AIMC Topic: Models, Neurological

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

Computers in biology and medicine Apr 29, 2019
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...
Cognition Models, Neurological Electroencephalography Databases, Factual Deep Learning Humans
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Deep convolutional models improve predictions of macaque V1 responses to natural images.

PLoS computational biology Apr 23, 2019
Despite great efforts over several decades, our best models of primary visual cortex (V1) still predict spiking activity quite poorly when probed with natural stimuli, highlighting our limited understanding of the nonlinear computations in V1. Recent...
Neural Networks, Computer Visual Perception Algorithms Macaca mulatta Visual Cortex Male Animals Models, Neurological Computational Biology Neurons
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Selection and Optimization of Temporal Spike Encoding Methods for Spiking Neural Networks.

IEEE transactions on neural networks and learning systems Apr 12, 2019
Spiking neural networks (SNNs) receive trains of spiking events as inputs. In order to design efficient SNN systems, real-valued signals must be optimally encoded into spike trains so that the task-relevant information is retained. This paper provide...
Signal-To-Noise Ratio Algorithms Reproducibility of Results Action Potentials Neural Networks, Computer Models, Neurological Signal Processing, Computer-Assisted
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Quantifying Information Conveyed by Large Neuronal Populations.

Neural computation Apr 12, 2019
Quantifying mutual information between inputs and outputs of a large neural circuit is an important open problem in both machine learning and neuroscience. However, evaluation of the mutual information is known to be generally intractable for large s...
Neural Networks, Computer Machine Learning Neurons Models, Neurological Humans
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Microstimulation in a spiking neural network model of the midbrain superior colliculus.

PLoS computational biology Apr 12, 2019
The midbrain superior colliculus (SC) generates a rapid saccadic eye movement to a sensory stimulus by recruiting a population of cells in its topographically organized motor map. Supra-threshold electrical microstimulation in the SC reveals that the...
Computational Biology Models, Neurological Action Potentials Neural Networks, Computer Animals Humans Electric Stimulation Nerve Net Saccades Neurons Haplorhini Superior Colliculi
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Hidden Bursting Firings and Bifurcation Mechanisms in Memristive Neuron Model With Threshold Electromagnetic Induction.

IEEE transactions on neural networks and learning systems Apr 11, 2019
Memristors can be employed to mimic biological neural synapses or to describe electromagnetic induction effects. To exhibit the threshold effect of electromagnetic induction, this paper presents a threshold flux-controlled memristor and examines its ...
Neurons Synapses Electrophysiological Phenomena Electromagnetic Fields Algorithms Computer Simulation Models, Neurological Neural Networks, Computer
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PCNN Mechanism and its Parameter Settings.

IEEE transactions on neural networks and learning systems Apr 11, 2019
The pulse-coupled neural network (PCNN) model is a third-generation artificial neural network without training that uses the synchronous pulse bursts of neurons to process digital images, but the lack of in-depth theoretical research limits its exten...
Models, Theoretical Models, Neurological Neurons Algorithms Image Processing, Computer-Assisted Electrophysiological Phenomena Image Interpretation, Computer-Assisted Neural Networks, Computer
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An optimal time interval of input spikes involved in synaptic adjustment of spike sequence learning.

Neural networks : the official journal of the International Neural Network Society Apr 1, 2019
The supervised learning methods for spiking neurons based on temporal encoding are important foundation for the development of spiking neural networks. During the learning process, the synaptic weights of a spiking neuron are adjusted to make the neu...
Algorithms Time Factors Models, Neurological Neural Networks, Computer Neurons Synapses Action Potentials Supervised Machine Learning
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Spatial Concept Learning: A Spiking Neural Network Implementation in Virtual and Physical Robots.

Computational intelligence and neuroscience Apr 1, 2019
This paper proposes an artificial spiking neural network (SNN) sustaining the cognitive abstract process of spatial concept learning, embedded in virtual and real robots. Based on an operant conditioning procedure, the robots learn the relationship o...
Neuronal Plasticity Models, Neurological Artificial Intelligence Humans Concept Formation Neurons Algorithms Neural Networks, Computer Learning
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Estimating Multiscale Direct Causality Graphs in Neural Spike-Field Networks.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Mar 28, 2019
Neural representations span various spatiotemporal scales of brain activity, from the spiking activity of single neurons to field activity measuring large-scale networks. The simultaneous analyses of spikes and fields to uncover causal interactions i...
Brain Reproducibility of Results Causality Neural Networks, Computer Algorithms Evoked Potentials Biophysics Models, Neurological Linear Models Humans
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