ArticleFrontiers in computational neuroscience2019
Back-Propagation Learning in Deep Spike-By-Spike Networks.
Article in Frontiers in computational neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Comparison of Diagnosis Accuracy between a Backpropagation Artificial Neural Network Model and Linear Regression in Digestive Disease Patients: an Empirical Research.Computational and mathematical methods in medicine · 2021Pooled it
- Interleaving cortex-analog mixing improves deep non-negative matrix factorization networks.Frontiers in computational neuroscience · 2025Article
- Learnability of the Boolean Innerproduct in Deep Neural Networks.Entropy (Basel, Switzerland) · 2022Article
- Current Characteristics Estimation of Si PV Modules Based on Artificial Neural Network Modeling.Materials (Basel, Switzerland) · 2019Article
- Back-Propagation Learning in Deep Spike-By-Spike Networks.Frontiers in computational neuroscience · 2019Article
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Authors and funding
2 authors.
Funding
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Abstract
Artificial neural networks (ANNs) are important building blocks in technical applications. They rely on noiseless continuous signals in stark contrast to the discrete action potentials stochastically exchanged among the neurons in real brains. We propose to bridge this gap with Spike-by-Spike (SbS) networks which represent a compromise between non-spiking and spiking versions of generative models. What is missing, however, are algorithms for finding weight sets that would optimize the output performances of deep SbS networks with many layers. Here, a learning rule for feed-forward SbS networks is derived. The properties of this approach are investigated and its functionality is demonstrated by simulations. In particular, a Deep Convolutional SbS network for classifying handwritten digits achieves a classification performance of roughly 99.3% on the MNIST test data when the learning rule is applied together with an optimizer. Thereby it approaches the benchmark results of ANNs without extensive parameter optimization. We envision this learning rule for SBS networks to provide a new basis for research in neuroscience and for technical applications, especially when they become implemented on specialized computational hardware.
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