Electrical Coupling within Thalamocortical Networks Cumulatively Reduces Cortical Correlation to Sensory Inputs.

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[1] § Materials and Methods ↔ Models.jl/src/TRNnetwork.jl, lines 43–104 · score 0.74 · delayed rectifier, regular sodium, rise, Na, Kd, Kt
[2] § Materials and Methods ↔ Models.jl/src/TRNmodel.jl, lines 52–126 · score 0.72 · delayed rectifier, regular sodium, Kd, Kt, NaT, K2

Paper

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The authors' code

Julia · 153 lines · 4.2 KB · no license · 1 match

@kwdef mutable struct simParams
names::Vector{String}
n::Int
per_neuron::Int
# mS/cm^2
g_caT::Float64 = 0.75
g_nat::Float64 = 60.5
g_kd::Float64 = 60.0
g_nap::Float64 = 0.0
g_kt::Float64 = 5.0
g_k2::Float64 = 0.5
g_ar::Float64 = 0.025
g_GtACR::Float64 = 10.0
g_L::Vector{Float64} = fill(0.1,n)
# mV
E_na::Float64 = 50.0
E_k::Float64 = -100.0
E_ca::Float64 = 125.0
E_ar::Float64 = -40.0
E_L::Float64 = -75.0
E_GtACR::Float64 = -70.0
E_AMPA::Float64 = 0.0
E_GABA::Float64 = -100.0
C::Float64 = 1.0 # membrance capacitance uF/cm^2
# DC pulses # uA/cm^2
bias::Vector{Float64} = zeros(n)
iDC::Vector{Float64} = zeros(n)
iStart::Vector{Vector{Float64}} = fill([0.0],n)
iStop::Vector{Vector{Float64}} = fill([0.0],n)
iexp::Vector{Float64} = zeros(n)
ieStart::Vector{Float64} = zeros(n)
iedecay::Vector{Float64} = fill(30.0,n)
# Silencing
GtACR_on::Vector{Vector{Float64}} = fill([0.0],n)
GtACR_off::Vector{Vector{Float64}} = fill([0.0],n)
# Alpha/Beta Synapses # uA/cm^2
Te1::Float64 = 5.0 #Exc rise time constant
Te2::Float64 = 35.0 #fall time constant
Ti1::Float64 = 5.0 #Inh
Ti2::Float64 = 35.0
A::Vector{Vector{Float64}} = fill([0.0],n)
tA::Vector{Vector{Float64}} = fill([0.0],n)
AI::Vector{Vector{Float64}} = fill([0.0],n)
tAI::Vector{Vector{Float64}} = fill([0.0],n)
# Electrical Synapses # mS/cm^2
gj::Matrix{Float64} = zeros(n,n)
end
function dsim!(du, u, p, t)
for i = 1:p.n
idx = p.per_neuron*(i-1)
v, m_nat, h_nat, m_nap, m_kd, m_kt, h_kt, m_k2, h_k2, m_caT, h_caT, m_ar = u[idx+1:idx+12]
# Inputs
## Applied current
Iapp = Iapp_f(p.bias[i],p.iDC[i],t,(p.iStart[i],p.iStop[i]))
