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Modeling flexible behavior with remapping-based hippocampal sequence learning.

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The authors' code

Jupyter notebook · 137 lines · 4.6 KB · MIT

  1. # %%
  2. import numpy as np
  3. import matplotlib.pyplot as plt
  4. import pickle
  6. # %%
  7. def get_result(reward, terminal):
  8. global Q
  9. result = dict()
  10. num = reward.shape[0]
  11. for t in range(rep):
  12. allroutes = []
  13. Q = np.array(np.zeros([num,num]))+0.1
  14. n_state = 0
  15. for i in range(trialnum):#1万回繰り返し学習を行う
  16. route = [n_state]
  17. while route[-1]+1 not in terminal:
  18. p_state = route[-1]
  19. n_actions = []
  20. for j in range(num):
  21. if reward[p_state,j] >= 1:
  22. n_actions.append(j)
  23. prob = Q[p_state,n_actions]
  24. n_state = np.random.choice(n_actions,p=prob/np.sum(prob)) #行動可能選択肢からランダムに選択
  25. rwd = reward[p_state,n_state]
  26. if n_state + 1 in terminal and check_rwd(route+[n_state], i):
  27. rwd += 100
  28. Q[p_state,n_state] = (1-alpha)*Q[p_state,n_state]+alpha*(rwd+gamma*Q[n_state,np.argmax(Q[n_state,])])
  29. route.append(n_state)
  30. n_state = np.where(reward[n_state, :] > 0)[0]
  31. allroutes.append(route)
  32. result[t] = allroutes
  33. return result
  35. # %%
  36. gamma = 0.6
  37. alpha = 0.4
  38. trialnum = 80
  39. swblock = 20
  40. rep = 40
  41. def check_rwd(route, count):
  42. if count < swblock:
  43. return len(route) == 3
  44. elif count < swblock*2:
  45. return len(route) == 5
  46. else:
  47. return len(route) >= 7
  49. # %%
  50. def get_reward(actdict):
  51. num = len(actdict)
  52. reward = np.zeros((num,num))
  53. for key,val in actdict.items():
  54. reward[key-1][np.array(val)-1] = 1
  55. return reward
  57. # %%
  58. def rwdplot(result):
  59. rwdrate = np.array([[check_rwd(r,n) for n,r in enumerate(route)] for _,route in result.items()])
  60. plt.figure(figsize = (10,4))
  61. for i in range(1,int(trialnum/swblock)-1):
  62. plt.plot([swblock*i-0.5,swblock*i-0.5], [-5,105], "--",color = "#555555")
  63. plt.errorbar(np.arange(trialnum), np.nanmean(rwdrate,0)*100, 100*np.nanstd(rwdrate,0)/np.sqrt(rep),fmt="go-")
  64. plt.xlabel("trial index", fontsize = 20)
  65. plt.ylabel("correct [%]", fontsize = 20)
  66. plt.xticks(np.arange(-0.5,trialnum+1,20),np.arange(0,trialnum+1,20), fontsize = 15)
  67. plt.yticks(fontsize = 15)
  68. plt.ylim([-5,105])
  69. #plt.savefig("./figure/dropby_2times_correct.png", bbox_inches = "tight")
  70. return rwdrate
  72. # %%
  73. actdict = {1: [3], 2:[4], 3: [7,10], 4: [7,10], 5:[9,11], \
  74. 6:[8,12], 7: [5], 8: [6], 9: [6], 10: [1], 11: [2], 12: [2]}
  75. terminal = [10,11,12]
  76. reward = get_reward(actdict)
  77. result4 = get_result(reward, terminal)
  78. rwdplot(result4)
  80. # %%
  81. actdict = {1: [3], 2: [3], 3: [6,8], 4:[7,9], 5: [7,9],\
  82. 6: [4], 7: [5], 8: [1], 9: [2]}
  83. terminal = [8,9]
  84. reward = get_reward(actdict)
  85. result3 = get_result(reward, terminal)
  86. rwdplot(result3)
  88. # %%
  89. actdict = {1: [2], 2: [4,6],3: [5,7], 4: [3], 5: [3], 6: [1], 7: [1]}
  90. terminal = [6,7]
  91. reward = get_reward(actdict)
  92. result2 = get_result(reward, terminal)
  93. rwdplot(result2)
  95. # %%
  96. actdict = {1: [2], 2: [4,5], 3: [4,5], 4: [3], 5: [1]}
  97. terminal = [5]
  98. reward = get_reward(actdict)
  99. result1 = get_result(reward, terminal)
  100. rwdplot(result1)
  102. # %%
  103. actdict = {1: [2], 2: [3,4], 3: [2], 4: [1]}
  104. terminal = [4]
  105. reward = get_reward(actdict)
  106. result0 = get_result(reward, terminal)
  107. rwdplot(result0)
  109. # %%
  110. plt.figure(figsize = (10,4))
  111. for i in range(1,int(trialnum/swblock)-1):
  112. plt.plot([swblock*i-0.5,swblock*i-0.5], [-5,105], "--",color = "#555555")
  114. clr = ["#00bbcc","#337744","#552255","#880088","#ff0000"]
  115. lbl = ["0 back","1 back","2 back","3 back","our model"]
  116. for n,result in enumerate([result0, result1,result2,result3]):
  117. rwdrate = np.array([[check_rwd(r,n) for n,r in enumerate(route)] for _,route in result.items()])
  118. plt.errorbar(np.arange(trialnum), np.nanmean(rwdrate,0)*100, 100*np.nanstd(rwdrate,0)/np.sqrt(rep),\
  119. color = clr[n], fmt="o-", label = lbl[n])
  120. with open("./pkls/dropby_2times{}.pkl".format(""), mode = "rb") as f:
  121. allresult = pickle.load(f)
  122. rwdrate = np.zeros((rep,trialnum))
  123. for x in range(rep):
  124. tmp = [res[-1]["rwd"] for r, res in enumerate(allresult[x])]
  125. rwdrate[x,:len(tmp)] = tmp
  126. plt.errorbar(np.arange(trialnum), np.nanmean(rwdrate,0)*100, 100*np.nanstd(rwdrate,0)/np.sqrt(rep),\
  127. color = clr[4], fmt="o-", label = lbl[4])
  128. plt.legend(bbox_to_anchor=(1.0, 1), loc='upper left', fontsize = 15)
  129. plt.xlabel("trial index", fontsize = 20)
  130. plt.ylabel("correct [%]", fontsize = 20)
  131. plt.xticks(np.arange(-0.5,trialnum+1,20),np.arange(0,trialnum+1,20), fontsize = 15)
  132. plt.yticks(fontsize = 15)
  133. plt.ylim([-5,105])
  134. plt.savefig("./figure/dropby_2time_correct_TD.png", bbox_inches = "tight")
  136. # %%

