Digital design of a spatial-pow-STDP learning block with high accuracy utilizing pow CORDIC for large-scale image classifier spatiotemporal SNN.

Mohammad Kazem Bahrami, Soheila Nazari

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In one paragraph

Article in Scientific reports, 2024. 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

2.1field-weighted citation impact, top 13% 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.

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 10 citations in OpenAlex.

Development of digital hardware for a spiking image recognition network employing a novel burst-based reinforcement learning approach.Scientific reports · 2025
Article

4 · The record

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5 · Who and what money

Authors and funding

2 authors at 1 institution in 1 country.

Mohammad Kazem BahramiFaculty of Electrical Engineering, Shahid Beheshti University, Tehran, 1983969411, Iran.

Soheila NazariFaculty of Electrical Engineering, Shahid Beheshti University, Tehran, 1983969411, Iran. so_nazari@sbu.ac.ir.

Shahid Beheshti University · IR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The paramount concern of highly accurate energy-efficient computing in machines with significant cognitive capabilities aims to enhance the accuracy and efficiency of bio-inspired Spiking Neural Networks (SNNs). This paper addresses this main objective by introducing a novel spatial power spike-timing-dependent plasticity (Spatial-Pow-STDP) learning rule as a digital block with high accuracy in a bio-inspired SNN model. Motivated by the demand for precise and accelerated computation that reduces high-cost resources in neural network applications, this paper presents a methodology based on COordinate Rotation DIgital Computer (CORDIC) definitions. The proposed designs of CORDIC algorithms for exponential (Exp CORDIC), natural logarithm (Ln CORDIC), and arbitrary power function (Pow CORDIC) are meticulously detailed and evaluated to ensure optimal acceleration and accuracy, which respectively show average errors near 10

Indexed as

AMPA and GABA neurotransmittersBio-Inspired SNNExp CORDICFPGA implementationImage classificationLIF neuronsLn CORDICPow CORDICSpatial-Pow-STDPUnsupervised learning

Identifiers

PMID38337032

DOI10.3389/fnins.2019.00095

PMCPMC10858263

OpenAlexW4391678300

What OpenQuestion holds

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LicenceCC BY

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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.