, repurposed brain systems. © The Author(s) 2016.

  9. Photon spectrometry utilizing neural networks

    International Nuclear Information System (INIS)

    Silveira, R.; Benevides, C.; Lima, F.; Vilela, E.

    2015-01-01

    Having in mind the time spent on the uneventful work of characterization of the radiation beams used in a ionizing radiation metrology laboratory, the Metrology Service of the Centro Regional de Ciencias Nucleares do Nordeste - CRCN-NE verified the applicability of artificial intelligence (artificial neural networks) to perform the spectrometry in photon fields. For this, was developed a multilayer neural network, as an application for the classification of patterns in energy, associated with a thermoluminescent dosimetric system (TLD-700 and TLD-600). A set of dosimeters was initially exposed to various well known medium energies, between 40 keV and 1.2 MeV, coinciding with the beams determined by ISO 4037 standard, for the dose of 10 mSv in the quantity Hp(10), on a chest phantom (ISO slab phantom) with the purpose of generating a set of training data for the neural network. Subsequently, a new set of dosimeters irradiated in unknown energies was presented to the network with the purpose to test the method. The methodology used in this work was suitable for application in the classification of energy beams, having obtained 100% of the classification performed. (authors)

  10. 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.

  11. Neurosecurity: security and privacy for neural devices.

    Science.gov (United States)

    Denning, Tamara; Matsuoka, Yoky; Kohno, Tadayoshi

    2009-07-01

    An increasing number of neural implantable devices will become available in the near future due to advances in neural engineering. This discipline holds the potential to improve many patients' lives dramatically by offering improved-and in some cases entirely new-forms of rehabilitation for conditions ranging from missing limbs to degenerative cognitive diseases. The use of standard engineering practices, medical trials, and neuroethical evaluations during the design process can create systems that are safe and that follow ethical guidelines; unfortunately, none of these disciplines currently ensure that neural devices are robust against adversarial entities trying to exploit these devices to alter, block, or eavesdrop on neural signals. The authors define "neurosecurity"-a version of computer science security principles and methods applied to neural engineering-and discuss why neurosecurity should be a critical consideration in the design of future neural devices.

  12. The Neural Border: Induction, Specification and Maturation of the territory that generates Neural Crest cells.

    Science.gov (United States)

    Pla, Patrick; Monsoro-Burq, Anne H

    2018-05-28

    The neural crest is induced at the edge between the neural plate and the nonneural ectoderm, in an area called the neural (plate) border, during gastrulation and neurulation. In recent years, many studies have explored how this domain is patterned, and how the neural crest is induced within this territory, that also participates to the prospective dorsal neural tube, the dorsalmost nonneural ectoderm, as well as placode derivatives in the anterior area. This review highlights the tissue interactions, the cell-cell signaling and the molecular mechanisms involved in this dynamic spatiotemporal patterning, resulting in the induction of the premigratory neural crest. Collectively, these studies allow building a complex neural border and early neural crest gene regulatory network, mostly composed by transcriptional regulations but also, more recently, including novel signaling interactions. Copyright © 2018. Published by Elsevier Inc.

  13. 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...

  14. Introduction to Concepts in Artificial Neural Networks

    Science.gov (United States)

    Niebur, Dagmar

    1995-01-01

    This introduction to artificial neural networks summarizes some basic concepts of computational neuroscience and the resulting models of artificial neurons. The terminology of biological and artificial neurons, biological and machine learning and neural processing is introduced. The concepts of supervised and unsupervised learning are explained with examples from the power system area. Finally, a taxonomy of different types of neurons and different classes of artificial neural networks is presented.

  15. Mode Choice Modeling Using Artificial Neural Networks

    OpenAIRE

    Edara, Praveen Kumar

    2003-01-01

    Artificial intelligence techniques have produced excellent results in many diverse fields of engineering. Techniques such as neural networks and fuzzy systems have found their way into transportation engineering. In recent years, neural networks are being used instead of regression techniques for travel demand forecasting purposes. The basic reason lies in the fact that neural networks are able to capture complex relationships and learn from examples and also able to adapt when new data becom...

  16. Dynamic training algorithm for dynamic neural networks

    International Nuclear Information System (INIS)

    Tan, Y.; Van Cauwenberghe, A.; Liu, Z.

    1996-01-01

    The widely used backpropagation algorithm for training neural networks based on the gradient descent has a significant drawback of slow convergence. A Gauss-Newton method based recursive least squares (RLS) type algorithm with dynamic error backpropagation is presented to speed-up the learning procedure of neural networks with local recurrent terms. Finally, simulation examples concerning the applications of the RLS type algorithm to identification of nonlinear processes using a local recurrent neural network are also included in this paper

  17. 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.

  18. Adaptive optimization and control using neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Mead, W.C.; Brown, S.K.; Jones, R.D.; Bowling, P.S.; Barnes, C.W.

    1993-10-22

    Recent work has demonstrated the ability of neural-network-based controllers to optimize and control machines with complex, non-linear, relatively unknown control spaces. We present a brief overview of neural networks via a taxonomy illustrating some capabilities of different kinds of neural networks. We present some successful control examples, particularly the optimization and control of a small-angle negative ion source.

  19. The quest for a Quantum Neural Network

    OpenAIRE

    Schuld, M.; Sinayskiy, I.; Petruccione, F.

    2014-01-01

    With the overwhelming success in the field of quantum information in the last decades, the "quest" for a Quantum Neural Network (QNN) model began in order to combine quantum computing with the striking properties of neural computing. This article presents a systematic approach to QNN research, which so far consists of a conglomeration of ideas and proposals. It outlines the challenge of combining the nonlinear, dissipative dynamics of neural computing and the linear, unitary dynamics of quant...

  20. NeuroMEMS: Neural Probe Microtechnologies

    Directory of Open Access Journals (Sweden)

    Sam Musallam

    2008-10-01

    Full Text Available Neural probe technologies have already had a significant positive effect on our understanding of the brain by revealing the functioning of networks of biological neurons. Probes are implanted in different areas of the brain to record and/or stimulate specific sites in the brain. Neural probes are currently used in many clinical settings for diagnosis of brain diseases such as seizers, epilepsy, migraine, Alzheimer’s, and dementia. We find these devices assisting paralyzed patients by allowing them to operate computers or robots using their neural activity. In recent years, probe technologies were assisted by rapid advancements in microfabrication and microelectronic technologies and thus are enabling highly functional and robust neural probes which are opening new and exciting avenues in neural sciences and brain machine interfaces. With a wide variety of probes that have been designed, fabricated, and tested to date, this review aims to provide an overview of the advances and recent progress in the microfabrication techniques of neural probes. In addition, we aim to highlight the challenges faced in developing and implementing ultralong multi-site recording probes that are needed to monitor neural activity from deeper regions in the brain. Finally, we review techniques that can improve the biocompatibility of the neural probes to minimize the immune response and encourage neural growth around the electrodes for long term implantation studies.

  1. Fuzzy neural network theory and application

    CERN Document Server

    Liu, Puyin

    2004-01-01

    This book systematically synthesizes research achievements in the field of fuzzy neural networks in recent years. It also provides a comprehensive presentation of the developments in fuzzy neural networks, with regard to theory as well as their application to system modeling and image restoration. Special emphasis is placed on the fundamental concepts and architecture analysis of fuzzy neural networks. The book is unique in treating all kinds of fuzzy neural networks and their learning algorithms and universal approximations, and employing simulation examples which are carefully designed to he

  2. Practical neural network recipies in C++

    CERN Document Server

    Masters

    2014-01-01

    This text serves as a cookbook for neural network solutions to practical problems using C++. It will enable those with moderate programming experience to select a neural network model appropriate to solving a particular problem, and to produce a working program implementing that network. The book provides guidance along the entire problem-solving path, including designing the training set, preprocessing variables, training and validating the network, and evaluating its performance. Though the book is not intended as a general course in neural networks, no background in neural works is assum

  3. Boolean Factor Analysis by Attractor Neural Network

    Czech Academy of Sciences Publication Activity Database

    Frolov, A. A.; Húsek, Dušan; Muraviev, I. P.; Polyakov, P.Y.

    2007-01-01

    Roč. 18, č. 3 (2007), s. 698-707 ISSN 1045-9227 R&D Projects: GA AV ČR 1ET100300419; GA ČR GA201/05/0079 Institutional research plan: CEZ:AV0Z10300504 Keywords : recurrent neural network * Hopfield-like neural network * associative memory * unsupervised learning * neural network architecture * neural network application * statistics * Boolean factor analysis * dimensionality reduction * features clustering * concepts search * information retrieval Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 2.769, year: 2007

  4. The LILARTI neural network system

    Energy Technology Data Exchange (ETDEWEB)

    Allen, J.D. Jr.; Schell, F.M.; Dodd, C.V.

    1992-10-01

    The material of this Technical Memorandum is intended to provide the reader with conceptual and technical background information on the LILARTI neural network system of detail sufficient to confer an understanding of the LILARTI method as it is presently allied and to facilitate application of the method to problems beyond the scope of this document. Of particular importance in this regard are the descriptive sections and the Appendices which include operating instructions, partial listings of program output and data files, and network construction information.

  5. Finite connectivity attractor neural networks

    International Nuclear Information System (INIS)

    Wemmenhove, B; Coolen, A C C

    2003-01-01

    We study a family of diluted attractor neural networks with a finite average number of (symmetric) connections per neuron. As in finite connectivity spin glasses, their equilibrium properties are described by order parameter functions, for which we derive an integral equation in replica symmetric approximation. A bifurcation analysis of this equation reveals the locations of the paramagnetic to recall and paramagnetic to spin-glass transition lines in the phase diagram. The line separating the retrieval phase from the spin-glass phase is calculated at zero temperature. All phase transitions are found to be continuous

  6. Neural Network Based Load Frequency Control for Restructuring ...

    African Journals Online (AJOL)

    Neural Network Based Load Frequency Control for Restructuring Power Industry. ... an artificial neural network (ANN) application of load frequency control (LFC) of a Multi-Area power system by using a neural network controller is presented.

  7. In-vitro differentiation induction of neural stem cells

    NARCIS (Netherlands)

    Balasubramaniyan, Veerakumar

    2006-01-01

    Neurale stamcellen maken de drie belangrijkste celtypes van ons zenuwstelsel aan. Veerakumar Balasubramaniyan onderzocht hoe neurale stamcellen kunnen worden aangezet tot het aanmaken van specifieke neurale celtypes. Met behulp van genetische technieken lukte het hem oligodendrocyten te verkrijgen:

  8. Neural network modeling of emotion

    Science.gov (United States)

    Levine, Daniel S.

    2007-03-01

    This article reviews the history and development of computational neural network modeling of cognitive and behavioral processes that involve emotion. The exposition starts with models of classical conditioning dating from the early 1970s. Then it proceeds toward models of interactions between emotion and attention. Then models of emotional influences on decision making are reviewed, including some speculative (not and not yet simulated) models of the evolution of decision rules. Through the late 1980s, the neural networks developed to model emotional processes were mainly embodiments of significant functional principles motivated by psychological data. In the last two decades, network models of these processes have become much more detailed in their incorporation of known physiological properties of specific brain regions, while preserving many of the psychological principles from the earlier models. Most network models of emotional processes so far have dealt with positive and negative emotion in general, rather than specific emotions such as fear, joy, sadness, and anger. But a later section of this article reviews a few models relevant to specific emotions: one family of models of auditory fear conditioning in rats, and one model of induced pleasure enhancing creativity in humans. Then models of emotional disorders are reviewed. The article concludes with philosophical statements about the essential contributions of emotion to intelligent behavior and the importance of quantitative theories and models to the interdisciplinary enterprise of understanding the interactions of emotion, cognition, and behavior.

  9. Imaging Posture Veils Neural Signals

    Directory of Open Access Journals (Sweden)

    Robert T Thibault

    2016-10-01

    Full Text Available Whereas modern brain imaging often demands holding body positions incongruent with everyday life, posture governs both neural activity and cognitive performance. Humans commonly perform while upright; yet, many neuroimaging methodologies require participants to remain motionless and adhere to non-ecological comportments within a confined space. This inconsistency between ecological postures and imaging constraints undermines the transferability and generalizability of many a neuroimaging assay.Here we highlight the influence of posture on brain function and behavior. Specifically, we challenge the tacit assumption that brain processes and cognitive performance are comparable across a spectrum of positions. We provide an integrative synthesis regarding the increasingly prominent influence of imaging postures on autonomic function, mental capacity, sensory thresholds, and neural activity. Arguing that neuroimagers and cognitive scientists could benefit from considering the influence posture wields on both general functioning and brain activity, we examine existing imaging technologies and the potential of portable and versatile imaging devices (e.g., functional near infrared spectroscopy. Finally, we discuss ways that accounting for posture may help unveil the complex brain processes of everyday cognition.

  10. Learning in Artificial Neural Systems

    Science.gov (United States)

    Matheus, Christopher J.; Hohensee, William E.

    1987-01-01

    This paper presents an overview and analysis of learning in Artificial Neural Systems (ANS's). It begins with a general introduction to neural networks and connectionist approaches to information processing. The basis for learning in ANS's is then described, and compared with classical Machine learning. While similar in some ways, ANS learning deviates from tradition in its dependence on the modification of individual weights to bring about changes in a knowledge representation distributed across connections in a network. This unique form of learning is analyzed from two aspects: the selection of an appropriate network architecture for representing the problem, and the choice of a suitable learning rule capable of reproducing the desired function within the given network. The various network architectures are classified, and then identified with explicit restrictions on the types of functions they are capable of representing. The learning rules, i.e., algorithms that specify how the network weights are modified, are similarly taxonomized, and where possible, the limitations inherent to specific classes of rules are outlined.

  11. Neural dynamics in reconfigurable silicon.

    Science.gov (United States)

    Basu, A; Ramakrishnan, S; Petre, C; Koziol, S; Brink, S; Hasler, P E

    2010-10-01

    A neuromorphic analog chip is presented that is capable of implementing massively parallel neural computations while retaining the programmability of digital systems. We show measurements from neurons with Hopf bifurcations and integrate and fire neurons, excitatory and inhibitory synapses, passive dendrite cables, coupled spiking neurons, and central pattern generators implemented on the chip. This chip provides a platform for not only simulating detailed neuron dynamics but also uses the same to interface with actual cells in applications such as a dynamic clamp. There are 28 computational analog blocks (CAB), each consisting of ion channels with tunable parameters, synapses, winner-take-all elements, current sources, transconductance amplifiers, and capacitors. There are four other CABs which have programmable bias generators. The programmability is achieved using floating gate transistors with on-chip programming control. The switch matrix for interconnecting the components in CABs also consists of floating-gate transistors. Emphasis is placed on replicating the detailed dynamics of computational neural models. Massive computational area efficiency is obtained by using the reconfigurable interconnect as synaptic weights, resulting in more than 50 000 possible 9-b accurate synapses in 9 mm(2).

  12. Chimera States in Neural Oscillators

    Science.gov (United States)

    Bahar, Sonya; Glaze, Tera

    2014-03-01

    Chimera states have recently been explored both theoretically and experimentally, in various coupled nonlinear oscillators, ranging from phase-oscillator models to coupled chemical reactions. In a chimera state, both coherent and incoherent (or synchronized and desynchronized) states occur simultaneously in populations of identical oscillators. We investigate chimera behavior in a population of neural oscillators using the Huber-Braun model, a Hodgkin-Huxley-like model originally developed to characterize the temperature-dependent bursting behavior of mammalian cold receptors. One population of neurons is allowed to synchronize, with each neuron receiving input from all the others in its group (global within-group coupling). Subsequently, a second population of identical neurons is placed under an identical global within-group coupling, and the two populations are also coupled to each other (between-group coupling). For certain values of the coupling constants, the neurons in the two populations exhibit radically different synchronization behavior. We will discuss the range of chimera activity in the model, and discuss its implications for actual neural activity, such as unihemispheric sleep.

  13. Improved transformer protection using probabilistic neural network ...

    African Journals Online (AJOL)

    user

    secure and dependable protection for power transformers. Owing to its superior learning and generalization capabilities Artificial. Neural Network (ANN) can considerably enhance the scope of WI method. ANN approach is faster, robust and easier to implement than the conventional waveform approach. The use of neural ...

  14. Neural network signal understanding for instrumentation

    DEFF Research Database (Denmark)

    Pau, L. F.; Johansen, F. S.

    1990-01-01

    understanding research is surveyed, and the selected implementation and its performance in terms of correct classification rates and robustness to noise are described. Formal results on neural net training time and sensitivity to weights are given. A theory for neural control using functional link nets is given...

  15. A Chip for an Implantable Neural Stimulator

    DEFF Research Database (Denmark)

    Gudnason, Gunnar; Bruun, Erik; Haugland, Morten

    2000-01-01

    This paper describes a chip for a multichannel neural stimulator for functional electrical stimulation (FES). The purpose of FES is to restore muscular control in disabled patients. The chip performs all the signal processing required in an implanted neural stimulator. The power and digital data...

  16. Hidden neural networks: application to speech recognition

    DEFF Research Database (Denmark)

    Riis, Søren Kamaric

    1998-01-01

    We evaluate the hidden neural network HMM/NN hybrid on two speech recognition benchmark tasks; (1) task independent isolated word recognition on the Phonebook database, and (2) recognition of broad phoneme classes in continuous speech from the TIMIT database. It is shown how hidden neural networks...

  17. Neural Network Classifier Based on Growing Hyperspheres

    Czech Academy of Sciences Publication Activity Database

    Jiřina Jr., Marcel; Jiřina, Marcel

    2000-01-01

    Roč. 10, č. 3 (2000), s. 417-428 ISSN 1210-0552. [Neural Network World 2000. Prague, 09.07.2000-12.07.2000] Grant - others:MŠMT ČR(CZ) VS96047; MPO(CZ) RP-4210 Institutional research plan: AV0Z1030915 Keywords : neural network * classifier * hyperspheres * big -dimensional data Subject RIV: BA - General Mathematics

  18. A high-speed analog neural processor

    NARCIS (Netherlands)

    Masa, P.; Masa, Peter; Hoen, Klaas; Hoen, Klaas; Wallinga, Hans

    1994-01-01

    Targeted at high-energy physics research applications, our special-purpose analog neural processor can classify up to 70 dimensional vectors within 50 nanoseconds. The decision-making process of the implemented feedforward neural network enables this type of computation to tolerate weight

  19. Neurophysiology and neural engineering: a review.

    Science.gov (United States)

    Prochazka, Arthur

    2017-08-01

    Neurophysiology is the branch of physiology concerned with understanding the function of neural systems. Neural engineering (also known as neuroengineering) is a discipline within biomedical engineering that uses engineering techniques to understand, repair, replace, enhance, or otherwise exploit the properties and functions of neural systems. In most cases neural engineering involves the development of an interface between electronic devices and living neural tissue. This review describes the origins of neural engineering, the explosive development of methods and devices commencing in the late 1950s, and the present-day devices that have resulted. The barriers to interfacing electronic devices with living neural tissues are many and varied, and consequently there have been numerous stops and starts along the way. Representative examples are discussed. None of this could have happened without a basic understanding of the relevant neurophysiology. I also consider examples of how neural engineering is repaying the debt to basic neurophysiology with new knowledge and insight. Copyright © 2017 the American Physiological Society.

  20. 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....

  1. Interpretable neural networks with BP-SOM

    NARCIS (Netherlands)

    Weijters, A.J.M.M.; Bosch, van den A.P.J.; Pobil, del A.P.; Mira, J.; Ali, M.

    1998-01-01

    Artificial Neural Networks (ANNS) are used successfully in industry and commerce. This is not surprising since neural networks are especially competitive for complex tasks for which insufficient domain-specific knowledge is available. However, interpretation of models induced by ANNS is often

  2. Deciphering the Cognitive and Neural Mechanisms Underlying ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    Deciphering the Cognitive and Neural Mechanisms Underlying Auditory Learning. This project seeks to understand the brain mechanisms necessary for people to learn to perceive sounds. Neural circuits and learning. The research team will test people with and without musical training to evaluate their capacity to learn ...

  3. 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

  4. NEURAL METHODS FOR THE FINANCIAL PREDICTION

    OpenAIRE

    Jerzy Balicki; Piotr Dryja; Waldemar Korłub; Piotr Przybyłek; Maciej Tyszka; Marcin Zadroga; Marcin Zakidalski

    2016-01-01

    Artificial neural networks can be used to predict share investment on the stock market, assess the reliability of credit client or predicting banking crises. Moreover, this paper discusses the principles of cooperation neural network algorithms with evolutionary method, and support vector machines. In addition, a reference is made to other methods of artificial intelligence, which are used in finance prediction.

  5. NEURAL METHODS FOR THE FINANCIAL PREDICTION

    Directory of Open Access Journals (Sweden)

    Jerzy Balicki

    2016-06-01

    Full Text Available Artificial neural networks can be used to predict share investment on the stock market, assess the reliability of credit client or predicting banking crises. Moreover, this paper discusses the principles of cooperation neural network algorithms with evolutionary method, and support vector machines. In addition, a reference is made to other methods of artificial intelligence, which are used in finance prediction.

  6. Neural Network to Solve Concave Games

    OpenAIRE

    Liu, Zixin; Wang, Nengfa

    2014-01-01

    The issue on neural network method to solve concave games is concerned. Combined with variational inequality, Ky Fan inequality, and projection equation, concave games are transformed into a neural network model. On the basis of the Lyapunov stable theory, some stability results are also given. Finally, two classic games’ simulation results are given to illustrate the theoretical results.

  7. Neural Network Algorithm for Particle Loading

    International Nuclear Information System (INIS)

    Lewandowski, J.L.V.

    2003-01-01

    An artificial neural network algorithm for continuous minimization is developed and applied to the case of numerical particle loading. It is shown that higher-order moments of the probability distribution function can be efficiently renormalized using this technique. A general neural network for the renormalization of an arbitrary number of moments is given

  8. Neural constructivism or self-organization?

    NARCIS (Netherlands)

    van der Maas, H.L.J.; Molenaar, P.C.M.

    2000-01-01

    Comments on the article by S. R. Quartz et al (see record 1998-00749-001) which discussed the constructivist perspective of interaction between cognition and neural processes during development and consequences for theories of learning. Three arguments are given to show that neural constructivism

  9. Memory in Neural Networks and Glasses

    NARCIS (Netherlands)

    Heerema, M.

    2000-01-01

    The thesis tries and models a neural network in a way which, at essential points, is biologically realistic. In a biological context, the changes of the synapses of the neural network are most often described by what is called `Hebb's learning rule'. On careful analysis it is, in fact, nothing but a

  10. Windowed active sampling for reliable neural learning

    NARCIS (Netherlands)

    Barakova, E.I; Spaanenburg, L

    The composition of the example set has a major impact on the quality of neural learning. The popular approach is focused on extensive pre-processing to bridge the representation gap between process measurement and neural presentation. In contrast, windowed active sampling attempts to solve these

  11. Neural Control of the Immune System

    Science.gov (United States)

    Sundman, Eva; Olofsson, Peder S.

