sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
70357e06b07a7500196afdd7b30cad508d9ec8a195fc1ad53d0dea4441b8073b | MATLAB | 145,984 | 484 | %% 400 EXAMPLES OF ASSEMBLY CONFIGURATIONS WITH 5 ASSEMBLIES IN A HONEYCOMB STRUCTURED TECTUM
% THERE ARE 40 CONFIGURATIONS WHICH ARE EMBEDDED IN TECTA OF INCREASING
% DEGREE v = 5 , ... 14
%
% THE ASSEMBLIES WERE CREATED IN HONEYCOMB STRUCTURED TECTUM OF DEGREE 8
% FROM n = 26 POINTS DRAWN FROM A 2D-NORMAL DISTRIBUTIO... |
205e3b6f4ce72aa18d663fe700e3c2cdc725bf4f3007ac5b8ad4d4a03edd2b23 | MATLAB | 146,205 | 4,901 | % VIEW_NII: Create or update a 3-View (Front, Top, Side) of the
% brain data that is specified by nii structure
%
% Usage: status = view_nii([h], nii, [option]) or
% status = view_nii(h, [option])
%
% Where, h is the figure on which the 3-View will be plotted;
% nii is the brain data in NIFTI format;
% o... |
b06ab672e55e7d8af7b20f704c1a7290b9897c63aa0c0d7f740495a4d81f2c4e | MATLAB | 158,444 | 3,873 | % ERPIMAGE - Plot a colored image of a collection of single-trial data epochs, optionally
% sorted on and/or aligned to an input sorting variable and smoothed across
% trials with a Gaussian weighted moving-average. (To return event-aligned data
% without plotting, use EEGALIGN). ... |
c9cf6a7fa3128a5589b88ca9aa9531baa66f496d878e1ee23e58c28670169abf | MATLAB | 160,806 | 4,409 | %%% Trauma film study
%============================================================================================
% Final analyses pipeline:
% Examining the effects of sleep in stress adaptation after a laboratory trauma
% Y. Azza, M. K. Kammerer, H.-V. Ngo, M. Ehsanifard, A. Wick, K. Junghans, I. Wilhelm-Groch
%... |
7cd5ee1c497f9209a6d20e789946d9afec948cce8010bc3481fe54ebee3b0d12 | MATLAB | 171,837 | 2,905 | function [cfg] = data2bids(cfg, varargin)
% DATA2BIDS is a helper function to convert MRI, MEG, EEG, iEEG or NIRS data to the
% Brain Imaging Data Structure. The overall idea is that you write a MATLAB script in
% which you call this function multiple times, once for each individually recorded
% data file (or data set... |
65cd50da2f428e950b72f7b8df806217d94c576958413006dd27c7fcfd7f96d2 | MATLAB | 180,372 | 4,104 | classdef Run_GUI_App < matlab.apps.AppBase
% Properties that correspond to app components
properties (Access = public)
figure1 matlab.ui.Figure
uipanel6_6 matlab.ui.container.Panel
uibuttongroup8_4 matlab.ui.container.ButtonGroup
radiobutton12_4 matlab.ui.con... |
21bbbb39e123b3f8e72b9bd234a24f4b6593521e3abfa94861ef866babfdf3d9 | MATLAB | 193,392 | 423 | %% 400 EXAMPLES OF ASSEMBLY CONFIGURATIONS WITH 10 ASSEMBLIES IN A HONEYCOMB STRUCTURED TECTUM
%
% THE ASSEMBLIES WERE CREATED IN HONEYCOMB STRUCTURED TECTUM OF DEGREE 12
% FROM n = 16 POINTS DRAWN FROM A 2D-NORMAL DISTRIBUTION WITH STANDARD
