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Flexible navigation with neuromodulated cognitive maps.

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The authors' code

Jupyter notebook · 262 lines · 6.3 KB · no license

  1. # %%
  2. import numpy as np
  3. import random
  4. import matplotlib.pyplot as plt
  5. import mod_models as mm
  6. import mod_evolution as me
  7. import sim_evo_I as se
  8. from mod_models import logger
  9. import inputools.Trajectory as it
  10. from tqdm import tqdm
  12. %load_ext autoreload
  13. %autoreload 2
  15. # %% [markdown]
  16. # ## Setup
  18. # %% [markdown]
  19. # #### Genome
  21. # %%
  22. # parameters that are not evolved
  23. FIXED_PARAMETERS = {
  24. 'N': 9,
  25. 'Nj': 9,
  26. 'dim': 2,
  28. 'lr_min': 1e-5,
  29. 'lr_tau': 100,
  30. 'wff_const': 5.5,
  31. 'wff_max': 4.5,
  32. 'wff_min': 0.01,
  33. 'wff_tau_max': 2000,
  34. 'wff_tau_min': 50,
  39. 'tau_ff': 10,
  40. 'tau_rec': 5,
  41. 'syn_ff_tau': 10,
  42. 'syn_ff_thr': 0.5,
  43. 'rate_func_beta': 0.3,
  44. 'rate_func_alpha': 60,
  45. }
  47. # Define the genome as a dict of parameters
  48. PARAMETERS = {
  49. 'tau_u': lambda: random.randint(1, 300),
  50. 'lr_max': lambda: round(random.uniform(5e-3, 0.1), 3),
  51. 'lr_min': lambda: round(random.uniform(1e-3, 1e-6), 3),
  52. 'lr_tau': lambda: random.randint(50, 300),
  53. 'wff_const': lambda: round(random.uniform(4.0, 10.0), 3),
  54. 'wff_max': lambda: round(random.uniform(2.0, 10.0), 3),
  55. 'wff_min': lambda: round(random.uniform(0., 2.0), 3),
  56. 'wff_tau_max': lambda: random.randint(1000, 8000),
  57. 'wff_tau_min': lambda: random.randint(10, 500),
  58. 'wff_tau_tau': lambda: random.randint(50, 400),
  59. 'wff_beta': lambda: round(random.uniform(0.1, 1.0), 3),
  60. 'wr_const': lambda: round(random.uniform(0.1, 10.0), 3),
  61. 'dim': lambda: random.choice((1, 2)),
  62. 'A': lambda: round(random.uniform(0.1, 4.0), 3),
  63. 'B': lambda: round(random.uniform(0.1, 3.0), 3),
  64. 'sigma_exc': lambda: round(random.uniform(0., 8.0), 3),
  65. 'sigma_inh': lambda: round(random.uniform(0., 8.0), 3),
  66. 'tau_ff': lambda: random.randint(1, 100),
  67. 'tau_rec': lambda: random.randint(1, 100),
  68. 'syn_ff_tau': lambda: random.randint(1, 100),
  69. 'syn_ff_thr': lambda: round(random.uniform(0., 1.0), 3),
  70. 'rate_func_beta': lambda: round(random.uniform(0.1, 1.0), 3),
  71. 'rate_func_alpha': lambda: random.randint(50, 80),
  72. }
  74. logger.info(f"Param len={len(PARAMETERS)}, FXPARAM len={len(FIXED_PARAMETERS)}")
  76. # %% [markdown]
  77. # #### Init
  79. # %%
  80. # Create an animal
  81. animal = it.AnimalTrajectory(dt=1,
  82. prob_turn=0.01,
  83. prob_speed=0.1,
  84. prob_rest=0.01,
  85. day_cycle=True)
  87. # input layer
  88. layer = ms.InputLayer(N=FIXED_PARAMETERS['Nj'],
  89. kind='place',
  90. bounds=(0.05, 0.95, 0.05, 0.95),
  91. sigma=0.04, max_rate=300, min_rate=5)
  93. dataset = it.make_dataset(n_samples=1,
  94. animal=animal,
  95. layer=layer,
  96. duration=50,
  97. timestep=100, dx=0.1)
  99. track = se.Track2D(dataset=dataset,
  100. Nj=FIXED_PARAMETERS['Nj'],
  101. wmax=FIXED_PARAMETERS['wff_max'])
  103. logger.info("init")
  105. # %% [markdown]
  106. # ## Settings
  108. # %%
  109. # ---| Evolution |---
  111. # Create the toolbox
  112. toolbox = me.make_toolbox(PARAMETERS=PARAMETERS.copy(),
  113. game=track,
  114. agent_class=se.Agent,