    2014-01-01

    Neural reflexes support homeostasis by modulating the function of organ systems. Recent advances in neuroscience and immunology have revealed that neural reflexes also regulate the immune system. Activation of the vagus nerve modulates leukocyte cytokine production and alleviates experimental shock and autoimmune disease, and recent data have…

  12. A new perspective on behavioral inconsistency and neural noise in aging: Compensatory speeding of neural communication

    Directory of Open Access Journals (Sweden)

    S. Lee Hong

    2012-09-01

    Full Text Available This paper seeks to present a new perspective on the aging brain. Here, we make connections between two key phenomena of brain aging: 1 increased neural noise or random background activity; and 2 slowing of brain activity. Our perspective proposes the possibility that the slowing of neural processing due to decreasing nerve conduction velocities leads to a compensatory speeding of neuron firing rates. These increased firing rates lead to a broader distribution of power in the frequency spectrum of neural oscillations, which we propose, can just as easily be interpreted as neural noise. Compensatory speeding of neural activity, as we present, is constrained by the: A availability of metabolic energy sources; and B competition for frequency bandwidth needed for neural communication. We propose that these constraints lead to the eventual inability to compensate for age-related declines in neural function that are manifested clinically as deficits in cognition, affect, and motor behavior.

  13. 22nd Italian Workshop on Neural Nets

    CERN Document Server

    Bassis, Simone; Esposito, Anna; Morabito, Francesco

    2013-01-01

    This volume collects a selection of contributions which has been presented at the 22nd Italian Workshop on Neural Networks, the yearly meeting of the Italian Society for Neural Networks (SIREN). The conference was held in Italy, Vietri sul Mare (Salerno), during May 17-19, 2012. The annual meeting of SIREN is sponsored by International Neural Network Society (INNS), European Neural Network Society (ENNS) and IEEE Computational Intelligence Society (CIS). The book – as well as the workshop-  is organized in three main components, two special sessions and a group of regular sessions featuring different aspects and point of views of artificial neural networks and natural intelligence, also including applications of present compelling interest.

  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. Signal Processing and Neural Network Simulator

    Science.gov (United States)

    Tebbe, Dennis L.; Billhartz, Thomas J.; Doner, John R.; Kraft, Timothy T.

    1995-04-01

    The signal processing and neural network simulator (SPANNS) is a digital signal processing simulator with the capability to invoke neural networks into signal processing chains. This is a generic tool which will greatly facilitate the design and simulation of systems with embedded neural networks. The SPANNS is based on the Signal Processing WorkSystemTM (SPWTM), a commercial-off-the-shelf signal processing simulator. SPW provides a block diagram approach to constructing signal processing simulations. Neural network paradigms implemented in the SPANNS include Backpropagation, Kohonen Feature Map, Outstar, Fully Recurrent, Adaptive Resonance Theory 1, 2, & 3, and Brain State in a Box. The SPANNS was developed by integrating SAIC's Industrial Strength Neural Networks (ISNN) Software into SPW.

  16. Neural networks with discontinuous/impact activations

    CERN Document Server

    Akhmet, Marat

    2014-01-01

    This book presents as its main subject new models in mathematical neuroscience. A wide range of neural networks models with discontinuities are discussed, including impulsive differential equations, differential equations with piecewise constant arguments, and models of mixed type. These models involve discontinuities, which are natural because huge velocities and short distances are usually observed in devices modeling the networks. A discussion of the models, appropriate for the proposed applications, is also provided. This book also: Explores questions related to the biological underpinning for models of neural networks\\ Considers neural networks modeling using differential equations with impulsive and piecewise constant argument discontinuities Provides all necessary mathematical basics for application to the theory of neural networks Neural Networks with Discontinuous/Impact Activations is an ideal book for researchers and professionals in the field of engineering mathematics that have an interest in app...

  17. International Conference on Artificial Neural Networks (ICANN)

    CERN Document Server

    Mladenov, Valeri; Kasabov, Nikola; Artificial Neural Networks : Methods and Applications in Bio-/Neuroinformatics

    2015-01-01

    The book reports on the latest theories on artificial neural networks, with a special emphasis on bio-neuroinformatics methods. It includes twenty-three papers selected from among the best contributions on bio-neuroinformatics-related issues, which were presented at the International Conference on Artificial Neural Networks, held in Sofia, Bulgaria, on September 10-13, 2013 (ICANN 2013). The book covers a broad range of topics concerning the theory and applications of artificial neural networks, including recurrent neural networks, super-Turing computation and reservoir computing, double-layer vector perceptrons, nonnegative matrix factorization, bio-inspired models of cell communities, Gestalt laws, embodied theory of language understanding, saccadic gaze shifts and memory formation, and new training algorithms for Deep Boltzmann Machines, as well as dynamic neural networks and kernel machines. It also reports on new approaches to reinforcement learning, optimal control of discrete time-delay systems, new al...

  18. Decentralized neural control application to robotics

    CERN Document Server

    Garcia-Hernandez, Ramon; Sanchez, Edgar N; Alanis, Alma y; Ruz-Hernandez, Jose A

    2017-01-01

    This book provides a decentralized approach for the identification and control of robotics systems. It also presents recent research in decentralized neural control and includes applications to robotics. Decentralized control is free from difficulties due to complexity in design, debugging, data gathering and storage requirements, making it preferable for interconnected systems. Furthermore, as opposed to the centralized approach, it can be implemented with parallel processors. This approach deals with four decentralized control schemes, which are able to identify the robot dynamics. The training of each neural network is performed on-line using an extended Kalman filter (EKF). The first indirect decentralized control scheme applies the discrete-time block control approach, to formulate a nonlinear sliding manifold. The second direct decentralized neural control scheme is based on the backstepping technique, approximated by a high order neural network. The third control scheme applies a decentralized neural i...

  19. Neural neworks in a management information systems

    Directory of Open Access Journals (Sweden)

    Jana Weinlichová

    2009-01-01

    Full Text Available For having retrospection for all over the data which are used, analyzed, evaluated and for a future incident predictions are used Management Information Systems and Business Intelligence. In case of not to be able to apply standard methods of data processing there can be with benefit applied an Artificial Intelligence. In this article will be referred to proofed abilities of Neural Networks. The Neural Networks is supported by many software products related to provide effective solution of manager issues. Those products are given as primary support for manager issues solving. We were tried to find reciprocally between products using Neural Networks and between Management Information Systems for finding a real possibility of applying Neural Networks as a direct part of Management Information Systems (MIS. In the article are presented possibilities to apply Neural Networks on different types of tasks in MIS.

  20. Rift Valley fever virus NSs inhibits host transcription independently of the degradation of dsRNA-dependent protein kinase PKR.

    Science.gov (United States)

    Kalveram, Birte; Lihoradova, Olga; Indran, Sabarish V; Lokugamage, Nandadeva; Head, Jennifer A; Ikegami, Tetsuro

    2013-01-20

    Rift Valley fever virus (RVFV) encodes one major virulence factor, the NSs protein. NSs suppresses host general transcription, including interferon (IFN)-β mRNA synthesis, and promotes degradation of the dsRNA-dependent protein kinase (PKR). We generated a novel RVFV mutant (rMP12-NSsR173A) specifically lacking the function to promote PKR degradation. rMP12-NSsR173A infection induces early phosphorylation of eIF2α through PKR activation, while retaining the function to inhibit host general transcription including IFN-β gene inhibition. MP-12 NSs but not R173A NSs binds to wt PKR. R173A NSs formed filamentous structure in nucleus in a mosaic pattern, which was distinct from MP-12 NSs filament pattern. Due to early phosphorylation of eIF2α, rMP12-NSsR173A could not efficiently accumulate viral proteins. Our results suggest that NSs-mediated host general transcription suppression occurs independently of PKR degradation, while the PKR degradation is important to inhibit the phosphorylation of eIF2α in infected cells undergoing host general transcription suppression. Copyright © 2012 Elsevier Inc. All rights reserved.

  1. Rift Valley fever virus NSs inhibits host transcription independently of the degradation of dsRNA-dependent Protein Kinase PKR

    OpenAIRE

    Kalveram, Birte; Lihoradova, Olga; Indran, Sabarish V.; Lokugamage, Nandadeva; Head, Jennifer A.; Ikegami, Tetsuro

    2012-01-01

    Rift Valley fever virus (RVFV) encodes one major virulence factor, the NSs protein. NSs suppresses host general transcription, including interferon (IFN)-β mRNA synthesis, and promotes degradation of the dsRNA-dependent protein kinase (PKR). We generated a novel RVFV mutant (rMP12-NSsR173A) specifically lacking the function to promote PKR degradation. rMP12-NSsR173A infection induces early phosphorylation of eIF2α through PKR activation, while retaining the function to inhibit host general tr...

  2. Noradrenergic modulation of neural erotic stimulus perception.

    Science.gov (United States)

    Graf, Heiko; Wiegers, Maike; Metzger, Coraline Danielle; Walter, Martin; Grön, Georg; Abler, Birgit

    2017-09-01

    We recently investigated neuromodulatory effects of the noradrenergic agent reboxetine and the dopamine receptor affine amisulpride in healthy subjects on dynamic erotic stimulus processing. Whereas amisulpride left sexual functions and neural activations unimpaired, we observed detrimental activations under reboxetine within the caudate nucleus corresponding to motivational components of sexual behavior. However, broadly impaired subjective sexual functioning under reboxetine suggested effects on further neural components. We now investigated the same sample under these two agents with static erotic picture stimulation as alternative stimulus presentation mode to potentially observe further neural treatment effects of reboxetine. 19 healthy males were investigated under reboxetine, amisulpride and placebo for 7 days each within a double-blind cross-over design. During fMRI static erotic picture were presented with preceding anticipation periods. Subjective sexual functions were assessed by a self-reported questionnaire. Neural activations were attenuated within the caudate nucleus, putamen, ventral striatum, the pregenual and anterior midcingulate cortex and in the orbitofrontal cortex under reboxetine. Subjective diminished sexual arousal under reboxetine was correlated with attenuated neural reactivity within the posterior insula. Again, amisulpride left neural activations along with subjective sexual functioning unimpaired. Neither reboxetine nor amisulpride altered differential neural activations during anticipation of erotic stimuli. Our results verified detrimental effects of noradrenergic agents on neural motivational but also emotional and autonomic components of sexual behavior. Considering the overlap of neural network alterations with those evoked by serotonergic agents, our results suggest similar neuromodulatory effects of serotonergic and noradrenergic agents on common neural pathways relevant for sexual behavior. Copyright © 2017 Elsevier B.V. and

  3. Neural Networks in Control Applications

    DEFF Research Database (Denmark)

    Sørensen, O.

    are examined. The models are separated into three groups representing input/output descriptions as well as state space descriptions: - Models, where all in- and outputs are measurable (static networks). - Models, where some inputs are non-measurable (recurrent networks). - Models, where some in- and some...... outputs are non-measurable (recurrent networks with incomplete state information). The three groups are ordered in increasing complexity, and for each group it is shown how to solve the problems concerning training and application of the specific model type. Of particular interest are the model types...... Kalmann filter) representing state space description. The potentials of neural networks for control of non-linear processes are also examined, focusing on three different groups of control concepts, all considered as generalizations of known linear control concepts to handle also non-linear processes...

  4. Primary neural leprosy: systematic review

    Directory of Open Access Journals (Sweden)

    Jose Antonio Garbino

    2013-06-01

    Full Text Available The authors proposed a systematic review on the current concepts of primary neural leprosy by consulting the following online databases: MEDLINE, Lilacs/SciELO, and Embase. Selected studies were classified based on the degree of recommendation and levels of scientific evidence according to the “Oxford Centre for Evidence-based Medicine”. The following aspects were reviewed: cutaneous clinical and laboratorial investigations, i.e. skin clinical exam, smears, and biopsy, and Mitsuda's reaction; neurological investigation (anamnesis, electromyography and nerve biopsy; serological investigation and molecular testing, i.e. serological testing for the detection of the phenolic glycolipid 1 (PGL-I and the polymerase chain reaction (PCR; and treatment (classification criteria for the definition of specific treatment, steroid treatment, and cure criteria.

  5. Neural Tube Defects and Pregnancy

    Directory of Open Access Journals (Sweden)

    Emine Çoşar

    2009-09-01

    Full Text Available OBJECTIVE: Neural tube defects are congenital malformations those mostly causing life-long morbidities. They are prevented by the periconseptional folic acid usage and prenatal diagnostic methods. MATERIALS-METHODS: Pregnants from Afyonkarahisar and neighbourhood cities applied to our hospital and determined NTD, were investigated. RESULTS: In our obstetrics clinic 1403 delivery were made and 43 of them had fetus with NTD. Among these fetuses 41.3% had meningomyelocel, 17.4% had meningocel, 21.7% had encephalocel, 8.7% had unencephali and 4.3% had iniencephali. CONCLUSION: Incidence of NTD is high in our region and geographic region, nutrition and other socioeconomic factors may be related to the high incidence. Education of the mother and periconceptional folic acid usage may reduce teh incidence of NTD.

  6. Collision avoidance using neural networks

    Science.gov (United States)

    Sugathan, Shilpa; Sowmya Shree, B. V.; Warrier, Mithila R.; Vidhyapathi, C. M.

    2017-11-01

    Now a days, accidents on roads are caused due to the negligence of drivers and pedestrians or due to unexpected obstacles that come into the vehicle’s path. In this paper, a model (robot) is developed to assist drivers for a smooth travel without accidents. It reacts to the real time obstacles on the four critical sides of the vehicle and takes necessary action. The sensor used for detecting the obstacle was an IR proximity sensor. A single layer perceptron neural network is used to train and test all possible combinations of sensors result by using Matlab (offline). A microcontroller (ARM Cortex-M3 LPC1768) is used to control the vehicle through the output data which is received from Matlab via serial communication. Hence, the vehicle becomes capable of reacting to any combination of real time obstacles.

  7. Neural networks: a biased overview

    International Nuclear Information System (INIS)

    Domany, E.

    1988-01-01

    An overview of recent activity in the field of neural networks is presented. The long-range aim of this research is to understand how the brain works. First some of the problems are stated and terminology defined; then an attempt is made to explain why physicists are drawn to the field, and their main potential contribution. In particular, in recent years some interesting models have been introduced by physicists. A small subset of these models is described, with particular emphasis on those that are analytically soluble. Finally a brief review of the history and recent developments of single- and multilayer perceptrons is given, bringing the situation up to date regarding the central immediate problem of the field: search for a learning algorithm that has an associated convergence theorem

  8. Application of neural network to CT

    International Nuclear Information System (INIS)

    Ma, Xiao-Feng; Takeda, Tatsuoki

    1999-01-01

    This paper presents a new method for two-dimensional image reconstruction by using a multilayer neural network. Multilayer neural networks are extensively investigated and practically applied to solution of various problems such as inverse problems or time series prediction problems. From learning an input-output mapping from a set of examples, neural networks can be regarded as synthesizing an approximation of multidimensional function (that is, solving the problem of hypersurface reconstruction, including smoothing and interpolation). From this viewpoint, neural networks are well suited to the solution of CT image reconstruction. Though a conventionally used object function of a neural network is composed of a sum of squared errors of the output data, we can define an object function composed of a sum of residue of an integral equation. By employing an appropriate line integral for this integral equation, we can construct a neural network that can be used for CT. We applied this method to some model problems and obtained satisfactory results. As it is not necessary to discretized the integral equation using this reconstruction method, therefore it is application to the problem of complicated geometrical shapes is also feasible. Moreover, in neural networks, interpolation is performed quite smoothly, as a result, inverse mapping can be achieved smoothly even in case of including experimental and numerical errors, However, use of conventional back propagation technique for optimization leads to an expensive computation cost. To overcome this drawback, 2nd order optimization methods or parallel computing will be applied in future. (J.P.N.)

  9. Nonequilibrium landscape theory of neural networks.

    Science.gov (United States)

    Yan, Han; Zhao, Lei; Hu, Liang; Wang, Xidi; Wang, Erkang; Wang, Jin

    2013-11-05

    The brain map project aims to map out the neuron connections of the human brain. Even with all of the wirings mapped out, the global and physical understandings of the function and behavior are still challenging. Hopfield quantified the learning and memory process of symmetrically connected neural networks globally through equilibrium energy. The energy basins of attractions represent memories, and the memory retrieval dynamics is determined by the energy gradient. However, the realistic neural networks are asymmetrically connected, and oscillations cannot emerge from symmetric neural networks. Here, we developed a nonequilibrium landscape-flux theory for realistic asymmetrically connected neural networks. We uncovered the underlying potential landscape and the associated Lyapunov function for quantifying the global stability and function. We found the dynamics and oscillations in human brains responsible for cognitive processes and physiological rhythm regulations are determined not only by the landscape gradient but also by the flux. We found that the flux is closely related to the degrees of the asymmetric connections in neural networks and is the origin of the neural oscillations. The neural oscillation landscape shows a closed-ring attractor topology. The landscape gradient attracts the network down to the ring. The flux is responsible for coherent oscillations on the ring. We suggest the flux may provide the driving force for associations among memories. We applied our theory to rapid-eye movement sleep cycle. We identified the key regulation factors for function through global sensitivity analysis of landscape topography against wirings, which are in good agreements with experiments.

  10. Nonequilibrium landscape theory of neural networks

    Science.gov (United States)

    Yan, Han; Zhao, Lei; Hu, Liang; Wang, Xidi; Wang, Erkang; Wang, Jin

    2013-01-01

    The brain map project aims to map out the neuron connections of the human brain. Even with all of the wirings mapped out, the global and physical understandings of the function and behavior are still challenging. Hopfield quantified the learning and memory process of symmetrically connected neural networks globally through equilibrium energy. The energy basins of attractions represent memories, and the memory retrieval dynamics is determined by the energy gradient. However, the realistic neural networks are asymmetrically connected, and oscillations cannot emerge from symmetric neural networks. Here, we developed a nonequilibrium landscape–flux theory for realistic asymmetrically connected neural networks. We uncovered the underlying potential landscape and the associated Lyapunov function for quantifying the global stability and function. We found the dynamics and oscillations in human brains responsible for cognitive processes and physiological rhythm regulations are determined not only by the landscape gradient but also by the flux. We found that the flux is closely related to the degrees of the asymmetric connections in neural networks and is the origin of the neural oscillations. The neural oscillation landscape shows a closed-ring attractor topology. The landscape gradient attracts the network down to the ring. The flux is responsible for coherent oscillations on the ring. We suggest the flux may provide the driving force for associations among memories. We applied our theory to rapid-eye movement sleep cycle. We identified the key regulation factors for function through global sensitivity analysis of landscape topography against wirings, which are in good agreements with experiments. PMID:24145451

  11. Permutation parity machines for neural synchronization

    International Nuclear Information System (INIS)

    Reyes, O M; Kopitzke, I; Zimmermann, K-H

    2009-01-01

    Synchronization of neural networks has been studied in recent years as an alternative to cryptographic applications such as the realization of symmetric key exchange protocols. This paper presents a first view of the so-called permutation parity machine, an artificial neural network proposed as a binary variant of the tree parity machine. The dynamics of the synchronization process by mutual learning between permutation parity machines is analytically studied and the results are compared with those of tree parity machines. It will turn out that for neural synchronization, permutation parity machines form a viable alternative to tree parity machines

  12. Enhancing neural-network performance via assortativity

    International Nuclear Information System (INIS)

    Franciscis, Sebastiano de; Johnson, Samuel; Torres, Joaquin J.

    2011-01-01

    The performance of attractor neural networks has been shown to depend crucially on the heterogeneity of the underlying topology. We take this analysis a step further by examining the effect of degree-degree correlations - assortativity - on neural-network behavior. We make use of a method recently put forward for studying correlated networks and dynamics thereon, both analytically and computationally, which is independent of how the topology may have evolved. We show how the robustness to noise is greatly enhanced in assortative (positively correlated) neural networks, especially if it is the hub neurons that store the information.

  13. Genetic algorithm for neural networks optimization

    Science.gov (United States)

    Setyawati, Bina R.; Creese, Robert C.; Sahirman, Sidharta

    2004-11-01

    This paper examines the forecasting performance of multi-layer feed forward neural networks in modeling a particular foreign exchange rates, i.e. Japanese Yen/US Dollar. The effects of two learning methods, Back Propagation and Genetic Algorithm, in which the neural network topology and other parameters fixed, were investigated. The early results indicate that the application of this hybrid system seems to be well suited for the forecasting of foreign exchange rates. The Neural Networks and Genetic Algorithm were programmed using MATLAB«.

  14. Stock market index prediction using neural networks

    Science.gov (United States)

    Komo, Darmadi; Chang, Chein-I.; Ko, Hanseok

    1994-03-01

    A neural network approach to stock market index prediction is presented. Actual data of the Wall Street Journal's Dow Jones Industrial Index has been used for a benchmark in our experiments where Radial Basis Function based neural networks have been designed to model these indices over the period from January 1988 to Dec 1992. A notable success has been achieved with the proposed model producing over 90% prediction accuracies observed based on monthly Dow Jones Industrial Index predictions. The model has also captured both moderate and heavy index fluctuations. The experiments conducted in this study demonstrated that the Radial Basis Function neural network represents an excellent candidate to predict stock market index.

  15. Mass reconstruction with a neural network

    International Nuclear Information System (INIS)

    Loennblad, L.; Peterson, C.; Roegnvaldsson, T.

    1992-01-01

    A feed-forward neural network method is developed for reconstructing the invariant mass of hadronic jets appearing in a calorimeter. The approach is illustrated in W→qanti q, where W-bosons are produced in panti p reactions at SPS collider energies. The neural network method yields results that are superior to conventional methods. This neural network application differs from the classification ones in the sense that an analog number (the mass) is computed by the network, rather than a binary decision being made. As a by-product our application clearly demonstrates the need for using 'intelligent' variables in instances when the amount of training instances is limited. (orig.)

  16. 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

  17. 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....

  18. Convolutional Neural Network for Image Recognition

    CERN Document Server

    Seifnashri, Sahand

    2015-01-01

    The aim of this project is to use machine learning techniques especially Convolutional Neural Networks for image processing. These techniques can be used for Quark-Gluon discrimination using calorimeters data, but unfortunately I didn’t manage to get the calorimeters data and I just used the Jet data fromminiaodsim(ak4 chs). The Jet data was not good enough for Convolutional Neural Network which is designed for ’image’ recognition. This report is made of twomain part, part one is mainly about implementing Convolutional Neural Network on unphysical data such as MNIST digits and CIFAR-10 dataset and part 2 is about the Jet data.

