AIMC Topic: Neurons

Clear Filters Showing 1111 to 1120 of 1455 articles

New results on anti-synchronization of switched neural networks with time-varying delays and lag signals.

Neural networks : the official journal of the International Neural Network Society
This paper investigates the problem of global exponential anti-synchronization of a class of switched neural networks with time-varying delays and lag signals. Considering the packed circuits, the controller is dependent on the output of the system a...

Analytically tractable studies of traveling waves of activity in integrate-and-fire neural networks.

Physical review. E
In contrast to other large-scale network models for propagation of electrical activity in neural tissue that have no analytical solutions for their dynamics, we show that for a specific class of integrate and fire neural networks the acceleration dep...

A neural model of the frontal eye fields with reward-based learning.

Neural networks : the official journal of the International Neural Network Society
Decision-making is a flexible process dependent on the accumulation of various kinds of information; however, the corresponding neural mechanisms are far from clear. We extended a layered model of the frontal eye field to a learning-based model, usin...

Analysis of complex neural circuits with nonlinear multidimensional hidden state models.

Proceedings of the National Academy of Sciences of the United States of America
A universal need in understanding complex networks is the identification of individual information channels and their mutual interactions under different conditions. In neuroscience, our premier example, networks made up of billions of nodes dynamica...

Proposal for an All-Spin Artificial Neural Network: Emulating Neural and Synaptic Functionalities Through Domain Wall Motion in Ferromagnets.

IEEE transactions on biomedical circuits and systems
Non-Boolean computing based on emerging post-CMOS technologies can potentially pave the way for low-power neural computing platforms. However, existing work on such emerging neuromorphic architectures have either focused on solely mimicking the neuro...

Mapping Generative Models onto a Network of Digital Spiking Neurons.

IEEE transactions on biomedical circuits and systems
Stochastic neural networks such as Restricted Boltzmann Machines (RBMs) have been successfully used in applications ranging from speech recognition to image classification, and are particularly interesting because of their potential for generative ta...

All Spin Artificial Neural Networks Based on Compound Spintronic Synapse and Neuron.

IEEE transactions on biomedical circuits and systems
Artificial synaptic devices implemented by emerging post-CMOS non-volatile memory technologies such as Resistive RAM (RRAM) have made great progress recently. However, it is still a big challenge to fabricate stable and controllable multilevel RRAM. ...

Intrinsic Plasticity for Natural Competition in Koniocortex-Like Neural Networks.

International journal of neural systems
In this paper, we use the neural property known as intrinsic plasticity to develop neural network models that resemble the koniocortex, the fourth layer of sensory cortices. These models evolved from a very basic two-layered neural network to a compl...

Memory recall and spike-frequency adaptation.

Physical review. E
The brain can reproduce memories from partial data; this ability is critical for memory recall. The process of memory recall has been studied using autoassociative networks such as the Hopfield model. This kind of model reliably converges to stored p...

Part 2-The firings of many neurons and their density; the neural network its connections and field of firings.

Neural networks : the official journal of the International Neural Network Society
This paper is concerned with the firing of many neurons and the synthesis of these firings to develop functions and their transforms which relate chemical and electrical phenomena to the physical world. The density of such functions in the most gener...