AIMC Topic: Neural Networks, Computer

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Multistability of neural networks with discontinuous non-monotonic piecewise linear activation functions and time-varying delays.

Neural networks : the official journal of the International Neural Network Society
This paper is concerned with the problem of coexistence and dynamical behaviors of multiple equilibrium points for neural networks with discontinuous non-monotonic piecewise linear activation functions and time-varying delays. The fixed point theorem...

Autoshaped choice in artificial neural networks: implications for behavioral economics and neuroeconomics.

Behavioural processes
An existing neural network model of conditioning was used to simulate autoshaped choice. In this phenomenon, pigeons first receive an autoshaping procedure with two keylight stimuli X and Y separately paired with food in a forward-delay manner, inter...

Rapid automated classification of anesthetic depth levels using GPU based parallelization of neural networks.

Journal of medical systems
The effect of anesthesia on the patient is referred to as depth of anesthesia. Rapid classification of appropriate depth level of anesthesia is a matter of great importance in surgical operations. Similarly, accelerating classification algorithms is ...

Universal Memcomputing Machines.

IEEE transactions on neural networks and learning systems
We introduce the notion of universal memcomputing machines (UMMs): a class of brain-inspired general-purpose computing machines based on systems with memory, whereby processing and storing of information occur on the same physical location. We analyt...

Delayed mutual information infers patterns of synaptic connectivity in a proprioceptive neural network.

Journal of computational neuroscience
Understanding the patterns of interconnections between neurons in complex networks is an enormous challenge using traditional physiological approaches. Here we combine the use of an information theoretic approach with intracellular recording to estab...

Modeling land use and land cover changes in a vulnerable coastal region using artificial neural networks and cellular automata.

Environmental monitoring and assessment
As one of the most vulnerable coasts in the continental USA, the Lower Mississippi River Basin (LMRB) region has endured numerous hazards over the past decades. The sustainability of this region has drawn great attention from the international, natio...

Analyses of a cirrhotic patient's evolution using self organizing mapping and Child-Pugh scoring.

Journal of medical systems
Due to the importance of cirrhosis evolution, this study examined cirrhotic patients using Self Organizing Mapping (SOM) based on the Child-Pugh scoring method. Because Colored Doppler Ultrasound (CDU) has too many parameters, scoring can be a very d...

A novel multiple instance learning method based on extreme learning machine.

Computational intelligence and neuroscience
Since real-world data sets usually contain large instances, it is meaningful to develop efficient and effective multiple instance learning (MIL) algorithm. As a learning paradigm, MIL is different from traditional supervised learning that handles the...

Attention modeled as information in learning multisensory integration.

Neural networks : the official journal of the International Neural Network Society
Top-down cognitive processes affect the way bottom-up cross-sensory stimuli are integrated. In this paper, we therefore extend a successful previous neural network model of learning multisensory integration in the superior colliculus (SC) by top-down...

Training spiking neural models using artificial bee colony.

Computational intelligence and neuroscience
Spiking neurons are models designed to simulate, in a realistic manner, the behavior of biological neurons. Recently, it has been proven that this type of neurons can be applied to solve pattern recognition problems with great efficiency. However, th...