  19. Applications of neural network to numerical analyses

    International Nuclear Information System (INIS)

    Takeda, Tatsuoki; Fukuhara, Makoto; Ma, Xiao-Feng; Liaqat, Ali

    1999-01-01

    Applications of a multi-layer neural network to numerical analyses are described. We are mainly concerned with the computed tomography and the solution of differential equations. In both cases as the objective functions for the training process of the neural network we employed residuals of the integral equation or the differential equations. This is different from the conventional neural network training where sum of the squared errors of the output values is adopted as the objective function. For model problems both the methods gave satisfactory results and the methods are considered promising for some kind of problems. (author)

  20. A neural network approach to burst detection.

    Science.gov (United States)

    Mounce, S R; Day, A J; Wood, A S; Khan, A; Widdop, P D; Machell, J

    2002-01-01

    This paper describes how hydraulic and water quality data from a distribution network may be used to provide a more efficient leakage management capability for the water industry. The research presented concerns the application of artificial neural networks to the issue of detection and location of leakage in treated water distribution systems. An architecture for an Artificial Neural Network (ANN) based system is outlined. The neural network uses time series data produced by sensors to directly construct an empirical model for predication and classification of leaks. Results are presented using data from an experimental site in Yorkshire Water's Keighley distribution system.

  1. Multistability in bidirectional associative memory neural networks

    International Nuclear Information System (INIS)

    Huang Gan; Cao Jinde

    2008-01-01

    In this Letter, the multistability issue is studied for Bidirectional Associative Memory (BAM) neural networks. Based on the existence and stability analysis of the neural networks with or without delay, it is found that the 2n-dimensional networks can have 3 n equilibria and 2 n equilibria of them are locally exponentially stable, where each layer of the BAM network has n neurons. Furthermore, the results has been extended to (n+m)-dimensional BAM neural networks, where there are n and m neurons on the two layers respectively. Finally, two numerical examples are presented to illustrate the validity of our results

  2. Multistability in bidirectional associative memory neural networks

    Science.gov (United States)

    Huang, Gan; Cao, Jinde

    2008-04-01

    In this Letter, the multistability issue is studied for Bidirectional Associative Memory (BAM) neural networks. Based on the existence and stability analysis of the neural networks with or without delay, it is found that the 2 n-dimensional networks can have 3 equilibria and 2 equilibria of them are locally exponentially stable, where each layer of the BAM network has n neurons. Furthermore, the results has been extended to (n+m)-dimensional BAM neural networks, where there are n and m neurons on the two layers respectively. Finally, two numerical examples are presented to illustrate the validity of our results.

  3. An Introduction to Neural Networks for Hearing Aid Noise Recognition.

    Science.gov (United States)

    Kim, Jun W.; Tyler, Richard S.

    1995-01-01

    This article introduces the use of multilayered artificial neural networks in hearing aid noise recognition. It reviews basic principles of neural networks, and offers an example of an application in which a neural network is used to identify the presence or absence of noise in speech. The ability of neural networks to "learn" the…

  4. Neural correlates and neural computations in posterior parietal cortex during perceptual decision-making

    Directory of Open Access Journals (Sweden)

    Alexander eHuk

    2012-10-01

    Full Text Available A recent line of work has found remarkable success in relating perceptual decision-making and the spiking activity in the macaque lateral intraparietal area (LIP. In this review, we focus on questions about the neural computations in LIP that are not answered by demonstrations of neural correlates of psychological processes. We highlight three areas of limitations in our current understanding of the precise neural computations that might underlie neural correlates of decisions: (1 empirical questions not yet answered by existing data; (2 implementation issues related to how neural circuits could actually implement the mechanisms suggested by both physiology and psychology; and (3 ecological constraints related to the use of well-controlled laboratory tasks and whether they provide an accurate window on sensorimotor computation. These issues motivate the adoption of a more general encoding-decoding framework that will be fruitful for more detailed contemplation of how neural computations in LIP relate to the formation of perceptual decisions.

  5. Deep Learning Neural Networks and Bayesian Neural Networks in Data Analysis

    Directory of Open Access Journals (Sweden)

    Chernoded Andrey

    2017-01-01

    Full Text Available Most of the modern analyses in high energy physics use signal-versus-background classification techniques of machine learning methods and neural networks in particular. Deep learning neural network is the most promising modern technique to separate signal and background and now days can be widely and successfully implemented as a part of physical analysis. In this article we compare Deep learning and Bayesian neural networks application as a classifiers in an instance of top quark analysis.

  6. Chondroitin sulfate effects on neural stem cell differentiation.

    Science.gov (United States)

    Canning, David R; Brelsford, Natalie R; Lovett, Neil W

    2016-01-01

    We have investigated the role chondroitin sulfate has on cell interactions during neural plate formation in the early chick embryo. Using tissue culture isolates from the prospective neural plate, we have measured neural gene expression profiles associated with neural stem cell differentiation. Removal of chondroitin sulfate from stage 4 neural plate tissue leads to altered associations of N-cadherin-positive neural progenitors and causes changes in the normal sequence of neural marker gene expression. Absence of chondroitin sulfate in the neural plate leads to reduced Sox2 expression and is accompanied by an increase in the expression of anterior markers of neural regionalization. Results obtained in this study suggest that the presence of chondroitin sulfate in the anterior chick embryo is instrumental in maintaining cells in the neural precursor state.

  7. Neural processing of auditory signals and modular neural control for sound tropism of walking machines

    DEFF Research Database (Denmark)

    Manoonpong, Poramate; Pasemann, Frank; Fischer, Joern

    2005-01-01

    and a neural preprocessing system together with a modular neural controller are used to generate a sound tropism of a four-legged walking machine. The neural preprocessing network is acting as a low-pass filter and it is followed by a network which discerns between signals coming from the left or the right....... The parameters of these networks are optimized by an evolutionary algorithm. In addition, a simple modular neural controller then generates the desired different walking patterns such that the machine walks straight, then turns towards a switched-on sound source, and then stops near to it....

  8. Local Dynamics in Trained Recurrent Neural Networks.

    Science.gov (United States)

    Rivkind, Alexander; Barak, Omri

    2017-06-23

    Learning a task induces connectivity changes in neural circuits, thereby changing their dynamics. To elucidate task-related neural dynamics, we study trained recurrent neural networks. We develop a mean field theory for reservoir computing networks trained to have multiple fixed point attractors. Our main result is that the dynamics of the network's output in the vicinity of attractors is governed by a low-order linear ordinary differential equation. The stability of the resulting equation can be assessed, predicting training success or failure. As a consequence, networks of rectified linear units and of sigmoidal nonlinearities are shown to have diametrically different properties when it comes to learning attractors. Furthermore, a characteristic time constant, which remains finite at the edge of chaos, offers an explanation of the network's output robustness in the presence of variability of the internal neural dynamics. Finally, the proposed theory predicts state-dependent frequency selectivity in the network response.

  9. 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.

  10. water demand prediction using artificial neural network

    African Journals Online (AJOL)

    user

    2017-01-01

    Jan 1, 2017 ... Interface for activation and deactivation of valves. •. Interface demand ... process could be done and monitored at the computer terminal as expected of a .... [15] Arbib, M. A.The Handbook of Brain Theory and Neural. Networks.

  11. Machine Learning Topological Invariants with Neural Networks

    Science.gov (United States)

    Zhang, Pengfei; Shen, Huitao; Zhai, Hui

    2018-02-01

    In this Letter we supervisedly train neural networks to distinguish different topological phases in the context of topological band insulators. After training with Hamiltonians of one-dimensional insulators with chiral symmetry, the neural network can predict their topological winding numbers with nearly 100% accuracy, even for Hamiltonians with larger winding numbers that are not included in the training data. These results show a remarkable success that the neural network can capture the global and nonlinear topological features of quantum phases from local inputs. By opening up the neural network, we confirm that the network does learn the discrete version of the winding number formula. We also make a couple of remarks regarding the role of the symmetry and the opposite effect of regularization techniques when applying machine learning to physical systems.

  12. Hopfield neural network in HEP track reconstruction

    International Nuclear Information System (INIS)

    Muresan, R.; Pentia, M.

    1997-01-01

    In experimental particle physics, pattern recognition problems, specifically for neural network methods, occur frequently in track finding or feature extraction. Track finding is a combinatorial optimization problem. Given a set of points in Euclidean space, one tries the reconstruction of particle trajectories, subject to smoothness constraints.The basic ingredients in a neural network are the N binary neurons and the synaptic strengths connecting them. In our case the neurons are the segments connecting all possible point pairs.The dynamics of the neural network is given by a local updating rule wich evaluates for each neuron the sign of the 'upstream activity'. An updating rule in the form of sigmoid function is given. The synaptic strengths are defined in terms of angle between the segments and the lengths of the segments implied in the track reconstruction. An algorithm based on Hopfield neural network has been developed and tested on the track coordinates measured by silicon microstrip tracking system

  13. Additive Feed Forward Control with Neural Networks

    DEFF Research Database (Denmark)

    Sørensen, O.

    1999-01-01

    This paper demonstrates a method to control a non-linear, multivariable, noisy process using trained neural networks. The basis for the method is a trained neural network controller acting as the inverse process model. A training method for obtaining such an inverse process model is applied....... A suitable 'shaped' (low-pass filtered) reference is used to overcome problems with excessive control action when using a controller acting as the inverse process model. The control concept is Additive Feed Forward Control, where the trained neural network controller, acting as the inverse process model......, is placed in a supplementary pure feed-forward path to an existing feedback controller. This concept benefits from the fact, that an existing, traditional designed, feedback controller can be retained without any modifications, and after training the connection of the neural network feed-forward controller...

  14. Hardware Acceleration of Adaptive Neural Algorithms.

    Energy Technology Data Exchange (ETDEWEB)

    James, Conrad D. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2017-11-01

    As tradit ional numerical computing has faced challenges, researchers have turned towards alternative computing approaches to reduce power - per - computation metrics and improve algorithm performance. Here, we describe an approach towards non - conventional computing that strengthens the connection between machine learning and neuroscience concepts. The Hardware Acceleration of Adaptive Neural Algorithms (HAANA) project ha s develop ed neural machine learning algorithms and hardware for applications in image processing and cybersecurity. While machine learning methods are effective at extracting relevant features from many types of data, the effectiveness of these algorithms degrades when subjected to real - world conditions. Our team has generated novel neural - inspired approa ches to improve the resiliency and adaptability of machine learning algorithms. In addition, we have also designed and fabricated hardware architectures and microelectronic devices specifically tuned towards the training and inference operations of neural - inspired algorithms. Finally, our multi - scale simulation framework allows us to assess the impact of microelectronic device properties on algorithm performance.

  15. Local Dynamics in Trained Recurrent Neural Networks

    Science.gov (United States)

    Rivkind, Alexander; Barak, Omri

    2017-06-01

    Learning a task induces connectivity changes in neural circuits, thereby changing their dynamics. To elucidate task-related neural dynamics, we study trained recurrent neural networks. We develop a mean field theory for reservoir computing networks trained to have multiple fixed point attractors. Our main result is that the dynamics of the network's output in the vicinity of attractors is governed by a low-order linear ordinary differential equation. The stability of the resulting equation can be assessed, predicting training success or failure. As a consequence, networks of rectified linear units and of sigmoidal nonlinearities are shown to have diametrically different properties when it comes to learning attractors. Furthermore, a characteristic time constant, which remains finite at the edge of chaos, offers an explanation of the network's output robustness in the presence of variability of the internal neural dynamics. Finally, the proposed theory predicts state-dependent frequency selectivity in the network response.

  16. Experimental Demonstrations of Optical Neural Computers

    OpenAIRE

    Hsu, Ken; Brady, David; Psaltis, Demetri

    1988-01-01

    We describe two experiments in optical neural computing. In the first a closed optical feedback loop is used to implement auto-associative image recall. In the second a perceptron-like learning algorithm is implemented with photorefractive holography.

  17. PREDIKSI FOREX MENGGUNAKAN MODEL NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    R. Hadapiningradja Kusumodestoni

    2015-11-01

    Full Text Available ABSTRAK Prediksi adalah salah satu teknik yang paling penting dalam menjalankan bisnis forex. Keputusan dalam memprediksi adalah sangatlah penting, karena dengan prediksi dapat membantu mengetahui nilai forex di waktu tertentu kedepan sehingga dapat mengurangi resiko kerugian. Tujuan dari penelitian ini dimaksudkan memprediksi bisnis fores menggunakan model neural network dengan data time series per 1 menit untuk mengetahui nilai akurasi prediksi sehingga dapat mengurangi resiko dalam menjalankan bisnis forex. Metode penelitian pada penelitian ini meliputi metode pengumpulan data kemudian dilanjutkan ke metode training, learning, testing menggunakan neural network. Setelah di evaluasi hasil penelitian ini menunjukan bahwa penerapan algoritma Neural Network mampu untuk memprediksi forex dengan tingkat akurasi prediksi 0.431 +/- 0.096 sehingga dengan prediksi ini dapat membantu mengurangi resiko dalam menjalankan bisnis forex. Kata kunci: prediksi, forex, neural network.

  18. NEURAL NETWORKS FOR STOCK MARKET OPTION PRICING

    Directory of Open Access Journals (Sweden)

    Sergey A. Sannikov

    2017-03-01

    Full Text Available Introduction: The use of neural networks for non-linear models helps to understand where linear model drawbacks, coused by their specification, reveal themselves. This paper attempts to find this out. The objective of research is to determine the meaning of “option prices calculation using neural networks”. Materials and Methods: We use two kinds of variables: endogenous (variables included in the model of neural network and variables affecting on the model (permanent disturbance. Results: All data are divided into 3 sets: learning, affirming and testing. All selected variables are normalised from 0 to 1. Extreme values of income were shortcut. Discussion and Conclusions: Using the 33-14-1 neural network with direct links we obtained two sets of forecasts. Optimal criteria of strategies in stock markets’ option pricing were developed.

  19. Hardware implementation of stochastic spiking neural networks.

    Science.gov (United States)

    Rosselló, Josep L; Canals, Vincent; Morro, Antoni; Oliver, Antoni

    2012-08-01

    Spiking Neural Networks, the last generation of Artificial Neural Networks, are characterized by its bio-inspired nature and by a higher computational capacity with respect to other neural models. In real biological neurons, stochastic processes represent an important mechanism of neural behavior and are responsible of its special arithmetic capabilities. In this work we present a simple hardware implementation of spiking neurons that considers this probabilistic nature. The advantage of the proposed implementation is that it is fully digital and therefore can be massively implemented in Field Programmable Gate Arrays. The high computational capabilities of the proposed model are demonstrated by the study of both feed-forward and recurrent networks that are able to implement high-speed signal filtering and to solve complex systems of linear equations.

  20. Neural adaptations to electrical stimulation strength training

    NARCIS (Netherlands)

    Hortobagyi, Tibor; Maffiuletti, Nicola A.

    2011-01-01

    This review provides evidence for the hypothesis that electrostimulation strength training (EST) increases the force of a maximal voluntary contraction (MVC) through neural adaptations in healthy skeletal muscle. Although electrical stimulation and voluntary effort activate muscle differently, there

  1. 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).

  2. Artificial neural networks for plasma spectroscopy analysis

    International Nuclear Information System (INIS)

    Morgan, W.L.; Larsen, J.T.; Goldstein, W.H.

    1992-01-01

    Artificial neural networks have been applied to a variety of signal processing and image recognition problems. Of the several common neural models the feed-forward, back-propagation network is well suited for the analysis of scientific laboratory data, which can be viewed as a pattern recognition problem. The authors present a discussion of the basic neural network concepts and illustrate its potential for analysis of experiments by applying it to the spectra of laser produced plasmas in order to obtain estimates of electron temperatures and densities. Although these are high temperature and density plasmas, the neural network technique may be of interest in the analysis of the low temperature and density plasmas characteristic of experiments and devices in gaseous electronics

  3. 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.

  4. Control of autonomous robot using neural networks

    Science.gov (United States)

    Barton, Adam; Volna, Eva

    2017-07-01

    The aim of the article is to design a method of control of an autonomous robot using artificial neural networks. The introductory part describes control issues from the perspective of autonomous robot navigation and the current mobile robots controlled by neural networks. The core of the article is the design of the controlling neural network, and generation and filtration of the training set using ART1 (Adaptive Resonance Theory). The outcome of the practical part is an assembled Lego Mindstorms EV3 robot solving the problem of avoiding obstacles in space. To verify models of an autonomous robot behavior, a set of experiments was created as well as evaluation criteria. The speed of each motor was adjusted by the controlling neural network with respect to the situation in which the robot was found.

  5. Neural activation in stress-related exhaustion

    DEFF Research Database (Denmark)

    Gavelin, Hanna Malmberg; Neely, Anna Stigsdotter; Andersson, Micael

    2017-01-01

    The primary purpose of this study was to investigate the association between burnout and neural activation during working memory processing in patients with stress-related exhaustion. Additionally, we investigated the neural effects of cognitive training as part of stress rehabilitation. Fifty...... association between burnout level and working memory performance was found, however, our findings indicate that frontostriatal neural responses related to working memory were modulated by burnout severity. We suggest that patients with high levels of burnout need to recruit additional cognitive resources...... to uphold task performance. Following cognitive training, increased neural activation was observed during 3-back in working memory-related regions, including the striatum, however, low sample size limits any firm conclusions....

  6. Front Propagation in Stochastic Neural Fields

    KAUST Repository

    Bressloff, Paul C.; Webber, Matthew A.

    2012-01-01

    We analyze the effects of extrinsic multiplicative noise on front propagation in a scalar neural field with excitatory connections. Using a separation of time scales, we represent the fluctuating front in terms of a diffusive-like displacement

  7. Diagnosis method utilizing neural networks

    International Nuclear Information System (INIS)

    Watanabe, K.; Tamayama, K.

    1990-01-01

    Studies have been made on the technique of neural networks, which will be used to identify a cause of a small anomalous state in the reactor coolant system of the ATR (Advance Thermal Reactor). Three phases of analyses were carried out in this study. First, simulation for 100 seconds was made to determine how the plant parameters respond after the occurence of a transient decrease in reactivity, flow rate and temperature of feed water and increase in the steam flow rate and steam pressure, which would produce a decrease of water level in a steam drum of the ATR. Next, the simulation data was analysed utilizing an autoregressive model. From this analysis, a total of 36 coherency functions up to 0.5 Hz in each transient were computed among nine important and detectable plant parameters: neutron flux, flow rate of coolant, steam or feed water, water level in the steam drum, pressure and opening area of control valve in a steam pipe, feed water temperature and electrical power. Last, learning of neural networks composed of 96 input, 4-9 hidden and 5 output layer units was done by use of the generalized delta rule, namely a back-propagation algorithm. These convergent computations were continued as far as the difference between the desired outputs, 1 for direct cause or 0 for four other ones and actual outputs reached less than 10%. (1) Coherency functions were not governed by decreasing rate of reactivity in the range of 0.41x10 -2 dollar/s to 1.62x10 -2 dollar /s or by decreasing depth of the feed water temperature in the range of 3 deg C to 10 deg C or by a change of 10% or less in the three other causes. Change in coherency functions only depended on the type of cause. (2) The direct cause from the other four ones could be discriminated with 0.94+-0.01 of output level. A maximum of 0.06 output height was found among the other four causes. (3) Calculation load which is represented as products of learning times and numbers of the hidden units did not depend on the

  8. Parameter extraction with neural networks

    Science.gov (United States)

    Cazzanti, Luca; Khan, Mumit; Cerrina, Franco

    1998-06-01

    In semiconductor processing, the modeling of the process is becoming more and more important. While the ultimate goal is that of developing a set of tools for designing a complete process (Technology CAD), it is also necessary to have modules to simulate the various technologies and, in particular, to optimize specific steps. This need is particularly acute in lithography, where the continuous decrease in CD forces the technologies to operate near their limits. In the development of a 'model' for a physical process, we face several levels of challenges. First, it is necessary to develop a 'physical model,' i.e. a rational description of the process itself on the basis of know physical laws. Second, we need an 'algorithmic model' to represent in a virtual environment the behavior of the 'physical model.' After a 'complete' model has been developed and verified, it becomes possible to do performance analysis. In many cases the input parameters are poorly known or not accessible directly to experiment. It would be extremely useful to obtain the values of these 'hidden' parameters from experimental results by comparing model to data. This is particularly severe, because the complexity and costs associated with semiconductor processing make a simple 'trial-and-error' approach infeasible and cost- inefficient. Even when computer models of the process already exists, obtaining data through simulations may be time consuming. Neural networks (NN) are powerful computational tools to predict the behavior of a system from an existing data set. They are able to adaptively 'learn' input/output mappings and to act as universal function approximators. In this paper we use artificial neural networks to build a mapping from the input parameters of the process to output parameters which are indicative of the performance of the process. Once the NN has been 'trained,' it is also possible to observe the process 'in reverse,' and to extract the values of the inputs which yield outputs

  9. Neural Elements for Predictive Coding

    Directory of Open Access Journals (Sweden)

    Stewart SHIPP

    2016-11-01

    Full Text Available Predictive coding theories of sensory brain function interpret the hierarchical construction of the cerebral cortex as a Bayesian, generative model capable of predicting the sensory data consistent with any given percept. Predictions are fed backwards in the hierarchy and reciprocated by prediction error in the forward direction, acting to modify the representation of the outside world at increasing levels of abstraction, and so to optimize the nature of perception over a series of iterations. This accounts for many ‘illusory’ instances of perception where what is seen (heard, etc is unduly influenced by what is expected, based on past experience. This simple conception, the hierarchical exchange of prediction and prediction error, confronts a rich cortical microcircuitry that is yet to be fully documented. This article presents the view that, in the current state of theory and practice, it is profitable to begin a two-way exchange: that predictive coding theory can support an understanding of cortical microcircuit function, and prompt particular aspects of future investigation, whilst existing knowledge of microcircuitry can, in return, influence theoretical development. As an example, a neural inference arising from the earliest formulations of predictive coding is that the source populations of forwards and backwards pathways should be completely separate, given their functional distinction; this aspect of circuitry – that neurons with extrinsically bifurcating axons do not project in both directions – has only recently been confirmed. Here, the computational architecture prescribed by a generalized (free-energy formulation of predictive coding is combined with the classic ‘canonical microcircuit’ and the laminar architecture of hierarchical extrinsic connectivity to produce a template schematic, that is further examined in the light of (a updates in the microcircuitry of primate visual cortex, and (b rapid technical advances made

  10. Neural Elements for Predictive Coding.

    Science.gov (United States)

    Shipp, Stewart

    2016-01-01

    Predictive coding theories of sensory brain function interpret the hierarchical construction of the cerebral cortex as a Bayesian, generative model capable of predicting the sensory data consistent with any given percept. Predictions are fed backward in the hierarchy and reciprocated by prediction error in the forward direction, acting to modify the representation of the outside world at increasing levels of abstraction, and so to optimize the nature of perception over a series of iterations. This accounts for many 'illusory' instances of perception where what is seen (heard, etc.) is unduly influenced by what is expected, based on past experience. This simple conception, the hierarchical exchange of prediction and prediction error, confronts a rich cortical microcircuitry that is yet to be fully documented. This article presents the view that, in the current state of theory and practice, it is profitable to begin a two-way exchange: that predictive coding theory can support an understanding of cortical microcircuit function, and prompt particular aspects of future investigation, whilst existing knowledge of microcircuitry can, in return, influence theoretical development. As an example, a neural inference arising from the earliest formulations of predictive coding is that the source populations of forward and backward pathways should be completely separate, given their functional distinction; this aspect of circuitry - that neurons with extrinsically bifurcating axons do not project in both directions - has only recently been confirmed. Here, the computational architecture prescribed by a generalized (free-energy) formulation of predictive coding is combined with the classic 'canonical microcircuit' and the laminar architecture of hierarchical extrinsic connectivity to produce a template schematic, that is further examined in the light of (a) updates in the microcircuitry of primate visual cortex, and (b) rapid technical advances made possible by transgenic neural

  11. Neural control of magnetic suspension systems

    Science.gov (United States)

    Gray, W. Steven

    1993-01-01

    The purpose of this research program is to design, build and test (in cooperation with NASA personnel from the NASA Langley Research Center) neural controllers for two different small air-gap magnetic suspension systems. The general objective of the program is to study neural network architectures for the purpose of control in an experimental setting and to demonstrate the feasibility of the concept. The specific objectives of the research program are: (1) to demonstrate through simulation and experimentation the feasibility of using neural controllers to stabilize a nonlinear magnetic suspension system; (2) to investigate through simulation and experimentation the performance of neural controllers designs under various types of parametric and nonparametric uncertainty; (3) to investigate through simulation and experimentation various types of neural architectures for real-time control with respect to performance and complexity; and (4) to benchmark in an experimental setting the performance of neural controllers against other types of existing linear and nonlinear compensator designs. To date, the first one-dimensional, small air-gap magnetic suspension system has been built, tested and delivered to the NASA Langley Research Center. The device is currently being stabilized with a digital linear phase-lead controller. The neural controller hardware is under construction. Two different neural network paradigms are under consideration, one based on hidden layer feedforward networks trained via back propagation and one based on using Gaussian radial basis functions trained by analytical methods related to stability conditions. Some advanced nonlinear control algorithms using feedback linearization and sliding mode control are in simulation studies.

