ArticleFrontiers in neuroscience2022
Analyzing time-to-first-spike coding schemes: A theoretical approach.
Article in Frontiers in neuroscience, 2022. 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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8 citing papers in PubMed.
- Volatile self-selective memristive neuron for millisecond-latency neuromorphic object detection at the edge.Nature communications · 2026Article
- Enhanced Release Probability without Changes in Synaptic Delay during Analogue-Digital Facilitation.Cells · 2024Article
- Sparse-firing regularization methods for spiking neural networks with time-to-first-spike coding.Scientific reports · 2023Article
- Phase information is conserved in sparse, synchronous population-rate-codes via phase-to-rate recoding.Nature communications · 2023Article
- Online spike-based recognition of digits with ultrafast microlaser neurons.Frontiers in computational neuroscience · 2023Article
- First-spike coding promotes accurate and efficient spiking neural networks for discrete events with rich temporal structures.Frontiers in neuroscience · 2023Article
- Enhanced representation learning with temporal coding in sparsely spiking neural networks.Frontiers in computational neuroscience · 2023Article
- Precise Spiking Motifs in Neurobiological and Neuromorphic Data.Brain sciences · 2022Review
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4 authors.
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Abstract
Spiking neural networks (SNNs) using time-to-first-spike (TTFS) codes, in which neurons fire at most once, are appealing for rapid and low power processing. In this theoretical paper, we focus on information coding and decoding in those networks, and introduce a new unifying mathematical framework that allows the comparison of various coding schemes. In an early proposal, called rank-order coding (ROC), neurons are maximally activated when inputs arrive in the order of their synaptic weights, thanks to a shunting inhibition mechanism that progressively desensitizes the neurons as spikes arrive. In another proposal, called NoM coding, only the first
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