  115. FIXED_PARAMETERS=FIXED_PARAMETERS.copy(),
  116. fitness_weights=(1., 1.))
  118. # ---| Run |---
  120. settings = {
  121. "NPOP": 25,
  122. "NGEN": 50,
  123. "CXPB": 0.5,
  124. "MUTPB": 0.2,
  125. "NLOG": 1,
  126. "TARGET": (40.5, 0),
  127. "TARGET_ERROR": 0.01,
  128. }
  130. # %% [markdown]
  131. # ## Run
  133. # %%
  134. agent = me.main(toolbox=toolbox,
  135. settings=settings)
  137. # genome
  138. print("\nGenome:")
  139. for k, v in agent.items():
  140. print(f"{k}: {v}")
  142. # %% [markdown]
  143. # ## Analysis
  145. # %%
  146. agent = me.load_best_individual()
  147. agent
  149. # %%
  150. ag = agent.copy()
  151. #ag['rate_func_alpha'] = 50
  152. #ag['rate_func_beta'] = 0.5
  153. #ag['eps'] = 10
  154. #ag['is_eps_scaled'] = True
  155. ag['N'] = 9
  156. ag['Nj'] = 9
  157. print(ag)
  158. net = mm.RateNetworkSimple(**ag)
  159. logger.info(net)
  161. # %%
  162. net = mm.RateNetworkSimple(**agent)
  163. logger.info(net)
  165. # %%
  166. # input layer
  167. layer = ms.InputLayer(N=ag['Nj'],
  168. kind='place',
  169. bounds=(0.05, 0.95, 0.05, 0.95),
  170. sigma=0.04, max_rate=10, min_rate=0)
  172. # model
  173. net.reset()
  174. net.set_plastic(plastic=True)
  176. # tuning
  177. rate_pc = it.get_network_tuning(model=net,
  178. layer=layer,
  179. mode='rate',
  180. dx=0.002,
  181. timestep=1,
  182. reset=False)
  185. nrows = net.n
  186. ncols = net.n
  187. fig, rows = plt.subplots(nrows, ncols, figsize=(5, 5))
  188. fig.suptitle(f"Place Cells tuning [{net.id}]")
  189. i = 0
  190. for cols in rows:
  191. for ax in cols:
  192. ax.imshow(rate_pc[:, i].reshape(int(np.sqrt(len(rate_pc))),
  193. int(np.sqrt(len(rate_pc)))),
  194. cmap='plasma')
  195. i += 1
  196. ax.set_xticks(())
  197. ax.set_yticks(())
  199. plt.show()
  201. # %%
  202. plt.figure(figsize=(3, 3))
  203. plt.imshow(net.Wff, cmap="plasma")
  204. plt.title("$W^{ff}$ weights")
  205. plt.xlabel('j')
  206. plt.ylabel('i')
  207. plt.show()
  209. print(np.around(net.Wff, 3))
  211. # %%
  212. np.around(rate_pc).shape
  215. # %%
  216. np.sort(net.Wff, axis=0)[-2:].T
  218. # %%
  219. np.diff((np.sort(net.Wff, axis=0)[-2:]), axis=0).sum()
  221. # %%
  222. ((np.sort(net.Wff, axis=0)[-2:].sum(axis=0))**2).sum()
  225. # %%
  226. wf = lambda x: 1 / (1 + np.exp(- 50 * (x - 0.85 * 5.)))
  228. w = np.ones((5, 5))*0.1 + 4.4*np.random.binomial(1, 0.1, (5, 5))
  229. w
  231. # %%
  232. np.around(wf(w.max(axis=0)).sum() / 5, 5)
  234. # %% [markdown]
  235. # ## Connectivity
  236. # ---
  238. # %%
  239. def mixture_of_gaussians_connectivity(size, amplitudes, sigmas):
  240. """ Create a connectivity pattern as a mixture of Gaussians """
  241. x, y = np.meshgrid(np.linspace(-1, 1, size), np.linspace(-1, 1, size))
  242. d = np.sqrt(x*x + y*y)
  244. pattern = np.zeros_like(d)
  245. for A, sigma in zip(amplitudes, sigmas):
  246. pattern += A * np.exp(-d**2 / (sigma**2))
  248. return pattern
  250. # Example parameters for the mixture of Gaussians
  251. amplitudes = [2.0, -2, 4] # Amplitudes for each Gaussian
  252. sigmas = [0.5, 1., 4] # Widths for each Gaussian
  254. size = 50
  255. mixture_pattern = mixture_of_gaussians_connectivity(size, amplitudes, sigmas)
  257. plt.figure(figsize=(6, 6))
  258. plt.imshow(mixture_pattern, cmap='RdBu')
  259. plt.colorbar()
  260. plt.title('Mixture of Gaussians Connectivity Pattern')
  261. plt.show()