% DEVIATION s UNIFORMLY IN sqrt([0.5 2.0])
%
% THE SIZE OF ASSEMBLIES IN TERM ... |
185b68c996be2296319b3eb27fd8659af182c70ed7d8e630fd0b41546aea57c1 | MATLAB | 200,000 | 344 | %% 400 EXAMPLES OF ASSEMBLY CONFIGURATIONS k ASSEMBLIES IN A HONEYCOMB STRUCTURED TECTUM
% THERE ARE 20 CONFIGURATIONS FOR k = 1, ... 20 EACH
%
% THE ASSEMBLIES WERE CREATED IN HONEYCOMB STRUCTURED TECTUM OF DEGREE 16
% FROM n = 26 POINTS DRAWN FROM A 2D-NORMAL DISTRIBUTION WITH STANDARD
% DEVIATION s UNIFORMLY IN sqrt... |
29747a376dabb438465bafc1f9bc519beab7fdce2932d847aac520a2ee5ffe99 | MATLAB | 200,000 | 385 | %% 400 EXAMPLES OF ASSEMBLY CONFIGURATIONS WITH 10 ASSEMBLIES IN A HONEYCOMB STRUCTURED TECTUM
%
% THE ASSEMBLIES WERE CREATED IN HONEYCOMB STRUCTURED TECTUM OF DEGREE 12
% FROM n = 16 POINTS DRAWN FROM A 2D-NORMAL DISTRIBUTION WITH STANDARD
% DEVIATION s UNIFORMLY IN sqrt([0.5 2.0])
%
% THE SIZE OF ASSEMBLIES IN TERM ... |
37279c64be7ea18f0eb01abd58081dc670dd1f5c2794fb322a8a475a55aede30 | MATLAB | 200,000 | 317 | %% 400 EXAMPLES OF ASSEMBLY CONFIGURATIONS WITH 10 ASSEMBLIES IN A HONEYCOMB STRUCTURED TECTUM
%
% THE ASSEMBLIES WERE CREATED IN HONEYCOMB STRUCTURED TECTUM OF DEGREE 12
% FROM n = 26 POINTS DRAWN FROM A 2D-NORMAL DISTRIBUTION WITH STANDARD
% DEVIATION s UNIFORMLY IN sqrt([1.0 2.5])
%
% THE SIZE OF ASSEMBLIES IN TERM ... |
39a3b400b7c1a6313569ecefa3a966ab598c62773b865edb45f5fe4432294f91 | MATLAB | 200,000 | 2,827 | function cmap = cmocean(ColormapName,varargin)
% cmocean returns perceptually-uniform colormaps created by Kristen Thyng.
%
%% Syntax
%
% cmocean
% cmap = cmocean('ColormapName')
% cmap = cmocean('-ColormapName')
% cmap = cmocean(...,NLevels)
% cmap = cmocean(...,'pivot',PivotValue)
% cmap = cmocean(...,... |
40e3c34377ddee5b698e1d072d59bb2820bc89d62b8500150e5dee9fcc56d7b4 | MATLAB | 200,000 | 245 | %% 400 EXAMPLES OF ASSEMBLY CONFIGURATIONS WITH 10 ASSEMBLIES IN A HONEYCOMB STRUCTURED TECTUM
%
% THE ASSEMBLIES WERE CREATED IN HONEYCOMB STRUCTURED TECTUM OF DEGREE 12
% FROM n = 36 POINTS DRAWN FROM A 2D-NORMAL DISTRIBUTION WITH STANDARD
% DEVIATION s UNIFORMLY IN sqrt([1.5 3.0])
%
% THE SIZE OF ASSEMBLIES IN TERM ... |
573826bf2357e18460a8394cfd8d7114002d30785730e9ee825d7bf7959fc7a9 | MATLAB | 200,000 | 275 | %% 400 EXAMPLES OF ASSEMBLY CONFIGURATIONS WITH 10 ASSEMBLIES IN A HONEYCOMB STRUCTURED TECTUM
%
% THE ASSEMBLIES WERE CREATED IN HONEYCOMB STRUCTURED TECTUM OF DEGREE 12
% WITH VARIABLE SIZE SO THAT THE MEAN ASSEMBLY SIZE WITHIN EVERY
% CONFIGURATION RANGES UNIFORMLY BETWEEN 6 AND 26.
%
% THE MEAN PAIRWISE OVERLAP OF ... |
5c1a3ac3f4ce452f25b69ddb638b4dc746dd1997ba90c11c26cc342a8bb5ef23 | MATLAB | 200,000 | 2,826 | function cmap = cmocean(ColormapName,varargin)
% cmocean returns perceptually-uniform colormaps created by Kristen Thyng.
