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Mild Focal Cooling Decouples Dendrites to Reconfigure Cortical Output.

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  1. [1] § Materials and Methods › Three‐Compartment Biophysical Model ↔ func/parameters_temp_mod.py, lines 8–52 · score 0.54 · membrane surface area, ratio, nad, distance, soma, channels
  2. [2] § Materials and Methods › Three‐Compartment Biophysical Model ↔ func/parameters_three_com.py, lines 8–52 · score 0.54 · membrane surface area, ratio, nad, distance, soma, channels

Paper

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The authors' code

Python · 181 lines · 5.5 KB · no license · 1 match

  1. """
  2. Parameters for three compartment neuron
  3. """
  4. import numpy as np
  5. import os
  8. def init_params(wd):
  9. N_e = 100
  10. N_i = 50
  11. soma = [0]
  12. basal = [1]
  13. oblique = [2]
  14. apical = [3]
  15. locs_e = np.array(
  16. basal + oblique + apical) # location of excitatory synapses
  17. locs_i = np.array(
  18. basal + soma + oblique + apical) # location of inhibitory synapses
  20. dist = np.array([0.001,0.001,25.0,40.0]) # location of each compartment (distance to soma)
  21. temp = np.asarray([34, 34, 34, 34]) # temperature of each compartment
  22. N = np.asarray([np.inf,np.inf,2e5, 2e5]) # number of nad channels in each compartment
  24. E_e = 0. # excitatory reversal potential (mv)
  25. E_i = -75. # inhibitory reversal potential (mv)
  26. tauA = np.array([0.1, 2.]) # AMPA synapse rise and decay time (ms)
  27. g_max_A = 0.2 * 1e-3 # AMPA conductance (uS)
  28. tauN = np.array([2., 75.]) # NMDA synapse rise and decay time (ms)
  29. g_max_N = 0.4 * 1e-3 # NMDA conductance (uS)
  30. tauG = np.array([1., 5.]) # GABA synapse rise and decay time (ms)
  31. g_max_G = 0.8 * 1e-3 # GABA conductance (uS)
  32. active_n = False #True
  33. tau_m = 25
  34. r_na = 0. # NMDA/AMPA ratio
  36. E_l = -70.0 # leak reversal
  37. E_k = -90.0 # K channel reversal
  38. E_na = 50.0 # Na reversal
  39. E_ca = 140.0 # Ca reversal
  40. gamma = 0.0006 # percentage of free Ca
  41. decay = 35.7 # Ca buffer time constant 200ms
  43. # axo-somatic compartment
  44. g_l = 0.03 # axo-somatic leak conductance (mS/cm2)
  45. c_m = 0.75 # saxo-omatic specific capacitance (uF/cm-2)
  46. g_na = 3000 # axo-somatic Na conductance (mS/cm2)
  47. g_nad = 0.0
  48. g_k = 300# 150 # axo-somatic K conductance (mS/cm2)
  49. g_m = 0.0 # axo-somatic Im conductance (mS/cm2)
  50. g_ca = 0.0 # axo-somatic Ca conductance (mS/cm2)
  51. g_kca = 0.0 # axo-somatic SK conductance (mS/cm2)
  52. area = 600 # axo-somatic membrane surface area (um2)
  54. # basal compartment
  55. g_l_b = 0.03 # basal leak conductance (mS/cm2)
  56. c_m_b = 0.75 # basal specific capacitance (uF/cm-2)
  57. g_na_b = 0.0 # basal Na conductance (mS/cm2)
  58. g_nad_b = 0.0
  59. g_k_b = 0.0 # basal K conductance (mS/cm2)
  60. g_m_b = 0.0 # basal Im conductance (mS/cm2)
  61. g_ca_b = 0.0 # basal Ca conductance (mS/cm2)
  62. g_kca_b = 0.0 # basal SK conductance (mS/cm2)
  63. rho_b = 1 # basal area/somatic area
  64. kappa_b = 10 # basal-somatic coupling resistance (MOhm)
  66. # distal compartment
  67. g_l_d = 0.03 # dendritic leak conductance (mS/cm2)
  68. c_m_d = 0.75 # dendritic specific capacitance (uF/cm-2)
  69. g_na_d = 0.0 # 0.8*4 # dendritic Na conductance (mS/cm2)
  70. g_nad_d = 20
  71. g_k_d = 0.0 # dendritic K conductance (mS/cm2)
  72. g_m_d = 0.1 # dendritic Im conductance (mS/cm2)
  73. g_ca_d = 0.3 # dendritic Ca conductance (mS/cm2)
  74. g_kca_d = 3 # dendritic SK conductance (mS/cm2)
  75. rho_d = 20 # dendritic area/somatic area
  76. kappa_d = 5 # dendritic - proximal coupling resistance (MOhm)
  78. # proximal compartment
  79. g_l_p = 0.03 # proximal dendritic leak conductance (mS/cm2)
  80. c_m_p = 0.75 # proximal dendritic specific capacitance (uF/cm-2)
  81. g_na_p = 0 # proximal dendritic Na conductance (mS/cm2)
  82. g_nad_p = 20
  83. g_k_p = 0.1*4 # proximal dendritic K conductance (mS/cm2)
  84. g_m_p = 0.0 # proximal dendritic Im conductance (mS/cm2)
  85. g_ca_p = 0. # proximal dendritic Ca conductance (mS/cm2)
  86. g_kca_p = 0.0 # proximal dendritic SK conductance (mS/cm2)
  87. rho_p = 15 # dendritic area/somatic area
  88. kappa_p = 5 # proximal-somatic coupling resistance (MOhm)
  90. g_ion = np.asarray([[g_na, g_k, g_m ,g_ca, g_kca,g_nad], [g_na_b,g_k_b, g_m_b ,g_ca_b, g_kca_b,g_nad_b],[g_na_p, g_k_p, g_m_p ,g_ca_p, g_kca_p, g_nad_p], [g_na_d, g_k_d, g_m_d ,g_ca_d, g_kca_d, g_nad_d]])
  91. g_ion = g_ion.T
  93. P = {
  94. 'soma': soma,
  95. 'basal': basal,
  96. 'oblique': oblique,
  97. 'apical': apical,
  98. 'locs_e': locs_e,
  99. 'locs_i': locs_i,
  100. 'N_e' : N_e,
  101. 'N_i' : N_i,
  102. 'E_l' : E_l,
  103. 'E_k' : E_k,
  104. 'E_na' : E_na,
  105. 'E_ca' : E_ca ,
  106. 'gamma' : gamma,
  107. 'decay' : decay,
  108. 'tau_m': tau_m,
  109. 'dist': dist,
  110. 'temp': temp,
  111. 'N': N,
  113. 'E_e': E_e,
  114. 'E_i': E_i,
  115. 'tauA': tauA,
  116. 'tauN': tauN,
  117. 'tauG': tauG,
  118. 'g_max_A': g_max_A,
  119. 'g_max_N': g_max_N,
  120. 'g_max_G': g_max_G,
  121. 'active_n' : active_n,
  122. 'r_na': r_na,
  124. # axo-somatic compartment
  125. 'g_l' : g_l,
  126. 'c_m' : c_m,
  127. 'area' : area,
  129. # basal compartment
  130. 'g_l_b' : g_l_b,
  131. 'c_m_b' : c_m_b,
  132. 'rho_b' : rho_b,
  133. 'kappa_b' : kappa_b,
  135. # dendritic compartment
  136. 'g_l_d' : g_l_d,
  137. 'c_m_d' : c_m_d,
  138. 'rho_d' : rho_d,
  139. 'kappa_d' : kappa_d,
  141. # proximal compartment
  142. 'g_l_p' : g_l_p,
  143. 'c_m_p' : c_m_p,
  144. 'rho_p' : rho_p,
  145. 'kappa_p' : kappa_p,
  147. # ionic conductances
  148. 'g_ion': g_ion,
  149. 'g_na': g_na,
  150. 'g_k': g_k,
  151. 'g_m': g_m,
  152. 'g_ca': g_ca,
  153. 'g_kca': g_kca,
  154. 'g_nad': g_nad,
  157. 'g_na_b': g_na_b,
  158. 'g_k_b': g_k_b,
  159. 'g_m_b': g_m_b,
  160. 'g_ca_b': g_ca_b,
  161. 'g_kca_b': g_kca_b,
  162. 'g_nad_b': g_nad_b,
  165. 'g_na_p': g_na_p,
  166. 'g_k_p': g_k_p,
  167. 'g_m_p': g_m_p ,
  168. 'g_ca_p': g_ca_p,
  169. 'g_kca_p': g_kca_p,
  170. 'g_nad_p': g_nad_p,
  172. 'g_na_d': g_na_d,
  173. 'g_k_d': g_k_d,
  174. 'g_m_d': g_m_d,
  175. 'g_ca_d': g_ca_d,
  176. 'g_kca_d': g_kca_d,
  177. 'g_nad_d': g_nad_d
  180. }
  181. return P

