AIMC Topic: Neural Networks, Computer

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Artificial neural network classifier predicts neuroblastoma patients' outcome.

BMC bioinformatics
BACKGROUND: More than fifty percent of neuroblastoma (NB) patients with adverse prognosis do not benefit from treatment making the identification of new potential targets mandatory. Hypoxia is a condition of low oxygen tension, occurring in poorly va...

Random synaptic feedback weights support error backpropagation for deep learning.

Nature communications
The brain processes information through multiple layers of neurons. This deep architecture is representationally powerful, but complicates learning because it is difficult to identify the responsible neurons when a mistake is made. In machine learnin...

Automatic detection and classification of leukocytes using convolutional neural networks.

Medical & biological engineering & computing
The detection and classification of white blood cells (WBCs, also known as Leukocytes) is a hot issue because of its important applications in disease diagnosis. Nowadays the morphological analysis of blood cells is operated manually by skilled opera...

PLAT: An Automated Fault and Behavioural Anomaly Detection Tool for PLC Controlled Manufacturing Systems.

Computational intelligence and neuroscience
Operational faults and behavioural anomalies associated with PLC control processes take place often in a manufacturing system. Real time identification of these operational faults and behavioural anomalies is necessary in the manufacturing industry. ...

Prediction of blood-brain barrier permeability of organic compounds.

Doklady. Biochemistry and biophysics
Using fragmental descriptors and artificial neural networks, a predictive model of the relationship between the structure of organic compounds and their blood-brain barrier permeability was constructed and the structural factors affecting the readine...

A new switching control for finite-time synchronization of memristor-based recurrent neural networks.

Neural networks : the official journal of the International Neural Network Society
In this paper, finite-time synchronization (FTS) of memristor-based recurrent neural networks (MNNs) with time-varying delays is investigated by designing a new switching controller. First, by using the differential inclusions theory and set-valued m...

Anti-correlations in the degree distribution increase stimulus detection performance in noisy spiking neural networks.

Journal of computational neuroscience
Neuronal circuits in the rodent barrel cortex are characterized by stable low firing rates. However, recent experiments show that short spike trains elicited by electrical stimulation in single neurons can induce behavioral responses. Hence, the unde...

Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments.

PLoS computational biology
Live-cell imaging has opened an exciting window into the role cellular heterogeneity plays in dynamic, living systems. A major critical challenge for this class of experiments is the problem of image segmentation, or determining which parts of a micr...

A new hyperbox selection rule and a pruning strategy for the enhanced fuzzy min-max neural network.

Neural networks : the official journal of the International Neural Network Society
In this paper, we extend our previous work on the Enhanced Fuzzy Min-Max (EFMM) neural network by introducing a new hyperbox selection rule and a pruning strategy to reduce network complexity and improve classification performance. Specifically, a ne...

A balanced motor primitive framework can simultaneously explain motor learning in unimanual and bimanual movements.

Neural networks : the official journal of the International Neural Network Society
Certain theoretical frameworks have successfully explained motor learning in either unimanual or bimanual movements. However, no single theoretical framework can comprehensively explain motor learning in both types of movement because the relationshi...