When can neuronal activity-dependent homeostatic plasticity maintain circuit-level properties?

Code ↔ Paper

2 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 2 matches

[1] § Results › Optimization of homeostatic mechanisms ↔ mainsimulateADHP.cpp, lines 1–53 · score 0.54 · activity dependent homeostatic, lower bound, ADHP mechanism, biases, plasticity, perturbations
[2] § Results › Optimization of homeostatic mechanisms ↔ mainADHPevol.cpp, lines 58–87 · score 0.53 · sliding window duration, upper bound, clipped, width, neuron, ADHP

Paper

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The authors' code

C++ · 239 lines · 8.6 KB · BSD-2-Clause · 1 match

// --------------------------------------------------------------
// Track the parameters and states of CTRNNs as they undergo
// Activity-Dependent Homeostatic Plasticity
// Record pyloric fitness before and after plasticity
//
// Run this script from the directory of the evolved pyloric circuit
// which you are perturbing/measuring, and specify the ADHP mechanism
// you want to test. If you don't want ADHP to be active (just to test
// pyloricness), then choose the nullADHP.dat file. Only the parameters
// indicated by your ADHP mechanism (first line) can be perturbed in the test.
// Ensure that the ./res.dat file specifies ranges (step doesn't matter)
// for each parameter that should be explored (lines read in order of plasticpars.dat)
//
// Test one specific point by setting num_ICs to 1 and the upper and lower bounds
// of the ranges in res.dat to the values you want
//
// --------------------------------------------------------------
#include "TSearch.h"
#include "CTRNN.h"
#include "random.h"
#include "pyloric.h"
// Simulation parameters
const double TransientDuration = 50; //Seconds to equilibrate before measuring pyloricness and activating ADHP
double PlasticDuration = 10000; //Seconds with ADHP running before re-measuring pyloricness (set to 0 if just measuring pyl)
const bool trackoutputs = true;
const int trackoutputsinterval = 1; //Track neural outputs for every X trials
const bool trackparams = false;
const int trackparamsinterval = 1; //Track biases for every X trials
const int trackingstepinterval = 2; //make the tracking files smaller by only recording every Xth step (though all steps are integrated)
const int num_ICs = 1; //how many initial points?
//Input Files
char Nfname[] = "./pyloriccircuit.ns";
// char HPfname[] = "./0/bestind.dat";
//null ADHP option
char HPfname[] = "../../nullADHP.dat";
char rangefname[] = "../../res.dat";
//Output Files
char Fitnessesfname[] = "./fit.dat"; //fitness of every point before and after regulation
char ICsfname[] = "./ics.dat"; //full parameters of every point before and after regulation
char biastrackfname[] = "./parstrack.dat"; //track all plastic parameters throughout the run (if trackparams==true)
char statestrackfname[] = "./statestrack.dat"; //track all three neural output timeseries throughout the run (if trackoutputs==true)
// Nervous system params
const int N = 3;
int CTRNNVectSize = N*N + 2*N;
int paramboundVectSize = 2*(CTRNNVectSize - N);
void GenPhenMapping(TVector<double> &gen, TVector<double> &phen, TVector<double> &parambounds)
{
int k = 1;
// Time-constants
for (int i = 1; i <= N; i++) {
phen(k) = MapSearchParameter(gen(k), .1, 2); // Time constants cannot be perturbed or regulated
k++;
}
int param_idx = 1;
// Bias
for (int i = 1; i <= N; i++) {
phen(k) = MapSearchParameter(gen(k), parambounds(param_idx), parambounds(param_idx+1));
k++;
param_idx += 2;
}
// Weights
for (int i = 1; i <= N; i++) {
for (int j = 1; j <= N; j++) {
phen(k) = MapSearchParameter(gen(k), parambounds(param_idx), parambounds(param_idx+1));
k++;
param_idx += 2;
}
}
}
int main(){
// Create files to hold data
ofstream fitnesses;
fitnesses.open(Fitnessesfname);
ofstream ICsfile;
ICsfile.open(ICsfname);
ofstream biastrack;
biastrack.open(biastrackfname);
ofstream statestrack;
statestrack.open(statestrackfname);
CTRNN Circuit(3);
// Set circuit parameters (start with the given pyloric solution)
ifstream ifs;
ifs.open(Nfname);
if (!ifs) {
cerr << "File not found: " << Nfname << endl;