lab_evolution-checkpoint.ipynb at commit ef9dd9f, no license · at the source

Overview

Authors: Krubeal Danieli1, Mikkel Elle Lepperød2
ORCID iDs: Mikkel Elle Lepperød
  1. Center for Integrative Neuroplasticity, FYSCELL, University of Oslo, Oslo, Norway
  2. Simula Research Laboratory, Oslo, Norway
Institutions: University of Oslo (Norway); Simula Research Laboratory (Norway)
Journal: PLoS computational biology, volume 22, issue 7, article e1013487
Dates: received 3 September 2025; accepted 15 June 2026; published online 28 July 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1371/journal.pcbi.1013487 · PMID 42520064 · PMCID PMC13450844 · OpenAlex W4414023443
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.1101/2025.09.04.674155
Status: code verified
Categories: none (in silico) (organism)
MeSH: Cognition*, Models, Neurological*, Spatial Navigation*, Animals, CA1 Region, Hippocampal, Computational Biology, Computer Simulation, Neuronal Plasticity, Place Cells, Reinforcement Machine Learning, Reward (* major topic)
Topic: Memory and Neural Mechanisms (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: European Commission (HORIZON2020, 945371); Universitetet i Oslo (945371); Norges Forskningsråd (#270053)
Citations: not cited yet (Europe PMC); 110 references in the paper

Abstract

Animals develop specialized cognitive maps during navigation, constructing environmental representations that facilitate efficient exploration and goal-directed planning. The hippocampal CA1 region is implicated as the primary neural substrate for cognitive mapping, housing spatially tuned cells that adapt based on behavioral patterns and internal states. Computational approaches to modeling these biological systems have employed various methodologies. Although labeled graphs with local spatial information and deep neural networks have provided computational frameworks for spatial navigation, significant limitations persist in modeling one-shot adaptive mapping. We introduce a biologically inspired place cell architecture that develops cognitive maps during exploration of novel environments. Our model implements a simulated agent for reward-driven navigation that forms spatial representations online. The architecture incorporates behaviorally relevant information through neuromodulatory signals that respond to environmental boundaries and reward locations. Learning combines rapid Hebbian plasticity, lateral competition, and targeted modulation of place cells. Analysis of the model across a variety of environments demonstrates that online map formation and reward-directed navigation can emerge within a single simulated trial, without the multi-epoch training typically required by reinforcement-learning approaches. The simulation results show that the agent successfully explores and navigates to target locations in various environments, adapting when reward positions change. Analysis of neuromodulated place cells reveals dynamic changes in neuronal density and place field size after behaviorally significant events. These findings align with experimental observations of reward effects on hippocampal spatial cells while providing computational support for the efficacy of biologically inspired approaches to cognitive mapping.

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

Repository

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

iKiru-hub/PCNN

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: ef9dd9f8a676a84aaefea558b1ac871ae6e9d654, 18 May 2026
Languages: Python (38), C++ (11), Jupyter (7), Shell (5), C (2), TypeScript (1)
Size: 742 files, 64 scripts
Software Heritage: not archived
Found in: “Data Availability”
Holds: README, environment (requirements.txt), tests, 4 notebooks
Not found: license file, CITATION.cff, continuous integration, documentation
Tools: Matplotlib (6 files), NumPy (6 files), SciPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
7 files
At the source: github.com/iKiru-hub/PCNN

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;
  • 6 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

The code and the simulation data are publicly available, and can be found at https://github.com/iKiru-hub/PCNN.

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

  • Funding: added European Commission: HORIZON2020, 945371; Universitetet i Oslo: 945371; Norges Forskningsråd: #270053

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 11 MeSH terms, 101 references.

Cite

This paper

Danieli, K., & Lepperød, M. E. (2026). Flexible navigation with neuromodulated cognitive maps. PLoS computational biology, 22(7), e1013487. https://doi.org/10.1371/journal.pcbi.1013487

BibTeX

@article{danieli2026flexible,
author = {Danieli, Krubeal and Lepperød, Mikkel Elle},
title = {{Flexible navigation with neuromodulated cognitive maps}},
journal = {PLoS computational biology},
year = {2026},
month = jul,
volume = {22},
number = {7},
pages = {e1013487},
publisher = {PLOS},
issn = {1553-734X},
doi = {10.1371/journal.pcbi.1013487},
url = {https://doi.org/10.1371/journal.pcbi.1013487},
pmid = {42520064},
pmcid = {PMC13450844}
}

RIS

TY - JOUR
AU - Danieli, Krubeal
AU - Lepperød, Mikkel Elle
TI - Flexible navigation with neuromodulated cognitive maps
T2 - PLoS computational biology
J2 - PLoS Comput Biol
PY - 2026
DA - 2026/07/28
VL - 22
IS - 7
SP - e1013487
SN - 1553-734X
PB - PLOS
DO - 10.1371/journal.pcbi.1013487
UR - https://doi.org/10.1371/journal.pcbi.1013487
LA - en
ER -

CSL-JSON

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"container-title-short": "PLoS Comput Biol",
"volume": "22",
"issue": "7",
"page": "e1013487",
"DOI": "10.1371/journal.pcbi.1013487",
"PMID": "42520064",
"PMCID": "PMC13450844",
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