FigS2.ipynb at commit e9ca9b8, under MIT · at the source

Overview

Authors: Yoshiki Ito1,2,3, Taro Toyoizumi2,4
ORCID iDs: Yoshiki Ito, Taro Toyoizumi
  1. Department of Neuroscience, Graduate School of Medicine, The University of Tokyo Tokyo Japan
  2. Laboratory for Neural Computation and Adaptation, RIKEN Center for Brain Science Saitama Japan
  3. Division of Visual Information Processing, National Institute for Physiological Sciences Okazaki Japan
  4. Department of Mathematical Informatics, Graduate School of Information Science and Technology, the University of Tokyo Tokyo Japan
Institutions: The University of Tokyo (Japan); RIKEN Center for Brain Science (Japan); National Institute for Physiological Sciences (Japan)
Journal: eLife, volume 14, article RP106506
Dates: published online 13 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.7554/elife.106506 · PMID 42125964 · PMCID PMC13171104 · OpenAlex W4411091049
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), autism (population), schizophrenia / psychosis (population), cognitive (subfield)
Keywords: hippocampal sequence, remapping, context, Amari-Hopfield model, schizophrenia, ASD, None
MeSH: Hippocampus*, Learning*, Models, Neurological*, Animals, Humans, Schizophrenia (* major topic)
Journal subjects: Neuroscience
Topic: Memory and Neural Mechanisms (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Japan Science and Technology Agency (10.52926/jpmjcr23n2); RIKEN (TRIP initiative: Quantum); Japan Society for the Promotion of Science (KAKENHI 25K24466)
Citations: not cited yet (Europe PMC); 96 references in the paper

