AIMC Topic: Neurons

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Toward neuroprosthetic real-time communication from in silico to biological neuronal network via patterned optogenetic stimulation.

Scientific reports May 5, 2020
Restoration of the communication between brain circuitry is a crucial step in the recovery of brain damage induced by traumatic injuries or neurological insults. In this work we present a study of real-time unidirectional communication between a spik...
Neural Networks, Computer Cells, Cultured Brain Humans Rats Models, Neurological Action Potentials Computer Simulation Animals In Vitro Techniques Immunohistochemistry Cerebral Cortex Electrophysiology Electrodes, Implanted Optogenetics Neurotransmitter Agents Light Neurons Video Recording Equipment Design Microscopy, Fluorescence Synapsins
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The NanoZoomer artificial intelligence connectomics pipeline for tracer injection studies of the marmoset brain.

Brain structure & function May 4, 2020
We describe our connectomics pipeline for processing anterograde tracer injection data for the brain of the common marmoset (Callithrix jacchus). Brain sections were imaged using a batch slide scanner (NanoZoomer 2.0-HT) and we used artificial intell...
Brain Animals Magnetic Resonance Imaging Artificial Intelligence Atlases as Topic Callithrix Neuroanatomical Tract-Tracing Techniques Neurons Connectome Neural Pathways
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Nonequilibrium Statistical Mechanics of Continuous Attractors.

Neural computation Apr 28, 2020
Continuous attractors have been used to understand recent neuroscience experiments where persistent activity patterns encode internal representations of external attributes like head direction or spatial location. However, the conditions under which ...
Neural Networks, Computer Humans Space Perception Data Interpretation, Statistical Neurons Place Cells
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Population coupling predicts the plasticity of stimulus responses in cortical circuits.

eLife Apr 21, 2020
Some neurons have stimulus responses that are stable over days, whereas other neurons have highly plastic stimulus responses. Using a recurrent network model, we explore whether this could be due to an underlying diversity in their synaptic plasticit...
Humans Neural Networks, Computer Brain Animals Mice Neurons Neuronal Plasticity Nerve Net Models, Neurological
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Dendrite P systems.

Neural networks : the official journal of the International Neural Network Society Apr 18, 2020
It was recently found that dendrites are not just a passive channel. They can perform mixed computation of analog and digital signals, and therefore can be abstracted as information processors. Moreover, dendrites possess a feedback mechanism. Motiva...
Dendrites Action Potentials Neurons Synapses Neural Networks, Computer Models, Neurological Humans
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Neuronal mechanisms for sequential activation of memory items: Dynamics and reliability.

PloS one Apr 16, 2020
In this article we present a biologically inspired model of activation of memory items in a sequence. Our model produces two types of sequences, corresponding to two different types of cerebral functions: activation of regular or irregular sequences....
Neurons Humans Reproducibility of Results Memory Animals Cerebral Cortex Synaptic Transmission Neural Networks, Computer Models, Neurological
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Noise can speed backpropagation learning and deep bidirectional pretraining.

Neural networks : the official journal of the International Neural Network Society Apr 11, 2020
We show that the backpropagation algorithm is a special case of the generalized Expectation-Maximization (EM) algorithm for iterative maximum likelihood estimation. We then apply the recent result that carefully chosen noise can speed the average con...
Probability Algorithms Deep Learning Neurons Neural Networks, Computer
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Extracting boolean and probabilistic rules from trained neural networks.

Neural networks : the official journal of the International Neural Network Society Apr 3, 2020
This paper presents two approaches to extracting rules from a trained neural network consisting of linear threshold functions. The first one leads to an algorithm that extracts rules in the form of Boolean functions. Compared with an existing one, th...
Probability Humans Neural Networks, Computer Algorithms Animals Neurons
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Learning in the machine: To share or not to share?

Neural networks : the official journal of the International Neural Network Society Mar 25, 2020
Weight-sharing is one of the pillars behind Convolutional Neural Networks and their successes. However, in physical neural systems such as the brain, weight-sharing is implausible. This discrepancy raises the fundamental question of whether weight-sh...
Brain Neurons Pattern Recognition, Automated Neural Networks, Computer Humans Machine Learning
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Before and beyond the Wilson-Cowan equations.

Journal of neurophysiology Mar 18, 2020
The Wilson-Cowan equations represent a landmark in the history of computational neuroscience. Along with the insights Wilson and Cowan offered for neuroscience, they crystallized an approach to modeling neural dynamics and brain function. Although th...
Neurosciences Animals Humans Models, Theoretical Neural Networks, Computer Neurons Brain
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