AI Medical Compendium Topic:
Models, Neurological

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Born to learn: The inspiration, progress, and future of evolved plastic artificial neural networks.

Neural networks : the official journal of the International Neural Network Society
Biological neural networks are systems of extraordinary computational capabilities shaped by evolution, development, and lifelong learning. The interplay of these elements leads to the emergence of biological intelligence. Inspired by such intricate ...

Identifying individuals with attention deficit hyperactivity disorder based on temporal variability of dynamic functional connectivity.

Scientific reports
Attention deficit hyperactivity disorder (ADHD) is a common disorder that emerges in school-age children. The diagnostic model based on neuroimaging features could be beneficial for ADHD in twofold: identifying individuals with ADHD and discovering t...

Evolving Spiking Neural Networks for online learning over drifting data streams.

Neural networks : the official journal of the International Neural Network Society
Nowadays huge volumes of data are produced in the form of fast streams, which are further affected by non-stationary phenomena. The resulting lack of stationarity in the distribution of the produced data calls for efficient and scalable algorithms fo...

A Survey of Cognitive Architectures in the Past 20 Years.

IEEE transactions on cybernetics
Building autonomous systems that achieve human level intelligence is one of the primary objectives in artificial intelligence (AI). It requires the study of a wide range of functions robustly across different phases of human cognition. This paper pre...

Analysis and Simulation of Capacitor-Less ReRAM-Based Stochastic Neurons for the in-Memory Spiking Neural Network.

IEEE transactions on biomedical circuits and systems
The stochastic neuron is a key for event-based probabilistic neural networks. We propose a stochastic neuron using a metal-oxide resistive random-access memory (ReRAM). The ReRAM's conducting filament with built-in stochasticity is used to mimic the ...

Deep(er) Learning.

The Journal of neuroscience : the official journal of the Society for Neuroscience
Animals successfully thrive in noisy environments with finite resources. The necessity to function with resource constraints has led evolution to design animal brains (and bodies) to be optimal in their use of computational power while being adaptabl...

Large-Scale, High-Resolution Comparison of the Core Visual Object Recognition Behavior of Humans, Monkeys, and State-of-the-Art Deep Artificial Neural Networks.

The Journal of neuroscience : the official journal of the Society for Neuroscience
Primates, including humans, can typically recognize objects in visual images at a glance despite naturally occurring identity-preserving image transformations (e.g., changes in viewpoint). A primary neuroscience goal is to uncover neuron-level mechan...

A molecular neuromorphic network device consisting of single-walled carbon nanotubes complexed with polyoxometalate.

Nature communications
In contrast to AI hardware, neuromorphic hardware is based on neuroscience, wherein constructing both spiking neurons and their dense and complex networks is essential to obtain intelligent abilities. However, the integration density of present neuro...

Scaling up molecular pattern recognition with DNA-based winner-take-all neural networks.

Nature
From bacteria following simple chemical gradients to the brain distinguishing complex odour information, the ability to recognize molecular patterns is essential for biological organisms. This type of information-processing function has been implemen...

Neuromorphic computing with multi-memristive synapses.

Nature communications
Neuromorphic computing has emerged as a promising avenue towards building the next generation of intelligent computing systems. It has been proposed that memristive devices, which exhibit history-dependent conductivity modulation, could efficiently r...