ArticleFrontiers in computational neuroscience2017
Conduction Delay Learning Model for Unsupervised and Supervised Classification of Spatio-Temporal Spike Patterns.
Article in Frontiers in computational neuroscience, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Incremental self-organization of spatio-temporal spike pattern detection.Scientific reports · 2025Article
- Spiking Neural Network Models of Interaural Time Difference Extraction via a Massively Collaborative Process.eNeuro · 2025Article
- Adapting to time: Why nature may have evolved a diverse set of neurons.PLoS computational biology · 2024Article
- Article
- Event-Based Computation for Touch Localization Based on Precise Spike Timing.Frontiers in neuroscience · 2020Article
- A Delay Learning Algorithm Based on Spike Train Kernels for Spiking Neurons.Frontiers in neuroscience · 2019Article
- Modeling Pitch Perception With an Active Auditory Model Extended by Octopus Cells.Frontiers in neuroscience · 2018Article
- A Neuro-Inspired System for Online Learning and Recognition of Parallel Spike Trains, Based on Spike Latency, and Heterosynaptic STDP.Frontiers in neuroscience · 2018Article
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Abstract
Precise spike timing is considered to play a fundamental role in communications and signal processing in biological neural networks. Understanding the mechanism of spike timing adjustment would deepen our understanding of biological systems and enable advanced engineering applications such as efficient computational architectures. However, the biological mechanisms that adjust and maintain spike timing remain unclear. Existing algorithms adopt a supervised approach, which adjusts the axonal conduction delay and synaptic efficacy until the spike timings approximate the desired timings. This study proposes a spike timing-dependent learning model that adjusts the axonal conduction delay and synaptic efficacy in both unsupervised and supervised manners. The proposed learning algorithm approximates the Expectation-Maximization algorithm, and classifies the input data encoded into spatio-temporal spike patterns. Even in the supervised classification, the algorithm requires no external spikes indicating the desired spike timings unlike existing algorithms. Furthermore, because the algorithm is consistent with biological models and hypotheses found in existing biological studies, it could capture the mechanism underlying biological delay learning.
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