  12. Nonlinear programming with feedforward neural networks.

    Energy Technology Data Exchange (ETDEWEB)

    Reifman, J.

    1999-06-02

    We provide a practical and effective method for solving constrained optimization problems by successively training a multilayer feedforward neural network in a coupled neural-network/objective-function representation. Nonlinear programming problems are easily mapped into this representation which has a simpler and more transparent method of solution than optimization performed with Hopfield-like networks and poses very mild requirements on the functions appearing in the problem. Simulation results are illustrated and compared with an off-the-shelf optimization tool.

  13. Feedforward Nonlinear Control Using Neural Gas Network

    OpenAIRE

    Machón-González, Iván; López-García, Hilario

    2017-01-01

    Nonlinear systems control is a main issue in control theory. Many developed applications suffer from a mathematical foundation not as general as the theory of linear systems. This paper proposes a control strategy of nonlinear systems with unknown dynamics by means of a set of local linear models obtained by a supervised neural gas network. The proposed approach takes advantage of the neural gas feature by which the algorithm yields a very robust clustering procedure. The direct model of the ...

  14. Neural networks and orbit control in accelerators

    International Nuclear Information System (INIS)

    Bozoki, E.; Friedman, A.

    1994-01-01

    An overview of the architecture, workings and training of Neural Networks is given. We stress the aspects which are important for the use of Neural Networks for orbit control in accelerators and storage rings, especially its ability to cope with the nonlinear behavior of the orbit response to 'kicks' and the slow drift in the orbit response during long-term operation. Results obtained for the two NSLS storage rings with several network architectures and various training methods for each architecture are given

  15. Drift chamber tracking with neural networks

    International Nuclear Information System (INIS)

    Lindsey, C.S.; Denby, B.; Haggerty, H.

    1992-10-01

    We discuss drift chamber tracking with a commercial log VLSI neural network chip. Voltages proportional to the drift times in a 4-layer drift chamber were presented to the Intel ETANN chip. The network was trained to provide the intercept and slope of straight tracks traversing the chamber. The outputs were recorded and later compared off line to conventional track fits. Two types of network architectures were studied. Applications of neural network tracking to high energy physics detector triggers is discussed

  16. Radioactive fallout and neural tube defects

    Directory of Open Access Journals (Sweden)

    Nejat Akar

    2015-10-01

    Full Text Available Possible link between radioactivity and the occurrence of neural tube defects is a long lasting debate since the Chernobyl nuclear fallout in 1986. A recent report on the incidence of neural defects in the west coast of USA, following Fukushima disaster, brought another evidence for effect of radioactive fallout on the occurrence of NTD’s. Here a literature review was performed focusing on this special subject.

  17. Neural networks, D0, and the SSC

    International Nuclear Information System (INIS)

    Barter, C.; Cutts, D.; Hoftun, J.S.; Partridge, R.A.; Sornborger, A.T.; Johnson, C.T.; Zeller, R.T.

    1989-01-01

    We outline several exploratory studies involving neural network simulations applied to pattern recognition in high energy physics. We describe the D0 data acquisition system and a natual means by which algorithms derived from neural networks techniques may be incorporated into recently developed hardware associated with the D0 MicroVAX farm nodes. Such applications to the event filtering needed by SSC detectors look interesting. 10 refs., 11 figs

  18. Culture of Mouse Neural Stem Cell Precursors

    OpenAIRE

    Currle, D. Spencer; Hu, Jia Sheng; Kolski-Andreaco, Aaron; Monuki, Edwin S.

    2007-01-01

    Primary neural stem cell cultures are useful for studying the mechanisms underlying central nervous system development. Stem cell research will increase our understanding of the nervous system and may allow us to develop treatments for currently incurable brain diseases and injuries. In addition, stem cells should be used for stem cell research aimed at the detailed study of mechanisms of neural differentiation and transdifferentiation and the genetic and environmental signals that direct the...

  19. Neural codes of seeing architectural styles

    OpenAIRE

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

    2017-01-01

    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 sugges...

  20. Neural network monitoring of resistive welding

    International Nuclear Information System (INIS)

    Quero, J.M.; Millan, R.L.; Franquelo, L.G.; Canas, J.

    1994-01-01

    Supervision of welding processes is one of the most important and complicated tasks in production lines. Artificial Neural Networks have been applied for modeling and control of ph physical processes. In our paper we propose the use of a neural network classifier for on-line non-destructive testing. This system has been developed and installed in a resistive welding station. Results confirm the validity of this novel approach. (Author) 6 refs

  1. Neural Network Models for Time Series Forecasts

    OpenAIRE

    Tim Hill; Marcus O'Connor; William Remus

    1996-01-01

    Neural networks have been advocated as an alternative to traditional statistical forecasting methods. In the present experiment, time series forecasts produced by neural networks are compared with forecasts from six statistical time series methods generated in a major forecasting competition (Makridakis et al. [Makridakis, S., A. Anderson, R. Carbone, R. Fildes, M. Hibon, R. Lewandowski, J. Newton, E. Parzen, R. Winkler. 1982. The accuracy of extrapolation (time series) methods: Results of a ...

  2. Neural neworks in a management information systems

    OpenAIRE

    Jana Weinlichová; Michael Štencl

    2009-01-01

    For having retrospection for all over the data which are used, analyzed, evaluated and for a future incident predictions are used Management Information Systems and Business Intelligence. In case of not to be able to apply standard methods of data processing there can be with benefit applied an Artificial Intelligence. In this article will be referred to proofed abilities of Neural Networks. The Neural Networks is supported by many software products related to provide effective solution of ma...

  3. Using neural networks in software repositories

    Science.gov (United States)

    Eichmann, David (Editor); Srinivas, Kankanahalli; Boetticher, G.

    1992-01-01

    The first topic is an exploration of the use of neural network techniques to improve the effectiveness of retrieval in software repositories. The second topic relates to a series of experiments conducted to evaluate the feasibility of using adaptive neural networks as a means of deriving (or more specifically, learning) measures on software. Taken together, these two efforts illuminate a very promising mechanism supporting software infrastructures - one based upon a flexible and responsive technology.

  4. Application of neural networks in CRM systems

    Directory of Open Access Journals (Sweden)

    Bojanowska Agnieszka

    2017-01-01

    Full Text Available The central aim of this study is to investigate how to apply artificial neural networks in Customer Relationship Management (CRM. The paper presents several business applications of neural networks in software systems designed to aid CRM, e.g. in deciding on the profitability of building a relationship with a given customer. Furthermore, a framework for a neural-network based CRM software tool is developed. Building beneficial relationships with customers is generating considerable interest among various businesses, and is often mentioned as one of the crucial objectives of enterprises, next to their key aim: to bring satisfactory profit. There is a growing tendency among businesses to invest in CRM systems, which together with an organisational culture of a company aid managing customer relationships. It is the sheer amount of gathered data as well as the need for constant updating and analysis of this breadth of information that may imply the suitability of neural networks for the application in question. Neural networks exhibit considerably higher computational capabilities than sequential calculations because the solution to a problem is obtained without the need for developing a special algorithm. In the majority of presented CRM applications neural networks constitute and are presented as a managerial decision-taking optimisation tool.

  5. Recent Advances in Neural Recording Microsystems

    Directory of Open Access Journals (Sweden)

    Benoit Gosselin

    2011-04-01

    Full Text Available The accelerating pace of research in neuroscience has created a considerable demand for neural interfacing microsystems capable of monitoring the activity of large groups of neurons. These emerging tools have revealed a tremendous potential for the advancement of knowledge in brain research and for the development of useful clinical applications. They can extract the relevant control signals directly from the brain enabling individuals with severe disabilities to communicate their intentions to other devices, like computers or various prostheses. Such microsystems are self-contained devices composed of a neural probe attached with an integrated circuit for extracting neural signals from multiple channels, and transferring the data outside the body. The greatest challenge facing development of such emerging devices into viable clinical systems involves addressing their small form factor and low-power consumption constraints, while providing superior resolution. In this paper, we survey the recent progress in the design and the implementation of multi-channel neural recording Microsystems, with particular emphasis on the design of recording and telemetry electronics. An overview of the numerous neural signal modalities is given and the existing microsystem topologies are covered. We present energy-efficient sensory circuits to retrieve weak signals from neural probes and we compare them. We cover data management and smart power scheduling approaches, and we review advances in low-power telemetry. Finally, we conclude by summarizing the remaining challenges and by highlighting the emerging trends in the field.

  6. Logarithmic learning for generalized classifier neural network.

    Science.gov (United States)

    Ozyildirim, Buse Melis; Avci, Mutlu

    2014-12-01

    Generalized classifier neural network is introduced as an efficient classifier among the others. Unless the initial smoothing parameter value is close to the optimal one, generalized classifier neural network suffers from convergence problem and requires quite a long time to converge. In this work, to overcome this problem, a logarithmic learning approach is proposed. The proposed method uses logarithmic cost function instead of squared error. Minimization of this cost function reduces the number of iterations used for reaching the minima. The proposed method is tested on 15 different data sets and performance of logarithmic learning generalized classifier neural network is compared with that of standard one. Thanks to operation range of radial basis function included by generalized classifier neural network, proposed logarithmic approach and its derivative has continuous values. This makes it possible to adopt the advantage of logarithmic fast convergence by the proposed learning method. Due to fast convergence ability of logarithmic cost function, training time is maximally decreased to 99.2%. In addition to decrease in training time, classification performance may also be improved till 60%. According to the test results, while the proposed method provides a solution for time requirement problem of generalized classifier neural network, it may also improve the classification accuracy. The proposed method can be considered as an efficient way for reducing the time requirement problem of generalized classifier neural network. Copyright © 2014 Elsevier Ltd. All rights reserved.

  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. Diabetic retinopathy screening using deep neural network.

    Science.gov (United States)

    Ramachandran, Nishanthan; Hong, Sheng Chiong; Sime, Mary J; Wilson, Graham A

    2017-09-07

    There is a burgeoning interest in the use of deep neural network in diabetic retinal screening. To determine whether a deep neural network could satisfactorily detect diabetic retinopathy that requires referral to an ophthalmologist from a local diabetic retinal screening programme and an international database. Retrospective audit. Diabetic retinal photos from Otago database photographed during October 2016 (485 photos), and 1200 photos from Messidor international database. Receiver operating characteristic curve to illustrate the ability of a deep neural network to identify referable diabetic retinopathy (moderate or worse diabetic retinopathy or exudates within one disc diameter of the fovea). Area under the receiver operating characteristic curve, sensitivity and specificity. For detecting referable diabetic retinopathy, the deep neural network had an area under receiver operating characteristic curve of 0.901 (95% confidence interval 0.807-0.995), with 84.6% sensitivity and 79.7% specificity for Otago and 0.980 (95% confidence interval 0.973-0.986), with 96.0% sensitivity and 90.0% specificity for Messidor. This study has shown that a deep neural network can detect referable diabetic retinopathy with sensitivities and specificities close to or better than 80% from both an international and a domestic (New Zealand) database. We believe that deep neural networks can be integrated into community screening once they can successfully detect both diabetic retinopathy and diabetic macular oedema. © 2017 Royal Australian and New Zealand College of Ophthalmologists.

  10. Preserving information in neural transmission.

    Science.gov (United States)

    Sincich, Lawrence C; Horton, Jonathan C; Sharpee, Tatyana O

    2009-05-13

    Along most neural pathways, the spike trains transmitted from one neuron to the next are altered. In the process, neurons can either achieve a more efficient stimulus representation, or extract some biologically important stimulus parameter, or succeed at both. We recorded the inputs from single retinal ganglion cells and the outputs from connected lateral geniculate neurons in the macaque to examine how visual signals are relayed from retina to cortex. We found that geniculate neurons re-encoded multiple temporal stimulus features to yield output spikes that carried more information about stimuli than was available in each input spike. The coding transformation of some relay neurons occurred with no decrement in information rate, despite output spike rates that averaged half the input spike rates. This preservation of transmitted information was achieved by the short-term summation of inputs that geniculate neurons require to spike. A reduced model of the retinal and geniculate visual responses, based on two stimulus features and their associated nonlinearities, could account for >85% of the total information available in the spike trains and the preserved information transmission. These results apply to neurons operating on a single time-varying input, suggesting that synaptic temporal integration can alter the temporal receptive field properties to create a more efficient representation of visual signals in the thalamus than the retina.

  11. Neural correlates of pediatric obesity.

    Science.gov (United States)

    Bruce, Amanda S; Martin, Laura E; Savage, Cary R

    2011-06-01

    Childhood obesity rates have increased over the last 40 years and have a detrimental impact on public health. While the causes of the obesity epidemic are complex, obesity ultimately arises from chronic imbalances between energy intake and expenditure. An emerging area of research in obesity has focused on the role of the brain in evaluating the rewarding properties of food and making decisions about what and how much to eat. This article reviews recent scientific literature regarding the brain's role in pediatric food motivation and childhood obesity. The article will begin by reviewing some of the recent literature discussing challenges associated with neuroimaging in children and the relevant developmental brain changes that occur in childhood and adolescence. The article will then review studies regarding neural mechanisms of food motivation and the ability to delay gratification in children and how these responses differ in obese compared to healthy weight children. Increasing our understanding about how brain function and behavior may differ in children will inform future research, obesity prevention, and interventions targeting childhood obesity. Copyright © 2011. Published by Elsevier Inc.

  12. 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.

  13. Echoes in correlated neural systems

    International Nuclear Information System (INIS)

    Helias, M; Tetzlaff, T; Diesmann, M

    2013-01-01

    Correlations are employed in modern physics to explain microscopic and macroscopic phenomena, like the fractional quantum Hall effect and the Mott insulator state in high temperature superconductors and ultracold atoms. Simultaneously probed neurons in the intact brain reveal correlations between their activity, an important measure to study information processing in the brain that also influences the macroscopic signals of neural activity, like the electroencephalogram (EEG). Networks of spiking neurons differ from most physical systems: the interaction between elements is directed, time delayed, mediated by short pulses and each neuron receives events from thousands of neurons. Even the stationary state of the network cannot be described by equilibrium statistical mechanics. Here we develop a quantitative theory of pairwise correlations in finite-sized random networks of spiking neurons. We derive explicit analytic expressions for the population-averaged cross correlation functions. Our theory explains why the intuitive mean field description fails, how the echo of single action potentials causes an apparent lag of inhibition with respect to excitation and how the size of the network can be scaled while maintaining its dynamical state. Finally, we derive a new criterion for the emergence of collective oscillations from the spectrum of the time-evolution propagator. (paper)

  14. Harnessing migraines for neural regeneration

    Directory of Open Access Journals (Sweden)

    Jonathan M Borkum

    2018-01-01

    Full Text Available The success of naturalistic or therapeutic neuroregeneration likely depends on an internal milieu that facilitates the survival, proliferation, migration, and differentiation of stem cells and their assimilation into neural networks. Migraine attacks are an integrated sequence of physiological processes that may protect the brain from oxidative stress by releasing growth factors, suppressing apoptosis, stimulating neurogenesis, encouraging mitochondrial biogenesis, reducing the production of oxidants, and upregulating antioxidant defenses. Thus, the migraine attack may constitute a physiologic environment conducive to stem cells. In this paper, key components of migraine are reviewed – neurogenic inflammation with release of calcitonin gene-related peptide (CGRP and substance P, plasma protein extravasation, platelet activation, release of serotonin by platelets and likely by the dorsal raphe nucleus, activation of endothelial nitric oxide synthase (eNOS, production of brain-derived neurotrophic factor (BDNF and, in migraine aura, cortical spreading depression – along with their potential neurorestorative aspects. The possibility is considered of using these components to facilitate successful stem cell transplantation. Potential methods for doing so are discussed, including chemical stimulation of the TRPA1 ion channel, conjoint activation of a subset of migraine components, invasive and noninvasive deep brain stimulation of the dorsal raphe nucleus, transcranial focused ultrasound, and stimulation of the Zusanli (ST36 acupuncture point.

  15. Differentiation between non-neural and neural contributors to ankle joint stiffness in cerebral palsy

    NARCIS (Netherlands)

    De Gooijer-van de Groep, K.L.; De Vlugt, E.; De Groot, J.H.; Van der Heijden-Maessen, H.C.M.; Wielheesen, D.H.M.; Van Wijlen-Hempel, R.M.S.; Arendzen, J.H.; Meskers, C.G.M.

    2013-01-01

    Background Spastic paresis in cerebral palsy (CP) is characterized by increased joint stiffness that may be of neural origin, i.e. improper muscle activation caused by e.g. hyperreflexia or non-neural origin, i.e. altered tissue viscoelastic properties (clinically: “spasticity” vs. “contracture”).

  16. Fast and Efficient Asynchronous Neural Computation with Adapting Spiking Neural Networks

    NARCIS (Netherlands)

    D. Zambrano (Davide); S.M. Bohte (Sander)

    2016-01-01

    textabstractBiological neurons communicate with a sparing exchange of pulses - spikes. It is an open question how real spiking neurons produce the kind of powerful neural computation that is possible with deep artificial neural networks, using only so very few spikes to communicate. Building on

  17. Neural Parallel Engine: A toolbox for massively parallel neural signal processing.

    Science.gov (United States)

    Tam, Wing-Kin; Yang, Zhi

    2018-05-01

    Large-scale neural recordings provide detailed information on neuronal activities and can help elicit the underlying neural mechanisms of the brain. However, the computational burden is also formidable when we try to process the huge data stream generated by such recordings. In this study, we report the development of Neural Parallel Engine (NPE), a toolbox for massively parallel neural signal processing on graphical processing units (GPUs). It offers a selection of the most commonly used routines in neural signal processing such as spike detection and spike sorting, including advanced algorithms such as exponential-component-power-component (EC-PC) spike detection and binary pursuit spike sorting. We also propose a new method for detecting peaks in parallel through a parallel compact operation. Our toolbox is able to offer a 5× to 110× speedup compared with its CPU counterparts depending on the algorithms. A user-friendly MATLAB interface is provided to allow easy integration of the toolbox into existing workflows. Previous efforts on GPU neural signal processing only focus on a few rudimentary algorithms, are not well-optimized and often do not provide a user-friendly programming interface to fit into existing workflows. There is a strong need for a comprehensive toolbox for massively parallel neural signal processing. A new toolbox for massively parallel neural signal processing has been created. It can offer significant speedup in processing signals from large-scale recordings up to thousands of channels. Copyright © 2018 Elsevier B.V. All rights reserved.

  18. Implantable neurotechnologies: a review of integrated circuit neural amplifiers.

    Science.gov (United States)

    Ng, Kian Ann; Greenwald, Elliot; Xu, Yong Ping; Thakor, Nitish V

    2016-01-01

    Neural signal recording is critical in modern day neuroscience research and emerging neural prosthesis programs. Neural recording requires the use of precise, low-noise amplifier systems to acquire and condition the weak neural signals that are transduced through electrode interfaces. Neural amplifiers and amplifier-based systems are available commercially or can be designed in-house and fabricated using integrated circuit (IC) technologies, resulting in very large-scale integration or application-specific integrated circuit solutions. IC-based neural amplifiers are now used to acquire untethered/portable neural recordings, as they meet the requirements of a miniaturized form factor, light weight and low power consumption. Furthermore, such miniaturized and low-power IC neural amplifiers are now being used in emerging implantable neural prosthesis technologies. This review focuses on neural amplifier-based devices and is presented in two interrelated parts. First, neural signal recording is reviewed, and practical challenges are highlighted. Current amplifier designs with increased functionality and performance and without penalties in chip size and power are featured. Second, applications of IC-based neural amplifiers in basic science experiments (e.g., cortical studies using animal models), neural prostheses (e.g., brain/nerve machine interfaces) and treatment of neuronal diseases (e.g., DBS for treatment of epilepsy) are highlighted. The review concludes with future outlooks of this technology and important challenges with regard to neural signal amplification.

