Evidence map›Paper›PMID 32848685›Full record

ArticleFrontiers in computational neuroscience2020

Necessary Conditions for Reliable Propagation of Slowly Time-Varying Firing Rate.

Navid Hasanzadeh, Mohammadreza Rezaei, Sayan Faraz, Milos R Popovic, Milad Lankarany

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in computational neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.6field-weighted citation impact, top 36% of its field
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

1 citing paper in PubMed, 5 citations in OpenAlex.

  1. 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

5 authors at 3 institutions in 2 countries.

Navid HasanzadehClinical and Computational Neuroscience, Krembil Research Institute, University Health Network, Toronto, ON, Canada.
Mohammadreza RezaeiClinical and Computational Neuroscience, Krembil Research Institute, University Health Network, Toronto, ON, Canada.
Sayan FarazClinical and Computational Neuroscience, Krembil Research Institute, University Health Network, Toronto, ON, Canada.
Milos R PopovicKITE Research Institute, Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada.
Milad LankaranyClinical and Computational Neuroscience, Krembil Research Institute, University Health Network, Toronto, ON, Canada.
University Health Network · CAUniversity of Tehran · IRUniversity of Toronto · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Reliable propagation of slow-modulations of the firing rate across multiple layers of a feedforward network (FFN) has proven difficult to capture in spiking neural models. In this paper, we explore necessary conditions for reliable and stable propagation of time-varying asynchronous spikes whose instantaneous rate of changes-in fairly short time windows [20-100] msec-represents information of slow fluctuations of the stimulus. Specifically, we study the effect of network size, level of background synaptic noise, and the variability of synaptic delays in an FFN with all-to-all connectivity. We show that network size and the level of background synaptic noise, together with the strength of synapses, are substantial factors enabling the propagation of asynchronous spikes in deep layers of an FFN. In contrast, the variability of synaptic delays has a minor effect on signal propagation.

Indexed as

feed-forward neural networkinformation propagationnetwork sizenoisesynaptic delaystime-varying rate coding

Identifiers

PMID32848685
PMCPMC7405925
OpenAlexW3045775993

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.