Evidence map›Paper›PMID 26441623›Full record

ArticleFrontiers in computational neuroscience2015

Thalamic neuron models encode stimulus information by burst-size modulation.

Daniel H Elijah, Inés Samengo, Marcelo A Montemurro

Abstract read
In one paragraph

Article in Frontiers in computational neuroscience, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

  1. Review
  2. Review
  3. The Augmentation of Retinogeniculate Communication during Thalamic Burst Mode.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2019
    Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Neural Coding With Bursts-Current State and Future Perspectives.Frontiers in computational neuroscience · 2018
    Review
  9. Article
  10. Article
4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

3 authors.

Daniel H ElijahFaculty of Life Sciences, The University of Manchester Manchester, UK.
Inés SamengoStatistical and Interdisciplinary Physics Group, Instituto Balseiro and Centro Atómico Bariloche San Carlos de Bariloche, Argentina.
Marcelo A MontemurroFaculty of Life Sciences, The University of Manchester Manchester, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Thalamic neurons have been long assumed to fire in tonic mode during perceptive states, and in burst mode during sleep and unconsciousness. However, recent evidence suggests that bursts may also be relevant in the encoding of sensory information. Here, we explore the neural code of such thalamic bursts. In order to assess whether the burst code is generic or whether it depends on the detailed properties of each bursting neuron, we analyzed two neuron models incorporating different levels of biological detail. One of the models contained no information of the biophysical processes entailed in spike generation, and described neuron activity at a phenomenological level. The second model represented the evolution of the individual ionic conductances involved in spiking and bursting, and required a large number of parameters. We analyzed the models' input selectivity using reverse correlation methods and information theory. We found that n-spike bursts from both models transmit information by modulating their spike count in response to changes to instantaneous input features, such as slope, phase, amplitude, etc. The stimulus feature that is most efficiently encoded by bursts, however, need not coincide with one of such classical features. We therefore searched for the optimal feature among all those that could be expressed as a linear transformation of the time-dependent input current. We found that bursting neurons transmitted 6 times more information about such more general features. The relevant events in the stimulus were located in a time window spanning ~100 ms before and ~20 ms after burst onset. Most importantly, the neural code employed by the simple and the biologically realistic models was largely the same, implying that the simple thalamic neuron model contains the essential ingredients that account for the computational properties of the thalamic burst code. Thus, our results suggest the n-spike burst code is a general property of thalamic neurons.

Indexed as

burstinformation theorymultivariate analysisneural codereverse correlationsingle neuron modelspike-triggered averagethalamus

Identifiers

PMID26441623
PMCPMC4585143

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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.