exit(EXIT_FAILURE);
}
ifs >> Circuit;
ifs.close();
ifstream rangefile;
rangefile.open(rangefname);
if (!rangefile) {
cerr << "File not found: " << rangefname << endl;
exit(EXIT_FAILURE);
}
// Set the proper HP parameters
ifstream HPifs;
HPifs.open(HPfname);
if (!HPifs) {
cerr << "File not found: " << HPfname << endl;
exit(EXIT_FAILURE);
}
Circuit.SetHPPhenotype(HPifs,StepSize);
HPifs.close();
bool ADHPon = false;
TVector<double> parambounds(1,paramboundVectSize); //vector to hold all ranges
if (Circuit.plasticitypars.Sum()>0){ //gets from the ADHP bestind.dat file
ADHPon = true;
int bound_idx = 1;
// Read in specified Ranges
for (int i=1;i<=Circuit.plasticitypars.UpperBound();i++){
if (Circuit.plasticitypars(i) == 1){
int step_throwaway;
rangefile >> parambounds(bound_idx);
rangefile >> parambounds(bound_idx+1);
rangefile >> step_throwaway;
}
bound_idx += 2;
}
}
else {PlasticDuration = 0;} //if no ADHP, then forego the plastic period
rangefile.close();
// Generate random circuit parameters within the allowed ranges
TVector<double> genotype(1,CTRNNVectSize);
TVector<double> phenotype(1,CTRNNVectSize);
for (int i = 0;i<num_ICs;i++){
long randomseed = static_cast<long>(time(NULL));
// long randomseed = 123456789; //if need repeats or direct compare
RandomState rs(randomseed+pow(i,2));
for (int j = 1; j <= genotype.Size(); j++)
{genotype[j] = rs.UniformRandom(-1,1);} //generate random genotype
GenPhenMapping(genotype,phenotype,parambounds); //map into proper ranges (or to specific values)
//use only the generated parameters that you need
int k = 1;
//check for biases
for(int j=1; j<=N; j++){
if (Circuit.plasticitypars[k]==1){
Circuit.SetNeuronBias(j,phenotype(k+N)); //start after time constants
}
k++;
}
//check for weights
for (int j=1; j<=N; j++){
for (int l=1; l<=N; l++){
if (Circuit.plasticitypars[k]==1){
Circuit.SetConnectionWeight(j,l,phenotype(k+N)); //started after time constants
}
k++;
}
}
//prepare circuit for run
Circuit.RandomizeCircuitOutput(0.5,0.5);
Circuit.WindowReset();
// Run for transient without ADHP
int tstep = 0;
for(double t=0;t<TransientDuration;t+=StepSize){
Circuit.EulerStep(StepSize,false);
if (trackoutputs && (i%trackoutputsinterval==0) && (tstep % trackingstepinterval==0)){
for (int j = 1; j <= Circuit.size; j++){
statestrack << Circuit.NeuronOutput(j) << " ";
}
statestrack << endl;}
tstep ++;
}
// Record initial parameters
ICsfile << Circuit.taus << " " << Circuit.biases << " ";
for(int j = 1; j <= N; j ++){
for(int k=1;k<=N;k++){
ICsfile << Circuit.ConnectionWeight(j,k) << " ";
}
}
ICsfile << endl;
// Run with HP for a time if ADHP is turned on
for(double t=0;t<PlasticDuration;t+=StepSize){
if (trackparams && (i%trackparamsinterval==0) && (tstep % trackingstepinterval == 0)){
for(int j = 1; j<= Circuit.plasticitypars.Sum(); j++){
biastrack << Circuit.ArbDParam(j) << " "; //record only the parameters that are changing throughout the run
}
biastrack << endl;
}
if (trackoutputs && (i%trackoutputsinterval==0) && (tstep % trackingstepinterval==0)){
for (int j = 1; j <= Circuit.size; j++){
statestrack << Circuit.NeuronOutput(j) << " ";
}
statestrack << endl;}
Circuit.EulerStep(StepSize,true);
tstep ++;
}
if (trackparams && (i%trackparamsinterval==0)) {biastrack << endl;}
if (trackoutputs && (i%trackoutputsinterval==0)) {statestrack << endl;}
// Record again, after HP
ICsfile << Circuit.taus << " " << Circuit.biases << " ";
for(int j = 1; j <= N; j ++){
for(int k=1;k<=N;k++){
ICsfile << Circuit.ConnectionWeight(j,k) << " ";
}
}
ICsfile << endl << endl;
// Test for Pyloricness (HP remains on if it was on during plastic period)
double fit = PyloricPerformance(Circuit,true);
fitnesses << fit << endl << endl;;
// fitnesses << Circuit.rhos << endl << endl; //proxy for whether HP is satisfied at the end, or whether it just ran into a boundary or is in a limit cycle
}
fitnesses.close();
ICsfile.close();
biastrack.close();
statestrack.close();
return 0;
}

mainsimulateADHP.cpp at commit 393a5cc, under BSD-2-Clause · at the source

Overview

Authors: Lindsay J Stolting¹, Randall D Beer¹

Cognitive Science Department and Program in Neuroscience, Indiana University, Bloomington, IN 47401 USA

