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Trainable movement control using spikes and muscle-twitch dynamics.

Code ↔ Paper

1 match 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 1 match
  1. [1] § Methods › Environment: Pong ↔ NeuromorphicPongControl/Visualization/visualize_gaussian_encoding.py, lines 1–11 · score 0.55 · Gaussian encoding, spiking signals, height, sensor, Weight

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

Python · 49 lines · 1.5 KB · MIT · 1 match

  1. '''Visualizes encoding from sensor values to spiking signal probability through Gaussian encoding'''
  2. import numpy as np
  3. import matplotlib.pyplot as plt
  4. import math
  5. import matplotlib.patches as patch
  7. plt.rcParams.update({'font.size': 15})
  8. plt.rcParams['font.weight'] = 'bold'
  9. plt.rcParams['axes.labelweight'] = 'bold'
  10. width = 2000
  11. height = 1601
  13. # Set your initial values here, nsensorneurons is the number of sensor neurons,
  14. # exponent is the width of the gaussian,
  15. # Sensorcenters is the location of the means.
  16. nsensorneurons = 10
  17. exponent = -25
  18. sensorcenters = np.linspace(-500, height+500, nsensorneurons)/height
  20. # Relative hit height of the ball (between 0 and 1)
  21. hitheight = 0.2
  24. fig, ax = plt.subplots(1)
  25. # Resolution
  26. points = np.arange(-1000, height + 500 + 500)/height
  27. for i, s in enumerate(sensorcenters):
  29. # Encoding avlues
  30. c1 = plt.Circle((hitheight, math.exp(exponent * (s - hitheight) ** 2)), radius=0.02, color="orange")
  31. ax.add_patch(c1)
  33. # Plot Gaussians
  34. sensoroutputs = []
  35. for p in points:
  36. sensoroutputs.append(math.exp(exponent * (s - p) ** 2))
  37. if i % 2 == 0:
  38. plt.plot(points, sensoroutputs, color="royalblue")
  39. else:
  40. plt.plot(points, sensoroutputs, color="green")
  42. # Plot environment width and ball height indicator
  43. plt.vlines([0, 1], 0, 1, color="black", linewidth=3, ls=":")
  44. plt.vlines([hitheight], 0, 1 , color="red", linewidth=2)
  45. plt.xlabel("Relative environment height")
  46. plt.ylabel("Sensor value")
  48. plt.tight_layout()
  49. plt.show()

visualize_gaussian_encoding.py at commit 2fd61c8, under MIT · at the source

Overview

Authors: Jordi Timmermans1,2, Lambert Schomaker1,2
ORCID iDs: Jordi Timmermans, Lambert Schomaker
  1. Department of Artificial Intelligence, Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Groningen, Netherlands
  2. Groningen Cognitive Systems and Materials Center (CogniGron), University of Groningen, Groningen, Netherlands
Institutions: University of Groningen (Netherlands)
Journal: Frontiers in neurorobotics, volume 20, article 1761767
Dates: received 5 December 2025; accepted 10 March 2026; published online 13 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3389/fnbot.2026.1761767 · PMID 42051242 · PMCID PMC13111196 · OpenAlex W7154134978
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: computational (subfield)
Methods: Complexity
Keywords: ballistic control, direct feedback alignment, global error, neuromorphic, neuromuscular inspiration, Pong, spiking neural networks, twitch
Topic: Advanced Memory and Neural Computing (Electrical and Electronic Engineering, Engineering), according to OpenAlex
Citations: not cited yet (Europe PMC); 53 references in the paper

