Spiking Neural Networks and Mathematical Models.

Mirto M Gasparinatou, Nikolaos Matzakos, Panagiotis Vlamos

Abstract readReview

In one paragraph

Review in Advances in experimental medicine and biology, 2023. 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

2.3field-weighted citation impact, top 12% 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, 2 citations in OpenAlex.

Dietary intakes of cysteine, glutamate, proline, and tryptophan are associated with hypertension risk in Chinese children and adolescents: a national cross-sectional study integrating machine learning.Frontiers in nutrition · 2026
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

3 authors at 2 institutions in 1 country.

Mirto M GasparinatouIonian University, Corfu, Greece. mgasparinatou@ionio.gr.

Nikolaos MatzakosSchool of Pedagogical and Technological Education, Marousi, Greece.

Panagiotis VlamosIonian University, Corfu, Greece.

Ionian University · GRSchool of Pedagogical and Technological Education · GR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Neural networks are applied in various scientific fields such as medicine, engineering, pharmacology, etc. Investigating operations of neural networks refers to estimating the relationship among single neurons and their contributions to the network as well. Hence, studying a single neuron is an essential process to solve complex brain problems. Mathematical models that simulate neurons and the way they transmit information are proven to be an indispensable tool for neuroscientists. Constructing appropriate mathematical models to simulate information transmission of a biological neural network is a challenge for researchers, as in the real world, identical neurons in terms of their electrophysiological characteristics in different brain regions do not contribute in the same way to information transmission within a neural network due to the intrinsic characteristics. This review highlights four mathematical, single-compartment models: Hodgkin-Huxley, Izhikevich, Leaky Integrate, and Fire and Morris-Lecar, and discusses comparison among them in terms of their biological plausibility, computational complexity, and applications, according to modern literature.

Indexed as

Models, NeurologicalNeural Networks, ComputerAction PotentialsBrainNeuronsHodgkin-HuxleyIzhikevichLeaky integrate and fireMathematical modelsMorris-LecarNeural networksSingle-compartment model

Identifiers

PMID37486481

DOI10.1142/s0218127412502057

OpenAlexW4385186516

What OpenQuestion holds

Textmetadata

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.