Evidence map›Paper›PMID 26705334›Full record

ArticleeLife2015

Dendritic nonlinearities are tuned for efficient spike-based computations in cortical circuits.

Balázs B Ujfalussy, Judit K Makara, Tiago Branco, Máté Lengyel

Abstract read
In one paragraph

Article in eLife, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

22 citing papers in PubMed.

  1. Article
  2. Information transfer and recovery for the sense of touch.Cerebral cortex (New York, N.Y. : 1991) · 2025
    Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. CaBiophysical journal · 2020
    Article
  13. Dendritic Spikes Expand the Range of Well Tolerated Population Noise Structures.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2019
    Article
  14. Simple framework for constructing functional spiking recurrent neural networks.Proceedings of the National Academy of Sciences of the United States of America · 2019
    Article
  15. Article
  16. Article
  17. Review
  18. Review
  19. Redundancy in synaptic connections enables neurons to learn optimally.Proceedings of the National Academy of Sciences of the United States of America · 2018
    Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Balázs B UjfalussyComputational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, United Kingdom.
Judit K MakaraLendület Laboratory of Neuronal Signaling, Institute of Experimental Medicine, Hungarian Academy of Sciences, Budapest, Hungary.
Tiago BrancoMRC Laboratory of Molecular Biology, Cambridge, United Kingdom.
Máté LengyelComputational and Biological Learning Lab, Department of Engineering, University of Cambridge, Cambridge, United Kingdom.

Funding

Medical Research Council MC_UP_1201/1Wellcome Trust 090915Wellcome Trust 098400
6 · The paper itself

Abstract

Cortical neurons integrate thousands of synaptic inputs in their dendrites in highly nonlinear ways. It is unknown how these dendritic nonlinearities in individual cells contribute to computations at the level of neural circuits. Here, we show that dendritic nonlinearities are critical for the efficient integration of synaptic inputs in circuits performing analog computations with spiking neurons. We developed a theory that formalizes how a neuron's dendritic nonlinearity that is optimal for integrating synaptic inputs depends on the statistics of its presynaptic activity patterns. Based on their in vivo preynaptic population statistics (firing rates, membrane potential fluctuations, and correlations due to ensemble dynamics), our theory accurately predicted the responses of two different types of cortical pyramidal cells to patterned stimulation by two-photon glutamate uncaging. These results reveal a new computational principle underlying dendritic integration in cortical neurons by suggesting a functional link between cellular and systems--level properties of cortical circuits.

Indexed as

Action PotentialsAnimalsDendritesGlutamic AcidModels, NeurologicalPyramidal CellsRats, Sprague-DawleySensorimotor CortexGlutamic Acidadaptationcomputationcortexdendritehumanmouseneuroscienceratstatistics

Identifiers

PMID26705334
PMCPMC4912838

What OpenQuestion holds

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Registered trials

None linked

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.