Results 81 to 90 of about 2,503,125 (316)

Investigating the cell of origin and novel molecular targets in Merkel cell carcinoma: a historic misnomer

Molecular Oncology, EarlyView.
This study indicates that Merkel cell carcinoma (MCC) does not originate from Merkel cells, and identifies gene, protein & cellular expression of immune‐linked and neuroendocrine markers in primary and metastatic Merkel cell carcinoma (MCC) tumor samples, linked to Merkel cell polyomavirus (MCPyV) status, with enrichment of B‐cell and other immune cell
Richie Jeremian, Sriraam Sivachandran, Melissa Galati, Brandon Ramchatesingh, Hibo Rijal, Johnny Hanna, Elena Netchiporouk, May Chergui, Margaret Redpath, Samy Abou Setah, Ivan V. Litvinov   +10 more
wiley   +1 more source

Dynamical Synapses Enhance Neural Information Processing: Gracefulness, Accuracy and Mobility

, 2012
Experimental data have revealed that neuronal connection efficacy exhibits two forms of short-term plasticity, namely, short-term depression (STD) and short-term facilitation (STF).
C. C. Alan Fung, Cortes J. M., Dayan P., He Wang, K. Y. Michael Wong, Romani S., Si Wu, Welling M.   +7 more
core   +2 more sources

Generating Neural Networks with Neural Networks

, 2018
Hypernetworks are neural networks that generate weights for another neural network. We formulate the hypernetwork training objective as a compromise between accuracy and diversity, where the diversity takes into account trivial symmetry transformations of the target network. We explain how this simple formulation generalizes variational inference.
openaire   +2 more sources

Associative Neural Network

Neural Processing Letters, 2002
An associative neural network (ASNN) is an ensemble-based method inspired by the function and structure of neural network correlations in brain. The method operates by simulating the short- and long-term memory of neural networks. The long-term memory is represented by ensemble of neural network weights, while the short-term memory is stored as a pool ...
openaire   +5 more sources

Variability in neural networks

eLife, 2018
Experiments on neurons in the heart system of the leech reveal why rhythmic behaviors differ between individuals.
Daniel R Kick, David J Schulz
openaire   +4 more sources

Current trends in single‐cell RNA sequencing applications in diabetes mellitus

FEBS Open Bio, EarlyView.
Single‐cell RNA sequencing is a powerful approach to decipher the cellular and molecular landscape at a single‐cell resolution. The rapid development of this technology has led to a wide range of applications, including the detection of cellular and molecular mechanisms and the identification and introduction of novel potential diagnostic and ...
Seyed Sajjad Zadian, Khodakaram Jahanbin, Shekoofeh Nikooei, Marzieh Rostaminejad, Arezoo Rahimi, Pedram Abdizadeh, Behnam Alipoor   +6 more
wiley   +1 more source

Electrophysiological approaches to informing therapeutic interventions with deep brain stimulation

npj Parkinson's Disease
Neuromodulation therapy comprises a range of non-destructive and adjustable methods for modulating neural activity using electrical stimulations, chemical agents, or mechanical interventions.
Atefeh Asadi, Alex I. Wiesman, Christoph Wiest, Sylvain Baillet, Huiling Tan, Muthuraman Muthuraman   +5 more
doaj   +1 more source

Towards Clinical Diagnosis: Automated Stroke Lesion Segmentation on Multi-Spectral MR Image Using Convolutional Neural Network

IEEE Access, 2018
The patient with ischemic stroke can benefit most from the earliest possible definitive diagnosis. While a quantitative evaluation of the stroke lesions on the magnetic resonance images (MRIs) is effective in clinical diagnosis, manually segmenting the ...
Zhiyang Liu, Chen Cao, Shuxue Ding, Zhiang Liu, Tong Han, Sheng Liu   +5 more
doaj   +1 more source

Modelling Identity Rules with Neural Networks [PDF]

, 2019
In this paper, we show that standard feed-forward and recurrent neural networks fail to learn abstract patterns based on identity rules. We propose Repetition Based Pattern (RBP) extensions to neural network structures that solve this problem ...
Kopparti, R. M., Weyde, T.
core   +1 more source