parameters_temp_mod.py at commit 286328f, no license · at the source

Overview

Authors: Meisam Habibi Matin1, Shulan Xiao1, Krishna Jayant1,2
ORCID iDs: Krishna Jayant
  1. Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA
  2. Purdue Institute for Integrative Neuroscience, Purdue University, West Lafayette, Indiana, USA
Institutions: Purdue University West Lafayette (United States)
Journal: Advanced science (Weinheim, Baden-Wurttemberg, Germany), volume 13, issue 34, article e20773
Dates: received 18 October 2025; accepted 26 March 2026; published online 9 April 2026; in print June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1002/advs.202520773 · PMID 41957535 · PMCID PMC13285124 · OpenAlex W7152686639
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: mouse (organism), systems (subfield)
Methods: Spectral & time-frequency, Statistics, Single-unit activity, calcium imaging
Keywords: dendritic excitability, differential sensitivity, focal cooling, neuromodulation, physiology
MeSH: Dendrites*, Neurons*, Somatosensory Cortex*, Animals, Cold Temperature, Mice, Vibrissae (* major topic)
Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: EMBRIO Institute (National Science Foundation) (2120200); National Institutes of Health (NIH R21EB029740); National Institutes of Health, New Innovator Award NIH DP2 (DP2MH136494); Human Frontier Science Program (RGY0069); Air Force Office of Scientific Research (FA9550‐23‐1‐0701, FA9550-23-1-0701); NIH HHS (NIH R21EB029740); NIMH NIH HHS (DP2 MH136494)
Citations: not cited yet (Europe PMC); 88 references in the paper

