Motor control by precisely timed spike patterns
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Abstract (arXiv)
A fundamental problem in neuroscience is to understand how sequences of action potentials ("spikes") encode information about sensory signals and motor outputs. Although traditional theories of neural coding assume that information is conveyed by the total number of spikes fired (spike rate), recent studies of sensory and motor activity have shown that far more information is carried by the millisecond-scale timing patterns of action potentials (spike timing). However, it is unknown whether or how subtle differences in spike timing drive differences in perception or behavior, leaving it unclear whether the information carried by spike timing actually plays a causal role in brain function. Here we demonstrate how a precise spike timing code is read out downstream by the muscles to control behavior. We provide both correlative and causal evidence to show that the nervous system uses millisecond-scale variations in the timing of spikes within multi-spike patterns to regulate a relatively simple behavior - respiration in the Bengalese finch, a songbird. These findings suggest that a fundamental assumption of current theories of motor coding requires revision, and that significant improvements in applications, such as neural prosthetic devices, can be achieved by using precise spike timing information.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 29 May 2016.
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Citing papers per year: 2023: 1; 2024: 1; 2025: 1.
3 papers.
- 2024Advancing Training Efficiency of Deep Spiking Neural Networks through Rate-based Backpropagationcited by 8
- 2023Evaluating Spiking Neural Network On Neuromorphic Platform For Human Activity Recognitioncited by 2
- 2025Predictive Spike Timing Enables Distributed Shortest Path Computation in Spiking Neural Networkscited by 0
- 2025Predictive Spike Timing Enables Distributed Shortest Path Computation in Spiking Neural Networks12 Sep 2025
- 2024Advancing Training Efficiency of Deep Spiking Neural Networks through Rate-based Backpropagation15 Oct 2024
- 2023Evaluating Spiking Neural Network On Neuromorphic Platform For Human Activity Recognition1 Aug 2023