Spiking Neuron Models: An Introduction
Spiking Neuron Models: An Introduction
Recognition by Variance: Learning Rules for Spatiotemporal Patterns
Neural Computation
LabelMe: A Database and Web-Based Tool for Image Annotation
International Journal of Computer Vision
Obstacle to training SpikeProp networks: cause of surges in training process
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Memory dynamics in attractor networks with saliency weights
Neural Computation
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IEEE Computational Intelligence Magazine
Computational Intelligence Applications for Defense [Research Frontier]
IEEE Computational Intelligence Magazine
Temporal Processing in primate motor control: relation between cortical and EMG activity
IEEE Transactions on Neural Networks
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During the past few decades, remarkable progress has been made in solving pattern recognition problems using networks of spiking neurons. However, the issue of pattern recognition involving computational process from sensory encoding to synaptic learning remains underexplored, as most existing models or algorithms target only part of the computational process. Furthermore, many learning algorithms proposed in the literature neglect or pay little attention to sensory information encoding, which makes them incompatible with neural-realistic sensory signals encoded from real-world stimuli. By treating sensory coding and learning as a systematic process, we attempt to build an integrated model based on spiking neural networks SNNs, which performs sensory neural encoding and supervised learning with precisely timed sequences of spikes. With emerging evidence of precise spike-timing neural activities, the view that information is represented by explicit firing times of action potentials rather than mean firing rates has been receiving increasing attention. The external sensory stimulation is first converted into spatiotemporal patterns using a latency-phase encoding method and subsequently transmitted to the consecutive network for learning. Spiking neurons are trained to reproduce target signals encoded with precisely timed spikes. We show that when a supervised spike-timing-based learning is used, different spatiotemporal patterns are recognized by different spike patterns with a high time precision in milliseconds.