AIMC Topic: Models, Neurological

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Competitive Learning in a Spiking Neural Network: Towards an Intelligent Pattern Classifier.

Sensors (Basel, Switzerland)
One of the modern trends in the design of human-machine interfaces (HMI) is to involve the so called spiking neuron networks (SNNs) in signal processing. The SNNs can be trained by simple and efficient biologically inspired algorithms. In particular,...

An efficient analytical reduction of detailed nonlinear neuron models.

Nature communications
Detailed conductance-based nonlinear neuron models consisting of thousands of synapses are key for understanding of the computational properties of single neurons and large neuronal networks, and for interpreting experimental results. Simulations of ...

The Design of Memristive Circuit for Affective Multi-Associative Learning.

IEEE transactions on biomedical circuits and systems
In this work, a memristive circuit with affective multi-associative learning function is proposed, which mimics the process of human affective formation. It mainly contains three modules: affective associative learning, affective formation, affective...

Interpreting neural decoding models using grouped model reliance.

PLoS computational biology
Machine learning algorithms are becoming increasingly popular for decoding psychological constructs based on neural data. However, as a step towards bridging the gap between theory-driven cognitive neuroscience and data-driven decoding approaches, th...

State-of-the-art methods in healthcare text classification system: AI paradigm.

Frontiers in bioscience (Landmark edition)
Machine learning has shown its importance in delivering healthcare solutions and revolutionizing the future of filtering huge amountd of textual content. The machine intelligence can adapt semantic relations among text to infer finer contextual infor...

Synchronization of Hindmarsh Rose Neurons.

Neural networks : the official journal of the International Neural Network Society
Modeling and implementation of biological neurons are key to the fundamental understanding of neural network architectures in the brain and its cognitive behavior. Synchronization of neuronal models play a significant role in neural signal processing...

A mean-field approach to the dynamics of networks of complex neurons, from nonlinear Integrate-and-Fire to Hodgkin-Huxley models.

Journal of neurophysiology
We present a mean-field formalism able to predict the collective dynamics of large networks of conductance-based interacting spiking neurons. We apply this formalism to several neuronal models, from the simplest Adaptive Exponential Integrate-and-Fir...

From Synaptic Interactions to Collective Dynamics in Random Neuronal Networks Models: Critical Role of Eigenvectors and Transient Behavior.

Neural computation
The study of neuronal interactions is at the center of several big collaborative neuroscience projects (including the Human Connectome Project, the Blue Brain Project, and the Brainome) that attempt to obtain a detailed map of the entire brain. Under...

Exploiting the stimuli encoding scheme of evolving Spiking Neural Networks for stream learning.

Neural networks : the official journal of the International Neural Network Society
Stream data processing has lately gained momentum with the arrival of new Big Data scenarios and applications dealing with continuously produced information flows. Unfortunately, traditional machine learning algorithms are not prepared to tackle the ...

Computational analysis of non-invasive deep brain stimulation based on interfering electric fields.

Physics in medicine and biology
Neuromodulation modalities are used as effective treatments for some brain disorders. Non-invasive deep brain stimulation (NDBS) via temporally interfering electric fields has emerged recently as a non-invasive strategy for electrically stimulating d...