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Neurons

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Hybrid neuromorphic hardware with sparing 2D synapse and CMOS neuron for character recognition.

Science bulletin
Neuromorphic computing enables efficient processing of data-intensive tasks, but requires numerous artificial synapses and neurons for certain functions, which leads to bulky systems and energy challenges. Achieving functionality with fewer synapses ...

Synchronization in STDP-driven memristive neural networks with time-varying topology.

Journal of biological physics
Synchronization is a widespread phenomenon in the brain. Despite numerous studies, the specific parameter configurations of the synaptic network structure and learning rules needed to achieve robust and enduring synchronization in neurons driven by s...

HybridSNN: Combining Bio-Machine Strengths by Boosting Adaptive Spiking Neural Networks.

IEEE transactions on neural networks and learning systems
Spiking neural networks (SNNs), inspired by the neuronal network in the brain, provide biologically relevant and low-power consuming models for information processing. Existing studies either mimic the learning mechanism of brain neural networks as c...

DEBI-NN: Distance-encoding biomorphic-informational neural networks for minimizing the number of trainable parameters.

Neural networks : the official journal of the International Neural Network Society
Modern artificial intelligence (AI) approaches mainly rely on neural network (NN) or deep NN methodologies. However, these approaches require large amounts of data to train, given, that the number of their trainable parameters has a polynomial relati...

Bio-inspired, task-free continual learning through activity regularization.

Biological cybernetics
The ability to sequentially learn multiple tasks without forgetting is a key skill of biological brains, whereas it represents a major challenge to the field of deep learning. To avoid catastrophic forgetting, various continual learning (CL) approach...

SWsnn: A Novel Simulator for Spiking Neural Networks.

Journal of computational biology : a journal of computational molecular cell biology
Spiking neural network (SNN) simulators play an important role in neural system modeling and brain function research. They can help scientists reproduce and explore neuronal activities in brain regions, neuroscience, brain-like computing, and other f...

Spiking neural P systems with lateral inhibition.

Neural networks : the official journal of the International Neural Network Society
As a member of the third generation of artificial neural network models, spiking neural P systems (SN P systems) have gained a hot research spot in recent years. This work introduces the phenomenon of lateral inhibition in biological nervous systems ...

DLATA: Deep Learning-Assisted transformation alignment of 2D brain slice histology.

Neuroscience letters
Accurate alignment of brain slices is crucial for the classification of neuron populations by brain region, and for quantitative analysis in in vitro brain studies. Current semi-automated alignment workflows require labor intensive labeling of featur...

Distribution Patterns of Subgroups of Inhibitory Neurons Divided by Calbindin 1.

Molecular neurobiology
The inhibitory neurons in the brain play an essential role in neural network firing patterns by releasing γ-aminobutyric acid (GABA) as the neurotransmitter. In the mouse brain, based on the protein molecular markers, inhibitory neurons are usually t...

Experimental validation of the free-energy principle with in vitro neural networks.

Nature communications
Empirical applications of the free-energy principle are not straightforward because they entail a commitment to a particular process theory, especially at the cellular and synaptic levels. Using a recently established reverse engineering technique, w...