%
%% Syntax
%
% cmocean
% cmap = cmocean('ColormapName')
% cmap = cmocean('-ColormapName')
% cmap = cmocean(...,NLevels)
% cmap = cmocean(...,'pivot',PivotValue)
% cmap = cmocean(...,... |
6e3f71df3fc3171198a5fcd4117a03782939c4704fed37ac0c9f40e412cfbd03 | MATLAB | 200,000 | 303 | %% 400 EXAMPLES OF ASSEMBLY CONFIGURATIONS k ASSEMBLIES IN A HONEYCOMB STRUCTURED TECTUM
% THERE ARE 20 CONFIGURATIONS FOR k = 1, ... 20 EACH
%
% THE ASSEMBLIES WERE CREATED IN HONEYCOMB STRUCTURED TECTUM OF DEGREE 16
% FROM n = 36 POINTS DRAWN FROM A 2D-NORMAL DISTRIBUTION WITH STANDARD
% DEVIATION s UNIFORMLY IN sqrt... |
6e7858554ee8bb4091c8a5cb53f86d892777c2ec101b10391c68f10bfad246d6 | MATLAB | 200,000 | 320 | %% 400 EXAMPLES OF ASSEMBLY CONFIGURATIONS WITH 10 ASSEMBLIES IN A HONEYCOMB STRUCTURED TECTUM
% THERE ARE 40 CONFIGURATIONS WHICH ARE EMBEDDED IN TECTA OF INCREASING
% DEGREE v = 8 , ... 17
%
% THE ASSEMBLIES WERE CREATED IN HONEYCOMB STRUCTURED TECTUM OF DEGREE 8
% FROM n = 26 POINTS DRAWN FROM A 2D-NORMAL DISTRIBUTI... |
74897ed3219eff8005b2d11cffee274950ac50c897043806c27e742f66dad1f1 | MATLAB | 200,000 | 340 | %% 400 EXAMPLES OF ASSEMBLY CONFIGURATIONS k ASSEMBLIES IN A HONEYCOMB STRUCTURED TECTUM
% THERE ARE 20 CONFIGURATIONS FOR k = 1, ... 20 EACH
%
% THE ASSEMBLIES WERE CREATED IN HONEYCOMB STRUCTURED TECTUM OF DEGREE 12
% FROM n = 26 POINTS DRAWN FROM A 2D-NORMAL DISTRIBUTION WITH STANDARD
% DEVIATION s UNIFORMLY IN sqrt... |
7583a23cbd6aa2bc2aa68b1ed90564467adc1777c94dfbd5358312676f55d166 | MATLAB | 200,000 | 790 | function [coef] = read_ctf_coef
% READ_CTF_COEF returns the spatial filter coefficients for the CTF MEG system
% that has been installed at the F.C. Donders Centre (id 1706)
%
% This function actually does not read the coefficients from a file, but the
% coefficients themselves are included in this function.
%
% The ... |
7d46e98e9f881d62561aa3ca58845efc171b724d87a42338072ab37a511f9514 | MATLAB | 200,000 | 4,739 | function varargout = BrainNet_Option(varargin)
%BrainNet Viewer, a graph-based brain network mapping tool, by Mingrui Xia
%Function for control panel
%-----------------------------------------------------------
% Copyright(c) 2017
% State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
... |
82d40d554537f59ff5e2123053d8582d050f21dc4859e23d4ee9e3f539041431 | MATLAB | 200,000 | 4,562 | function varargout = BrainNet(varargin)
%BrainNet Viewer, a graph-based brain network mapping tool, by Mingrui Xia
%-----------------------------------------------------------
% Copyright(c) 2018
% Beijing Normal University
% Written by Mingrui Xia
% Mail to Author: <a href="mingruixia@gmail.com">Mingrui Xia</a>
% V... |
8fcc507b1ff69ada113fafed118846460426c3481ec3c0048ad4540f00c80020 | MATLAB | 200,000 | 407 | %% 400 EXAMPLES OF ASSEMBLY CONFIGURATIONS k ASSEMBLIES IN A HONEYCOMB STRUCTURED TECTUM
% THERE ARE 20 CONFIGURATIONS FOR k = 1, ... 20 EACH
%
% THE ASSEMBLIES WERE CREATED IN HONEYCOMB STRUCTURED TECTUM OF DEGREE 16
% FROM n = 16 POINTS DRAWN FROM A 2D-NORMAL DISTRIBUTION WITH STANDARD
% DEVIATION s UNIFORMLY IN sqrt... |