Abstract

Animals flexibly change their behavior depending on context. It is reported that the hippocampus is one of the most prominent regions for contextual behaviors, and its sequential activity shows context dependency. However, how such context-dependent sequential activity is established through reorganization of neuronal activity (remapping) remains unclear. To better understand the formation of hippocampal activity and its contribution to context-dependent flexible behavior, we present a novel biologically plausible reinforcement learning model. In this model, Context selector promotes the formation of context-dependent sequential activity and allows for flexible switching of behavior in multiple contexts. This model reproduces a variety of findings from neural activity, optogenetic inactivation, human fMRI, and clinical research. Furthermore, our model predicts that imbalances in the ratio between sensory and contextual representations in Context selector account for schizophrenia and autism spectrum disorder-like behaviors.

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

Repository

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

toppo365/flexiblemodel

License: MIT
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: e9ca9b8189785688829f8d7cb3cbb08fe65673ea, 13 May 2026
Languages: Jupyter (11), Python (1)
Size: 24 files, 12 scripts
Software Heritage: archived
Found in: “Data availability”
Holds: README, license file, 11 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (12 files), Matplotlib (11 files), SciPy (3 files)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
14 files
At the source: github.com/toppo365/flexiblemodel

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:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 12 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

All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. All source code is provided in https://github.com/toppo365/flexiblemodel (copy archived at Ito, 2026).

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

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 2 authors, 7 keywords, 6 MeSH terms, 3 funders, 91 references.

Cite

This paper

Ito, Y., & Toyoizumi, T. (2026). Modeling flexible behavior with remapping-based hippocampal sequence learning. eLife, 14, RP106506. https://doi.org/10.7554/elife.106506

BibTeX

@article{ito2026modeling,
author = {Ito, Yoshiki and Toyoizumi, Taro},
title = {{Modeling flexible behavior with remapping-based hippocampal sequence learning}},
journal = {eLife},
year = {2026},
month = may,
volume = {14},
pages = {RP106506},
publisher = {eLife Sciences Publications, Ltd},
issn = {2050-084X},
doi = {10.7554/elife.106506},
url = {https://doi.org/10.7554/elife.106506},
pmid = {42125964},
pmcid = {PMC13171104}
}

RIS

TY - JOUR
AU - Ito, Yoshiki
AU - Toyoizumi, Taro
TI - Modeling flexible behavior with remapping-based hippocampal sequence learning
T2 - eLife
J2 - eLife
PY - 2026
DA - 2026/05/13
VL - 14
SP - RP106506
SN - 2050-084X
PB - eLife Sciences Publications, Ltd
DO - 10.7554/elife.106506
UR - https://doi.org/10.7554/elife.106506
LA - en
ER -

CSL-JSON

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"id": "10.7554/elife.106506",
"type": "article-journal",
"title": "Modeling flexible behavior with remapping-based hippocampal sequence learning",
"container-title": "eLife",
"author": [
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"family": "Ito",
"given": "Yoshiki"
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{
"family": "Toyoizumi",
"given": "Taro"
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],
"container-title-short": "eLife",
"volume": "14",
"page": "RP106506",
"DOI": "10.7554/elife.106506",
"PMID": "42125964",
"PMCID": "PMC13171104",
"ISSN": "2050-084X",
"publisher": "eLife Sciences Publications, Ltd",
"URL": "https://doi.org/10.7554/elife.106506",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
13
]
]
}
}

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