Evidence map›Paper›PMID 24298251›Full record

ArticleFrontiers in computational neuroscience2013

Propagating synchrony in feed-forward networks.

Sven Jahnke, Raoul-Martin Memmesheimer, Marc Timme

Abstract read
In one paragraph

Article in Frontiers in computational neuroscience, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

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  5. Memory replay in balanced recurrent networks.PLoS computational biology · 2017
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  9. A Unified Dynamic Model for Learning, Replay, and Sharp-Wave/Ripples.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2015
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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

3 authors.

Sven JahnkeNetwork Dynamics, Max Planck Institute for Dynamics and Self-Organization (MPIDS) Göttingen, Germany ; Bernstein Center for Computational Neuroscience (BCCN) Göttingen, Germany ; Fakultät für Physik, Georg-August-Universität Göttingen Göttingen, Germany.
Raoul-Martin Memmesheimer
Marc Timme

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Coordinated patterns of precisely timed action potentials (spikes) emerge in a variety of neural circuits but their dynamical origin is still not well understood. One hypothesis states that synchronous activity propagating through feed-forward chains of groups of neurons (synfire chains) may dynamically generate such spike patterns. Additionally, synfire chains offer the possibility to enable reliable signal transmission. So far, mostly densely connected chains, often with all-to-all connectivity between groups, have been theoretically and computationally studied. Yet, such prominent feed-forward structures have not been observed experimentally. Here we analytically and numerically investigate under which conditions diluted feed-forward chains may exhibit synchrony propagation. In addition to conventional linear input summation, we study the impact of non-linear, non-additive summation accounting for the effect of fast dendritic spikes. The non-linearities promote synchronous inputs to generate precisely timed spikes. We identify how non-additive coupling relaxes the conditions on connectivity such that it enables synchrony propagation at connectivities substantially lower than required for linearly coupled chains. Although the analytical treatment is based on a simple leaky integrate-and-fire neuron model, we show how to generalize our methods to biologically more detailed neuron models and verify our results by numerical simulations with, e.g., Hodgkin Huxley type neurons.

Indexed as

mathematical neurosciencenetworksnon-additive couplingnon-linear dendritesspike patternsynchronysynfire chains

Identifiers

PMID24298251
PMCPMC3828753

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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.