  19. 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

  20. Neural systems for tactual memories.

    Science.gov (United States)

    Bonda, E; Petrides, M; Evans, A

    1996-04-01

    1. The aim of this study was to investigate the neural systems involved in the memory processing of experiences through touch. 2. Regional cerebral blood flow was measured with positron emission tomography by means of the water bolus H2(15)O methodology in human subjects as they performed tasks involving different levels of tactual memory. In one of the experimental tasks, the subjects had to palpate nonsense shapes to match each one to a previously learned set, thus requiring constant reference to long-term memory. The other experimental task involved judgements of the recent recurrence of shapes during the scanning period. A set of three control tasks was used to control for the type of exploratory movements and sensory processing inherent in the two experimental tasks. 3. Comparisons of the distribution of activity between the experimental and the control tasks were carried out by means of the subtraction method. In relation to the control conditions, the two experimental tasks requiring memory resulted in significant changes within the posteroventral insula and the central opercular region. In addition, the task requiring recall from long-term memory yielded changes in the perirhinal cortex. 4. The above findings demonstrated that a ventrally directed parietoinsular pathway, leading to the posteroventral insula and the perirhinal cortex, constitutes a system by which long-lasting representations of tactual experiences are formed. It is proposed that the posteroventral insula is involved in tactual feature analysis, by analogy with the similar role of the inferotemporal cortex in vision, whereas the perirhinal cortex is further involved in the integration of these features into long-lasting representations of somatosensory experiences.

  1. The equilibrium of neural firing: A mathematical theory

    Energy Technology Data Exchange (ETDEWEB)

    Lan, Sizhong, E-mail: lsz@fuyunresearch.org [Fuyun Research, Beijing, 100055 (China)

    2014-12-15

    Inspired by statistical thermodynamics, we presume that neuron system has equilibrium condition with respect to neural firing. We show that, even with dynamically changeable neural connections, it is inevitable for neural firing to evolve to equilibrium. To study the dynamics between neural firing and neural connections, we propose an extended communication system where noisy channel has the tendency towards fixed point, implying that neural connections are always attracted into fixed points such that equilibrium can be reached. The extended communication system and its mathematics could be useful back in thermodynamics.

  2. 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.

  3. Prototype-Incorporated Emotional Neural Network.

    Science.gov (United States)

    Oyedotun, Oyebade K; Khashman, Adnan

    2017-08-15

    Artificial neural networks (ANNs) aim to simulate the biological neural activities. Interestingly, many ''engineering'' prospects in ANN have relied on motivations from cognition and psychology studies. So far, two important learning theories that have been subject of active research are the prototype and adaptive learning theories. The learning rules employed for ANNs can be related to adaptive learning theory, where several examples of the different classes in a task are supplied to the network for adjusting internal parameters. Conversely, the prototype-learning theory uses prototypes (representative examples); usually, one prototype per class of the different classes contained in the task. These prototypes are supplied for systematic matching with new examples so that class association can be achieved. In this paper, we propose and implement a novel neural network algorithm based on modifying the emotional neural network (EmNN) model to unify the prototype- and adaptive-learning theories. We refer to our new model as ``prototype-incorporated EmNN''. Furthermore, we apply the proposed model to two real-life challenging tasks, namely, static hand-gesture recognition and face recognition, and compare the result to those obtained using the popular back-propagation neural network (BPNN), emotional BPNN (EmNN), deep networks, an exemplar classification model, and k-nearest neighbor.

  4. Tuning Neural Phase Entrainment to Speech.

    Science.gov (United States)

    Falk, Simone; Lanzilotti, Cosima; Schön, Daniele

    2017-08-01

    Musical rhythm positively impacts on subsequent speech processing. However, the neural mechanisms underlying this phenomenon are so far unclear. We investigated whether carryover effects from a preceding musical cue to a speech stimulus result from a continuation of neural phase entrainment to periodicities that are present in both music and speech. Participants listened and memorized French metrical sentences that contained (quasi-)periodic recurrences of accents and syllables. Speech stimuli were preceded by a rhythmically regular or irregular musical cue. Our results show that the presence of a regular cue modulates neural response as estimated by EEG power spectral density, intertrial coherence, and source analyses at critical frequencies during speech processing compared with the irregular condition. Importantly, intertrial coherences for regular cues were indicative of the participants' success in memorizing the subsequent speech stimuli. These findings underscore the highly adaptive nature of neural phase entrainment across fundamentally different auditory stimuli. They also support current models of neural phase entrainment as a tool of predictive timing and attentional selection across cognitive domains.

  5. Race modulates neural activity during imitation

    Science.gov (United States)

    Losin, Elizabeth A. Reynolds; Iacoboni, Marco; Martin, Alia; Cross, Katy A.; Dapretto, Mirella

    2014-01-01

    Imitation plays a central role in the acquisition of culture. People preferentially imitate others who are self-similar, prestigious or successful. Because race can indicate a person's self-similarity or status, race influences whom people imitate. Prior studies of the neural underpinnings of imitation have not considered the effects of race. Here we measured neural activity with fMRI while European American participants imitated meaningless gestures performed by actors of their own race, and two racial outgroups, African American, and Chinese American. Participants also passively observed the actions of these actors and their portraits. Frontal, parietal and occipital areas were differentially activated while participants imitated actors of different races. More activity was present when imitating African Americans than the other racial groups, perhaps reflecting participants' reported lack of experience with and negative attitudes towards this group, or the group's lower perceived social status. This pattern of neural activity was not found when participants passively observed the gestures of the actors or simply looked at their faces. Instead, during face-viewing neural responses were overall greater for own-race individuals, consistent with prior race perception studies not involving imitation. Our findings represent a first step in elucidating neural mechanisms involved in cultural learning, a process that influences almost every aspect of our lives but has thus far received little neuroscientific study. PMID:22062193

  6. Using neural networks to describe tracer correlations

    Directory of Open Access Journals (Sweden)

    D. J. Lary

    2004-01-01

    Full Text Available Neural networks are ideally suited to describe the spatial and temporal dependence of tracer-tracer correlations. The neural network performs well even in regions where the correlations are less compact and normally a family of correlation curves would be required. For example, the CH4-N2O correlation can be well described using a neural network trained with the latitude, pressure, time of year, and methane volume mixing ratio (v.m.r.. In this study a neural network using Quickprop learning and one hidden layer with eight nodes was able to reproduce the CH4-N2O correlation with a correlation coefficient between simulated and training values of 0.9995. Such an accurate representation of tracer-tracer correlations allows more use to be made of long-term datasets to constrain chemical models. Such as the dataset from the Halogen Occultation Experiment (HALOE which has continuously observed CH4  (but not N2O from 1991 till the present. The neural network Fortran code used is available for download.

  7. 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.

  8. Neural network based multiscale image restoration approach

    Science.gov (United States)

    de Castro, Ana Paula A.; da Silva, José D. S.

    2007-02-01

    This paper describes a neural network based multiscale image restoration approach. Multilayer perceptrons are trained with artificial images of degraded gray level circles, in an attempt to make the neural network learn inherent space relations of the degraded pixels. The present approach simulates the degradation by a low pass Gaussian filter blurring operation and the addition of noise to the pixels at pre-established rates. The training process considers the degraded image as input and the non-degraded image as output for the supervised learning process. The neural network thus performs an inverse operation by recovering a quasi non-degraded image in terms of least squared. The main difference of the approach to existing ones relies on the fact that the space relations are taken from different scales, thus providing relational space data to the neural network. The approach is an attempt to come up with a simple method that leads to an optimum solution to the problem. Considering different window sizes around a pixel simulates the multiscale operation. In the generalization phase the neural network is exposed to indoor, outdoor, and satellite degraded images following the same steps use for the artificial circle image.

  9. Artificial neural network intelligent method for prediction

    Science.gov (United States)

    Trifonov, Roumen; Yoshinov, Radoslav; Pavlova, Galya; Tsochev, Georgi

    2017-09-01

    Accounting and financial classification and prediction problems are high challenge and researchers use different methods to solve them. Methods and instruments for short time prediction of financial operations using artificial neural network are considered. The methods, used for prediction of financial data as well as the developed forecasting system with neural network are described in the paper. The architecture of a neural network used four different technical indicators, which are based on the raw data and the current day of the week is presented. The network developed is used for forecasting movement of stock prices one day ahead and consists of an input layer, one hidden layer and an output layer. The training method is algorithm with back propagation of the error. The main advantage of the developed system is self-determination of the optimal topology of neural network, due to which it becomes flexible and more precise The proposed system with neural network is universal and can be applied to various financial instruments using only basic technical indicators as input data.

  10. 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.

  11. Inverting radiometric measurements with a neural network

    Science.gov (United States)

    Measure, Edward M.; Yee, Young P.; Balding, Jeff M.; Watkins, Wendell R.

    1992-02-01

    A neural network scheme for retrieving remotely sensed vertical temperature profiles was applied to observed ground based radiometer measurements. The neural network used microwave radiance measurements and surface measurements of temperature and pressure as inputs. Because the microwave radiometer is capable of measuring 4 oxygen channels at 5 different elevation angles (9, 15, 25, 40, and 90 degs), 20 microwave measurements are potentially available. Because these measurements have considerable redundancy, a neural network was experimented with, accepting as inputs microwave measurements taken at 53.88 GHz, 40 deg; 57.45 GHz, 40 deg; and 57.45, 90 deg. The primary test site was located at White Sands Missile Range (WSMR), NM. Results are compared with measurements made simultaneously with balloon borne radiosonde instruments and with radiometric temperature retrievals made using more conventional retrieval algorithms. The neural network was trained using a Widrow-Hoff delta rule procedure. Functions of date to include season dependence in the retrieval process and functions of time to include diurnal effects were used as inputs to the neural network.

  12. Kernel Temporal Differences for Neural Decoding

    Science.gov (United States)

    Bae, Jihye; Sanchez Giraldo, Luis G.; Pohlmeyer, Eric A.; Francis, Joseph T.; Sanchez, Justin C.; Príncipe, José C.

    2015-01-01

    We study the feasibility and capability of the kernel temporal difference (KTD)(λ) algorithm for neural decoding. KTD(λ) is an online, kernel-based learning algorithm, which has been introduced to estimate value functions in reinforcement learning. This algorithm combines kernel-based representations with the temporal difference approach to learning. One of our key observations is that by using strictly positive definite kernels, algorithm's convergence can be guaranteed for policy evaluation. The algorithm's nonlinear functional approximation capabilities are shown in both simulations of policy evaluation and neural decoding problems (policy improvement). KTD can handle high-dimensional neural states containing spatial-temporal information at a reasonable computational complexity allowing real-time applications. When the algorithm seeks a proper mapping between a monkey's neural states and desired positions of a computer cursor or a robot arm, in both open-loop and closed-loop experiments, it can effectively learn the neural state to action mapping. Finally, a visualization of the coadaptation process between the decoder and the subject shows the algorithm's capabilities in reinforcement learning brain machine interfaces. PMID:25866504

  13. 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.

  14. Estimation of neural energy in microelectrode signals

    Science.gov (United States)

    Gaumond, R. P.; Clement, R.; Silva, R.; Sander, D.

    2004-09-01

    We considered the problem of determining the neural contribution to the signal recorded by an intracortical electrode. We developed a linear least-squares approach to determine the energy fraction of a signal attributable to an arbitrary number of autocorrelation-defined signals buried in noise. Application of the method requires estimation of autocorrelation functions Rap(tgr) characterizing the action potential (AP) waveforms and Rn(tgr) characterizing background noise. This method was applied to the analysis of chronically implanted microelectrode signals from motor cortex of rat. We found that neural (AP) energy consisted of a large-signal component which grows linearly with the number of threshold-detected neural events and a small-signal component unrelated to the count of threshold-detected AP signals. The addition of pseudorandom noise to electrode signals demonstrated the algorithm's effectiveness for a wide range of noise-to-signal energy ratios (0.08 to 39). We suggest, therefore, that the method could be of use in providing a measure of neural response in situations where clearly identified spike waveforms cannot be isolated, or in providing an additional 'background' measure of microelectrode neural activity to supplement the traditional AP spike count.

  15. Neural network models of categorical perception.

    Science.gov (United States)

    Damper, R I; Harnad, S R

    2000-05-01

    Studies of the categorical perception (CP) of sensory continua have a long and rich history in psychophysics. In 1977, Macmillan, Kaplan, and Creelman introduced the use of signal detection theory to CP studies. Anderson and colleagues simultaneously proposed the first neural model for CP, yet this line of research has been less well explored. In this paper, we assess the ability of neural-network models of CP to predict the psychophysical performance of real observers with speech sounds and artificial/novel stimuli. We show that a variety of neural mechanisms are capable of generating the characteristics of CP. Hence, CP may not be a special model of perception but an emergent property of any sufficiently powerful general learning system.

  16. Modelling collective cell migration of neural crest.

    Science.gov (United States)

    Szabó, András; Mayor, Roberto

    2016-10-01

    Collective cell migration has emerged in the recent decade as an important phenomenon in cell and developmental biology and can be defined as the coordinated and cooperative movement of groups of cells. Most studies concentrate on tightly connected epithelial tissues, even though collective migration does not require a constant physical contact. Movement of mesenchymal cells is more independent, making their emergent collective behaviour less intuitive and therefore lending importance to computational modelling. Here we focus on such modelling efforts that aim to understand the collective migration of neural crest cells, a mesenchymal embryonic population that migrates large distances as a group during early vertebrate development. By comparing different models of neural crest migration, we emphasize the similarity and complementary nature of these approaches and suggest a future direction for the field. The principles derived from neural crest modelling could aid understanding the collective migration of other mesenchymal cell types. Copyright © 2016 Elsevier Ltd. All rights reserved.

  17. The neural basis of financial risk taking.

    Science.gov (United States)

    Kuhnen, Camelia M; Knutson, Brian

    2005-09-01

    Investors systematically deviate from rationality when making financial decisions, yet the mechanisms responsible for these deviations have not been identified. Using event-related fMRI, we examined whether anticipatory neural activity would predict optimal and suboptimal choices in a financial decision-making task. We characterized two types of deviations from the optimal investment strategy of a rational risk-neutral agent as risk-seeking mistakes and risk-aversion mistakes. Nucleus accumbens activation preceded risky choices as well as risk-seeking mistakes, while anterior insula activation preceded riskless choices as well as risk-aversion mistakes. These findings suggest that distinct neural circuits linked to anticipatory affect promote different types of financial choices and indicate that excessive activation of these circuits may lead to investing mistakes. Thus, consideration of anticipatory neural mechanisms may add predictive power to the rational actor model of economic decision making.

  18. Deep Neural Network Detects Quantum Phase Transition

    Science.gov (United States)

    Arai, Shunta; Ohzeki, Masayuki; Tanaka, Kazuyuki

    2018-03-01

    We detect the quantum phase transition of a quantum many-body system by mapping the observed results of the quantum state onto a neural network. In the present study, we utilized the simplest case of a quantum many-body system, namely a one-dimensional chain of Ising spins with the transverse Ising model. We prepared several spin configurations, which were obtained using repeated observations of the model for a particular strength of the transverse field, as input data for the neural network. Although the proposed method can be employed using experimental observations of quantum many-body systems, we tested our technique with spin configurations generated by a quantum Monte Carlo simulation without initial relaxation. The neural network successfully identified the strength of transverse field only from the spin configurations, leading to consistent estimations of the critical point of our model Γc = J.

  19. Recurrent Neural Network for Computing Outer Inverse.

    Science.gov (United States)

    Živković, Ivan S; Stanimirović, Predrag S; Wei, Yimin

    2016-05-01

    Two linear recurrent neural networks for generating outer inverses with prescribed range and null space are defined. Each of the proposed recurrent neural networks is based on the matrix-valued differential equation, a generalization of dynamic equations proposed earlier for the nonsingular matrix inversion, the Moore-Penrose inversion, as well as the Drazin inversion, under the condition of zero initial state. The application of the first approach is conditioned by the properties of the spectrum of a certain matrix; the second approach eliminates this drawback, though at the cost of increasing the number of matrix operations. The cases corresponding to the most common generalized inverses are defined. The conditions that ensure stability of the proposed neural network are presented. Illustrative examples present the results of numerical simulations.

  20. Deciphering Neural Codes of Memory during Sleep

    Science.gov (United States)

    Chen, Zhe; Wilson, Matthew A.

    2017-01-01

    Memories of experiences are stored in the cerebral cortex. Sleep is critical for consolidating hippocampal memory of wake experiences into the neocortex. Understanding representations of neural codes of hippocampal-neocortical networks during sleep would reveal important circuit mechanisms on memory consolidation, and provide novel insights into memory and dreams. Although sleep-associated ensemble spike activity has been investigated, identifying the content of memory in sleep remains challenging. Here, we revisit important experimental findings on sleep-associated memory (i.e., neural activity patterns in sleep that reflect memory processing) and review computational approaches for analyzing sleep-associated neural codes (SANC). We focus on two analysis paradigms for sleep-associated memory, and propose a new unsupervised learning framework (“memory first, meaning later”) for unbiased assessment of SANC. PMID:28390699

  1. Electrospun Nanofibrous Materials for Neural Tissue Engineering

    Directory of Open Access Journals (Sweden)

    Yee-Shuan Lee

    2011-02-01

    Full Text Available The use of biomaterials processed by the electrospinning technique has gained considerable interest for neural tissue engineering applications. The tissue engineering strategy is to facilitate the regrowth of nerves by combining an appropriate cell type with the electrospun scaffold. Electrospinning can generate fibrous meshes having fiber diameter dimensions at the nanoscale and these fibers can be nonwoven or oriented to facilitate neurite extension via contact guidance. This article reviews studies evaluating the effect of the scaffold’s architectural features such as fiber diameter and orientation on neural cell function and neurite extension. Electrospun meshes made of natural polymers, proteins and compositions having electrical activity in order to enhance neural cell function are also discussed.

  2. Dysfunction of Rapid Neural Adaptation in Dyslexia.

    Science.gov (United States)

    Perrachione, Tyler K; Del Tufo, Stephanie N; Winter, Rebecca; Murtagh, Jack; Cyr, Abigail; Chang, Patricia; Halverson, Kelly; Ghosh, Satrajit S; Christodoulou, Joanna A; Gabrieli, John D E

    2016-12-21

    Identification of specific neurophysiological dysfunctions resulting in selective reading difficulty (dyslexia) has remained elusive. In addition to impaired reading development, individuals with dyslexia frequently exhibit behavioral deficits in perceptual adaptation. Here, we assessed neurophysiological adaptation to stimulus repetition in adults and children with dyslexia for a wide variety of stimuli, spoken words, written words, visual objects, and faces. For every stimulus type, individuals with dyslexia exhibited significantly diminished neural adaptation compared to controls in stimulus-specific cortical areas. Better reading skills in adults and children with dyslexia were associated with greater repetition-induced neural adaptation. These results highlight a dysfunction of rapid neural adaptation as a core neurophysiological difference in dyslexia that may underlie impaired reading development. Reduced neurophysiological adaptation may relate to prior reports of reduced behavioral adaptation in dyslexia and may reveal a difference in brain functions that ultimately results in a specific reading impairment. Copyright © 2016 Elsevier Inc. All rights reserved.

  3. Open quantum generalisation of Hopfield neural networks

    Science.gov (United States)

    Rotondo, P.; Marcuzzi, M.; Garrahan, J. P.; Lesanovsky, I.; Müller, M.

    2018-03-01

    We propose a new framework to understand how quantum effects may impact on the dynamics of neural networks. We implement the dynamics of neural networks in terms of Markovian open quantum systems, which allows us to treat thermal and quantum coherent effects on the same footing. In particular, we propose an open quantum generalisation of the Hopfield neural network, the simplest toy model of associative memory. We determine its phase diagram and show that quantum fluctuations give rise to a qualitatively new non-equilibrium phase. This novel phase is characterised by limit cycles corresponding to high-dimensional stationary manifolds that may be regarded as a generalisation of storage patterns to the quantum domain.

  4. Neural Correlates of Boredom in Music Perception

    Directory of Open Access Journals (Sweden)

    Ashkan Fakhr Tabatabaie

    2014-11-01

    Full Text Available Introduction: Music can elicit powerful emotional responses, the neural correlates of which have not been properly understood. An important aspect about the quality of any musical piece is its ability to elicit a sense of excitement in the listeners. In this study, we investigated the neural correlates of boredom evoked by music in human subjects. Methods: We used EEG recording in nine subjects while they were listening to total number of 10 short-length (83 sec musical pieces with various boredom indices. Subjects evaluated boringness of musical pieces while their EEG was recording. Results: Using short time Fourier analysis, we found that beta2 rhythm was (16-20 Hz significantly lower whenever the subjects rated the music as boring in comparison to nonboring. Discussion: The results demonstrate that the music modulates neural activity of various partsof the brain and can be measured using EEG.

  5. Normalization as a canonical neural computation

    Science.gov (United States)

    Carandini, Matteo; Heeger, David J.

    2012-01-01

    There is increasing evidence that the brain relies on a set of canonical neural computations, repeating them across brain regions and modalities to apply similar operations to different problems. A promising candidate for such a computation is normalization, in which the responses of neurons are divided by a common factor that typically includes the summed activity of a pool of neurons. Normalization was developed to explain responses in the primary visual cortex and is now thought to operate throughout the visual system, and in many other sensory modalities and brain regions. Normalization may underlie operations such as the representation of odours, the modulatory effects of visual attention, the encoding of value and the integration of multisensory information. Its presence in such a diversity of neural systems in multiple species, from invertebrates to mammals, suggests that it serves as a canonical neural computation. PMID:22108672

  6. Reconstruction of neutron spectra through neural networks

    International Nuclear Information System (INIS)

    Vega C, H.R.; Hernandez D, V.M.; Manzanares A, E.

    2003-01-01

    A neural network has been used to reconstruct the neutron spectra starting from the counting rates of the detectors of the Bonner sphere spectrophotometric system. A group of 56 neutron spectra was selected to calculate the counting rates that would produce in a Bonner sphere system, with these data and the spectra it was trained the neural network. To prove the performance of the net, 12 spectra were used, 6 were taken of the group used for the training, 3 were obtained of mathematical functions and those other 3 correspond to real spectra. When comparing the original spectra of those reconstructed by the net we find that our net has a poor performance when reconstructing monoenergetic spectra, this attributes it to those characteristic of the spectra used for the training of the neural network, however for the other groups of spectra the results of the net are appropriate with the prospective ones. (Author)

  7. A quantum-implementable neural network model

    Science.gov (United States)

    Chen, Jialin; Wang, Lingli; Charbon, Edoardo

    2017-10-01

    A quantum-implementable neural network, namely quantum probability neural network (QPNN) model, is proposed in this paper. QPNN can use quantum parallelism to trace all possible network states to improve the result. Due to its unique quantum nature, this model is robust to several quantum noises under certain conditions, which can be efficiently implemented by the qubus quantum computer. Another advantage is that QPNN can be used as memory to retrieve the most relevant data and even to generate new data. The MATLAB experimental results of Iris data classification and MNIST handwriting recognition show that much less neuron resources are required in QPNN to obtain a good result than the classical feedforward neural network. The proposed QPNN model indicates that quantum effects are useful for real-life classification tasks.

