AIMC Topic: Neurons

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Attractor dynamics of a Boolean model of a brain circuit controlled by multiple parameters.

Chaos (Woodbury, N.Y.) Oct 1, 2018
Studies of Boolean recurrent neural networks are briefly introduced with an emphasis on the attractor dynamics determined by the sequence of distinct attractors observed in the limit cycles. We apply this framework to a simplified model of the basal ...
Nonlinear Dynamics Basal Ganglia Computer Simulation Feedback Animals Models, Theoretical Neuronal Plasticity Neurons Software Algorithms Brain Humans Neural Networks, Computer
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Propagation delays determine neuronal activity and synaptic connectivity patterns emerging in plastic neuronal networks.

Chaos (Woodbury, N.Y.) Oct 1, 2018
In plastic neuronal networks, the synaptic strengths are adapted to the neuronal activity. Specifically, spike-timing-dependent plasticity (STDP) is a fundamental mechanism that modifies the synaptic strengths based on the relative timing of pre- and...
Neural Networks, Computer Algorithms Brain Humans Models, Neurological Action Potentials Plastics Neurons Computer Simulation Time Factors Neuronal Plasticity Synaptic Transmission Nerve Net Axons Dendrites Nonlinear Dynamics Normal Distribution Synapses Oscillometry
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Global firing rate contrast enhancement in E/I neuronal networks by recurrent synchronized inhibition.

Chaos (Woodbury, N.Y.) Oct 1, 2018
Inhibitory synchronization is commonly observed and may play some important functional roles in excitatory/inhibitory (E/I) neuronal networks. The firing rate contrast enhancement is a general feature of information processing in sensory pathways, an...
Synaptic Transmission Neural Inhibition Sensory Receptor Cells Neurons Synapses Electronic Data Processing Humans Neural Networks, Computer Models, Neurological Animals Action Potentials
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Large-scale Exploration of Neuronal Morphologies Using Deep Learning and Augmented Reality.

Neuroinformatics Oct 1, 2018
Recently released large-scale neuron morphological data has greatly facilitated the research in neuroinformatics. However, the sheer volume and complexity of these data pose significant challenges for efficient and accurate neuron exploration. In thi...
Deep Learning Imaging, Three-Dimensional Humans Neurons Random Allocation Neural Networks, Computer Databases, Factual
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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...
Models, Neurological Unsupervised Machine Learning Neural Networks, Computer Neurons Neuronal Plasticity
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Working Memory: Delay Activity, Yes! Persistent Activity? Maybe Not.

The Journal of neuroscience : the official journal of the Society for Neuroscience Aug 8, 2018
Persistent spiking has been thought to underlie working memory (WM). However, virtually all of the evidence for this comes from studies that averaged spiking across time and across trials, which masks the details. On single trials, activity often occ...
Humans Neural Networks, Computer Time Factors Action Potentials Research Design Animals Cerebral Cortex Nerve Net Haplorhini Fixation, Ocular Memory, Short-Term Gamma Rhythm Neurons Saccades Energy Metabolism
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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...
Nerve Net Supervised Machine Learning Neurons Cerebellum Time Factors Neuronal Plasticity Models, Neurological Humans Algorithms Animals
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Smart Data Analytics approach to model Complex Biochemical Oscillations in Hippocampal Neurons.

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
Calcium spiking can be used for drug screening studies in pharmaceutical industries. However, performing experiments for multiple drugs and doses are highly expensive. The oscillatory behavior of calcium spiking data demonstrates extreme nonlinearity...
Rats Drug Evaluation, Preclinical Fluorescence Hippocampus Neurons Data Science Calcium Signaling Neural Networks, Computer Microscopy, Confocal Algorithms Animals
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FPGA implementation of deep-learning recurrent neural networks with sub-millisecond real-time latency for BCI-decoding of large-scale neural sensors (104 nodes).

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
Advances in neurotechnology are expected to provide access to thousands of neural channel recordings including neuronal spiking, multiunit activity and local field potentials. In addition, recent studies have shown that deep learning, in particular r...
Brain-Computer Interfaces Neural Networks, Computer Algorithms Humans Neurons
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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...
Neural Networks, Computer Machine Learning Neurons Deep Learning Models, Neurological Action Potentials
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