AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Neurons

Showing 191 to 200 of 1319 articles

Clear Filters

Empirical modeling and prediction of neuronal dynamics.

Biological cybernetics
Mathematical modeling of neuronal dynamics has experienced a fast growth in the last decades thanks to the biophysical formalism introduced by Hodgkin and Huxley in the 1950s. Other types of models (for instance, integrate and fire models), although ...

Physics-informed neural wavefields with Gabor basis functions.

Neural networks : the official journal of the International Neural Network Society
Recently, Physics-Informed Neural Networks (PINNs) have gained significant attention for their versatile interpolation capabilities in solving partial differential equations (PDEs). Despite their potential, the training can be computationally demandi...

Graph Representation Learning for Large-Scale Neuronal Morphological Analysis.

IEEE transactions on neural networks and learning systems
The analysis of neuronal morphological data is essential to investigate the neuronal properties and brain mechanisms. The complex morphologies, absence of annotations, and sheer volume of these data pose significant challenges in neuronal morphologic...

Floating-Point Approximation Enabling Cost-Effective and High-Precision Digital Implementation of FitzHugh-Nagumo Neural Networks.

IEEE transactions on biomedical circuits and systems
The study of neuron interactions and hardware implementations are crucial research directions in neuroscience, particularly in developing large-scale biological neural networks. The FitzHugh-Nagumo (FHN) model is a popular neuron model with highly bi...

Emergence of Emotion Selectivity in Deep Neural Networks Trained to Recognize Visual Objects.

PLoS computational biology
Recent neuroimaging studies have shown that the visual cortex plays an important role in representing the affective significance of visual input. The origin of these affect-specific visual representations is debated: they are intrinsic to the visual ...

A lightweight and gradient-stable neural layer.

Neural networks : the official journal of the International Neural Network Society
To enhance resource efficiency and model deployability of neural networks, we propose a neural-layer architecture based on Householder weighting and absolute-value activating, called Householder-absolute neural layer or simply Han-layer. Compared to ...

A fully spiking coupled model of a deep neural network and a recurrent attractor explains dynamics of decision making in an object recognition task.

Journal of neural engineering
Object recognition and making a choice regarding the recognized object is pivotal for most animals. This process in the brain contains information representation and decision making steps which both take different amount of times for different object...

Functional loops: Monitoring functional organization of deep neural networks using algebraic topology.

Neural networks : the official journal of the International Neural Network Society
Various topological methods have emerged in recent years to investigate the inner workings of deep neural networks (DNNs) based on the structural and weight information. However, their effectiveness is restricted due to the stratified structure and v...

A universal ANN-to-SNN framework for achieving high accuracy and low latency deep Spiking Neural Networks.

Neural networks : the official journal of the International Neural Network Society
Spiking Neural Networks (SNNs) have become one of the most prominent next-generation computational models owing to their biological plausibility, low power consumption, and the potential for neuromorphic hardware implementation. Among the various met...

Bayesian inference is facilitated by modular neural networks with different time scales.

PLoS computational biology
Various animals, including humans, have been suggested to perform Bayesian inferences to handle noisy, time-varying external information. In performing Bayesian inference by the brain, the prior distribution must be acquired and represented by sampli...