AIMC Topic: Neural Networks, Computer

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Sensitivity to Stimulus Irregularity Is Inherent in Neural Networks.

Neural computation
Behavior is controlled by complex neural networks in which neurons process thousands of inputs. However, even short spike trains evoked in a single cortical neuron were demonstrated to be sufficient to influence behavior in vivo. Specifically, irregu...

A New Nonlinear Sparse Component Analysis for a Biologically Plausible Model of Neurons.

Neural computation
It is known that brain can create a sparse representation of the environment in both sensory and mnemonic forms (Olshausen & Field, 2004). Such sparse representation can be combined in downstream areas to create rich multisensory responses to support...

Learning with Precise Spike Times: A New Decoding Algorithm for Liquid State Machines.

Neural computation
There is extensive evidence that biological neural networks encode information in the precise timing of the spikes generated and transmitted by neurons, which offers several advantages over rate-based codes. Here we adopt a vector space formulation o...

An Improved Full Convolutional Network Combined with Conditional Random Fields for Brain MR Image Segmentation Algorithm and its 3D Visualization Analysis.

Journal of medical systems
Existing brain region segmentation algorithms based on deep convolutional neural networks (CNN) are inefficient for object boundary segmentation. In order to enhance the segmentation accuracy of brain tissue, this paper proposed an object region segm...

Hybrid scattering-LSTM networks for automated detection of sleep arousals.

Physiological measurement
OBJECTIVE: Early detection of sleep arousal in polysomnographic (PSG) signals is crucial for monitoring or diagnosing sleep disorders and reducing the risk of further complications, including heart disease and blood pressure fluctuations.

Maximal flexibility in dynamic functional connectivity with critical dynamics revealed by fMRI data analysis and brain network modelling.

Journal of neural engineering
OBJECTIVE: The exploration of time-varying functional connectivity (FC) through human neuroimaging techniques provides important new insights on the spatio-temporal organization of functional communication in the brain's networks and its alterations ...

SpikeDeeptector: a deep-learning based method for detection of neural spiking activity.

Journal of neural engineering
OBJECTIVE: In electrophysiology, microelectrodes are the primary source for recording neural data (single unit activity). These microelectrodes can be implanted individually or in the form of arrays containing dozens to hundreds of channels. Recordin...

Convolutional neural networks for skull-stripping in brain MR imaging using silver standard masks.

Artificial intelligence in medicine
Manual annotation is considered to be the "gold standard" in medical imaging analysis. However, medical imaging datasets that include expert manual segmentation are scarce as this step is time-consuming, and therefore expensive. Moreover, single-rate...

Using deep maxout neural networks to improve the accuracy of function prediction from protein interaction networks.

PloS one
Protein-protein interaction network data provides valuable information that infers direct links between genes and their biological roles. This information brings a fundamental hypothesis for protein function prediction that interacting proteins tend ...

Energy-Efficient Intelligent ECG Monitoring for Wearable Devices.

IEEE transactions on biomedical circuits and systems
Wearable intelligent ECG monitoring devices can perform automatic ECG diagnosis in real time and send out alert signal together with abnormal ECG signal for doctor's further analysis. This provides a means for the patient to identify their heart prob...