ArticlePLoS computational biology2014
Spike-threshold adaptation predicted by membrane potential dynamics in vivo.
Article in PLoS computational biology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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Who cites it
46 citing papers in PubMed.
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- Heterogeneity in Neuronal Dynamics Is Learned by Gradient Descent for Temporal Processing Tasks.Neural computation · 2023Article
- Biologically-inspired neuronal adaptation improves learning in neural networks.Communicative & integrative biology · 2023Article
- A dynamic spike threshold with correlated noise predicts observed patterns of negative interval correlations in neuronal spike trains.Biological cybernetics · 2022Article
- Cortical Representation of Touch in Silico.Neuroinformatics · 2022Article
- Neural synchrony in cortical networks: mechanisms and implications for neural information processing and coding.Frontiers in integrative neuroscience · 2022Article
- Reliable Sensory Processing in Mouse Visual Cortex through Cooperative Interactions between Somatostatin and Parvalbumin Interneurons.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2021Article
- Monosynaptic inference via finely-timed spikes.Journal of computational neuroscience · 2021Article
- Polynomial, piecewise-Linear, Step (PLS): A Simple, Scalable, and Efficient Framework for Modeling Neurons.Frontiers in neuroinformatics · 2021Article
- Axonal NaScience advances · 2020Article
- Unsupervised Adaptive Weight Pruning for Energy-Efficient Neuromorphic Systems.Frontiers in neuroscience · 2020Article
- Homogeneous Intrinsic Neuronal Excitability Induces Overfitting to Sensory Noise: A Robot Model of Neurodevelopmental Disorder.Frontiers in psychiatry · 2020Article
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3 authors.
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Abstract
Neurons encode information in sequences of spikes, which are triggered when their membrane potential crosses a threshold. In vivo, the spiking threshold displays large variability suggesting that threshold dynamics have a profound influence on how the combined input of a neuron is encoded in the spiking. Threshold variability could be explained by adaptation to the membrane potential. However, it could also be the case that most threshold variability reflects noise and processes other than threshold adaptation. Here, we investigated threshold variation in auditory neurons responses recorded in vivo in barn owls. We found that spike threshold is quantitatively predicted by a model in which the threshold adapts, tracking the membrane potential at a short timescale. As a result, in these neurons, slow voltage fluctuations do not contribute to spiking because they are filtered by threshold adaptation. More importantly, these neurons can only respond to input spikes arriving together on a millisecond timescale. These results demonstrate that fast adaptation to the membrane potential captures spike threshold variability in vivo.
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