AI Medical Compendium Topic

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Models, Neurological

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Statistical field theory of the transmission of nerve impulses.

Theoretical biology & medical modelling
BACKGROUND: Stochastic processes leading voltage-gated ion channel dynamics on the nerve cell membrane are a sufficient condition to describe membrane conductance through statistical mechanics of disordered and complex systems.

Evolving Deep Neural Networks via Cooperative Coevolution With Backpropagation.

IEEE transactions on neural networks and learning systems
Deep neural networks (DNNs), characterized by sophisticated architectures capable of learning a hierarchy of feature representations, have achieved remarkable successes in various applications. Learning DNN's parameters is a crucial but challenging t...

Constraints on Hebbian and STDP learned weights of a spiking neuron.

Neural networks : the official journal of the International Neural Network Society
We analyse mathematically the constraints on weights resulting from Hebbian and STDP learning rules applied to a spiking neuron with weight normalisation. In the case of pure Hebbian learning, we find that the normalised weights equal the promotion p...

Recursive Threshold Logic-A Bioinspired Reconfigurable Dynamic Logic System With Crossbar Arrays.

IEEE transactions on biomedical circuits and systems
The neuron behavioral models are inspired by the principle of the firing of neurons, and weighted accumulation of charge for a given set of input stimuli. Biological neurons show dynamic behavior through its feedback and feedforward time-dependent re...

Noise suppression ability and its mechanism analysis of scale-free spiking neural network under white Gaussian noise.

PloS one
With the continuous improvement of automation and informatization, the electromagnetic environment has become increasingly complex. Traditional protection methods for electronic systems are facing with serious challenges. Biological nervous system ha...

Interpreting and Improving Adversarial Robustness of Deep Neural Networks With Neuron Sensitivity.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Deep neural networks (DNNs) are vulnerable to adversarial examples where inputs with imperceptible perturbations mislead DNNs to incorrect results. Despite the potential risk they bring, adversarial examples are also valuable for providing insights i...

Transforming task representations to perform novel tasks.

Proceedings of the National Academy of Sciences of the United States of America
An important aspect of intelligence is the ability to adapt to a novel task without any direct experience (zero shot), based on its relationship to previous tasks. Humans can exhibit this cognitive flexibility. By contrast, models that achieve superh...

Strong inhibitory signaling underlies stable temporal dynamics and working memory in spiking neural networks.

Nature neuroscience
Cortical neurons process information on multiple timescales, and areas important for working memory (WM) contain neurons capable of integrating information over a long timescale. However, the underlying mechanisms for the emergence of neuronal timesc...

Changes to information in working memory depend on distinct removal operations.

Nature communications
Holding information in working memory is essential for cognition, but removing unwanted thoughts is equally important. Here we use multivariate pattern analyses of brain activity to demonstrate the successful manipulation and removal of information f...

A Robust Collision Perception Visual Neural Network With Specific Selectivity to Darker Objects.

IEEE transactions on cybernetics
Building an efficient and reliable collision perception visual system is a challenging problem for future robots and autonomous vehicles. The biological visual neural networks, which have evolved over millions of years in nature and are working perfe...