ArticleFrontiers in computational neuroscience2014
The neuronal response at extended timescales: long-term correlations without long-term memory.
Article in Frontiers in computational neuroscience, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- A biophysical perspective on the resilience of neuronal excitability across timescales.Nature reviews. Neuroscience · 2023Review
- Dynamical Timescale Explains Marginal Stability in Excitability Dynamics.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2017Article
- The neuronal response at extended timescales: a linearized spiking input-output relation.Frontiers in computational neuroscience · 2014Article
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Authors and funding
2 authors.
Funding
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Abstract
Long term temporal correlations frequently appear at many levels of neural activity. We show that when such correlations appear in isolated neurons, they indicate the existence of slow underlying processes and lead to explicit conditions on the dynamics of these processes. Moreover, although these slow processes can potentially store information for long times, we demonstrate that this does not imply that the neuron possesses a long memory of its input, even if these processes are bidirectionally coupled with neuronal response. We derive these results for a broad class of biophysical neuron models, and then fit a specific model to recent experiments. The model reproduces the experimental results, exhibiting long term (days-long) correlations due to the interaction between slow variables and internal fluctuations. However, its memory of the input decays on a timescale of minutes. We suggest experiments to test these predictions directly.
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Registered trials
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