Iapp += Iexp_f(p.iexp[i],p.iedecay[i],t,p.ieStart[i])
## External Synapses
### AMPAergic
A, vpre = ExtSyn_f(t,p.tA[i],p.A[i])
### GABAergic
AI, vpreI = ExtSyn_f(t,p.tAI[i],p.AI[i])
# Channels
## Regular sodium
dm_nat, dh_nat = Na_t(v, m_nat, h_nat)
## Persistent sodium
dm_nap = Na_p(v, m_nap)
## Delayed rectifier
dm_kd = K_rect(v, m_kd)
## Transient K = A current, McCormick/Huguenard 1992
dm_kt, dh_kt = K_A(v, m_kt, h_kt)
## GK2
dm_k2, dh_k2 = K2(v, m_k2, h_k2)
## T current, as implemented by Traub 2005, which cites Destexhe 1996
dm_caT, dh_caT = Ca_T(v, m_caT, h_caT)
## Anonymous rectifier, AR; Traub 2005 calls this 'h'. ?!
dm_ar = AR(v, m_ar)
Ina = (p.g_nat*(m_nat^3.0)*h_nat + p.g_nap*m_nap) * (v-p.E_na)
Ik = (p.g_kd*(m_kd^4.0) + p.g_kt*(m_kt^4.0)*h_kt + p.g_k2*m_k2*h_k2) * (v-p.E_k)
ICa = (p.g_caT*(m_caT^2.0)*h_caT) * (v-p.E_ca)
if endswith(p.names[i],"SOM")||endswith(p.names[i],"HO")
ICa *= 0.5
end
IAR = (p.g_ar*m_ar) * (v-p.E_ar)
IL = (p.g_L[i]) * (v-p.E_L)
IGtACR = GtACR_f(p.g_GtACR,t,(p.GtACR_on[i],p.GtACR_off[i])) * (v-p.E_GtACR)
# Synapses
## Excitatory input
du[idx+13] = p.Te1*K_syn(vpre)*(1.0-u[idx+13]) - p.Te2*u[idx+13]
Esyn1 = A*u[idx+13] * (v-p.E_AMPA)
## Inhibitory input
du[idx+14] = p.Ti1*K_syn(vpreI)*(1.0-u[idx+14]) - p.Ti2*u[idx+14]
Isyn1 = AI*u[idx+14] * (v-p.E_GABA)
## Electrical synapses TRN
Gsyn = Gsyn_f(v,u[1:p.per_neuron:end],p.gj[:,i])
Summed_Isyn = Esyn1 + Isyn1 + Gsyn
# Final equations
du[idx+1] = (-1.0/p.C)*(Ina + Ik +ICa + IAR + IL + IGtACR + Iapp + Summed_Isyn)
du[idx+2] = dm_nat
du[idx+3] = dh_nat
du[idx+4] = dm_nap
du[idx+5] = dm_kd
du[idx+6] = dm_kt
du[idx+7] = dh_kt
du[idx+8] = dm_k2
du[idx+9] = dh_k2
du[idx+10] = dm_caT
du[idx+11] = dh_caT
du[idx+12] = dm_ar
end
return nothing
end
function initialconditions(numNeurons, bias = true)
u_init = [-70.0, 0.039, 0.85, 0.1, 0.023, 0.24, 0.21, 0.029, 0.76, 0.043, 0.12, 0.29]
if bias == false
u_init = [-72.8, 0.03, 0.9, 0.076, 0.018, 0.18, 0.3, 0.024, 0.8, 0.03, 0.19, 0.4]
end
u_init = [u_init; zeros(2)]
per_neuron = length(u_init)
u0 = repeat(u_init, numNeurons)
return u0, per_neuron
end