  8. Eddy Current Flaw Characterization Using Neural Networks

    International Nuclear Information System (INIS)

    Song, S. J.; Park, H. J.; Shin, Y. K.

    1998-01-01

    Determination of location, shape and size of a flaw from its eddy current testing signal is one of the fundamental issues in eddy current nondestructive evaluation of steam generator tubes. Here, we propose an approach to this problem; an inversion of eddy current flaw signal using neural networks trained by finite element model-based synthetic signatures. Total 216 eddy current signals from four different types of axisymmetric flaws in tubes are generated by finite element models of which the accuracy is experimentally validated. From each simulated signature, total 24 eddy current features are extracted and among them 13 features are finally selected for flaw characterization. Based on these features, probabilistic neural networks discriminate flaws into four different types according to the location and the shape, and successively back propagation neural networks determine the size parameters of the discriminated flaw

  9. Neural Network Classifiers for Local Wind Prediction.

    Science.gov (United States)

    Kretzschmar, Ralf; Eckert, Pierre; Cattani, Daniel; Eggimann, Fritz

    2004-05-01

    This paper evaluates the quality of neural network classifiers for wind speed and wind gust prediction with prediction lead times between +1 and +24 h. The predictions were realized based on local time series and model data. The selection of appropriate input features was initiated by time series analysis and completed by empirical comparison of neural network classifiers trained on several choices of input features. The selected input features involved day time, yearday, features from a single wind observation device at the site of interest, and features derived from model data. The quality of the resulting classifiers was benchmarked against persistence for two different sites in Switzerland. The neural network classifiers exhibited superior quality when compared with persistence judged on a specific performance measure, hit and false-alarm rates.

  10. Metabolic neural mapping in neonatal rats

    International Nuclear Information System (INIS)

    DiRocco, R.J.; Hall, W.G.

    1981-01-01

    Functional neural mapping by 14 C-deoxyglucose autoradiography in adult rats has shown that increases in neural metabolic rate that are coupled to increased neurophysiological activity are more evident in axon terminals and dendrites than neuron cell bodies. Regions containing architectonically well-defined concentrations of terminals and dendrites (neuropil) have high metabolic rates when the neuropil is physiologically active. In neonatal rats, however, we find that regions containing well-defined groupings of neuron cell bodies have high metabolic rates in 14 C-deoxyglucose autoradiograms. The striking difference between the morphological appearance of 14 C-deoxyglucose autoradiograms obtained from neonatal and adult rats is probably related to developmental changes in morphometric features of differentiating neurons, as well as associated changes in type and locus of neural work performed

  11. 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.

  12. File list: ALL.Neu.05.AllAg.Induced_neural_progenitors [Chip-atlas[Archive

    Lifescience Database Archive (English)

    Full Text Available ALL.Neu.05.AllAg.Induced_neural_progenitors mm9 All antigens Neural Induced neural progeni....biosciencedbc.jp/kyushu-u/mm9/assembled/ALL.Neu.05.AllAg.Induced_neural_progenitors.bed ...

  13. File list: ALL.Neu.10.AllAg.Induced_neural_progenitors [Chip-atlas[Archive

    Lifescience Database Archive (English)

    Full Text Available ALL.Neu.10.AllAg.Induced_neural_progenitors mm9 All antigens Neural Induced neural progeni....biosciencedbc.jp/kyushu-u/mm9/assembled/ALL.Neu.10.AllAg.Induced_neural_progenitors.bed ...

  14. Neural complexity: A graph theoretic interpretation

    Science.gov (United States)

    Barnett, L.; Buckley, C. L.; Bullock, S.

    2011-04-01

    One of the central challenges facing modern neuroscience is to explain the ability of the nervous system to coherently integrate information across distinct functional modules in the absence of a central executive. To this end, Tononi [Proc. Natl. Acad. Sci. USA.PNASA60027-842410.1073/pnas.91.11.5033 91, 5033 (1994)] proposed a measure of neural complexity that purports to capture this property based on mutual information between complementary subsets of a system. Neural complexity, so defined, is one of a family of information theoretic metrics developed to measure the balance between the segregation and integration of a system’s dynamics. One key question arising for such measures involves understanding how they are influenced by network topology. Sporns [Cereb. Cortex53OPAV1047-321110.1093/cercor/10.2.127 10, 127 (2000)] employed numerical models in order to determine the dependence of neural complexity on the topological features of a network. However, a complete picture has yet to be established. While De Lucia [Phys. Rev. EPLEEE81539-375510.1103/PhysRevE.71.016114 71, 016114 (2005)] made the first attempts at an analytical account of this relationship, their work utilized a formulation of neural complexity that, we argue, did not reflect the intuitions of the original work. In this paper we start by describing weighted connection matrices formed by applying a random continuous weight distribution to binary adjacency matrices. This allows us to derive an approximation for neural complexity in terms of the moments of the weight distribution and elementary graph motifs. In particular, we explicitly establish a dependency of neural complexity on cyclic graph motifs.

  15. Cotton genotypes selection through artificial neural networks.

    Science.gov (United States)

    Júnior, E G Silva; Cardoso, D B O; Reis, M C; Nascimento, A F O; Bortolin, D I; Martins, M R; Sousa, L B

    2017-09-27

    Breeding programs currently use statistical analysis to assist in the identification of superior genotypes at various stages of a cultivar's development. Differently from these analyses, the computational intelligence approach has been little explored in genetic improvement of cotton. Thus, this study was carried out with the objective of presenting the use of artificial neural networks as auxiliary tools in the improvement of the cotton to improve fiber quality. To demonstrate the applicability of this approach, this research was carried out using the evaluation data of 40 genotypes. In order to classify the genotypes for fiber quality, the artificial neural networks were trained with replicate data of 20 genotypes of cotton evaluated in the harvests of 2013/14 and 2014/15, regarding fiber length, uniformity of length, fiber strength, micronaire index, elongation, short fiber index, maturity index, reflectance degree, and fiber quality index. This quality index was estimated by means of a weighted average on the determined score (1 to 5) of each characteristic of the HVI evaluated, according to its industry standards. The artificial neural networks presented a high capacity of correct classification of the 20 selected genotypes based on the fiber quality index, so that when using fiber length associated with the short fiber index, fiber maturation, and micronaire index, the artificial neural networks presented better results than using only fiber length and previous associations. It was also observed that to submit data of means of new genotypes to the neural networks trained with data of repetition, provides better results of classification of the genotypes. When observing the results obtained in the present study, it was verified that the artificial neural networks present great potential to be used in the different stages of a genetic improvement program of the cotton, aiming at the improvement of the fiber quality of the future cultivars.

  16. The Effects of GABAergic Polarity Changes on Episodic Neural Network Activity in Developing Neural Systems

    Directory of Open Access Journals (Sweden)

    Wilfredo Blanco

    2017-09-01

    Full Text Available Early in development, neural systems have primarily excitatory coupling, where even GABAergic synapses are excitatory. Many of these systems exhibit spontaneous episodes of activity that have been characterized through both experimental and computational studies. As development progress the neural system goes through many changes, including synaptic remodeling, intrinsic plasticity in the ion channel expression, and a transformation of GABAergic synapses from excitatory to inhibitory. What effect each of these, and other, changes have on the network behavior is hard to know from experimental studies since they all happen in parallel. One advantage of a computational approach is that one has the ability to study developmental changes in isolation. Here, we examine the effects of GABAergic synapse polarity change on the spontaneous activity of both a mean field and a neural network model that has both glutamatergic and GABAergic coupling, representative of a developing neural network. We find some intuitive behavioral changes as the GABAergic neurons go from excitatory to inhibitory, shared by both models, such as a decrease in the duration of episodes. We also find some paradoxical changes in the activity that are only present in the neural network model. In particular, we find that during early development the inter-episode durations become longer on average, while later in development they become shorter. In addressing this unexpected finding, we uncover a priming effect that is particularly important for a small subset of neurons, called the “intermediate neurons.” We characterize these neurons and demonstrate why they are crucial to episode initiation, and why the paradoxical behavioral change result from priming of these neurons. The study illustrates how even arguably the simplest of developmental changes that occurs in neural systems can present non-intuitive behaviors. It also makes predictions about neural network behavioral changes

  17. Neural networks prove effective at NOx reduction

    Energy Technology Data Exchange (ETDEWEB)

    Radl, B.J. [Pegasus Technologies, Mentor, OH (USA)

    2000-05-01

    The availability of low cost computer hardware and software is opening up possibilities for the use of artificial intelligence concepts, notably neural networks, in power plant control applications, delivering lower costs, greater efficiencies and reduced emissions. One example of a neural network system is the NeuSIGHT combustion optimisation system, developed by Pegasus Technologies, a subsidiary of KFx Inc. It can help reduce NOx emissions, improve heat rate and enable either deferral or elimination of capital expenditures. on other NOx control technologies, such as low NOx burners, SNCR and SCR. This paper illustrates these benefits using three recent case studies. 4 figs.

  18. Top tagging with deep neural networks [Vidyo

    CERN Multimedia

    CERN. Geneva

    2017-01-01

    Recent literature on deep neural networks for top tagging has focussed on image based techniques or multivariate approaches using high level jet substructure variables. Here, we take a sequential approach to this task by using anordered sequence of energy deposits as training inputs. Unlike previous approaches, this strategy does not result in a loss of information during pixelization or the calculation of high level features. We also propose new preprocessing methods that do not alter key physical quantities such as jet mass. We compare the performance of this approach to standard tagging techniques and present results evaluating the robustness of the neural network to pileup.

  19. A neural theory of visual attention

    DEFF Research Database (Denmark)

    Bundesen, Claus; Habekost, Thomas; Kyllingsbæk, Søren

    2005-01-01

    A neural theory of visual attention (NTVA) is presented. NTVA is a neural interpretation of C. Bundesen's (1990) theory of visual attention (TVA). In NTVA, visual processing capacity is distributed across stimuli by dynamic remapping of receptive fields of cortical cells such that more processing...... resources (cells) are devoted to behaviorally important objects than to less important ones. By use of the same basic equations used in TVA, NTVA accounts for a wide range of known attentional effects in human performance (reaction times and error rates) and a wide range of effects observed in firing rates...

  20. Avoiding object by robot using neural network

    International Nuclear Information System (INIS)

    Prasetijo, D.W.

    1997-01-01

    A Self controlling robot is necessary in the robot application in which operator control is difficult. Serial method such as process on the computer of van newman is difficult to be applied for self controlling robot. In this research, Neural network system for robotic control system was developed by performance expanding at the SCARA. In this research, it was shown that SCARA with application at Neural network system can avoid blocking objects without influence by number and density of the blocking objects, also departure and destination paint. robot developed by this study also can control its moving by self

  1. Associative memory model with spontaneous neural activity

    Science.gov (United States)

    Kurikawa, Tomoki; Kaneko, Kunihiko

    2012-05-01

    We propose a novel associative memory model wherein the neural activity without an input (i.e., spontaneous activity) is modified by an input to generate a target response that is memorized for recall upon the same input. Suitable design of synaptic connections enables the model to memorize input/output (I/O) mappings equaling 70% of the total number of neurons, where the evoked activity distinguishes a target pattern from others. Spontaneous neural activity without an input shows chaotic dynamics but keeps some similarity with evoked activities, as reported in recent experimental studies.

  2. Neural Activity Reveals Preferences Without Choices

    Science.gov (United States)

    Smith, Alec; Bernheim, B. Douglas; Camerer, Colin

    2014-01-01

    We investigate the feasibility of inferring the choices people would make (if given the opportunity) based on their neural responses to the pertinent prospects when they are not engaged in actual decision making. The ability to make such inferences is of potential value when choice data are unavailable, or limited in ways that render standard methods of estimating choice mappings problematic. We formulate prediction models relating choices to “non-choice” neural responses and use them to predict out-of-sample choices for new items and for new groups of individuals. The predictions are sufficiently accurate to establish the feasibility of our approach. PMID:25729468

  3. Musical Audio Synthesis Using Autoencoding Neural Nets

    OpenAIRE

    Sarroff, Andy; Casey, Michael A.

    2014-01-01

    With an optimal network topology and tuning of hyperpa-\\ud rameters, artificial neural networks (ANNs) may be trained\\ud to learn a mapping from low level audio features to one\\ud or more higher-level representations. Such artificial neu-\\ud ral networks are commonly used in classification and re-\\ud gression settings to perform arbitrary tasks. In this work\\ud we suggest repurposing autoencoding neural networks as\\ud musical audio synthesizers. We offer an interactive musi-\\ud cal audio synt...

  4. Neural network tagging in a toy model

    International Nuclear Information System (INIS)

    Milek, Marko; Patel, Popat

    1999-01-01

    The purpose of this study is a comparison of Artificial Neural Network approach to HEP analysis against the traditional methods. A toy model used in this analysis consists of two types of particles defined by four generic properties. A number of 'events' was created according to the model using standard Monte Carlo techniques. Several fully connected, feed forward multi layered Artificial Neural Networks were trained to tag the model events. The performance of each network was compared to the standard analysis mechanisms and significant improvement was observed

  5. Alpha spectral analysis via artificial neural networks

    International Nuclear Information System (INIS)

    Kangas, L.J.; Hashem, S.; Keller, P.E.; Kouzes, R.T.; Troyer, G.L.

    1994-10-01

    An artificial neural network system that assigns quality factors to alpha particle energy spectra is discussed. The alpha energy spectra are used to detect plutonium contamination in the work environment. The quality factors represent the levels of spectral degradation caused by miscalibration and foreign matter affecting the instruments. A set of spectra was labeled with a quality factor by an expert and used in training the artificial neural network expert system. The investigation shows that the expert knowledge of alpha spectra quality factors can be transferred to an ANN system

  6. Human Face Recognition Using Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Răzvan-Daniel Albu

    2009-10-01

    Full Text Available In this paper, I present a novel hybrid face recognition approach based on a convolutional neural architecture, designed to robustly detect highly variable face patterns. The convolutional network extracts successively larger features in a hierarchical set of layers. With the weights of the trained neural networks there are created kernel windows used for feature extraction in a 3-stage algorithm. I present experimental results illustrating the efficiency of the proposed approach. I use a database of 796 images of 159 individuals from Reims University which contains quite a high degree of variability in expression, pose, and facial details.

  7. Target recognition based on convolutional neural network

    Science.gov (United States)

    Wang, Liqiang; Wang, Xin; Xi, Fubiao; Dong, Jian

    2017-11-01

    One of the important part of object target recognition is the feature extraction, which can be classified into feature extraction and automatic feature extraction. The traditional neural network is one of the automatic feature extraction methods, while it causes high possibility of over-fitting due to the global connection. The deep learning algorithm used in this paper is a hierarchical automatic feature extraction method, trained with the layer-by-layer convolutional neural network (CNN), which can extract the features from lower layers to higher layers. The features are more discriminative and it is beneficial to the object target recognition.

  8. Neural correlates underlying musical semantic memory.

    Science.gov (United States)

    Groussard, M; Viader, F; Landeau, B; Desgranges, B; Eustache, F; Platel, H

    2009-07-01

    Numerous functional imaging studies have examined the neural basis of semantic memory mainly using verbal and visuospatial materials. Musical material also allows an original way to explore semantic memory processes. We used PET imaging to determine the neural substrates that underlie musical semantic memory using different tasks and stimuli. The results of three PET studies revealed a greater involvement of the anterior part of the temporal lobe. Concerning clinical observations and our neuroimaging data, the musical lexicon (and most widely musical semantic memory) appears to be sustained by a temporo-prefrontal cerebral network involving right and left cerebral regions.

  9. Livermore Big Artificial Neural Network Toolkit

    Energy Technology Data Exchange (ETDEWEB)

    2016-07-01

    LBANN is a toolkit that is designed to train artificial neural networks efficiently on high performance computing architectures. It is optimized to take advantages of key High Performance Computing features to accelerate neural network training. Specifically it is optimized for low-latency, high bandwidth interconnects, node-local NVRAM, node-local GPU accelerators, and high bandwidth parallel file systems. It is built on top of the open source Elemental distributed-memory dense and spars-direct linear algebra and optimization library that is released under the BSD license. The algorithms contained within LBANN are drawn from the academic literature and implemented to work within a distributed-memory framework.

  10. Quantitative phase microscopy using deep neural networks

    Science.gov (United States)

    Li, Shuai; Sinha, Ayan; Lee, Justin; Barbastathis, George

    2018-02-01

    Deep learning has been proven to achieve ground-breaking accuracy in various tasks. In this paper, we implemented a deep neural network (DNN) to achieve phase retrieval in a wide-field microscope. Our DNN utilized the residual neural network (ResNet) architecture and was trained using the data generated by a phase SLM. The results showed that our DNN was able to reconstruct the profile of the phase target qualitatively. In the meantime, large error still existed, which indicated that our approach still need to be improved.

  11. Neural network approach to radiologic lesion detection

    International Nuclear Information System (INIS)

    Newman, F.D.; Raff, U.; Stroud, D.

    1989-01-01

    An area of artificial intelligence that has gained recent attention is the neural network approach to pattern recognition. The authors explore the use of neural networks in radiologic lesion detection with what is known in the literature as the novelty filter. This filter uses a linear model; images of normal patterns become training vectors and are stored as columns of a matrix. An image of an abnormal pattern is introduced and the abnormality or novelty is extracted. A VAX 750 was used to encode the novelty filter, and two experiments have been examined

  12. Hindcasting of storm waves using neural networks

    Digital Repository Service at National Institute of Oceanography (India)

    Rao, S.; Mandal, S.

    Department NN neural network net i weighted sum of the inputs of neuron i o k network output at kth output node P total number of training pattern s i output of neuron i t k target output at kth output node 1. Introduction Severe storms occur in Bay of Bengal...), forecasting of runoff (Crespo and Mora, 1993), concrete strength (Kasperkiewicz et al., 1995). The uses of neural network in the coastal the wave conditions will change from year to year, thus a proper statistical and climatological treatment requires several...

  13. Neural networks advances and applications 2

    CERN Document Server

    Gelenbe, E

    1992-01-01

    The present volume is a natural follow-up to Neural Networks: Advances and Applications which appeared one year previously. As the title indicates, it combines the presentation of recent methodological results concerning computational models and results inspired by neural networks, and of well-documented applications which illustrate the use of such models in the solution of difficult problems. The volume is balanced with respect to these two orientations: it contains six papers concerning methodological developments and five papers concerning applications and examples illustrating the theoret

  14. Stimulation and recording electrodes for neural prostheses

    CERN Document Server

    Pour Aryan, Naser; Rothermel, Albrecht

    2015-01-01

    This book provides readers with basic principles of the electrochemistry of the electrodes used in modern, implantable neural prostheses. The authors discuss the boundaries and conditions in which the electrodes continue to function properly for long time spans, which are required when designing neural stimulator devices for long-term in vivo applications. Two kinds of electrode materials, titanium nitride and iridium are discussed extensively, both qualitatively and quantitatively. The influence of the counter electrode on the safety margins and electrode lifetime in a two electrode system is explained. Electrode modeling is handled in a final chapter.

  15. Inherently stochastic spiking neurons for probabilistic neural computation

    KAUST Repository

    Al-Shedivat, Maruan; Naous, Rawan; Neftci, Emre; Cauwenberghs, Gert; Salama, Khaled N.

    2015-01-01

    . Our analysis and simulations show that the proposed neuron circuit satisfies a neural computability condition that enables probabilistic neural sampling and spike-based Bayesian learning and inference. Our findings constitute an important step towards

  16. Wave transmission prediction of multilayer floating breakwater using neural network

    Digital Repository Service at National Institute of Oceanography (India)

    Mandal, S.; Patil, S.G.; Hegde, A.V.

    In the present study, an artificial neural network method has been applied for wave transmission prediction of multilayer floating breakwater. Two neural network models are constructed based on the parameters which influence the wave transmission...

  17. Stability prediction of berm breakwater using neural network

    Digital Repository Service at National Institute of Oceanography (India)

    Mandal, S.; Rao, S.; Manjunath, Y.R.

    In the present study, an artificial neural network method has been applied to predict the stability of berm breakwaters. Four neural network models are constructed based on the parameters which influence the stability of breakwater. Training...

  18. Parameter Identification by Bayes Decision and Neural Networks

    DEFF Research Database (Denmark)

    Kulczycki, P.; Schiøler, Henrik

    1994-01-01

    The problem of parameter identification by Bayes point estimation using neural networks is investigated.......The problem of parameter identification by Bayes point estimation using neural networks is investigated....

  19. An Artificial Neural Network Controller for Intelligent Transportation Systems Applications

    Science.gov (United States)

    1996-01-01

    An Autonomous Intelligent Cruise Control (AICC) has been designed using a feedforward artificial neural network, as an example for utilizing artificial neural networks for nonlinear control problems arising in intelligent transportation systems appli...

  20. Analysis of some meteorological parameters using artificial neural ...

    African Journals Online (AJOL)

    Analysis of some meteorological parameters using artificial neural network method for ... The mean daily data for sunshine hours, maximum temperature, cloud cover and ... The study used artificial neural networks (ANN) for the estimation.

  1. Emerging trends in neuro engineering and neural computation

    CERN Document Server

    Lee, Kendall; Garmestani, Hamid; Lim, Chee

    2017-01-01

    This book focuses on neuro-engineering and neural computing, a multi-disciplinary field of research attracting considerable attention from engineers, neuroscientists, microbiologists and material scientists. It explores a range of topics concerning the design and development of innovative neural and brain interfacing technologies, as well as novel information acquisition and processing algorithms to make sense of the acquired data. The book also highlights emerging trends and advances regarding the applications of neuro-engineering in real-world scenarios, such as neural prostheses, diagnosis of neural degenerative diseases, deep brain stimulation, biosensors, real neural network-inspired artificial neural networks (ANNs) and the predictive modeling of information flows in neuronal networks. The book is broadly divided into three main sections including: current trends in technological developments, neural computation techniques to make sense of the neural behavioral data, and application of these technologie...

  2. Stability of Neutral Fractional Neural Networks with Delay

    Institute of Scientific and Technical Information of China (English)

    LI Yan; JIANG Wei; HU Bei-bei

    2016-01-01

    This paper studies stability of neutral fractional neural networks with delay. By introducing the definition of norm and using the uniform stability, the sufficient condition for uniform stability of neutral fractional neural networks with delay is obtained.

  3. One weird trick for parallelizing convolutional neural networks

    OpenAIRE

    Krizhevsky, Alex

    2014-01-01

    I present a new way to parallelize the training of convolutional neural networks across multiple GPUs. The method scales significantly better than all alternatives when applied to modern convolutional neural networks.

