This paper presents a deterministic and adaptive spike model derived from radial basis functionsand a leaky integrate-and-fire sampler developed for training spiking neural networks without directweight manipulation. ...
This paper presents a deterministic and adaptive spike model derived from radial basis functions
and a leaky integrate-and-fire sampler developed for training spiking neural networks without direct
weight manipulation. Several algorithms have been proposed for training spiking neural networks
through biologically-plausible learning mechanisms, such as spike-timing-dependent synaptic plasticity
and Hebbian plasticity. These algorithms typically rely on the ability to update the synaptic strengths,
or weights, directly, through a weight update rule in which the weight increment can be decided
and implemented based on the training equations. However, in several potential applications of
adaptive spiking neural networks, including neuroprosthetic devices and CMOS/memristor nanoscale
neuromorphic chips, the weights cannot be manipulated directly and, instead, tend to change over time
by virtue of the pre- and postsynaptic neural activity. This paper presents an indirect learning method
that induces changes in the synaptic weights by modulating spike-timing-dependent plasticity by means
of controlled input spike trains. In place of the weights, the algorithm manipulates the input spike trains
used to stimulate the input neurons by determining a sequence of spike timings that minimize a desired
objective function and, indirectly, induce the desired synaptic plasticity in the network.
Epigenetic alterations are fundamental hallmarks of cancer genomes. We surveyed the landscape of DNA methylation alterations in gastric cancer by analyzing genome-wide CG dinucleotide (CpG) methylation profiles of 240...
Epigenetic alterations are fundamental hallmarks of cancer genomes. We surveyed the landscape of DNA methylation alterations in gastric cancer by analyzing genome-wide CG dinucleotide (CpG) methylation profiles of 240 gastric cancers (203 tumors and 37 cell lines) and 94 matched normal gastric tissues. Cancer-specific epigenetic alterations were observed in 44% of CpGs, comprising both tumor hyper- and hypomethylation. Twenty-five percent of the methylation alterations were significantly associated with changes in tumor gene expression. Whereas most methylation-expression correlations were negative, several positively correlated methylation-expression interactions were also observed, associated with CpG sites exhibiting atypical transcription start site distances and gene body localization. Methylation clustering of the tumors revealed a CpG island methylator phenotype (CIMP) subgroup associated with widespread hypermethylation, young patient age, and adverse patient outcome in a disease stage–independent manner. CIMP cell lines displayed sensitivity to 5-aza-2′-deoxycytidine, a clinically approved demethylating drug. We also identified long-range regions of epigenetic silencing (LRESs) in CIMP tumors. Combined analysis of the methylation, gene expression, and drug treatment data suggests that certain LRESs may silence specific genes within the region, rather than all genes. Finally, we discovered regions of long-range tumor hypomethylation, associated with increased chromosomal instability. Our results provide insights into the epigenetic impact of environmental and biological agents on gastric epithelial cells, which may contribute to cancer.
Music Information Retrieval (MIR) is an interdisciplinary field that facilitates indexing and content-based organization of music databases. Music classification and clustering is one of the major topics in MIR. Music...
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We investigated the relation between diet pill use and eating disorder subtype, purging and other compensatory behaviors, body mass index (BMI), tobacco and caffeine use, alcohol abuse or dependence, personality chara...
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