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Neurons

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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 ...

A GPU-based computational framework that bridges neuron simulation and artificial intelligence.

Nature communications
Biophysically detailed multi-compartment models are powerful tools to explore computational principles of the brain and also serve as a theoretical framework to generate algorithms for artificial intelligence (AI) systems. However, the expensive comp...

Signatures of task learning in neural representations.

Current opinion in neurobiology
While neural plasticity has long been studied as the basis of learning, the growth of large-scale neural recording techniques provides a unique opportunity to study how learning-induced activity changes are coordinated across neurons within the same ...

An exact mapping from ReLU networks to spiking neural networks.

Neural networks : the official journal of the International Neural Network Society
Deep spiking neural networks (SNNs) offer the promise of low-power artificial intelligence. However, training deep SNNs from scratch or converting deep artificial neural networks to SNNs without loss of performance has been a challenge. Here we propo...

Learning heterogeneous delays in a layer of spiking neurons for fast motion detection.

Biological cybernetics
The precise timing of spikes emitted by neurons plays a crucial role in shaping the response of efferent biological neurons. This temporal dimension of neural activity holds significant importance in understanding information processing in neurobiolo...

Predictive learning by a burst-dependent learning rule.

Neurobiology of learning and memory
Humans and other animals are able to quickly generalize latent dynamics of spatiotemporal sequences, often from a minimal number of previous experiences. Additionally, internal representations of external stimuli must remain stable, even in the prese...