  4. Artificial Neural Network Analysis of Xinhui Pericarpium Citri ...

    African Journals Online (AJOL)

    Methods: Artificial neural networks (ANN) models, including general regression neural network (GRNN) and multi-layer ... N-hexane (HPLC grade) was purchased from. Fisher Scientific. ..... Simultaneous Quantification of Seven Flavonoids in.

  5. Classification of Urinary Calculi using Feed-Forward Neural Networks

    African Journals Online (AJOL)

    NJD

    Genetic algorithms were used for optimization of neural networks and for selection of the ... Urinary calculi, infrared spectroscopy, classification, neural networks, variable ..... note that the best accuracy is obtained for whewellite, weddellite.

  6. Nano-topography Enhances Communication in Neural Cells Networks

    KAUST Repository

    Onesto, V.; Cancedda, L.; Coluccio, M. L.; Nanni, M.; Pesce, M.; Malara, N.; Cesarelli, M.; Di Fabrizio, Enzo M.; Amato, F.; Gentile, F.

    2017-01-01

    Neural cells are the smallest building blocks of the central and peripheral nervous systems. Information in neural networks and cell-substrate interactions have been heretofore studied separately. Understanding whether surface nano-topography can

  7. Infrared neural stimulation (INS) inhibits electrically evoked neural responses in the deaf white cat

    Science.gov (United States)

    Richter, Claus-Peter; Rajguru, Suhrud M.; Robinson, Alan; Young, Hunter K.

    2014-03-01

    Infrared neural stimulation (INS) has been used in the past to evoke neural activity from hearing and partially deaf animals. All the responses were excitatory. In Aplysia californica, Duke and coworkers demonstrated that INS also inhibits neural responses [1], which similar observations were made in the vestibular system [2, 3]. In deaf white cats that have cochleae with largely reduced spiral ganglion neuron counts and a significant degeneration of the organ of Corti, no cochlear compound action potentials could be observed during INS alone. However, the combined electrical and optical stimulation demonstrated inhibitory responses during irradiation with infrared light.

  8. Advanced Applications of Neural Networks and Artificial Intelligence: A Review

    OpenAIRE

    Koushal Kumar; Gour Sundar Mitra Thakur

    2012-01-01

    Artificial Neural Network is a branch of Artificial intelligence and has been accepted as a new computing technology in computer science fields. This paper reviews the field of Artificial intelligence and focusing on recent applications which uses Artificial Neural Networks (ANN’s) and Artificial Intelligence (AI). It also considers the integration of neural networks with other computing methods Such as fuzzy logic to enhance the interpretation ability of data. Artificial Neural Networks is c...

  9. Insights into neural crest development from studies of avian embryos

    OpenAIRE

    Gandhi, Shashank; Bronner, Marianne E.

    2018-01-01

    The neural crest is a multipotent and highly migratory cell type that contributes to many of the defining features of vertebrates, including the skeleton of the head and most of the peripheral nervous system. 150 years after the discovery of the neural crest, avian embryos remain one of the most important model organisms for studying neural crest development. In this review, we describe aspects of neural crest induction, migration and axial level differences, highlighting what is known about ...

  10. Application of radial basis neural network for state estimation of ...

    African Journals Online (AJOL)

    An original application of radial basis function (RBF) neural network for power system state estimation is proposed in this paper. The property of massive parallelism of neural networks is employed for this. The application of RBF neural network for state estimation is investigated by testing its applicability on a IEEE 14 bus ...

  11. 38 CFR 17.149 - Sensori-neural aids.

    Science.gov (United States)

    2010-07-01

    ... 38 Pensions, Bonuses, and Veterans' Relief 1 2010-07-01 2010-07-01 false Sensori-neural aids. 17... Prosthetic, Sensory, and Rehabilitative Aids § 17.149 Sensori-neural aids. (a) Notwithstanding any other provision of this part, VA will furnish needed sensori-neural aids (i.e., eyeglasses, contact lenses...

  12. Prediction based chaos control via a new neural network

    International Nuclear Information System (INIS)

    Shen Liqun; Wang Mao; Liu Wanyu; Sun Guanghui

    2008-01-01

    In this Letter, a new chaos control scheme based on chaos prediction is proposed. To perform chaos prediction, a new neural network architecture for complex nonlinear approximation is proposed. And the difficulty in building and training the neural network is also reduced. Simulation results of Logistic map and Lorenz system show the effectiveness of the proposed chaos control scheme and the proposed neural network

  13. Neural networks in economic modelling : An empirical study

    NARCIS (Netherlands)

    Verkooijen, W.J.H.

    1996-01-01

    This dissertation addresses the statistical aspects of neural networks and their usability for solving problems in economics and finance. Neural networks are discussed in a framework of modelling which is generally accepted in econometrics. Within this framework a neural network is regarded as a

  14. Tensor Basis Neural Network v. 1.0 (beta)

    Energy Technology Data Exchange (ETDEWEB)

    2017-03-28

    This software package can be used to build, train, and test a neural network machine learning model. The neural network architecture is specifically designed to embed tensor invariance properties by enforcing that the model predictions sit on an invariant tensor basis. This neural network architecture can be used in developing constitutive models for applications such as turbulence modeling, materials science, and electromagnetism.

  15. Time series prediction with simple recurrent neural networks ...

    African Journals Online (AJOL)

    A hybrid of the two called Elman-Jordan (or Multi-recurrent) neural network is also being used. In this study, we evaluated the performance of these neural networks on three established bench mark time series prediction problems. Results from the experiments showed that Jordan neural network performed significantly ...

  16. Artificial Neural Network Modeling of an Inverse Fluidized Bed ...

    African Journals Online (AJOL)

    A Radial Basis Function neural network has been successfully employed for the modeling of the inverse fluidized bed reactor. In the proposed model, the trained neural network represents the kinetics of biological decomposition of pollutants in the reactor. The neural network has been trained with experimental data ...

  17. Collaborative Recurrent Neural Networks forDynamic Recommender Systems

    Science.gov (United States)

    2016-11-22

    JMLR: Workshop and Conference Proceedings 63:366–381, 2016 ACML 2016 Collaborative Recurrent Neural Networks for Dynamic Recommender Systems Young...an unprece- dented scale. Although such activity logs are abundantly available, most approaches to recommender systems are based on the rating...Recurrent Neural Network, Recommender System , Neural Language Model, Collaborative Filtering 1. Introduction As ever larger parts of the population

  18. 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)

  19. Efficient computation in adaptive artificial spiking neural networks

    NARCIS (Netherlands)

    D. Zambrano (Davide); R.B.P. Nusselder (Roeland); H.S. Scholte; S.M. Bohte (Sander)

    2017-01-01

    textabstractArtificial Neural Networks (ANNs) are bio-inspired models of neural computation that have proven highly effective. Still, ANNs lack a natural notion of time, and neural units in ANNs exchange analog values in a frame-based manner, a computationally and energetically inefficient form of

  20. 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)

  1. The gamma model : a new neural network for temporal processing

    NARCIS (Netherlands)

    Vries, de B.

    1992-01-01

    In this paper we develop the gamma neural model, a new neural net architecture for processing of temporal patterns. Time varying patterns are normally segmented into a sequence of static patterns that are successively presented to a neural net. In the approach presented here segmentation is avoided.

  2. Analysis of neural networks in terms of domain functions

    NARCIS (Netherlands)

    van der Zwaag, B.J.; Slump, Cornelis H.; Spaanenburg, Lambert

    Despite their success-story, artificial neural networks have one major disadvantage compared to other techniques: the inability to explain comprehensively how a trained neural network reaches its output; neural networks are not only (incorrectly) seen as a "magic tool" but possibly even more as a

  3. Rhesus monkey neural stem cell transplantation promotes neural regeneration in rats with hippocampal lesions

    Directory of Open Access Journals (Sweden)

    Li-juan Ye

    2016-01-01

    Full Text Available Rhesus monkey neural stem cells are capable of differentiating into neurons and glial cells. Therefore, neural stem cell transplantation can be used to promote functional recovery of the nervous system. Rhesus monkey neural stem cells (1 × 105 cells/μL were injected into bilateral hippocampi of rats with hippocampal lesions. Confocal laser scanning microscopy demonstrated that green fluorescent protein-labeled transplanted cells survived and grew well. Transplanted cells were detected at the lesion site, but also in the nerve fiber-rich region of the cerebral cortex and corpus callosum. Some transplanted cells differentiated into neurons and glial cells clustering along the ventricular wall, and integrated into the recipient brain. Behavioral tests revealed that spatial learning and memory ability improved, indicating that rhesus monkey neural stem cells noticeably improve spatial learning and memory abilities in rats with hippocampal lesions.

  4. Bidirectional neural interface: Closed-loop feedback control for hybrid neural systems.

    Science.gov (United States)

    Chou, Zane; Lim, Jeffrey; Brown, Sophie; Keller, Melissa; Bugbee, Joseph; Broccard, Frédéric D; Khraiche, Massoud L; Silva, Gabriel A; Cauwenberghs, Gert

    2015-01-01

    Closed-loop neural prostheses enable bidirectional communication between the biological and artificial components of a hybrid system. However, a major challenge in this field is the limited understanding of how these components, the two separate neural networks, interact with each other. In this paper, we propose an in vitro model of a closed-loop system that allows for easy experimental testing and modification of both biological and artificial network parameters. The interface closes the system loop in real time by stimulating each network based on recorded activity of the other network, within preset parameters. As a proof of concept we demonstrate that the bidirectional interface is able to establish and control network properties, such as synchrony, in a hybrid system of two neural networks more significantly more effectively than the same system without the interface or with unidirectional alternatives. This success holds promise for the application of closed-loop systems in neural prostheses, brain-machine interfaces, and drug testing.

  5. 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.

  6. Neural Ranking Models with Weak Supervision

    NARCIS (Netherlands)

    Dehghani, M.; Zamani, H.; Severyn, A.; Kamps, J.; Croft, W.B.

    2017-01-01

    Despite the impressive improvements achieved by unsupervised deep neural networks in computer vision and NLP tasks, such improvements have not yet been observed in ranking for information retrieval. The reason may be the complexity of the ranking problem, as it is not obvious how to learn from

  7. Feature to prototype transition in neural networks

    Science.gov (United States)

    Krotov, Dmitry; Hopfield, John

    Models of associative memory with higher order (higher than quadratic) interactions, and their relationship to neural networks used in deep learning are discussed. Associative memory is conventionally described by recurrent neural networks with dynamical convergence to stable points. Deep learning typically uses feedforward neural nets without dynamics. However, a simple duality relates these two different views when applied to problems of pattern classification. From the perspective of associative memory such models deserve attention because they make it possible to store a much larger number of memories, compared to the quadratic case. In the dual description, these models correspond to feedforward neural networks with one hidden layer and unusual activation functions transmitting the activities of the visible neurons to the hidden layer. These activation functions are rectified polynomials of a higher degree rather than the rectified linear functions used in deep learning. The network learns representations of the data in terms of features for rectified linear functions, but as the power in the activation function is increased there is a gradual shift to a prototype-based representation, the two extreme regimes of pattern recognition known in cognitive psychology. Simons Center for Systems Biology.

  8. 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.

  9. Artificial neural networks in neutron dosimetry

    Energy Technology Data Exchange (ETDEWEB)

    Vega C, H.R.; Hernandez D, V.M.; Manzanares A, E.; Mercado, G.A.; Perales M, W.A.; Robles R, J.A. [Unidades Academicas de Estudios Nucleares, UAZ, A.P. 336, 98000 Zacatecas (Mexico); Gallego, E.; Lorente, A. [Depto. de Ingenieria Nuclear, Universidad Politecnica de Madrid, (Spain)

    2005-07-01

    An artificial neural network has been designed to obtain the neutron doses using only the Bonner spheres spectrometer's count rates. Ambient, personal and effective neutron doses were included. 187 neutron spectra were utilized to calculate the Bonner count rates and the neutron doses. The spectra were transformed from lethargy to energy distribution and were re-binned to 31 energy groups using the MCNP 4C code. Re-binned spectra, UTA4 response matrix and fluence-to-dose coefficients were used to calculate the count rates in Bonner spheres spectrometer and the doses. Count rates were used as input and the respective doses were used as output during neural network training. Training and testing was carried out in Mat lab environment. The artificial neural network performance was evaluated using the {chi}{sup 2}- test, where the original and calculated doses were compared. The use of Artificial Neural Networks in neutron dosimetry is an alternative procedure that overcomes the drawbacks associated in this ill-conditioned problem. (Author)

  10. Some Properties of the Assembly Neural Networks

    Czech Academy of Sciences Publication Activity Database

    Húsek, Dušan; Goltsev, A.

    2002-01-01

    Roč. 12, č. 1 (2002), s. 15-32 ISSN 1210-0552 R&D Projects: GA MŠk LN00B096 Keywords : neuron * neural assembly * neuural column subnetwork * generalization * recognition * perceptron * the nearest-neighbor method Subject RIV: BA - General Mathematics

  11. Feedforward Nonlinear Control Using Neural Gas Network

    Directory of Open Access Journals (Sweden)

    Iván Machón-González

    2017-01-01

    Full Text Available Nonlinear systems control is a main issue in control theory. Many developed applications suffer from a mathematical foundation not as general as the theory of linear systems. This paper proposes a control strategy of nonlinear systems with unknown dynamics by means of a set of local linear models obtained by a supervised neural gas network. The proposed approach takes advantage of the neural gas feature by which the algorithm yields a very robust clustering procedure. The direct model of the plant constitutes a piece-wise linear approximation of the nonlinear system and each neuron represents a local linear model for which a linear controller is designed. The neural gas model works as an observer and a controller at the same time. A state feedback control is implemented by estimation of the state variables based on the local transfer function that was provided by the local linear model. The gradient vectors obtained by the supervised neural gas algorithm provide a robust procedure for feedforward nonlinear control, that is, supposing the inexistence of disturbances.

  12. Burst firing enhances neural output correlation

    Directory of Open Access Journals (Sweden)

    Ho Ka eChan

    2016-05-01

    Full Text Available Neurons communicate and transmit information predominantly through spikes. Given that experimentally observed neural spike trains in a variety of brain areas can be highly correlated, it is important to investigate how neurons process correlated inputs. Most previous work in this area studied the problem of correlation transfer analytically by making significant simplifications on neural dynamics. Temporal correlation between inputs that arises from synaptic filtering, for instance, is often ignored when assuming that an input spike can at most generate one output spike. Through numerical simulations of a pair of leaky integrate-and-fire (LIF neurons receiving correlated inputs, we demonstrate that neurons in the presence of synaptic filtering by slow synapses exhibit strong output correlations. We then show that burst firing plays a central role in enhancing output correlations, which can explain the above-mentioned observation because synaptic filtering induces bursting. The observed changes of correlations are mostly on a long time scale. Our results suggest that other features affecting the prevalence of neural burst firing in biological neurons, e.g., adaptive spiking mechanisms, may play an important role in modulating the overall level of correlations in neural networks.

  13. Foreign currency rate forecasting using neural networks

    Science.gov (United States)

    Pandya, Abhijit S.; Kondo, Tadashi; Talati, Amit; Jayadevappa, Suryaprasad

    2000-03-01

    Neural networks are increasingly being used as a forecasting tool in many forecasting problems. This paper discusses the application of neural networks in predicting daily foreign exchange rates between the USD, GBP as well as DEM. We approach the problem from a time-series analysis framework - where future exchange rates are forecasted solely using past exchange rates. This relies on the belief that the past prices and future prices are very close related, and interdependent. We present the result of training a neural network with historical USD-GBP data. The methodology used in explained, as well as the training process. We discuss the selection of inputs to the network, and present a comparison of using the actual exchange rates and the exchange rate differences as inputs. Price and rate differences are the preferred way of training neural network in financial applications. Results of both approaches are present together for comparison. We show that the network is able to learn the trends in the exchange rate movements correctly, and present the results of the prediction over several periods of time.

  14. A Neural Region of Abstract Working Memory

    Science.gov (United States)

    Cowan, Nelson; Li, Dawei; Moffitt, Amanda; Becker, Theresa M.; Martin, Elizabeth A.; Saults, J. Scott; Christ, Shawn E.

    2011-01-01

    Over 350 years ago, Descartes proposed that the neural basis of consciousness must be a brain region in which sensory inputs are combined. Using fMRI, we identified at least one such area for working memory, the limited information held in mind, described by William James as the trailing edge of consciousness. Specifically, a region in the left…

  15. Artificial Astrocytes Improve Neural Network Performance

    Science.gov (United States)

    Porto-Pazos, Ana B.; Veiguela, Noha; Mesejo, Pablo; Navarrete, Marta; Alvarellos, Alberto; Ibáñez, Oscar; Pazos, Alejandro; Araque, Alfonso

    2011-01-01

    Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. However, the actual impact of astrocytes in neural network function is largely unknown and its application in artificial intelligence remains untested. We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance. Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN) and artificial neuron-glia networks (NGN) to solve classification problems. We show that the degree of success of NGN is superior to NN. Analysis of performances of NN with different number of neurons or different architectures indicate that the effects of NGN cannot be accounted for an increased number of network elements, but rather they are specifically due to astrocytes. Furthermore, the relative efficacy of NGN vs. NN increases as the complexity of the network increases. These results indicate that artificial astrocytes improve neural network performance, and established the concept of Artificial Neuron-Glia Networks, which represents a novel concept in Artificial Intelligence with implications in computational science as well as in the understanding of brain function. PMID:21526157

  16. Radioactive fallout and neural tube defects

    African Journals Online (AJOL)

    Nejat Akar

    2015-07-10

    Jul 10, 2015 ... It is a prenatal failure of the embryonic neural tube to close over the ... and the ability of radioisotopes to attach to cells, tissues, and ... The Egyptian Journal of Medical Human Genetics .... Stem Cells 1997;15(Suppl 2):255–60.

  17. IMPLEMENTATION OF NEURAL - CRYPTOGRAPHIC SYSTEM USING FPGA

    Directory of Open Access Journals (Sweden)

    KARAM M. Z. OTHMAN

    2011-08-01

    Full Text Available Modern cryptography techniques are virtually unbreakable. As the Internet and other forms of electronic communication become more prevalent, electronic security is becoming increasingly important. Cryptography is used to protect e-mail messages, credit card information, and corporate data. The design of the cryptography system is a conventional cryptography that uses one key for encryption and decryption process. The chosen cryptography algorithm is stream cipher algorithm that encrypt one bit at a time. The central problem in the stream-cipher cryptography is the difficulty of generating a long unpredictable sequence of binary signals from short and random key. Pseudo random number generators (PRNG have been widely used to construct this key sequence. The pseudo random number generator was designed using the Artificial Neural Networks (ANN. The Artificial Neural Networks (ANN providing the required nonlinearity properties that increases the randomness statistical properties of the pseudo random generator. The learning algorithm of this neural network is backpropagation learning algorithm. The learning process was done by software program in Matlab (software implementation to get the efficient weights. Then, the learned neural network was implemented using field programmable gate array (FPGA.

  18. Neural Entrainment to Speech Modulates Speech Intelligibility

    NARCIS (Netherlands)

    Riecke, Lars; Formisano, Elia; Sorger, Bettina; Baskent, Deniz; Gaudrain, Etienne

    2018-01-01

    Speech is crucial for communication in everyday life. Speech-brain entrainment, the alignment of neural activity to the slow temporal fluctuations (envelope) of acoustic speech input, is a ubiquitous element of current theories of speech processing. Associations between speech-brain entrainment and

  19. Neural and Behavioral Correlates of Song Prosody

    Science.gov (United States)

    Gordon, Reyna Leigh

    2010-01-01

    This dissertation studies the neural basis of song, a universal human behavior. The relationship of words and melodies in the perception of song at phonological, semantic, melodic, and rhythmic levels of processing was investigated using the fine temporal resolution of Electroencephalography (EEG). The observations reported here may shed light on…

  20. Neural networks to predict exosphere temperature corrections

    Science.gov (United States)

    Choury, Anna; Bruinsma, Sean; Schaeffer, Philippe

    2013-10-01

    Precise orbit prediction requires a forecast of the atmospheric drag force with a high degree of accuracy. Artificial neural networks are universal approximators derived from artificial intelligence and are widely used for prediction. This paper presents a method of artificial neural networking for prediction of the thermosphere density by forecasting exospheric temperature, which will be used by the semiempirical thermosphere Drag Temperature Model (DTM) currently developed. Artificial neural network has shown to be an effective and robust forecasting model for temperature prediction. The proposed model can be used for any mission from which temperature can be deduced accurately, i.e., it does not require specific training. Although the primary goal of the study was to create a model for 1 day ahead forecast, the proposed architecture has been generalized to 2 and 3 days prediction as well. The impact of artificial neural network predictions has been quantified for the low-orbiting satellite Gravity Field and Steady-State Ocean Circulation Explorer in 2011, and an order of magnitude smaller orbit errors were found when compared with orbits propagated using the thermosphere model DTM2009.

  1. Deformable image registration using convolutional neural networks

    NARCIS (Netherlands)

    Eppenhof, Koen A.J.; Lafarge, Maxime W.; Moeskops, Pim; Veta, Mitko; Pluim, Josien P.W.

    2018-01-01

    Deformable image registration can be time-consuming and often needs extensive parameterization to perform well on a specific application. We present a step towards a registration framework based on a three-dimensional convolutional neural network. The network directly learns transformations between

  2. Energy Complexity of Recurrent Neural Networks

    Czech Academy of Sciences Publication Activity Database

    Šíma, Jiří

    2014-01-01

    Roč. 26, č. 5 (2014), s. 953-973 ISSN 0899-7667 R&D Projects: GA ČR GAP202/10/1333 Institutional support: RVO:67985807 Keywords : neural network * finite automaton * energy complexity * optimal size Subject RIV: IN - Informatics, Computer Science Impact factor: 2.207, year: 2014

  3. Continual Learning through Evolvable Neural Turing Machines

    DEFF Research Database (Denmark)

    Lüders, Benno; Schläger, Mikkel; Risi, Sebastian

    2016-01-01

    Continual learning, i.e. the ability to sequentially learn tasks without catastrophic forgetting of previously learned ones, is an important open challenge in machine learning. In this paper we take a step in this direction by showing that the recently proposed Evolving Neural Turing Machine (ENTM...

  4. Epileptiform spike detection via convolutional neural networks

    DEFF Research Database (Denmark)

    Johansen, Alexander Rosenberg; Jin, Jing; Maszczyk, Tomasz

    2016-01-01

    The EEG of epileptic patients often contains sharp waveforms called "spikes", occurring between seizures. Detecting such spikes is crucial for diagnosing epilepsy. In this paper, we develop a convolutional neural network (CNN) for detecting spikes in EEG of epileptic patients in an automated...