911d4f2992236475b9be5779c3c8578a4bcb0818f9b9419dd61f35a32053631a | MATLAB | 200,000 | 238 | %% 400 EXAMPLES OF ASSEMBLY CONFIGURATIONS WITH 10 ASSEMBLIES IN A HONEYCOMB STRUCTURED TECTUM
%
% THE ASSEMBLIES WERE CREATED IN HONEYCOMB STRUCTURED TECTUM OF DEGREE 12
% FROM n = 36 POINTS DRAWN FROM A 2D-NORMAL DISTRIBUTION WITH STANDARD
% DEVIATION s UNIFORMLY IN sqrt([1.5 3.0])
%
% THE SIZE OF ASSEMBLIES IN TERM ... |
9e53318d3577955a0d428aa178a2de4034a5af176f31860c098144f4d4a089e8 | MATLAB | 200,000 | 4,433 | function res=OPERA_par(varargin)
Version='2.9';
SubVersion='2.9.5';
%%
%
% _______________________________________________________________________
% | |
% | OPERA models for physchem, environmental fate and tox properties. |
%... |
aa441f718abbfcb44070350598940c360302c0f96cde20a83ff743519f409756 | MATLAB | 200,000 | 4,412 | function res=OPERA(varargin)
Version='2.9';
SubVersion='2.9.5';
%%
%
% _______________________________________________________________________
% | |
% | OPERA models for physchem, environmental fate and tox properties. |
% ... |
b70dc1d414628b036f8da434053d8f5ebea8084b8acec91b56787a2d2a36e14a | MATLAB | 200,000 | 290 | %% 400 EXAMPLES OF ASSEMBLY CONFIGURATIONS WITH 10 ASSEMBLIES IN A HONEYCOMB STRUCTURED TECTUM
%
% THE ASSEMBLIES WERE CREATED IN HONEYCOMB STRUCTURED TECTUM OF DEGREE 12
% FROM n = 26 POINTS DRAWN FROM A 2D-NORMAL DISTRIBUTION WITH STANDARD
% DEVIATION s UNIFORMLY IN sqrt([1.0 2.5])
%
% THE SIZE OF ASSEMBLIES IN TERM ... |
c14cfc198e745e10ad4f211a3fce660cb29f53972170d5fa536f932604576cc7 | MATLAB | 200,008 | 2,693 | function dat = TOL_data
% TOL_data, digitized data storage for Duvelle and Grieves (2025)
% Main code to store data for: Duvelle and Grieves (2025) Tolman's Sunburst Maze 80
% years on: A meta-analysis reveals poor replicability and little evidence for
% shortcutting. European Journal of Neuroscience. DOI: 10.1111/ej... |
59d9d417351a9162bfa2c84b88bd0e414d5c4d7daaac39a1ea20797e2987bf27 | MATLAB | 200,021 | 4,383 | %% Script to analyze Aversive Pavlovian Learning Doric and TDT data.
%Script for the analysis of photometry signals during Aversive Pavlovian
%Learning of individual mice. Different functions for correcting, dff/zscore calculations and plotting.
%author:Zacharoula Kagiampaki
%version:26.05.2024 (new version)
%modific... |
1f2a285a3111640ab85db3d78045e1165171bc146125fed32983e466fb63541c | MATLAB | 200,084 | 4,323 | %% Script for cohort analysis of APL (Aversive Pavlovian Learning) photometry data for both single injected and double injected mice.
% Selection of groups to be compared, data extraction from the individual
% analysis script (signal corrections, dff, zscores), average/sem of dff
% and zscores for both whole traces an... |
a44a31bf317c94e1d75f42f74bd72e08d15a40e324d3b625b2033f11b258819c | Mathematica | 30,097 | 365 | (* ::Package:: *)
(* ::Input::Initialization:: *)
<<NDSolve`FEM`;
(*Note: the preset[] function is required to run before this to build the basic topology/geometry of the network manifold, providing variables such as edgeList, vertexPolyFacesByLines, etc.*)
(* ::Input::Initialization:: *)
(*setupFEM[globalScale]: ... |
c8d1a5445c1e1c5b02814d21b22d122e133586e1a51dbe27b81d681a818af3be | Mathematica | 56,534 | 1,329 | (* Content-type: application/vnd.wolfram.mathematica *)
