ArticleFrontiers in neuroscience2013
Synthesis of neural networks for spatio-temporal spike pattern recognition and processing.
Article in Frontiers in neuroscience, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Potassium homeostasis during disease progression of Alzheimer's disease.The Journal of physiology · 2025Article
- Adapting to time: Why nature may have evolved a diverse set of neurons.PLoS computational biology · 2024Article
- Loss of neuron network coherence induced by virus-infected astrocytes: a model study.Scientific reports · 2023Article
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- Event-Based Feature Extraction Using Adaptive Selection Thresholds.Sensors (Basel, Switzerland) · 2020Article
- Echo View Cells From Bio-Inspired Sonar.Frontiers in neurorobotics · 2020Article
- Generating Brain Waves, the Power of Astrocytes.Frontiers in neuroscience · 2019Review
- Deep Learning With Spiking Neurons: Opportunities and Challenges.Frontiers in neuroscience · 2018Review
- Feature Representations for Neuromorphic Audio Spike Streams.Frontiers in neuroscience · 2018Article
- Skimming Digits: Neuromorphic Classification of Spike-Encoded Images.Frontiers in neuroscience · 2016Article
- A Review of Control Strategies in Closed-Loop Neuroprosthetic Systems.Frontiers in neuroscience · 2016Review
- Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades.Frontiers in neuroscience · 2015Article
- Spatiotemporal features for asynchronous event-based data.Frontiers in neuroscience · 2015Article
- Racing to learn: statistical inference and learning in a single spiking neuron with adaptive kernels.Frontiers in neuroscience · 2014Article
- A framework for plasticity implementation on the SpiNNaker neural architecture.Frontiers in neuroscience · 2014Article
- The ripple pond: enabling spiking networks to see.Frontiers in neuroscience · 2013Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The advent of large scale neural computational platforms has highlighted the lack of algorithms for synthesis of neural structures to perform predefined cognitive tasks. The Neural Engineering Framework (NEF) offers one such synthesis, but it is most effective for a spike rate representation of neural information, and it requires a large number of neurons to implement simple functions. We describe a neural network synthesis method that generates synaptic connectivity for neurons which process time-encoded neural signals, and which makes very sparse use of neurons. The method allows the user to specify-arbitrarily-neuronal characteristics such as axonal and dendritic delays, and synaptic transfer functions, and then solves for the optimal input-output relationship using computed dendritic weights. The method may be used for batch or online learning and has an extremely fast optimization process. We demonstrate its use in generating a network to recognize speech which is sparsely encoded as spike times.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.