AIMC Topic: Neurons

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A White-Box Testing for Deep Neural Networks Based on Neuron Coverage.

IEEE transactions on neural networks and learning systems
With the introduction of neuron coverage as a testing criterion for deep neural networks (DNNs), covering more neurons to detect more internal logic of DNNs became the main goal of many research studies. While some works had made progress, some new c...

Prototype-Based Interpretation of the Functionality of Neurons in Winner-Take-All Neural Networks.

IEEE transactions on neural networks and learning systems
Prototype-based learning (PbL) using a winner-take-all (WTA) network based on minimum Euclidean distance (ED-WTA) is an intuitive approach to multiclass classification. By constructing meaningful class centers, PbL provides higher interpretability an...

An advanced approach for the electrical responses of discrete fractional-order biophysical neural network models and their dynamical responses.

Scientific reports
The multiple activities of neurons frequently generate several spiking-bursting variations observed within the neurological mechanism. We show that a discrete fractional-order activated nerve cell framework incorporating a Caputo-type fractional diff...

Iontronic Nanopore Model for Artificial Neurons: The Requisites of Spiking.

The journal of physical chemistry letters
Brain-inspired neuromorphic computing is currently being investigated for effective artificial intelligence (AI) systems. The development of artificial neurons and synapses is imperative to creating efficient computational biomimetic networks. Here w...

Neuron Contact Detection Based on Pipette Precise Positioning for Robotic Brain-Slice Patch Clamps.

Sensors (Basel, Switzerland)
A patch clamp is the "gold standard" method for studying ion-channel biophysics and pharmacology. Due to the complexity of the operation and the heavy reliance on experimenter experience, more and more researchers are focusing on patch-clamp automati...

A Deep Learning Approach for Neuronal Cell Body Segmentation in Neurons Expressing GCaMP Using a Swin Transformer.

eNeuro
Neuronal cell body analysis is crucial for quantifying changes in neuronal sizes under different physiological and pathologic conditions. Neuronal cell body detection and segmentation mainly rely on manual or pseudo-manual annotations. Manual annotat...

Learning smooth dendrite morphological neurons by stochastic gradient descent for pattern classification.

Neural networks : the official journal of the International Neural Network Society
This article presents a learning algorithm for dendrite morphological neurons (DMN) based on stochastic gradient descent (SGD). In particular, we focus on a DMN topology that comprises spherical dendrites, smooth maximum activation function nodes, an...

Pumping machine fault diagnosis based on fused RDC-RBF.

PloS one
At present, the fault diagnosis of pumping units in major oil fields in China is time-consuming and inefficient, and there is no universal problem for high requirements of hardware resources. In this study, a fault fusion diagnosis method of pumping ...

Brain-inspired neural circuit evolution for spiking neural networks.

Proceedings of the National Academy of Sciences of the United States of America
In biological neural systems, different neurons are capable of self-organizing to form different neural circuits for achieving a variety of cognitive functions. However, the current design paradigm of spiking neural networks is based on structures de...

Brain-guided manifold transferring to improve the performance of spiking neural networks in image classification.

Journal of computational neuroscience
Spiking neural networks (SNNs), as the third generation of neural networks, are based on biological models of human brain neurons. In this work, a shallow SNN plays the role of an explicit image decoder in the image classification. An LSTM-based EEG ...