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Variational autoencoder: An unsupervised model for encoding and decoding fMRI activity in visual cortex.

NeuroImage
Goal-driven and feedforward-only convolutional neural networks (CNN) have been shown to be able to predict and decode cortical responses to natural images or videos. Here, we explored an alternative deep neural network, variational auto-encoder (VAE)...

Cascaded Tuning to Amplitude Modulation for Natural Sound Recognition.

The Journal of neuroscience : the official journal of the Society for Neuroscience
The auditory system converts the physical properties of a sound waveform to neural activities and processes them for recognition. During the process, the tuning to amplitude modulation (AM) is successively transformed by a cascade of brain regions. T...

A self-organized recurrent neural network for estimating the effective connectivity and its application to EEG data.

Computers in biology and medicine
OBJECTIVE: Effective connectivity is an important notion in neuroscience research, useful for detecting the interactions between regions of the brain.

DoGNet: A deep architecture for synapse detection in multiplexed fluorescence images.

PLoS computational biology
Neuronal synapses transmit electrochemical signals between cells through the coordinated action of presynaptic vesicles, ion channels, scaffolding and adapter proteins, and membrane receptors. In situ structural characterization of numerous synaptic ...

Electrical stimulation in a spiking neural network model of monkey superior colliculus.

Progress in brain research
The superior colliculus (SC) generates saccades by recruiting a population of cells in its topographically organized motor map. Supra-threshold electrical stimulation in the SC produces a normometric saccade with little effect of the stimulation para...

All-optical spiking neurosynaptic networks with self-learning capabilities.

Nature
Software implementations of brain-inspired computing underlie many important computational tasks, from image processing to speech recognition, artificial intelligence and deep learning applications. Yet, unlike real neural tissue, traditional computi...

SleepEEGNet: Automated sleep stage scoring with sequence to sequence deep learning approach.

PloS one
Electroencephalogram (EEG) is a common base signal used to monitor brain activities and diagnose sleep disorders. Manual sleep stage scoring is a time-consuming task for sleep experts and is limited by inter-rater reliability. In this paper, we propo...

Defining Image Memorability Using the Visual Memory Schema.

IEEE transactions on pattern analysis and machine intelligence
Memorability of an image is a characteristic determined by the human observers' ability to remember images they have seen. Yet recent work on image memorability defines it as an intrinsic property that can be obtained independent of the observer. The...

Implications of asymmetric neural activity patterns in the basal ganglia outflow in the integrative neural network model for cervical dystonia.

Progress in brain research
Cervical dystonia (CD) is characterized by abnormal twisting and turning of the head with associated head oscillations. It is the most common form of dystonia, which is a third most common movement disorder. Despite frequent occurrence there is pauci...

Assessing cognitive mental workload via EEG signals and an ensemble deep learning classifier based on denoising autoencoders.

Computers in biology and medicine
To estimate the reliability and cognitive states of operator performance in a human-machine collaborative environment, we propose a novel human mental workload (MW) recognizer based on deep learning principles and utilizing the features of the electr...