ArticleScientific reports2021
Clustering and control for adaptation uncovers time-warped spike time patterns in cortical networks in vivo.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 12 citations in OpenAlex.
- The wave-coded brain: transforming spike patterns through space-time dynamics.Biological cybernetics · 2026Article
- Modeling and simulation of neocortical micro- and mesocircuitry (Part II, Physiology and experimentation).eLife · 2026Article
- Sensory Encoding Alternates With Hippocampal Ripples across Cycles of Forebrain Spiking Cascades.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Neural coding of space by time.Biological cybernetics · 2024Article
- Cortical cell assemblies and their underlying connectivity: An in silico study.PLoS computational biology · 2024Article
- Global and local neuronal coding of tactile information in the barrel cortex.Frontiers in neuroscience · 2023Article
- From End to End: Gaining, Sorting, and Employing High-Density Neural Single Unit Recordings.Frontiers in neuroinformatics · 2022Review
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Authors and funding
5 authors at 4 institutions in 4 countries.
Funding
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Abstract
How information in the nervous system is encoded by patterns of action potentials (i.e. spikes) remains an open question. Multi-neuron patterns of single spikes are a prime candidate for spike time encoding but their temporal variability requires further characterisation. Here we show how known sources of spike count variability affect stimulus-evoked spike time patterns between neurons separated over multiple layers and columns of adult rat somatosensory cortex in vivo. On subsets of trials (clusters) and after controlling for stimulus-response adaptation, spike time differences between pairs of neurons are "time-warped" (compressed/stretched) by trial-to-trial changes in shared excitability, explaining why fixed spike time patterns and noise correlations are seldom reported. We show that predicted cortical state is correlated between groups of 4 neurons, introducing the possibility of spike time pattern modulation by population-wide trial-to-trial changes in excitability (i.e. cortical state). Under the assumption of state-dependent coding, we propose an improved potential encoding capacity.
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