Institutions: Indiana University Bloomington (United States); Indiana University (United States)

Journal: Journal of computational neuroscience, volume 54, issue 2, pages 365-391

Dates: received 8 February 2026; accepted 27 April 2026; published online 13 May 2026; in print 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1007/s10827-026-00936-7 · PMID 42126467 · PMCID PMC13233920 · OpenAlex W7161026767

Open access: hybrid, a free copy (OpenAlex)

Status: code verified

Categories: computational modeling (no new data) (modality), none (in silico) (organism), systems (subfield)

Methods: Machine learning, Single-unit activity, calcium imaging, Smoothing, state filtering, decompositions, Connectivity

Keywords: Activity-dependent homeostatic plasticity, Pyloric network, Circuit dynamics, Central pattern generators

MeSH: Homeostasis*, Models, Neurological*, Nerve Net*, Neuronal Plasticity*, Neurons*, Action Potentials, Animals, Computer Simulation, Ganglia, Invertebrate, Pylorus (* major topic)

Topic: Neurobiology and Insect Physiology Research (Cellular and Molecular Neuroscience, Neuroscience), according to OpenAlex

Funding: Eli Lilly and Company; Lilly Endowment

Citations: not cited yet (Europe PMC); 102 references in the paper

Abstract

Neural circuits are remarkably robust to perturbations that threaten their function. Activity-dependent homeostatic plasticity (ADHP) is a stabilizing mechanism that supports robustness by tuning neuronal ion conductances to combat chronic over- or under-activity. Its restorative capacity has been demonstrated in the pyloric circuit of the crustacean stomatogastric ganglion, whose neurons must burst in a specific order to coordinate digestive muscles. After disruption by physical and pharmacological manipulations, this circuit reliably recovers not only the activity levels of constituent neurons, but also the proper burst order. But how could ADHP, operating only on local information about each neuron’s average activity, maintain higher-order circuit properties? We explored this question in a computational model of the pyloric pattern generator. We first optimized a set of pyloric-like networks, then optimized ADHP mechanisms for each network to restore its pyloric character after parametric perturbations. This was possible for some networks and impossible for others, so we aimed to explain this disparity. We found that successful homeostatic regulators target average neural activity levels which happen to occur only among pyloric circuits and not among non-pyloric ones, within the set of reachable circuit configurations. Therefore, in subsets of parameter space where such dissociation is possible, activity carries indirect information about burst order, which ADHP can exploit to maintain pyloricness. Other subsets, whose pyloric averages are inseparable from non-pyloric ones, cannot be perfectly regulated. This separability property may explain differences in recovery capacity across perturbations and across individuals.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

Zenodo 18509900

License: bsd-2-clause-netbsd

State: the link answers, verified on 28 September 2026

Evidence: files inventoried

Size: 1 file

Software Heritage: not checked

Found in: “Code Availability”

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

Tools: Matplotlib (2 files), NumPy (2 files)

Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)

28 September 2026: the link answers (HTTP 200)

18 files

CTRNN.cpp, C++, 657 lines
CTRNN.h, C/C++, 226 lines
No Timing Requirements/HPLCstats.ipynb, Jupyter, 144 lines
TSearch.cpp, C++, 1,022 lines
TSearch.h, C/C++, 228 lines
Timing Requirements/15/test.ipynb, Jupyter, 11 lines
VectorMatrix.h, C/C++, 517 lines
mainADHPevol.cpp, C++, 308 lines
mainADHPmetaparspace.cpp, C++, 265 lines
mainavgpyloric.cpp, C++, 411 lines
mainpyloricevol.cpp, C++, 161 lines
mainsimulateADHP.cpp, C++, 239 lines
pyloric.cpp, C++, 346 lines
pyloric.h, C/C++, 47 lines
random.cpp, C++, 208 lines
random.h, C/C++, 77 lines
LICENSE, License, 24 lines
README.md, Text, 58 lines

At the source: doi.org/10.5281/zenodo.18509900

ljstolting/adhp-maintains-pyloricness

License: BSD-2-Clause

State: the link answers, verified on 28 September 2026

Evidence: files inventoried

Commit: 393a5cc2481d2342d881d05ec8964c3aed43b4f8, 16 June 2026

Languages: C++ (9), C/C++ (5), Jupyter (2)