Abstract

The biological sensorimotor system is a source of inspiration for the design of neuromorphic ballistic control systems. A large portion of sensorimotor-inspired research focuses on the sensory encoding and information processing stages of the system. However, research on broader task-performance systems, involving actuator control on the output side remains scarce. In this work, we develop and train a neuromuscular-inspired model to perform ballistic control. In the model, a spiking neural network's output spikes are used to generate twitch-like signals. These twitches are the basis for generating a continuous fluctuating output signal that is used to operate an actuator. We refer to the the used model as the Twitch Neural Network (TwNN). As a test case, the model is trained to control the paddle of an adapted version of the game of Pong. An adapted version of the Direct Feedback Alignment learning rule, specifically for integrate-and-fire neurons, is introduced. The new rule avoids the update-locking problem of backpropagation, allowing network weight updates in parallel. The model output consists of one group of agonist-innervating motor neurons, and one group of antagonist-innervating motor neurons. We find that it is possible to teach a neuromuscular-inspired system to control the paddle in the game of Pong with the adapted Direct Feedback Alignment learning rule. The best-performing baseline model achieved a hit rate of 96%. By applying logarithmic scaling to the output activity, a hit rate of 98% could be achieved. Finally, by replacing the neuromorphically unrealistic exact summation steps with leaky integrators in training, the range of good learning parameters became more narrow and clear. The best-performing model reaches a hit rate of 99%. Threshold analysis during training has shown that learning is robust to a variety of neuron thresholds. Noise analysis has shown that the system is robust to membrane potential noise during inference for uniform noise up to values in the order of around 0.1-1% of the neuron threshold value per time step.

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 1 match between paragraphs and lines of code.

JordiTi/NeuromorphicPongControl

License: MIT
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 2fd61c8bc5286adf6d5fff491be3bc605652d835, 13 March 2026
Languages: Python (19)
Size: 30 files, 19 scripts
Software Heritage: not archived
Found in: the text, “Contributions”
Holds: README, license file, environment (requirements.txt)
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (19 files), Matplotlib (14 files), imageio (2 files), pandas (2 files), SciPy (2 files), seaborn (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
21 files
At the source: github.com/JordiTi/NeuromorphicPongControl

Zenodo 17832222

License: MIT
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data availability statement”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (22 files), Matplotlib (16 files), imageio (3 files), pandas (2 files), SciPy (2 files), seaborn (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
  • 29 September 2026: the link answers (HTTP 200)
24 files
At the source: doi.org/10.5281/zenodo.17832222

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.

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;
  • 41 scripts, each with its path and the digest of its content;
  • 1 match 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

Datasets cited

Data availability statement

The data generated to create the plots can be found in the Zenodo repository https://doi.org/10.5281/zenodo.17832288. The software written for this study can be found in the Zenodo repository https://doi.org/10.5281/zenodo.17832222.

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

Versions

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 2 authors, 8 keywords, 46 references.

Cite

This paper

Timmermans, J., & Schomaker, L. (2026). Trainable movement control using spikes and muscle-twitch dynamics. Frontiers in neurorobotics, 20, 1761767. https://doi.org/10.3389/fnbot.2026.1761767

BibTeX

@article{timmermans2026trainable,
author = {Timmermans, Jordi and Schomaker, Lambert},
title = {{Trainable movement control using spikes and muscle-twitch dynamics}},
journal = {Frontiers in neurorobotics},
year = {2026},
month = apr,
volume = {20},
pages = {1761767},
publisher = {Frontiers Media SA},
issn = {1662-5218},
doi = {10.3389/fnbot.2026.1761767},
url = {https://doi.org/10.3389/fnbot.2026.1761767},
pmid = {42051242},
pmcid = {PMC13111196}
}

RIS

TY - JOUR
AU - Timmermans, Jordi
AU - Schomaker, Lambert
TI - Trainable movement control using spikes and muscle-twitch dynamics
T2 - Frontiers in neurorobotics
J2 - Front Neurorobot
PY - 2026
DA - 2026/04/13
VL - 20
SP - 1761767
SN - 1662-5218
PB - Frontiers Media SA
DO - 10.3389/fnbot.2026.1761767
UR - https://doi.org/10.3389/fnbot.2026.1761767
LA - en
ER -

CSL-JSON

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