Spiking neural network with local plasticity and sparse connectivity for audio classification [PDF]
Известия высших учебных заведений: Прикладная нелинейная динамикаPurpose. Studying the possibility of implementing a data classification method based on a spiking neural network, which has a low number of connections and is trained based on local plasticity rules, such as Spike-Timing-Dependent Plasticity.
Rybka, Roman Борисович +4 more
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Using a Low-Power Spiking Continuous Time Neuron (SCTN) for Sound Signal Processing
Sensors, 2021 This work presents a new approach based on a spiking neural network for sound preprocessing and classification. The proposed approach is biologically inspired by the biological neuron’s characteristic using spiking neurons, and Spike-Timing-Dependent ...
Moshe Bensimon +2 more
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Neural Sampling by Irregular Gating Inhibition of Spiking Neurons and Attractor Networks [PDF]
, 2016 A long tradition in theoretical neuroscience casts sensory processing in the brain as the process of inferring the maximally consistent interpretations of imperfect sensory input.
Indiveri, Giacomo, Muller, Lorenz K.
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A parallel supercomputer implementation of a biological inspired neural network and its use for pattern recognition [PDF]
, 2012 : A parallel implementation of a large spiking neural network is proposed and evaluated. The neural network implements the binding by synchrony process using the Oscillatory Dynamic Link Matcher (ODLM).
Bergeron, Jocelyn +6 more
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Sa-SNN: spiking attention neural network for image classification [PDF]
PeerJ Computer ScienceSpiking neural networks (SNNs) are known as third generation neural networks due to their energy efficient and low power consumption. SNNs have received a lot of attention due to their biological plausibility. SNNs are closer to the way biological neural
Yongping Dan +3 more
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A generative spike train model with time-structured higher order correlations [PDF]
, 2013 Emerging technologies are revealing the spiking activity in ever larger neural ensembles. Frequently, this spiking is far from independent, with correlations in the spike times of different cells.
Eric eShea-Brown +4 more
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Stochastic dynamics of a finite-size spiking neural network
, 2006 We present a simple Markov model of spiking neural dynamics that can be analytically solved to characterize the stochastic dynamics of a finite-size spiking neural network.
Chow, C. C., Soula, H.
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Phase diagram of spiking neural networks [PDF]
Frontiers in Computational Neuroscience, 2015 oscillations are studied in this ...
openaire +4 more sources
Real Spike: Learning Real-Valued Spikes for Spiking Neural Networks
, 2022 Accepted by ...
Guo, Yufei +7 more
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Equivalence of Additive and Multiplicative Coupling in Spiking Neural Networks
IEEE Access, 2023 Spiking neural network models characterize the emergent collective dynamics of circuits of biological neurons and help engineer neuro-inspired solutions across fields. Most dynamical systems’ models of spiking neural networks typically exhibit one
Georg Borner +2 more
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