AI Medical Compendium Topic:
Neurons

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Fast and robust active neuron segmentation in two-photon calcium imaging using spatiotemporal deep learning.

Proceedings of the National Academy of Sciences of the United States of America
Calcium imaging records large-scale neuronal activity with cellular resolution in vivo. Automated, fast, and reliable active neuron segmentation is a critical step in the analysis workflow of utilizing neuronal signals in real-time behavioral studies...

Autonomous patch-clamp robot for functional characterization of neurons in vivo: development and application to mouse visual cortex.

Journal of neurophysiology
Patch clamping is the gold standard measurement technique for cell-type characterization in vivo, but it has low throughput, is difficult to scale, and requires highly skilled operation. We developed an autonomous robot that can acquire multiple cons...

Phase relations of theta oscillations in a computer model of the hippocampal CA1 field: Key role of Schaffer collaterals.

Neural networks : the official journal of the International Neural Network Society
The hippocampal theta rhythm (4-12 Hz) is one of the most important electrophysiological processes in the hippocampus, it participates in cognitive hippocampal functions, such as navigation in space, novelty detection, and declarative memory. We use ...

Identification of piecewise linear dynamical systems using physically-interpretable neural-fuzzy networks: Methods and applications to origami structures.

Neural networks : the official journal of the International Neural Network Society
Self-locking origami structures are characterized by their piecewise linear constitutive relations between force and deformation, which, in practice, are always completely opaque and unmeasurable: the number of piecewise segments, the positions of no...

An unsupervised neuromorphic clustering algorithm.

Biological cybernetics
Brains perform complex tasks using a fraction of the power that would be required to do the same on a conventional computer. New neuromorphic hardware systems are now becoming widely available that are intended to emulate the more power efficient, hi...

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
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...

Spatial Concept Learning: A Spiking Neural Network Implementation in Virtual and Physical Robots.

Computational intelligence and neuroscience
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...

A Novel Hardware Systolic Architecture of a Self-Organizing Map Neural Network.

Computational intelligence and neuroscience
In this article, we propose to design a new modular architecture for a self-organizing map (SOM) neural network. The proposed approach, called systolic-SOM (SSOM), is based on the use of a generic model inspired by a systolic movement. This model is ...

On-line prediction of ferrous ion concentration in goethite process based on self-adjusting structure RBF neural network.

Neural networks : the official journal of the International Neural Network Society
Outlet ferrous ion concentration is an essential indicator to manipulate the goethite process in the zinc hydrometallurgy plant. However, it cannot be measured on-line, which leads to the delay of this feedback information. In this study, a self-adju...

Evolutionary Spiking Neural Networks for Solving Supervised Classification Problems.

Computational intelligence and neuroscience
This paper presents a grammatical evolution (GE)-based methodology to automatically design third generation artificial neural networks (ANNs), also known as spiking neural networks (SNNs), for solving supervised classification problems. The proposal ...