  5. Convolutional Neural Networks for SAR Image Segmentation

    DEFF Research Database (Denmark)

    Malmgren-Hansen, David; Nobel-Jørgensen, Morten

    2015-01-01

    Segmentation of Synthetic Aperture Radar (SAR) images has several uses, but it is a difficult task due to a number of properties related to SAR images. In this article we show how Convolutional Neural Networks (CNNs) can easily be trained for SAR image segmentation with good results. Besides...

  6. 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...

  7. Convolutional Neural Networks - Generalizability and Interpretations

    DEFF Research Database (Denmark)

    Malmgren-Hansen, David

    from data despite it being limited in amount or context representation. Within Machine Learning this thesis focuses on Convolutional Neural Networks for Computer Vision. The research aims to answer how to explore a model's generalizability to the whole population of data samples and how to interpret...

  8. 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...

  9. Visualization of neural networks using saliency maps

    DEFF Research Database (Denmark)

    Mørch, Niels J.S.; Kjems, Ulrik; Hansen, Lars Kai

    1995-01-01

    The saliency map is proposed as a new method for understanding and visualizing the nonlinearities embedded in feedforward neural networks, with emphasis on the ill-posed case, where the dimensionality of the input-field by far exceeds the number of examples. Several levels of approximations...

  10. A fully implantable rodent neural stimulator

    Science.gov (United States)

    Perry, D. W. J.; Grayden, D. B.; Shepherd, R. K.; Fallon, J. B.

    2012-02-01

    The ability to electrically stimulate neural and other excitable tissues in behaving experimental animals is invaluable for both the development of neural prostheses and basic neurological research. We developed a fully implantable neural stimulator that is able to deliver two channels of intra-cochlear electrical stimulation in the rat. It is powered via a novel omni-directional inductive link and includes an on-board microcontroller with integrated radio link, programmable current sources and switching circuitry to generate charge-balanced biphasic stimulation. We tested the implant in vivo and were able to elicit both neural and behavioural responses. The implants continued to function for up to five months in vivo. While targeted to cochlear stimulation, with appropriate electrode arrays the stimulator is well suited to stimulating other neurons within the peripheral or central nervous systems. Moreover, it includes significant on-board data acquisition and processing capabilities, which could potentially make it a useful platform for telemetry applications, where there is a need to chronically monitor physiological variables in unrestrained animals.

  11. Fast Fingerprint Classification with Deep Neural Network

    DEFF Research Database (Denmark)

    Michelsanti, Daniel; Guichi, Yanis; Ene, Andreea-Daniela

    2018-01-01

    . In this work we evaluate the performance of two pre-trained convolutional neural networks fine-tuned on the NIST SD4 benchmark database. The obtained results show that this approach is comparable with other results in the literature, with the advantage of a fast feature extraction stage....

  12. Neural understanding of low-resolution images

    NARCIS (Netherlands)

    Spaanenburg, L; DeGraaf, J; Nijhuis, JAG; Stevens, [No Value; Wichers, W

    1998-01-01

    Neural networks can be applied for a number of innovative applications in a production environment, ranging from security & safety in the environmental conditions to the product control & diagnosis. For visual monitoring the use of low-resolution images is promising to bridge the time elapse between

  13. Novel quantum inspired binary neural network algorithm

    Indian Academy of Sciences (India)

    This parameter is taken as the threshold of neuron for learning of neural network. This algorithm is tested with three benchmark datasets and ... Author Affiliations. OM PRAKASH PATEL1 ARUNA TIWARI. Department of Computer Science and Engineering, Indian Institute of Technology Indore, Indore 453552, India ...

  14. Nonlinear Time Series Analysis via Neural Networks

    Science.gov (United States)

    Volná, Eva; Janošek, Michal; Kocian, Václav; Kotyrba, Martin

    This article deals with a time series analysis based on neural networks in order to make an effective forex market [Moore and Roche, J. Int. Econ. 58, 387-411 (2002)] pattern recognition. Our goal is to find and recognize important patterns which repeatedly appear in the market history to adapt our trading system behaviour based on them.

  15. Artificial Neural Networks and Instructional Technology.

    Science.gov (United States)

    Carlson, Patricia A.

    1991-01-01

    Artificial neural networks (ANN), part of artificial intelligence, are discussed. Such networks are fed sample cases (training sets), learn how to recognize patterns in the sample data, and use this experience in handling new cases. Two cognitive roles for ANNs (intelligent filters and spreading, associative memories) are examined. Prototypes…

  16. Application of neural networks in experimental physics

    International Nuclear Information System (INIS)

    Kisel', I.V.; Neskromnyj, V.N.; Ososkov, G.A.

    1993-01-01

    The theoretical foundations of numerous models of artificial neural networks (ANN) and their applications to the actual problems of associative memory, optimization and pattern recognition are given. This review contains also numerous using of ANN in the experimental physics both as the hardware realization of fast triggering systems for even selection and for the following software implementation of the trajectory data recognition

  17. Integrated Neural Flight and Propulsion Control System

    Science.gov (United States)

    Kaneshige, John; Gundy-Burlet, Karen; Norvig, Peter (Technical Monitor)

    2001-01-01

    This paper describes an integrated neural flight and propulsion control system. which uses a neural network based approach for applying alternate sources of control power in the presence of damage or failures. Under normal operating conditions, the system utilizes conventional flight control surfaces. Neural networks are used to provide consistent handling qualities across flight conditions and for different aircraft configurations. Under damage or failure conditions, the system may utilize unconventional flight control surface allocations, along with integrated propulsion control, when additional control power is necessary for achieving desired flight control performance. In this case, neural networks are used to adapt to changes in aircraft dynamics and control allocation schemes. Of significant importance here is the fact that this system can operate without emergency or backup flight control mode operations. An additional advantage is that this system can utilize, but does not require, fault detection and isolation information or explicit parameter identification. Piloted simulation studies were performed on a commercial transport aircraft simulator. Subjects included both NASA test pilots and commercial airline crews. Results demonstrate the potential for improving handing qualities and significantly increasing survivability rates under various simulated failure conditions.

  18. Integrating neural network technology and noise analysis

    International Nuclear Information System (INIS)

    Uhrig, R.E.; Oak Ridge National Lab., TN

    1995-01-01

    The integrated use of neural network and noise analysis technologies offers advantages not available by the use of either technology alone. The application of neural network technology to noise analysis offers an opportunity to expand the scope of problems where noise analysis is useful and unique ways in which the integration of these technologies can be used productively. The two-sensor technique, in which the responses of two sensors to an unknown driving source are related, is used to demonstration such integration. The relationship between power spectral densities (PSDs) of accelerometer signals is derived theoretically using noise analysis to demonstrate its uniqueness. This relationship is modeled from experimental data using a neural network when the system is working properly, and the actual PSD of one sensor is compared with the PSD of that sensor predicted by the neural network using the PSD of the other sensor as an input. A significant deviation between the actual and predicted PSDs indicate that system is changing (i.e., failing). Experiments carried out on check values and bearings illustrate the usefulness of the methodology developed. (Author)

  19. 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.

  20. Neural network segmentation of magnetic resonance images

    International Nuclear Information System (INIS)

    Frederick, B.

    1990-01-01

    Neural networks are well adapted to the task of grouping input patterns into subsets which share some similarity. Moreover, once trained, they can generalize their classification rules to classify new data sets. Sets of pixel intensities from magnetic resonance (MR) images provide a natural input to a neural network; by varying imaging parameters, MR images can reflect various independent physical parameters of tissues in their pixel intensities. A neural net can then be trained to classify physically similar tissue types based on sets of pixel intensities resulting from different imaging studies on the same subject. This paper reports that a neural network classifier for image segmentation was implanted on a Sun 4/60, and was tested on the task of classifying tissues of canine head MR images. Four images of a transaxial slice with different imaging sequences were taken as input to the network (three spin-echo images and an inversion recovery image). The training set consisted of 691 representative samples of gray matter, white matter, cerebrospinal fluid, bone, and muscle preclassified by a neuroscientist. The network was trained using a fast backpropagation algorithm to derive the decision criteria to classify any location in the image by its pixel intensities, and the image was subsequently segmented by the classifier

  1. Neural networks in continuous optical media

    International Nuclear Information System (INIS)

    Anderson, D.Z.

    1987-01-01

    The authors' interest is to see to what extent neural models can be implemented using continuous optical elements. Thus these optical networks represent a continuous distribution of neuronlike processors rather than a discrete collection. Most neural models have three characteristic features: interconnections; adaptivity; and nonlinearity. In their optical representation the interconnections are implemented with linear one- and two-port optical elements such as lenses and holograms. Real-time holographic media allow these interconnections to become adaptive. The nonlinearity is achieved with gain, for example, from two-beam coupling in photorefractive media or a pumped dye medium. Using these basic optical elements one can in principle construct continuous representations of a number of neural network models. The authors demonstrated two devices based on continuous optical elements: an associative memory which recalls an entire object when addressed with a partial object and a tracking novelty filter which identifies time-dependent features in an optical scene. These devices demonstrate the potential of distributed optical elements to implement more formal models of neural networks

  2. Neural Chaos and Free Will Problem

    Czech Academy of Sciences Publication Activity Database

    Andrey, Ladislav

    1997-01-01

    Roč. 4, č. 1 (1997), s. 23 ISSN 1355-8250. [The Brain and Self Workshop: Toward a Science of Consciousness. Elsinore, 21.08.1997-24.08.1997] R&D Projects: GA ČR GA201/95/0992 Keywords : free will and agency * attention * emotion * neural networks and connectionism * nonlinear dynamics

  3. Image Encryption and Chaotic Cellular Neural Network

    Science.gov (United States)

    Peng, Jun; Zhang, Du

    Machine learning has been playing an increasingly important role in information security and assurance. One of the areas of new applications is to design cryptographic systems by using chaotic neural network due to the fact that chaotic systems have several appealing features for information security applications. In this chapter, we describe a novel image encryption algorithm that is based on a chaotic cellular neural network. We start by giving an introduction to the concept of image encryption and its main technologies, and an overview of the chaotic cellular neural network. We then discuss the proposed image encryption algorithm in details, which is followed by a number of security analyses (key space analysis, sensitivity analysis, information entropy analysis and statistical analysis). The comparison with the most recently reported chaos-based image encryption algorithms indicates that the algorithm proposed in this chapter has a better security performance. Finally, we conclude the chapter with possible future work and application prospects of the chaotic cellular neural network in other information assurance and security areas.

  4. Based on BP Neural Network Stock Prediction

    Science.gov (United States)

    Liu, Xiangwei; Ma, Xin

    2012-01-01

    The stock market has a high profit and high risk features, on the stock market analysis and prediction research has been paid attention to by people. Stock price trend is a complex nonlinear function, so the price has certain predictability. This article mainly with improved BP neural network (BPNN) to set up the stock market prediction model, and…

  5. Training Deep Spiking Neural Networks Using Backpropagation.

    Science.gov (United States)

    Lee, Jun Haeng; Delbruck, Tobi; Pfeiffer, Michael

    2016-01-01

    Deep spiking neural networks (SNNs) hold the potential for improving the latency and energy efficiency of deep neural networks through data-driven event-based computation. However, training such networks is difficult due to the non-differentiable nature of spike events. In this paper, we introduce a novel technique, which treats the membrane potentials of spiking neurons as differentiable signals, where discontinuities at spike times are considered as noise. This enables an error backpropagation mechanism for deep SNNs that follows the same principles as in conventional deep networks, but works directly on spike signals and membrane potentials. Compared with previous methods relying on indirect training and conversion, our technique has the potential to capture the statistics of spikes more precisely. We evaluate the proposed framework on artificially generated events from the original MNIST handwritten digit benchmark, and also on the N-MNIST benchmark recorded with an event-based dynamic vision sensor, in which the proposed method reduces the error rate by a factor of more than three compared to the best previous SNN, and also achieves a higher accuracy than a conventional convolutional neural network (CNN) trained and tested on the same data. We demonstrate in the context of the MNIST task that thanks to their event-driven operation, deep SNNs (both fully connected and convolutional) trained with our method achieve accuracy equivalent with conventional neural networks. In the N-MNIST example, equivalent accuracy is achieved with about five times fewer computational operations.

  6. Neutron spectrometry with artificial neural networks

    International Nuclear Information System (INIS)

    Vega C, H.R.; Hernandez D, V.M.; Manzanares A, E.; Rodriguez, J.M.; Mercado S, G.A.; Iniguez de la Torre Bayo, M.P.; Barquero, R.; Arteaga A, T.

    2005-01-01

    An artificial neural network has been designed to obtain the neutron spectra from the Bonner spheres spectrometer's count rates. The neural network was trained using 129 neutron spectra. These include isotopic neutron sources; reference and operational spectra from accelerators and nuclear reactors, spectra from mathematical functions as well as few energy groups and monoenergetic spectra. The spectra were transformed from lethargy to energy distribution and were re-bin ned to 31 energy groups using the MCNP 4C code. Re-binned spectra and UTA4 response matrix were used to calculate the expected count rates in Bonner spheres spectrometer. These count rates were used as input and the respective spectrum was used as output during neural network training. After training the network was tested with the Bonner spheres count rates produced by a set of neutron spectra. This set contains data used during network training as well as data not used. Training and testing was carried out in the Mat lab program. To verify the network unfolding performance the original and unfolded spectra were compared using the χ 2 -test and the total fluence ratios. The use of Artificial Neural Networks to unfold neutron spectra in neutron spectrometry is an alternative procedure that overcomes the drawbacks associated in this ill-conditioned problem. (Author)

  7. Artificial neural networks in neutron dosimetry

    International Nuclear Information System (INIS)

    Vega C, H.R.; Hernandez D, V.M.; Manzanares A, E.; Mercado, G.A.; Perales M, W.A.; Robles R, J.A.; Gallego, E.; Lorente, A.

    2005-01-01

    An artificial neural network has been designed to obtain the neutron doses using only the Bonner spheres spectrometer's count rates. Ambient, personal and effective neutron doses were included. 187 neutron spectra were utilized to calculate the Bonner count rates and the neutron doses. The spectra were transformed from lethargy to energy distribution and were re-binned to 31 energy groups using the MCNP 4C code. Re-binned spectra, UTA4 response matrix and fluence-to-dose coefficients were used to calculate the count rates in Bonner spheres spectrometer and the doses. Count rates were used as input and the respective doses were used as output during neural network training. Training and testing was carried out in Mat lab environment. The artificial neural network performance was evaluated using the χ 2 - test, where the original and calculated doses were compared. The use of Artificial Neural Networks in neutron dosimetry is an alternative procedure that overcomes the drawbacks associated in this ill-conditioned problem. (Author)

  8. Separable explanations of neural network decisions

    DEFF Research Database (Denmark)

    Rieger, Laura

    2017-01-01

    Deep Taylor Decomposition is a method used to explain neural network decisions. When applying this method to non-dominant classifications, the resulting explanation does not reflect important features for the chosen classification. We propose that this is caused by the dense layers and propose...

  9. 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…

  10. Neural Control of the Lower Urinary Tract

    Science.gov (United States)

    de Groat, William C.; Griffiths, Derek; Yoshimura, Naoki

    2015-01-01

    This article summarizes anatomical, neurophysiological, pharmacological, and brain imaging studies in humans and animals that have provided insights into the neural circuitry and neurotransmitter mechanisms controlling the lower urinary tract. The functions of the lower urinary tract to store and periodically eliminate urine are regulated by a complex neural control system in the brain, spinal cord, and peripheral autonomic ganglia that coordinates the activity of smooth and striated muscles of the bladder and urethral outlet. The neural control of micturition is organized as a hierarchical system in which spinal storage mechanisms are in turn regulated by circuitry in the rostral brain stem that initiates reflex voiding. Input from the forebrain triggers voluntary voiding by modulating the brain stem circuitry. Many neural circuits controlling the lower urinary tract exhibit switch-like patterns of activity that turn on and off in an all-or-none manner. The major component of the micturition switching circuit is a spinobulbospinal parasympathetic reflex pathway that has essential connections in the periaqueductal gray and pontine micturition center. A computer model of this circuit that mimics the switching functions of the bladder and urethra at the onset of micturition is described. Micturition occurs involuntarily in infants and young children until the age of 3 to 5 years, after which it is regulated voluntarily. Diseases or injuries of the nervous system in adults can cause the re-emergence of involuntary micturition, leading to urinary incontinence. Neuroplasticity underlying these developmental and pathological changes in voiding function is discussed. PMID:25589273

  11. Vibration monitoring with artificial neural networks

    International Nuclear Information System (INIS)

    Alguindigue, I.

    1991-01-01

    Vibration monitoring of components in nuclear power plants has been used for a number of years. This technique involves the analysis of vibration data coming from vital components of the plant to detect features which reflect the operational state of machinery. The analysis leads to the identification of potential failures and their causes, and makes it possible to perform efficient preventive maintenance. Earlydetection is important because it can decrease the probability of catastrophic failures, reduce forced outgage, maximize utilization of available assets, increase the life of the plant, and reduce maintenance costs. This paper documents our work on the design of a vibration monitoring methodology based on neural network technology. This technology provides an attractive complement to traditional vibration analysis because of the potential of neural network to operate in real-time mode and to handle data which may be distorted or noisy. Our efforts have been concentrated on the analysis and classification of vibration signatures collected from operating machinery. Two neural networks algorithms were used in our project: the Recirculation algorithm for data compression and the Backpropagation algorithm to perform the actual classification of the patterns. Although this project is in the early stages of development it indicates that neural networks may provide a viable methodology for monitoring and diagnostics of vibrating components. Our results to date are very encouraging

  12. Towards semen quality assessment using neural networks

    DEFF Research Database (Denmark)

    Linneberg, Christian; Salamon, P.; Svarer, C.

    1994-01-01

    The paper presents the methodology and results from a neural net based classification of human sperm head morphology. The methodology uses a preprocessing scheme in which invariant Fourier descriptors are lumped into “energy” bands. The resulting networks are pruned using optimal brain damage. Pe...

  13. Parameter estimation using compensatory neural networks

    Indian Academy of Sciences (India)

    of interconnections among neurons but also reduces the total computing time for training. The suggested model has properties of the basic neuron ..... Engelbrecht A P, Cloete I, Geldenhuys J, Zurada J M 1995 Automatic scaling using gamma learning for feedforward neural networks. From natural to artificial computing.

  14. Neutron spectrometry using artificial neural networks

    International Nuclear Information System (INIS)

    Vega-Carrillo, Hector Rene; Martin Hernandez-Davila, Victor; Manzanares-Acuna, Eduardo; Mercado Sanchez, Gema A.; Pilar Iniguez de la Torre, Maria; Barquero, Raquel; Palacios, Francisco; Mendez Villafane, Roberto; Arteaga Arteaga, Tarcicio; Manuel Ortiz Rodriguez, Jose

    2006-01-01

    An artificial neural network has been designed to obtain neutron spectra from Bonner spheres spectrometer count rates. The neural network was trained using 129 neutron spectra. These include spectra from isotopic neutron sources; reference and operational spectra from accelerators and nuclear reactors, spectra based on mathematical functions as well as few energy groups and monoenergetic spectra. The spectra were transformed from lethargy to energy distribution and were re-binned to 31 energy groups using the MCNP 4C code. The re-binned spectra and the UTA4 response matrix were used to calculate the expected count rates in Bonner spheres spectrometer. These count rates were used as input and their respective spectra were used as output during the neural network training. After training, the network was tested with the Bonner spheres count rates produced by folding a set of neutron spectra with the response matrix. This set contains data used during network training as well as data not used. Training and testing was carried out using the Matlab ( R) program. To verify the network unfolding performance, the original and unfolded spectra were compared using the root mean square error. The use of artificial neural networks to unfold neutron spectra in neutron spectrometry is an alternative procedure that overcomes the drawbacks associated with this ill-conditioned problem

  15. Learning drifting concepts with neural networks

    NARCIS (Netherlands)

    Biehl, Michael; Schwarze, Holm

    1993-01-01

    The learning of time-dependent concepts with a neural network is studied analytically and numerically. The linearly separable target rule is represented by an N-vector, whose time dependence is modelled by a random or deterministic drift process. A single-layer network is trained online using

  16. Learning from large scale neural simulations

    DEFF Research Database (Denmark)

    Serban, Maria

    2017-01-01

    Large-scale neural simulations have the marks of a distinct methodology which can be fruitfully deployed to advance scientific understanding of the human brain. Computer simulation studies can be used to produce surrogate observational data for better conceptual models and new how...

  17. Improved transformer protection using probabilistic neural network ...

    African Journals Online (AJOL)

    This article presents a novel technique to distinguish between magnetizing inrush current and internal fault current of power transformer. An algorithm has been developed around the theme of the conventional differential protection method in which parallel combination of Probabilistic Neural Network (PNN) and Power ...

  18. Artificial astrocytes improve neural network performance.

    Directory of Open Access Journals (Sweden)

    Ana B Porto-Pazos

    Full Text Available Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. However, the actual impact of astrocytes in neural network function is largely unknown and its application in artificial intelligence remains untested. We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance. Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN and artificial neuron-glia networks (NGN to solve classification problems. We show that the degree of success of NGN is superior to NN. Analysis of performances of NN with different number of neurons or different architectures indicate that the effects of NGN cannot be accounted for an increased number of network elements, but rather they are specifically due to astrocytes. Furthermore, the relative efficacy of NGN vs. NN increases as the complexity of the network increases. These results indicate that artificial astrocytes improve neural network performance, and established the concept of Artificial Neuron-Glia Networks, which represents a novel concept in Artificial Intelligence with implications in computational science as well as in the understanding of brain function.

  19. Artificial astrocytes improve neural network performance.

    Science.gov (United States)

    Porto-Pazos, Ana B; Veiguela, Noha; Mesejo, Pablo; Navarrete, Marta; Alvarellos, Alberto; Ibáñez, Oscar; Pazos, Alejandro; Araque, Alfonso

    2011-04-19

    Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. However, the actual impact of astrocytes in neural network function is largely unknown and its application in artificial intelligence remains untested. We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance. Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN) and artificial neuron-glia networks (NGN) to solve classification problems. We show that the degree of success of NGN is superior to NN. Analysis of performances of NN with different number of neurons or different architectures indicate that the effects of NGN cannot be accounted for an increased number of network elements, but rather they are specifically due to astrocytes. Furthermore, the relative efficacy of NGN vs. NN increases as the complexity of the network increases. These results indicate that artificial astrocytes improve neural network performance, and established the concept of Artificial Neuron-Glia Networks, which represents a novel concept in Artificial Intelligence with implications in computational science as well as in the understanding of brain function.

  20. Tinnitus and neural plasticity of the brain

    NARCIS (Netherlands)

    Bartels, Hilke; Staal, Michiel J.; Albers, Frans W. J.

    Objective: To describe the current ideas about the manifestations of neural plasticity in generating tinnitus. Data Sources: Recently published source articles were identified using MEDLINE, PubMed, and Cochrane Library according to the key words mentioned below. Study Selection: Review articles and