Evidence map›Paper›PMID 29937707›Full record

ArticleFrontiers in neuroscience2018

A Digital Hardware Realization for Spiking Model of Cutaneous Mechanoreceptor.

Nima Salimi-Nezhad, Mahmood Amiri, Egidio Falotico, Cecilia Laschi

Abstract read
In one paragraph

Article in Frontiers in neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Artificial sensory system based on memristive devices.Exploration (Beijing, China) · 2024
    Review
  3. Article
  4. Article
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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

4 authors.

Nima Salimi-NezhadMedical Biology Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran.
Mahmood AmiriMedical Biology Research Center, Kermanshah University of Medical Sciences, Kermanshah, Iran.
Egidio FaloticoThe BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Italy.
Cecilia LaschiThe BioRobotics Institute, Scuola Superiore Sant'Anna, Pontedera, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Inspired by the biology of human tactile perception, a hardware neuromorphic approach is proposed for spiking model of mechanoreceptors to encode the input force. In this way, a digital circuit is designed for a slowly adapting type I (SA-I) and fast adapting type I (FA-I) mechanoreceptors to be implemented on a low-cost digital hardware, such as field-programmable gate array (FPGA). This system computationally replicates the neural firing responses of both afferents. Then, comparative simulations are shown. The spiking models of mechanoreceptors are first simulated in MATLAB and next the digital neuromorphic circuits simulated in VIVADO are also compared to show that obtained results are in good agreement both quantitatively and qualitatively. Finally, we test the performance of the proposed digital mechanoreceptors in hardware using a prepared experimental set up. Hardware synthesis and physical realization on FPGA indicate that the digital mechanoreceptors are able to replicate essential characteristics of different firing patterns including bursting and spiking responses of the SA-I and FA-I mechanoreceptors. In addition to parallel computation, a main advantage of this method is that the mechanoreceptor digital circuits can be implemented in real-time through low-power neuromorphic hardware. This novel engineering framework is generally suitable for use in robotic and hand-prosthetic applications, so progressing the state of the art for tactile sensing.

Indexed as

hardware implementationmechanoreceptorneuromorphic circuitspiking modeltactile sensing

Identifiers

PMID29937707
PMCPMC6003138

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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.