Task-State fMRI-Derived Whole-Brain Functional Topology-Constrained Spiking Neural Network with an Embedded Auditory Core Circuit for Speech Recognition.

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Paper

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The authors' code

MATLAB · 22 lines · 665 B · no license

function f=enframe(x,win,inc)
nx=length(x(:)); %xÎªÓïÒôÐÅºÅ winÎª´° incÎªÖ¡³¤»ò´°º¯Êý
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nwin=length(win);
if (nwin == 1)
len = win;
else
len = nwin;
end
if (nargin < 3)
inc = len;
end
nf = fix((nx-len+inc)/inc);
f=zeros(nf,len);
indf= inc*(0:(nf-1)).';
inds = (1:len);
f(:) = x(indf(:,ones(1,len))+inds(ones(nf,1),:));
if (nwin > 1)
w = win(:)';
f = f .* w(ones(nf,1),:);
end

enframe.m at commit 84c1c71, no license · at the source

Overview

Authors: Lei Guo^1,2, Yaxin Yang¹

ORCID iDs: Lei Guo

Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300131, China
State Key Laboratory of Intelligent Power Distribution Equipment and System, Hebei University of Technology, Tianjin 300401, China

Institutions: Hebei University of Technology (China)

Journal: Biomimetics (Basel, Switzerland), volume 11, issue 7, article 481

Dates: received 21 May 2026; accepted 6 July 2026; published online 9 July 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.3390/biomimetics11070481 · PMID 42505514 · PMCID PMC13406740 · OpenAlex W7167826407

Open access: gold, a free copy (OpenAlex)

Status: code verified

Categories: fMRI (modality)

Methods: Spectral & time-frequency, Connectivity, Statistics, Smoothing, state filtering, decompositions, fMRI & imaging

Keywords: spiking neural network, task-state fMRI, speech recognition, whole-brain functional topology, auditory core circuit

Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex

Funding: National Natural Science Foundation of China (52477232); Postgraduate Innovation Foundation of Hebei province (CXZZSS2026026)

Citations: not cited yet (Europe PMC); 41 references in the paper

Abstract

The topology of spiking neural networks (SNNs) plays an important role in determining their dynamic representation ability, recognition performance, and biological interpretability in speech recognition. However, most existing SNN reservoirs are constructed using random, regular, or manually designed connectivity patterns, which may not reflect the functional organization of the human brain during speech perception. In this study, we propose a task-state fMRI-constrained SNN framework for speech recognition. Human fMRI data acquired during naturalistic English audiobook listening are used offline to derive a task-state whole-brain functional topology, which serves as a biologically inspired structural prior for the recurrent connectivity of the SNN reservoir. Because the fMRI and downstream isolated-digit recognition tasks use different speech paradigms, this topology is interpreted as a general speech-listening prior rather than a digit-specific neural representation. The Schaefer-400 cortical parcellation is used to define 400 whole-brain functional nodes, all of which are retained to preserve distributed cortical interactions during speech listening. Within this topology, 7 SomMotB_Aud parcels are identified as auditory core nodes and analyzed as an embedded auditory circuit. Compared with resting-state fMRI, task-state fMRI shows enhanced functional connectivity among these auditory nodes, indicating task-related auditory-circuit activation. The resulting 400-node task-state topology is mapped onto the recurrent connectivity of the SNN reservoir. This mapping is regarded as a topology-constrained computational abstraction rather than a direct model of biological information transmission. During recognition, speech spike trains are the only external input, while fMRI data are used only for offline topology construction. Experimental comparisons with baseline SNNs show that the proposed topology improves recognition performance and biological interpretability. Resting-state topology comparison, auditory-core contribution analysis, threshold-sensitivity analysis, and statistical testing are further used to evaluate robustness. These findings suggest that speech-evoked whole-brain functional organization may provide an effective topology prior for biologically inspired speech recognition models.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above.

syhtsr/fMRI-SNN

License: none: the authors keep all their rights

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Commit: 84c1c71a57586b57e4738a6cf778f8fc22748ccf, 9 March 2024

Languages: MATLAB (11)

Size: 12 files, 11 scripts

Software Heritage: not archived

Found in: “Data Availability Statement”

Holds: README

Not found: license file, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: Signal Processing Toolbox (1 file)

Availability: 1 check, the latest on 27 September 2026: the link answers

27 September 2026: the link answers

12 files

code/Data/enframe.m, MATLAB, 22 lines
code/Data/preprocessing.m, MATLAB, 109 lines
code/Data/vad.m, MATLAB, 121 lines
code/Firing/Fir.m, MATLAB, 130 lines
code/Firing/firstin.m, MATLAB, 28 lines
code/Firing/istdpfunction.m, MATLAB, 13 lines
code/Firing/net_data.m, MATLAB, 99 lines
code/Firing/stdpfunction.m, MATLAB, 11 lines
code/Firing/t2function.m, MATLAB, 5 lines
code/test.m, MATLAB, 296 lines
code/train.m, MATLAB, 114 lines
README.md, Text, 16 lines

At the source: github.com/syhtsr/fMRI-SNN.git

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

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1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
11 scripts, each with its path and the digest of its content;
no match between paragraphs and code yet;
neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data Availability Statement

The task-state fMRI data used for whole-brain functional topology construction and auditory-core circuit analysis were obtained from the English subset of OpenNeuro ds003643, Le Petit Prince: A multilingual fMRI corpus using ecological stimuli, which included 49 participants after quality control. The speech recognition experiments used the TI46 corpus (LDC93S9). Complete code reproducing the simulation results is publicly available at https://github.com/syhtsr/fMRI-SNN.git (accessed on 5 July 2026).

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 5 keywords, 2 funders, 37 references.

Cite

This paper

Guo, L., & Yang, Y. (2026). Task-State fMRI-Derived Whole-Brain Functional Topology-Constrained Spiking Neural Network with an Embedded Auditory Core Circuit for Speech Recognition. Biomimetics (Basel, Switzerland), 11(7), 481. https://doi.org/10.3390/biomimetics11070481

BibTeX

@article{guo2026task, author = {Guo, Lei and Yang, Yaxin}, title = {{Task-State fMRI-Derived Whole-Brain Functional Topology-Constrained Spiking Neural Network with an Embedded Auditory Core Circuit for Speech Recognition}}, journal = {Biomimetics (Basel, Switzerland)}, year = {2026}, month = jul, volume = {11}, number = {7}, pages = {481}, publisher = {Multidisciplinary Digital Publishing Institute (MDPI)}, issn = {2313-7673}, doi = {10.3390/biomimetics11070481}, url = {https://doi.org/10.3390/biomimetics11070481}, pmid = {42505514}, pmcid = {PMC13406740} }

RIS

TY - JOUR AU - Guo, Lei AU - Yang, Yaxin TI - Task-State fMRI-Derived Whole-Brain Functional Topology-Constrained Spiking Neural Network with an Embedded Auditory Core Circuit for Speech Recognition T2 - Biomimetics (Basel, Switzerland) J2 - Biomimetics (Basel) PY - 2026 DA - 2026/07/09 VL - 11 IS - 7 SP - 481 SN - 2313-7673 PB - Multidisciplinary Digital Publishing Institute (MDPI) DO - 10.3390/biomimetics11070481 UR - https://doi.org/10.3390/biomimetics11070481 LA - en ER -

CSL-JSON

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