TRNnetwork.jl at commit 98ca09d, no license · at the source

Overview

Authors: Austin J. Mendoza¹, Julie S. Haas¹

ORCID iDs: Austin J. Mendoza, Julie S. Haas

Department of Biological Sciences, Lehigh University, Bethlehem, Pennsylvania 18015

Institutions: Lehigh University (United States)

Journal: eNeuro, volume 13, issue 6, pages ENEURO.0029-26.2026

Dates: received 29 January 2026; accepted 21 May 2026; published online 12 June 2026; in print June 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.1523/eneuro.0029-26.2026 · PMID 42230149 · PMCID PMC13271814 · OpenAlex W4413717947

Open access: gold, a free copy (OpenAlex)

Preprint: doi:10.1101/2025.08.21.671565

Status: code verified

Methods: Evoked potentials, Single-unit activity, calcium imaging

Keywords: cortex, electrical synapse, gap junction, thalamic reticular nucleus, thalamocortical, thalamus

MeSH: Cerebral Cortex*, Electrical Synapses*, Neurons*, Thalamic Nuclei*, Thalamus*, Action Potentials, Animals, Models, Neurological, Neural Pathways (* major topic)

Journal subjects: Research Article: New Research, Neuronal Excitability

Topic: Neural dynamics and brain function (Cognitive Neuroscience, Neuroscience), according to OpenAlex

Funding: HHS | NIH | National Institute of Neurological Disorders and Stroke (NINDS) (NS128713)

Citations: not cited yet (Europe PMC); 81 references in the paper

Abstract

Thalamocortical (TC) cells relay sensory information to the cortex as well as driving their own feedback inhibition through collateral excitation of the thalamic reticular nucleus (TRN). Inhibitory TRN cells are extensively coupled through electrical synapses. While electrical synapses are most often noted for synchronizing rhythmic forms of neuronal activity, their modulation of transient neuronal signals is less understood. Here we sought to characterize how electrical synapses embedded within a network of TRN neurons regulate the processing of ongoing sensory inputs during relay from thalamus to cortex. We constructed a thalamocortical network consisting of reciprocally connected Hodgkin–Huxley-style TC and TRN cells and one cortical output cell summing the TC activity. TRN cells were each electrically coupled to two neighboring cells, forming a ring topology. TC cells received synaptic inputs in sequence, with inputs separated by 10–50 ms, allowing us to assess the functional radius of an electrical synapse by comparing the cumulative effects of each additional TRN electrical synapse on responses within the network. Electrical synapse strength altered both TRN and TC spike response rates and latencies with each additional electrical synapse. Coupling within TRN modulated cortical integration of TC inputs by unexpectedly increasing response rates, duration, and reducing spike correlation to the input sequence that was presented to the TC layer. Thus, embedded TRN electrical synapses exert powerful influence on thalamocortical relay, highlighting the multisynaptic influences of electrically coupled cells on more complex and realistic networks of the brain.

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

Repository

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

jhaaslab/RingModel

License: none: the authors keep all their rights

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Commit: 98ca09d0c59885497564791baa94f1b79a9045a7, 16 September 2025

Languages: Julia (22), MATLAB (10)

Size: 257 files, 32 scripts

Software Heritage: not archived

Found in: “Code accessibility”

Holds: README, environment (Manifest.toml, Project.toml, Models.jl/Manifest.toml, Models.jl/Project.toml), documentation

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

Tools: DifferentialEquations.jl (10 files), Plots.jl (2 files), Makie (1 file)

Availability: 1 check, the latest on 27 September 2026: the link answers

27 September 2026: the link answers

33 files

Models.jl/src/Channels.jl, Julia, 105 lines
Models.jl/src/Inputs.jl, Julia, 49 lines
Models.jl/src/Models.jl, Julia, 84 lines
Models.jl/src/RunUtils.jl, Julia, 57 lines
Models.jl/src/Synapses.jl, Julia, 106 lines
Models.jl/src/TC_TRNnetwork.jl, Julia, 208 lines
Models.jl/src/TRNmodel.jl, Julia, 128 lines, 1 match
Models.jl/src/TRNnetwork.jl, Julia, 153 lines, 1 match
Run_templates/RunTC_TRNnetwork.jl, Julia, 147 lines
Run_templates/RunTRNmodel.jl, Julia, 111 lines
Run_templates/RunTRNnetwork.jl, Julia, 146 lines
Run_templates/Run_simple.jl, Julia, 49 lines
Run_templates/Run_vminit.jl, Julia, 30 lines
matlab/dataAnalysis.m, MATLAB, 64 lines
matlab/plotFR.m, MATLAB, 246 lines
matlab/plotVm.m, MATLAB, 170 lines
networks/createGJnetwork.jl, Julia, 104 lines
networks/run_scripts/calcBias.jl, Julia, 81 lines
networks/run_scripts/calcCC.jl, Julia, 72 lines
networks/run_scripts/calcRin.jl, Julia, 68 lines
networks/run_scripts/calcUinit.jl, Julia, 61 lines
simulations/no_noise/RunTC_TRNnetwork.jl, Julia, 165 lines
simulations/no_noise/dataAnalysis.m, MATLAB, 185 lines
simulations/no_noise/makePlots.m, MATLAB, 150 lines
simulations/no_noise/testCorr.m, MATLAB, 155 lines
simulations/w_noise/RunTC_TRNnetwork.jl, Julia, 170 lines
simulations/w_noise/control/RunTC_TRNnetwork.jl, Julia, 159 lines
simulations/w_noise/control/dataAnalysis.m, MATLAB, 190 lines
simulations/w_noise/control/makePlots.m, MATLAB, 219 lines
simulations/w_noise/dataAnalysis.m, MATLAB, 230 lines
simulations/w_noise/getVMplot.jl, Julia, 123 lines
simulations/w_noise/makePlots.m, MATLAB, 60 lines
README.md, Text, 52 lines