Size: 2,221 files, 16 scripts

Software Heritage: not archived

Found in: the Zenodo archive record

Holds: README, license file, 2 notebooks

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

Tools: Matplotlib (2 files), NumPy (2 files)

Availability: 1 check, the latest on 28 September 2026: the link answers

28 September 2026: the link answers

18 files

CTRNN.cpp, C++, 657 lines
CTRNN.h, C/C++, 226 lines
No Timing Requirements/HPLCstats.ipynb, Jupyter, 144 lines
TSearch.cpp, C++, 1,022 lines
TSearch.h, C/C++, 228 lines
Timing Requirements/15/test.ipynb, Jupyter, 11 lines
VectorMatrix.h, C/C++, 517 lines
mainADHPevol.cpp, C++, 308 lines, 1 match
mainADHPmetaparspace.cpp, C++, 265 lines
mainavgpyloric.cpp, C++, 411 lines
mainpyloricevol.cpp, C++, 161 lines
mainsimulateADHP.cpp, C++, 239 lines, 1 match
pyloric.cpp, C++, 346 lines
pyloric.h, C/C++, 47 lines
random.cpp, C++, 208 lines
random.h, C/C++, 77 lines
LICENSE, License, 24 lines
README.md, Text, 57 lines

At the source: github.com/ljstolting/ADHP-maintains-pyloricness/tree/Paper-code

Code Availability

C++ and Python code required to reproduce all data is publicly available on Zenodo at https://doi.org/10.5281/zenodo.18509900

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

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.

What the map holds:

2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
32 scripts, each with its path and the digest of its content;
2 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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

All code required to reproduce these simulations is publicly available on Zenodo at https://doi.org/10.5281/zenodo.18509900

C++ and Python code required to reproduce all data is publicly available on Zenodo at https://doi.org/10.5281/zenodo.18509900

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

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 2, 28 September 2026

Publisher: n/a → Springer Science+Business Media
Funding: added Eli Lilly and Company; Lilly Endowment

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 4 keywords, 10 MeSH terms, 99 references.

Cite

This paper

Stolting, L. J., & Beer, R. D. (2026). When can neuronal activity-dependent homeostatic plasticity maintain circuit-level properties? Journal of computational neuroscience, 54(2), 365-391. https://doi.org/10.1007/s10827-026-00936-7

BibTeX

@article{stolting2026when, author = {Stolting, Lindsay J and Beer, Randall D}, title = {{When can neuronal activity-dependent homeostatic plasticity maintain circuit-level properties?}}, journal = {Journal of computational neuroscience}, year = {2026}, month = may, volume = {54}, number = {2}, pages = {365--391}, publisher = {Springer Science+Business Media}, issn = {0929-5313}, doi = {10.1007/s10827-026-00936-7}, url = {https://doi.org/10.1007/s10827-026-00936-7}, pmid = {42126467}, pmcid = {PMC13233920} }

RIS

TY - JOUR AU - Stolting, Lindsay J AU - Beer, Randall D TI - When can neuronal activity-dependent homeostatic plasticity maintain circuit-level properties? T2 - Journal of computational neuroscience J2 - J Comput Neurosci PY - 2026 DA - 2026/05/13 VL - 54 IS - 2 SP - 365 EP - 391 SN - 0929-5313 PB - Springer Science+Business Media DO - 10.1007/s10827-026-00936-7 UR - https://doi.org/10.1007/s10827-026-00936-7 LA - en ER -

CSL-JSON

{ "id": "10.1007/s10827-026-00936-7", "type": "article-journal", "title": "When can neuronal activity-dependent homeostatic plasticity maintain circuit-level properties?", "container-title": "Journal of computational neuroscience", "author": [ { "family": "Stolting", "given": "Lindsay J" }, { "family": "Beer", "given": "Randall D" } ], "container-title-short": "J Comput Neurosci", "volume": "54", "issue": "2", "page": "365-391", "DOI": "10.1007/s10827-026-00936-7", "PMID": "42126467", "PMCID": "PMC13233920", "ISSN": "0929-5313", "publisher": "Springer Science+Business Media", "URL": "https://doi.org/10.1007/s10827-026-00936-7", "language": "en", "issued": { "date-parts": [ [ 2026, 5, 13 ] ] } }

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When can neuronal activity-dependent homeostatic plasticity maintain circuit-level properties?

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ljstolting/adhp-maintains-pyloricness

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