AIMC Topic:
Neurons

Clear Filters Showing 1251 to 1260 of 1328 articles

Frequency-dependent response in cortical network with periodic electrical stimulation.

Chaos (Woodbury, N.Y.) Jul 1, 2020
Electrical stimulation can shape oscillations in brain activity. However, the mechanism of how periodic electrical stimulation modulates brain oscillations by time-delayed neural networks is poorly understood at present. To address this question, we ...
Humans Neural Networks, Computer Neurons Electric Stimulation Brain
View on PubMed DOI

Dynamics and bifurcations in multistable 3-cell neural networks.

Chaos (Woodbury, N.Y.) Jul 1, 2020
We disclose the generality of the intrinsic mechanisms underlying multistability in reciprocally inhibitory 3-cell circuits composed of simplified, low-dimensional models of oscillatory neurons, as opposed to those of a detailed Hodgkin-Huxley type [...
Synapses Humans Neural Networks, Computer Models, Neurological Neurons
View on PubMed DOI

Deep neural networks capture texture sensitivity in V2.

Journal of vision Jul 1, 2020
Deep convolutional neural networks (CNNs) trained on visual objects have shown intriguing ability to predict some response properties of visual cortical neurons. However, the factors (e.g., if the model is trained or not, receptive field size) and co...
Neural Networks, Computer Signal Processing, Computer-Assisted Humans Contrast Sensitivity Neurons Form Perception Visual Cortex
View on PubMed DOI

What is the Key Conceptual or Methodological Bottleneck to Controlling Neural Biology?

Cell systems Jun 24, 2020
Neurostimulation techniques allow us to manipulate the activity of nervous systems, and even that of single neurons. In this piece, researchers discuss what they see as the current key bottlenecks to controlling neural biology.
Neurons Biology Humans Neural Networks, Computer
View on PubMed DOI

Sparse Scanning Electron Microscopy Data Acquisition and Deep Neural Networks for Automated Segmentation in Connectomics.

Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada Jun 1, 2020
With the growing importance of three-dimensional and very large field of view imaging, acquisition time becomes a serious bottleneck. Additionally, dose reduction is of importance when imaging material like biological tissue that is sensitive to elec...
Image Processing, Computer-Assisted Neurons Imaging, Three-Dimensional Microscopy, Electron, Scanning Connectome Neural Networks, Computer Deep Learning
View on PubMed DOI

Distinct subtypes of inhibitory interneurons differentially promote the propagation of rate and temporal codes in the feedforward neural network.

Chaos (Woodbury, N.Y.) May 1, 2020
Sensory information is believed to be encoded in neuronal spikes using two different neural codes, the rate code (spike firing rate) and the temporal code (precisely-timed spikes). Since the sensory cortex has a highly hierarchical feedforward struct...
Perception Neural Networks, Computer Parvalbumins Interneurons Pyramidal Cells Action Potentials Neurons Excitatory Postsynaptic Potentials
View on PubMed DOI

A simple locally active memristor and its application in HR neurons.

Chaos (Woodbury, N.Y.) May 1, 2020
This paper proposes a simple locally active memristor whose state equation only consists of linear terms and an easily implementable function and design for its circuit emulator. The effectiveness of the circuit emulator is validated using breadboard...
Neurons Algorithms Neural Networks, Computer
View on PubMed DOI

Extreme value theory of evolving phenomena in complex dynamical systems: Firing cascades in a model of a neural network.

Chaos (Woodbury, N.Y.) Apr 1, 2020
We extend the scope of the dynamical theory of extreme values to include phenomena that do not happen instantaneously but evolve over a finite, albeit unknown at the onset, time interval. We consider complex dynamical systems composed of many individ...
Models, Neurological Neurons Neural Networks, Computer Humans Computer Simulation
View on PubMed DOI

Computational cannula microscopy of neurons using neural networks.

Optics letters Apr 1, 2020
Computational cannula microscopy is a minimally invasive imaging technique that can enable high-resolution imaging deep inside tissue. Here, we apply artificial neural networks to enable real-time, power-efficient image reconstructions that are more ...
Animals Image Processing, Computer-Assisted Rats Rats, Sprague-Dawley Neurons Neural Networks, Computer Microscopy, Fluorescence Signal-To-Noise Ratio
View on PubMed DOI

3D high resolution generative deep-learning network for fluorescence microscopy imaging.

Optics letters Apr 1, 2020
Microscopic fluorescence imaging serves as a basic tool in many research areas including biology, medicine, and chemistry. With the help of optical clearing, large volume imaging of a mouse brain and even a whole body has been enabled. However, const...
Deep Learning Signal-To-Noise Ratio Imaging, Three-Dimensional Microscopy, Fluorescence Neurons
View on PubMed DOI
Popular Topics
Recent Journals