At the source: github.com/jhaaslab/RingModel

Code accessibility

The code/software described in the paper is freely available online at https://github.com/jhaaslab/RingModel. The code is available as Extended Data (https://doi.org/10.1523/ENEURO.0029-26.2026.d1).

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

Tracing map

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Version 2, 28 September 2026

Authors: added Austin J. Mendoza (0000-0001-6823-6624); Julie S. Haas (0000-0002-1571-8512); removed Austin J. Mendoza; Julie S. Haas

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 2 authors, 6 keywords, 9 MeSH terms, 1 funder, 80 references.

Cite

This paper

Mendoza, A. J., & Haas, J. S. (2026). Electrical Coupling within Thalamocortical Networks Cumulatively Reduces Cortical Correlation to Sensory Inputs. eNeuro, 13(6), ENEURO.0029-26.2026. https://doi.org/10.1523/eneuro.0029-26.2026

BibTeX

@article{mendoza2026electrical, author = {Mendoza, Austin J. and Haas, Julie S.}, title = {{Electrical Coupling within Thalamocortical Networks Cumulatively Reduces Cortical Correlation to Sensory Inputs}}, journal = {eNeuro}, year = {2026}, month = jun, volume = {13}, number = {6}, pages = {ENEURO.0029--26.2026}, publisher = {Society for Neuroscience}, issn = {2373-2822}, doi = {10.1523/eneuro.0029-26.2026}, url = {https://doi.org/10.1523/eneuro.0029-26.2026}, pmid = {42230149}, pmcid = {PMC13271814} }

RIS

TY - JOUR AU - Mendoza, Austin J. AU - Haas, Julie S. TI - Electrical Coupling within Thalamocortical Networks Cumulatively Reduces Cortical Correlation to Sensory Inputs T2 - eNeuro J2 - eNeuro PY - 2026 DA - 2026/06/16 VL - 13 IS - 6 SP - ENEURO.0029 EP - 26.2026 SN - 2373-2822 PB - Society for Neuroscience DO - 10.1523/eneuro.0029-26.2026 UR - https://doi.org/10.1523/eneuro.0029-26.2026 LA - en ER -

CSL-JSON

{ "id": "10.1523/eneuro.0029-26.2026", "type": "article-journal", "title": "Electrical Coupling within Thalamocortical Networks Cumulatively Reduces Cortical Correlation to Sensory Inputs", "container-title": "eNeuro", "author": [ { "family": "Mendoza", "given": "Austin J." }, { "family": "Haas", "given": "Julie S." } ], "container-title-short": "eNeuro", "volume": "13", "issue": "6", "page": "ENEURO.0029-26.2026", "DOI": "10.1523/eneuro.0029-26.2026", "PMID": "42230149", "PMCID": "PMC13271814", "ISSN": "2373-2822", "publisher": "Society for Neuroscience", "URL": "https://doi.org/10.1523/eneuro.0029-26.2026", "language": "en", "issued": { "date-parts": [ [ 2026, 6, 16 ] ] } }

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