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Neurons

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Neuromorphic Nanoionics for Human-Machine Interaction: From Materials to Applications.

Advanced materials (Deerfield Beach, Fla.)
Human-machine interaction (HMI) technology has undergone significant advancements in recent years, enabling seamless communication between humans and machines. Its expansion has extended into various emerging domains, including human healthcare, mach...

Modular Spiking Neural Membrane Systems for Image Classification.

International journal of neural systems
A variant of membrane computing models called Spiking Neural P systems (SNP systems) closely mimics the structure and behavior of biological neurons. As third-generation neural networks, SNP systems have flexible architectures allowing the design of ...

Improving Classification Performance in Dendritic Neuron Models through Practical Initialization Strategies.

Sensors (Basel, Switzerland)
A dendritic neuron model (DNM) is a deep neural network model with a unique dendritic tree structure and activation function. Effective initialization of its model parameters is crucial for its learning performance. This work proposes a novel initial...

Efficient Spiking Neural Networks with Biologically Similar Lithium-Ion Memristor Neurons.

ACS applied materials & interfaces
Benefiting from the brain-inspired event-driven feature and asynchronous sparse coding approach, spiking neural networks (SNNs) are becoming a potentially energy-efficient replacement for conventional artificial neural networks. However, neuromorphic...

Unsupervised learning of perceptual feature combinations.

PLoS computational biology
In many situations it is behaviorally relevant for an animal to respond to co-occurrences of perceptual, possibly polymodal features, while these features alone may have no importance. Thus, it is crucial for animals to learn such feature combination...

Neurobiologically realistic neural network enables cross-scale modeling of neural dynamics.

Scientific reports
Fundamental principles underlying computation in multi-scale brain networks illustrate how multiple brain areas and their coordinated activity give rise to complex cognitive functions. Whereas brain activity has been studied at the micro- to meso-sca...

Mode combinability: Exploring convex combinations of permutation aligned models.

Neural networks : the official journal of the International Neural Network Society
We explore element-wise convex combinations of two permutation-aligned neural network parameter vectors Θ and Θ of size d. We conduct extensive experiments by examining various distributions of such model combinations parametrized by elements of the ...

A topological deep learning framework for neural spike decoding.

Biophysical journal
The brain's spatial orientation system uses different neuron ensembles to aid in environment-based navigation. Two of the ways brains encode spatial information are through head direction cells and grid cells. Brains use head direction cells to deter...

Predicting Single Neuron Responses of the Primary Visual Cortex with Deep Learning Model.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Modeling neuron responses to stimuli can shed light on next-generation technologies such as brain-chip interfaces. Furthermore, high-performing models can serve to help formulate hypotheses and reveal the mechanisms underlying neural responses. Here ...

Fluorescent Neuronal Cells v2: multi-task, multi-format annotations for deep learning in microscopy.

Scientific data
Fluorescent Neuronal Cells v2 is a collection of fluorescence microscopy images and the corresponding ground-truth annotations, designed to foster innovative research in the domains of Life Sciences and Deep Learning. This dataset encompasses three i...