AI Medical Compendium Topic:
Neurons

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Multiscale segmentation- and error-guided iterative convolutional neural network for cerebral neuron segmentation in microscopic images.

Microscopy research and technique
This article uses microscopy images obtained from diverse anatomical regions of macaque brain for neuron semantic segmentation. The complex structure of brain, the large intra-class staining intensity difference within neuron class, the small inter-c...

On the Tuning of the Computation Capability of Spiking Neural Membrane Systems with Communication on Request.

International journal of neural systems
Spiking neural P systems (abbreviated as SNP systems) are models of computation that mimic the behavior of biological neurons. The spiking neural P systems with communication on request (abbreviated as SNQP systems) are a recently developed class of ...

A disector-based framework for the automatic optical fractionator.

Journal of chemical neuroanatomy
Stereology-based methods provide the current state-of-the-art approaches for accurate quantification of numbers and other morphometric parameters of biological objects in stained tissue sections. The advent of artificial intelligence (AI)-based deep ...

Exact mean-field models for spiking neural networks with adaptation.

Journal of computational neuroscience
Networks of spiking neurons with adaption have been shown to be able to reproduce a wide range of neural activities, including the emergent population bursting and spike synchrony that underpin brain disorders and normal function. Exact mean-field mo...

On Spiking Neural Membrane Systems with Neuron and Synapse Creation.

International journal of neural systems
Spiking neural membrane systems are models of computation inspired by the natural functioning of the brain using the concepts of neurons and synapses, and represent a way of building computational systems of a biological inspiration. A variant of suc...

BackEISNN: A deep spiking neural network with adaptive self-feedback and balanced excitatory-inhibitory neurons.

Neural networks : the official journal of the International Neural Network Society
Spiking neural networks (SNNs) transmit information through discrete spikes that perform well in processing spatial-temporal information. Owing to their nondifferentiable characteristic, difficulties persist in designing SNNs that deliver good perfor...

Sequence Learning in a Single Trial: A Spiking Neurons Model Based on Hippocampal Circuitry.

IEEE transactions on neural networks and learning systems
In contrast with our everyday experience using brain circuits, it can take a prohibitively long time to train a computational system to produce the correct sequence of outputs in the presence of a series of inputs. This suggests that something import...

BiCoSS: Toward Large-Scale Cognition Brain With Multigranular Neuromorphic Architecture.

IEEE transactions on neural networks and learning systems
The further exploration of the neural mechanisms underlying the biological activities of the human brain depends on the development of large-scale spiking neural networks (SNNs) with different categories at different levels, as well as the correspond...

How to Analyze the Neurodynamic Characteristics of Pulse-Coupled Neural Networks? A Theoretical Analysis and Case Study of Intersecting Cortical Model.

IEEE transactions on cybernetics
The intersecting cortical model (ICM), initially designed for image processing, is a special case of the biologically inspired pulse-coupled neural-network (PCNN) models. Although the ICM has been widely used, few studies concern the internal activit...

A Sparse and Spike-Timing-Based Adaptive Photoencoder for Augmenting Machine Vision for Spiking Neural Networks.

Advanced materials (Deerfield Beach, Fla.)
The representation of external stimuli in the form of action potentials or spikes constitutes the basis of energy efficient neural computation that emerging spiking neural networks (SNNs) aspire to imitate. With recent evidence suggesting that inform...