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Comparing SNNs and RNNs on neuromorphic vision datasets: Similarities and differences.

Neural networks : the official journal of the International Neural Network Society
Neuromorphic data, recording frameless spike events, have attracted considerable attention for the spatiotemporal information components and the event-driven processing fashion. Spiking neural networks (SNNs) represent a family of event-driven models...

Self healable neuromorphic memtransistor elements for decentralized sensory signal processing in robotics.

Nature communications
Sensory information processing in robot skins currently rely on a centralized approach where signal transduction (on the body) is separated from centralized computation and decision-making, requiring the transfer of large amounts of data from periphe...

SpiFoG: an efficient supervised learning algorithm for the network of spiking neurons.

Scientific reports
There has been a lot of research on supervised learning in spiking neural network (SNN) for a couple of decades to improve computational efficiency. However, evolutionary algorithm based supervised learning for SNN has not been investigated thoroughl...

Spiking Neural P Systems with Delay on Synapses.

International journal of neural systems
Based on the feature and communication of neurons in animal neural systems, spiking neural P systems (SN P systems) were proposed as a kind of powerful computing model. Considering the length of axons and the information transmission speed on synapse...

A Mean-Field Description of Bursting Dynamics in Spiking Neural Networks with Short-Term Adaptation.

Neural computation
Bursting plays an important role in neural communication. At the population level, macroscopic bursting has been identified in populations of neurons that do not express intrinsic bursting mechanisms. For the analysis of phase transitions between bur...

Time-resolved neurotransmitter detection in mouse brain tissue using an artificial intelligence-nanogap.

Scientific reports
The analysis of neurotransmitters in the brain helps to understand brain functions and diagnose Parkinson's disease. Pharmacological inhibition experiments, electrophysiological measurement of action potentials, and mass analysers have been applied f...

Artificial Intelligence and Machine Learning in Arrhythmias and Cardiac Electrophysiology.

Circulation. Arrhythmia and electrophysiology
Artificial intelligence (AI) and machine learning (ML) in medicine are currently areas of intense exploration, showing potential to automate human tasks and even perform tasks beyond human capabilities. Literacy and understanding of AI/ML methods are...

Minimal Spiking Neuron for Solving Multilabel Classification Tasks.

Neural computation
The multispike tempotron (MST) is a powersul, single spiking neuron model that can solve complex supervised classification tasks. It is also internally complex, computationally expensive to evaluate, and unsuitable for neuromorphic hardware. Here we ...