AI Medical Compendium Topic:
Neurons

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DeepBranch: Deep Neural Networks for Branch Point Detection in Biomedical Images.

IEEE transactions on medical imaging
Morphology reconstruction of tree-like structures in volumetric images, such as neurons, retinal blood vessels, and bronchi, is of fundamental interest for biomedical research. 3D branch points play an important role in many reconstruction applicatio...

Self-adaptive STDP-based learning of a spiking neuron with nanocomposite memristive weights.

Nanotechnology
Neuromorphic systems consisting of artificial neurons and memristive synapses could provide a much better performance and a significantly more energy-efficient approach to the implementation of different types of neural network algorithms than tradit...

Stable memory with unstable synapses.

Nature communications
What is the physiological basis of long-term memory? The prevailing view in Neuroscience attributes changes in synaptic efficacy to memory acquisition, implying that stable memories correspond to stable connectivity patterns. However, an increasing b...

Deep CovDenseSNN: A hierarchical event-driven dynamic framework with spiking neurons in noisy environment.

Neural networks : the official journal of the International Neural Network Society
Neurons in the brain use an event signal, termed spike, encode temporal information for neural computation. Spiking neural networks (SNNs) take this advantage to serve as biological relevant models. However, the effective encoding of sensory informat...

Responses of midbrain auditory neurons to two different environmental sounds-A new approach on cross-sound modeling.

Bio Systems
When modeling auditory responses to environmental sounds, results are satisfactory if both training and testing are restricted to datasets of one type of sound. To predict 'cross-sound' responses (i.e., to predict the response to one type of sound e....

A biologically plausible supervised learning method for spiking neural networks using the symmetric STDP rule.

Neural networks : the official journal of the International Neural Network Society
Spiking neural networks (SNNs) possess energy-efficient potential due to event-based computation. However, supervised training of SNNs remains a challenge as spike activities are non-differentiable. Previous SNNs training methods can be generally cat...

Design Space Exploration of Hardware Spiking Neurons for Embedded Artificial Intelligence.

Neural networks : the official journal of the International Neural Network Society
Machine learning is yielding unprecedented interest in research and industry, due to recent success in many applied contexts such as image classification and object recognition. However, the deployment of these systems requires huge computing capabil...

Gated spiking neural network using Iterative Free-Energy Optimization and rank-order coding for structure learning in memory sequences (INFERNO GATE).

Neural networks : the official journal of the International Neural Network Society
We present a framework based on iterative free-energy optimization with spiking neural networks for modeling the fronto-striatal system (PFC-BG) for the generation and recall of audio memory sequences. In line with neuroimaging studies carried out in...

Decoding and encoding (de)mixed population responses.

Current opinion in neurobiology
A central tenet of neuroscience is that the brain works through large populations of interacting neurons. With recent advances in recording techniques, the inner working of these populations has come into full view. Analyzing the resulting large-scal...

Event-driven implementation of deep spiking convolutional neural networks for supervised classification using the SpiNNaker neuromorphic platform.

Neural networks : the official journal of the International Neural Network Society
Neural networks have enabled great advances in recent times due mainly to improved parallel computing capabilities in accordance to Moore's Law, which allowed reducing the time needed for the parameter learning of complex, multi-layered neural archit...