ArticlePLoS computational biology2016
Enhanced Sensitivity to Rapid Input Fluctuations by Nonlinear Threshold Dynamics in Neocortical Pyramidal Neurons.
Article in PLoS computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed.
- Emergence of sparse coding, balance and decorrelation from a biologically-grounded spiking neural network model of learning in the primary visual cortex.PLoS computational biology · 2025Article
- Firing rate distributions in plastic networks of spiking neurons.Network neuroscience (Cambridge, Mass.) · 2025Article
- Learning what matters: Synaptic plasticity with invariance to second-order input correlations.PLoS computational biology · 2024Article
- Article
- Kv1 channels regulate variations in spike patterning and temporal reliability in the avian cochlear nucleus angularis.Journal of neurophysiology · 2022Article
- Neural synchrony in cortical networks: mechanisms and implications for neural information processing and coding.Frontiers in integrative neuroscience · 2022Article
- Monosynaptic inference via finely-timed spikes.Journal of computational neuroscience · 2021Article
- Modeling the Short-Term Dynamics ofThe Journal of neuroscience : the official journal of the Society for Neuroscience · 2020Article
- Somatodendritic consistency check for temporal feature segmentation.Nature communications · 2020Article
- Spike threshold adaptation diversifies neuronal operating modes in the auditory brain stem.Journal of neurophysiology · 2019Article
- Inferring and validating mechanistic models of neural microcircuits based on spike-train data.Nature communications · 2019Article
- Adaptation of Inhibition Mediates Retinal Sensitization.Current biology : CB · 2019Article
- Optimal Localist and Distributed Coding of Spatiotemporal Spike Patterns Through STDP and Coincidence Detection.Frontiers in computational neuroscience · 2018Article
- Sparse Computation in Adaptive Spiking Neural Networks.Frontiers in neuroscience · 2018Article
- Dynamical Timescale Explains Marginal Stability in Excitability Dynamics.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2017Article
- Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size.PLoS computational biology · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The way in which single neurons transform input into output spike trains has fundamental consequences for network coding. Theories and modeling studies based on standard Integrate-and-Fire models implicitly assume that, in response to increasingly strong inputs, neurons modify their coding strategy by progressively reducing their selective sensitivity to rapid input fluctuations. Combining mathematical modeling with in vitro experiments, we demonstrate that, in L5 pyramidal neurons, the firing threshold dynamics adaptively adjust the effective timescale of somatic integration in order to preserve sensitivity to rapid signals over a broad range of input statistics. For that, a new Generalized Integrate-and-Fire model featuring nonlinear firing threshold dynamics and conductance-based adaptation is introduced that outperforms state-of-the-art neuron models in predicting the spiking activity of neurons responding to a variety of in vivo-like fluctuating currents. Our model allows for efficient parameter extraction and can be analytically mapped to a Generalized Linear Model in which both the input filter--describing somatic integration--and the spike-history filter--accounting for spike-frequency adaptation--dynamically adapt to the input statistics, as experimentally observed. Overall, our results provide new insights on the computational role of different biophysical processes known to underlie adaptive coding in single neurons and support previous theoretical findings indicating that the nonlinear dynamics of the firing threshold due to Na+-channel inactivation regulate the sensitivity to rapid input fluctuations.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.