A Sparse and Spike‐Timing‐Based Adaptive Photoencoder for Augmenting Machine Vision for Spiking Neural Networks
Sign Up to like & getrecommendations! Published in 2022 at "Advanced Materials"
DOI: 10.1002/adma.202202535
Abstract: The representation of external stimuli in the form of action potentials or spikes constitutes the basis of energy efficient neural computation that emerging spiking neural networks (SNNs) aspire to imitate. With recent evidence suggesting that… read more here.
Keywords: spike; photoencoder; spiking neural; timing based ... See more keywords
The maximum points-based supervised learning rule for spiking neural networks
Sign Up to like & getrecommendations! Published in 2019 at "Soft Computing"
DOI: 10.1007/s00500-018-3576-0
Abstract: As the third generation of neural networks, Spiking Neural Networks (SNNs) have made great success in pattern recognition fields. However, the existing training methods for SNNs are not efficient enough because of the temporal encoding… read more here.
Keywords: based supervised; maximum points; neural networks; supervised learning ... See more keywords
Research on learning mechanism designing for equilibrated bipolar spiking neural networks
Sign Up to like & getrecommendations! Published in 2020 at "Artificial Intelligence Review"
DOI: 10.1007/s10462-020-09818-5
Abstract: Artificial Intelligence (AI) has become very popular due to both the increasing demands from applications and the booming of computer techniques. Spiking Neural Network (SNN), as the third generation of Artificial Neural Network, receives more… read more here.
Keywords: neural networks; equilibrated bipolar; spiking neural; learning mechanism ... See more keywords
Logic Negation with Spiking Neural P Systems
Sign Up to like & getrecommendations! Published in 2020 at "Neural Processing Letters"
DOI: 10.1007/s11063-020-10324-6
Abstract: Nowadays, the success of neural networks as reasoning systems is doubtless. Nonetheless, one of the drawbacks of such reasoning systems is that they work as black-boxes and the acquired knowledge is not human readable. In… read more here.
Keywords: reasoning systems; spiking neural; logic negation; negation spiking ... See more keywords
Training a Hidden Markov Model with a Bayesian Spiking Neural Network
Sign Up to like & getrecommendations! Published in 2018 at "Journal of Signal Processing Systems"
DOI: 10.1007/s11265-016-1153-2
Abstract: It is of some interest to understand how statistically based mechanisms for signal processing might be integrated with biologically motivated mechanisms such as neural networks. This paper explores a novel hybrid approach for classifying segments… read more here.
Keywords: hidden markov; markov model; model; spiking neural ... See more keywords
Design of spiking neural networks for blood pressure prediction during general anesthesia: considerations for optimizing results
Sign Up to like & getrecommendations! Published in 2017 at "Evolving Systems"
DOI: 10.1007/s12530-017-9176-x
Abstract: The ability to predict blood pressure changes during general anesthesia would assist anesthetists minimize the risk of complications due to hypotensive events. However, such prediction is not trivial. Evolving spiking neural networks are a relatively… read more here.
Keywords: spiking neural; prediction; blood pressure; design ... See more keywords
Low power, ultrafast synaptic plasticity in 1R-ferroelectric tunnel memristive structure for spiking neural networks
Sign Up to like & getrecommendations! Published in 2019 at "AEU - International Journal of Electronics and Communications"
DOI: 10.1016/j.aeue.2019.01.003
Abstract: Abstract This paper presents the design and implementation of a low power and ultrafast spike-timing dependent plasticity (STDP) of the spiking neural network (SNN) in a crossbar structure based on the ferroelectric tunnel memristor (FTM)… read more here.
Keywords: plasticity; power; low power; structure ... See more keywords
A new scalable parallel adder based on spiking neural P systems, dendritic behavior, rules on the synapses and astrocyte-like control to compute multiple signed numbers
Sign Up to like & getrecommendations! Published in 2018 at "Neurocomputing"
DOI: 10.1016/j.neucom.2018.08.076
Abstract: Abstract This brief presents a scalable parallel neural adder circuit based on spiking neural P systems along with dendritic delays, dendritic feedback, rules on the synapses and astrocyte-like control to create a compact and highly… read more here.
Keywords: adder; based spiking; rules synapses; spiking neural ... See more keywords
LEGION-based image segmentation by means of spiking neural networks using normalized synaptic weights implemented on a compact scalable neuromorphic architecture
Sign Up to like & getrecommendations! Published in 2019 at "Neurocomputing"
DOI: 10.1016/j.neucom.2019.04.037
Abstract: Abstract LEGION (Locally Excitatory, Globally Inhibitory Oscillator Network) topology has demonstrated good capabilities in scene segmentation applications. However, the implementation of LEGION algorithm requires machines with high performance to process a set of complex differential… read more here.
Keywords: scalable neuromorphic; neural networks; segmentation; spiking neural ... See more keywords
Simplified and yet Turing universal spiking neural P systems with polarizations optimized by anti-spikes
Sign Up to like & getrecommendations! Published in 2020 at "Neurocomputing"
DOI: 10.1016/j.neucom.2020.07.051
Abstract: Abstract Spiking neural P systems with polarizations (PSN P systems) are a class of neural-inspired computation models, where the firing condition of rules is the neuron-associated polarization. It has previously been shown that PSN P… read more here.
Keywords: turing universal; systems polarizations; anti spikes; spiking neural ... See more keywords
STiDi-BP: Spike time displacement based error backpropagation in multilayer spiking neural networks
Sign Up to like & getrecommendations! Published in 2021 at "Neurocomputing"
DOI: 10.1016/j.neucom.2020.11.052
Abstract: Abstract Error backpropagation is the most common approach for direct training of spiking neural networks. However, the non-differentiability of spiking neurons makes the backpropagation of error a challenge. In this paper, we introduce a new… read more here.
Keywords: neural networks; error backpropagation; error; time ... See more keywords