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Inferring a network from dynamical signals at its nodes.

PLoS computational biology
We give an approximate solution to the difficult inverse problem of inferring the topology of an unknown network from given time-dependent signals at the nodes. For example, we measure signals from individual neurons in the brain, and infer how they ...

Deep-learned spike representations and sorting via an ensemble of auto-encoders.

Neural networks : the official journal of the International Neural Network Society
Spike sorting refers to the technique of detecting signals generated by single neurons from multi-neuron recordings and is a valuable tool for analyzing the relationships between individual neuronal activity patterns and specific behaviors. Since the...

Artificial Intelligence-Electrocardiography to Predict Incident Atrial Fibrillation: A Population-Based Study.

Circulation. Arrhythmia and electrophysiology
BACKGROUND: An artificial intelligence (AI) algorithm applied to electrocardiography during sinus rhythm has recently been shown to detect concurrent episodic atrial fibrillation (AF). We sought to characterize the value of AI-enabled electrocardiogr...

Neuromorphic Engineering: From Biological to Spike-Based Hardware Nervous Systems.

Advanced materials (Deerfield Beach, Fla.)
The human brain is a sophisticated, high-performance biocomputer that processes multiple complex tasks in parallel with high efficiency and remarkably low power consumption. Scientists have long been pursuing an artificial intelligence (AI) that can ...

Evolution-Communication Spiking Neural P Systems.

International journal of neural systems
Spiking neural P systems (SNP systems) are a class of distributed and parallel computation models, which are inspired by the way in which neurons process information through spikes, where the integrate-and-fire behavior of neurons and the distributio...

Network structure of cascading neural systems predicts stimulus propagation and recovery.

Journal of neural engineering
OBJECTIVE: Many neural systems display spontaneous, spatiotemporal patterns of neural activity that are crucial for information processing. While these cascading patterns presumably arise from the underlying network of synaptic connections between ne...

Population coding in the cerebellum: a machine learning perspective.

Journal of neurophysiology
The cere resembles a feedforward, three-layer network of neurons in which the "hidden layer" consists of Purkinje cells (P-cells) and the output layer consists of deep cerebellar nucleus (DCN) neurons. In this analogy, the output of each DCN neuron i...

Pattern Recognition of Spiking Neural Networks Based on Visual Mechanism and Supervised Synaptic Learning.

Neural plasticity
Electrophysiological studies have shown that mammalian primary visual cortex are selective for the orientations of visual stimuli. Inspired by this mechanism, we propose a hierarchical spiking neural network (SNN) for image classification. Grayscale ...

Passive Nonlinear Dendritic Interactions as a Computational Resource in Spiking Neural Networks.

Neural computation
Nonlinear interactions in the dendritic tree play a key role in neural computation. Nevertheless, modeling frameworks aimed at the construction of large-scale, functional spiking neural networks, such as the Neural Engineering Framework, tend to assu...