AI Medical Compendium Topic:
Models, Neurological

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Abstract concept learning in a simple neural network inspired by the insect brain.

PLoS computational biology
The capacity to learn abstract concepts such as 'sameness' and 'difference' is considered a higher-order cognitive function, typically thought to be dependent on top-down neocortical processing. It is therefore surprising that honey bees apparantly h...

Learning of spatiotemporal patterns in a spiking neural network with resistive switching synapses.

Science advances
The human brain is a complex integrated spatiotemporal system, where space (which neuron fires) and time (when a neuron fires) both carry information to be processed by cognitive functions. To parallel the energy efficiency and computing functionalit...

Digital Multiplierless Realization of Coupled Wilson Neuron Model.

IEEE transactions on biomedical circuits and systems
The human brain is composed of 10 neurons with a switching speed of about 1 ms. Studying spiking neural networks, including the modeling, simulation, and implementation of the biological neuron models, helps us to learn about the brain and the relate...

Evaluating (and Improving) the Correspondence Between Deep Neural Networks and Human Representations.

Cognitive science
Decades of psychological research have been aimed at modeling how people learn features and categories. The empirical validation of these theories is often based on artificial stimuli with simple representations. Recently, deep neural networks have r...

Toward Fast Neural Computing using All-Photonic Phase Change Spiking Neurons.

Scientific reports
The rapid growth of brain-inspired computing coupled with the inefficiencies in the CMOS implementations of neuromrphic systems has led to intense exploration of efficient hardware implementations of the functional units of the brain, namely, neurons...

Domain Wall Motion-Based Dual-Threshold Activation Unit for Low-Power Classification of Non-Linearly Separable Functions.

IEEE transactions on biomedical circuits and systems
Recently, a great deal of scientific endeavour has been devoted to developing spin-based neuromorphic platforms owing to the ultra-low-power benefits offered by spin devices and the inherent correspondence between spintronic phenomena and the desired...

Monostable multivibrators as novel artificial neurons.

Neural networks : the official journal of the International Neural Network Society
Retriggerable and non-retriggerable monostable multivibrators are simple timers with a single characteristic, their period. Motivated by the fact that monostable multivibrators are implementable in large quantities as counters in digital programmable...

Handwritten-Digit Recognition by Hybrid Convolutional Neural Network based on HfO Memristive Spiking-Neuron.

Scientific reports
Although there is a huge progress in complementary-metal-oxide-semiconductor (CMOS) technology, construction of an artificial neural network using CMOS technology to realize the functionality comparable with that of human cerebral cortex containing 1...

Application of chaos in a recurrent neural network to control in ill-posed problems: a novel autonomous robot arm.

Biological cybernetics
Inspired by a viewpoint that complex/chaotic dynamics would play an important role in biological systems including the brain, chaotic dynamics introduced in a recurrent neural network was applied to robot control in ill-posed situations. By computer ...

Neural circuits for learning context-dependent associations of stimuli.

Neural networks : the official journal of the International Neural Network Society
The use of reinforcement learning combined with neural networks provides a powerful framework for solving certain tasks in engineering and cognitive science. Previous research shows that neural networks have the power to automatically extract feature...