Abstract

Focal cooling modulates cortical computations, yet how principal neurons respond remains unclear. We demonstrate that mild focal cooling of the barrel cortex (S1) while impacting behavior creates a steep translaminar temperature gradient with ∼4°C drop in layer 5 (L5)—a range where conduction velocity changes are minimal. L5 neurons integrate self‐motion and touch by encoding whisker dynamics, and because their apical tuft dendrites lie proximal to the cooled surface, the gradient implicates possible dendritic mechanisms in mediating behavioral disruption. In vitro experiments confirm this: focal cooling (100 µm radius) selectively increases impedance and input‐output transformations in L5 tuft but not basal dendrites, yet paradoxically impairs recovery from inactivation of apical dendritic Na+ channels, reducing somato‐dendritic coupling. These results challenge the view that cooling acts mainly through slowed conduction. Instead, suggesting that cooling decouples basal‐tuft integration and dynamically regulates cortical gain, revealing a potent neuromodulatory mechanism with implications for sensory‐motor computation.

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.

shulanx1/simpl5pn_temperature_modulation

License: none: the authors keep all their rights
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 286328f7f10d71d367f570d40b386b4ca22fbd52, 1 November 2024
Languages: Python (13), MATLAB (4)
Size: 192 files, 17 scripts
Software Heritage: not archived
Found in: the text, “Three‐Compartment Biophysical Model”
Holds: README, tests
Not found: license file, CITATION.cff, environment file, continuous integration, documentation
Tools: NumPy (13 files), Matplotlib (5 files), SciPy (5 files), Numba (2 files), Statistics and Machine Learning Toolbox (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
18 files
At the source: github.com/shulanx1/simpl5pn_temperature_modulation

Zenodo 14020643

License: CC-BY-4.0
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data, Materials, and Software Availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (13 files), Matplotlib (5 files), SciPy (5 files), Numba (2 files), Statistics and Machine Learning Toolbox (1 file)
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
  • 29 September 2026: the link answers (HTTP 200)
17 files
At the source: doi.org/10.5281/zenodo.14020643

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

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  • 2 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

Datasets cited

Data Availability Statement

The data that support the findings of this study are openly available in Zenodo at https://doi.org/10.5281/zenodo.14020289, reference number 14020289.

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

Data, Materials, and Software Availability

Upon acceptance, all additional data will be uploaded to Zenodo, and the code will be uploaded to GitHub repositories, wherever applicable. The Zenodo link with the currently presented data in the manuscript is https://doi.org/10.5281/zenodo.14020289. This link will be made live upon acceptance with the entire data set Link to code with biophysical simulations https://doi.org/10.5281/zenodo.14020643. Dynamic clamp board https://doi.org/10.5281/zenodo.11303939

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

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 5 keywords, 7 MeSH terms, 7 funders, 88 references.

Cite

This paper

Habibi Matin, M., Xiao, S., & Jayant, K. (2026). Mild Focal Cooling Decouples Dendrites to Reconfigure Cortical Output. Advanced science (Weinheim, Baden-Wurttemberg, Germany), 13(34), e20773. https://doi.org/10.1002/advs.202520773

BibTeX

@article{habibimatin2026mild,
author = {Habibi Matin, Meisam and Xiao, Shulan and Jayant, Krishna},
title = {{Mild Focal Cooling Decouples Dendrites to Reconfigure Cortical Output}},
journal = {Advanced science (Weinheim, Baden-Wurttemberg, Germany)},
year = {2026},
month = apr,
volume = {13},
number = {34},
pages = {e20773},
publisher = {Wiley},
issn = {2198-3844},
doi = {10.1002/advs.202520773},
url = {https://doi.org/10.1002/advs.202520773},
pmid = {41957535},
pmcid = {PMC13285124}
}

RIS

TY - JOUR
AU - Habibi Matin, Meisam
AU - Xiao, Shulan
AU - Jayant, Krishna
TI - Mild Focal Cooling Decouples Dendrites to Reconfigure Cortical Output
T2 - Advanced science (Weinheim, Baden-Wurttemberg, Germany)
J2 - Adv Sci (Weinh)
PY - 2026
DA - 2026/04/09
VL - 13
IS - 34
SP - e20773
SN - 2198-3844
PB - Wiley
DO - 10.1002/advs.202520773
UR - https://doi.org/10.1002/advs.202520773
LA - en
ER -

CSL-JSON

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