AIMC Topic: Models, Neurological

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Multivariate machine learning distinguishes cross-network dynamic functional connectivity patterns in state and trait neuropathic pain.

Pain Sep 1, 2018
Communication within the brain is dynamic. Chronic pain can also be dynamic, with varying intensities experienced over time. Little is known of how brain dynamics are disrupted in chronic pain, or relates to patients' pain assessed at various timesca...
Nerve Net Executive Function Models, Neurological Neuralgia Humans Machine Learning Brain
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Spiking Neural Networks with Unsupervised Learning Based on STDP Using Resistive Synaptic Devices and Analog CMOS Neuron Circuit.

Journal of nanoscience and nanotechnology Sep 1, 2018
We designed the CMOS analog integrate and fire (I&F) neuron circuit can drive resistive synaptic device. The neuron circuit consists of a current mirror for spatial integration, a capacitor for temporal integration, asymmetric negative and positive p...
Neural Networks, Computer Models, Neurological Unsupervised Machine Learning Neuronal Plasticity Neurons
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Computational Principles of Supervised Learning in the Cerebellum.

Annual review of neuroscience Jul 8, 2018
Supervised learning plays a key role in the operation of many biological and artificial neural networks. Analysis of the computations underlying supervised learning is facilitated by the relatively simple and uniform architecture of the cerebellum, a...
Animals Time Factors Neuronal Plasticity Humans Algorithms Nerve Net Neurons Models, Neurological Cerebellum Supervised Machine Learning
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Improving EEG-Based Motor Imagery Classification via Spatial and Temporal Recurrent Neural Networks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2018
Motor imagery (MI) based Brain-Computer Interface (BCI) is an important active BCI paradigm for recognizing movement intention of severely disabled persons. There are extensive studies about MI-based intention recognition, most of which heavily rely ...
Movement Neural Networks, Computer Humans Deep Learning Models, Neurological Electroencephalography Brain-Computer Interfaces
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A Deep Learning Approach for the Classification of Neuronal Cell Types.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2018
Classification of neurons from extracellular recordings is mainly limited to putatively excitatory or inhibitory units based on the spike shape and firing patterns. Narrow waveforms are considered to be fast spiking inhibitory neurons and broad wavef...
Models, Neurological Action Potentials Neurons Machine Learning Deep Learning Neural Networks, Computer
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Biophysically interpretable recurrent neural network for functional magnetic resonance imaging analysis and sparsity based causal architecture discovery.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2018
Recent efforts use state-of-the-art Recurrent Neural Networks (RNN) to gain insight into neuroscience. A limitation of these works is that the used generic RNNs lack biophysical meaning, making the interpretation of the results in a neuroscience cont...
Models, Neurological Brain Mapping Brain Magnetic Resonance Imaging Neural Networks, Computer
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[Review of the research of spiking neuron network based on memristor].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi Jun 25, 2018
The rapid development of artificial intelligence put forward higher requirements for the computational speed, resource consumption and the biological interpretation of computational neuroscience. Spiking neuron networks can carry a large amount of in...
Neurons Models, Neurological Action Potentials Neural Networks, Computer Neuronal Plasticity
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Correlations between synapses in pairs of neurons slow down dynamics in randomly connected neural networks.

Physical review. E Jun 1, 2018
Networks of randomly connected neurons are among the most popular models in theoretical neuroscience. The connectivity between neurons in the cortex is however not fully random, the simplest and most prominent deviation from randomness found in exper...
Models, Neurological Action Potentials Neural Networks, Computer Animals Time Factors Neurons Synapses
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Introduction.

International journal of neural systems Jun 1, 2018
Neural Networks, Computer Brain Animals Models, Neurological Humans Neurons Parkinson Disease Periodicals as Topic
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A Real-Time Reconfigurable Multichip Architecture for Large-Scale Biophysically Accurate Neuron Simulation.

IEEE transactions on biomedical circuits and systems Apr 1, 2018
Simulation of brain neurons in real-time using biophysically meaningful models is a prerequisite for comprehensive understanding of how neurons process information and communicate with each other, in effect efficiently complementing in-vivo experimen...
Animals Olivary Nucleus Models, Neurological Neural Networks, Computer Mice Neurons Computer Simulation
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