(*** Wolfram Notebook File ***)
(* http://www.wolfram.com/nb *)
(* CreatedBy='Wolfram 14.2' *)
(*CacheID: 234*)
(* Internal cache information:
NotebookFileLineBreakTest
NotebookFileLineBreakTest
NotebookDataPosition[ 154, 7]
NotebookDataLength[ ... |
8110093b6b3e049bb42da6bce02d6b99b71943bbf1157e6ec77ba81ef4deb612 | Mathematica | 200,000 | 4,258 | (* Content-type: application/vnd.wolfram.mathematica *)
(*** Wolfram Notebook File ***)
(* http://www.wolfram.com/nb *)
(* CreatedBy='Mathematica 12.2' *)
(*CacheID: 234*)
(* Internal cache information:
NotebookFileLineBreakTest
NotebookFileLineBreakTest
NotebookDataPosition[ 158, 7]
NotebookDataLengt... |
d4c7331e742d3e637eba300cd2c4783f06b11d27217ad61b01f2c485ba3387a7 | Mathematica | 200,000 | 4,891 | (* Content-type: application/vnd.wolfram.mathematica *)
(*** Wolfram Notebook File ***)
(* http://www.wolfram.com/nb *)
(* CreatedBy='Mathematica 12.2' *)
(*CacheID: 234*)
(* Internal cache information:
NotebookFileLineBreakTest
NotebookFileLineBreakTest
NotebookDataPosition[ 158, 7]
NotebookDataLengt... |
f8c6ebeb9c2246487427ffdc41a1f9e085c60c1ebaad61b8f563703eee1dd974 | Mathematica | 200,000 | 4,506 | (* Content-type: application/vnd.wolfram.mathematica *)
(*** Wolfram Notebook File ***)
(* http://www.wolfram.com/nb *)
(* CreatedBy='Mathematica 12.2' *)
(*CacheID: 234*)
(* Internal cache information:
NotebookFileLineBreakTest
NotebookFileLineBreakTest
NotebookDataPosition[ 158, 7]
NotebookDataLengt... |
4cb7c6dc4476c619e0507ab9f9970b3e1fcb4c334c6fe89b246026e619d06246 | NEURON | 30 | 3 | module thalamus-go
go 1.23.2
|
456de3889ae337d9ae8387a49ec89a3ccca10d6996cff59d8f966f5edf4c5939 | NEURON | 81 | 8 | NEURON {
SUFFIX hh02
NONSPECIFIC_CURRENT il
}
BREAKPOINT {
il = 0
}
|
059c445fc23c4a1c7ee4a9e418bfa7f637e89aa817ee97258ad8a1c30cbfb1a8 | NEURON | 109 | 8 | NEURON {
SUFFIX hh
USEION na READ ena, ina WRITE ina
}
BREAKPOINT { ina = 5*(v - ena) }
INITIAL {}
|
cdb3ffb6fdf5c16f7b6c2eafb24b762c8cb29eb44a1b8f70b2a03c2211ec6c57 | NEURON | 113 | 12 | NEURON {
VOLTAGE_PROCESS v_clamp
GLOBAL v0
}
PARAMETER {
v0 = -60 (mV)
}
BREAKPOINT {
v = v0
}
|
ace372045ccc8a5abea6fed87144fcb24d4f0e083e06757d950818f7f4d3f845 | NEURON | 128 | 13 | NEURON {
VOLTAGE_PROCESS vmech
}
INITIAL { c = 0 }
STATE { c }
PARAMETER {
tau_c = 150 (ms)
}
BREAKPOINT { v = 42 }
|
14b5a5f332b1f9b905a308697cfd45d870cfcbe7382fa2cf2f3844e2805ad868 | NEURON | 145 | 13 | NEURON {
JUNCTION_PROCESS gj
NONSPECIFIC_CURRENT i
RANGE g
}
INITIAL {}
PARAMETER { g = 1 }
BREAKPOINT {
i = g*(v - v_peer)
}
|
3f746aa62e238f272799226ea98a2421bff530d008491d464cdc35e0d7f1e7d5 | NEURON | 149 | 14 | NEURON {
JUNCTION_PROCESS gj0
NONSPECIFIC_CURRENT i
RANGE g
}
INITIAL {}
PARAMETER {
g = 1
}
BREAKPOINT {
i = g*(v - v_peer)
}
|
e45509cd75ffa512e0dc91c483b045b1ee6147f5ed44b17b58688d5800fa7016 | NEURON | 164 | 15 | NEURON {
SUFFIX decay
USEION x WRITE xd
RANGE tau
}
PARAMETER { tau = 5 }
BREAKPOINT {
SOLVE dX METHOD cnexp
}
DERIVATIVE dX {
xd' = -tau*xd
}
|
0ee7b9f8fc8febe04f1808216c674213b26a2f401304adc1c9c36e273f121f2d | NEURON | 166 | 15 | NEURON {
JUNCTION_PROCESS gj1
NONSPECIFIC_CURRENT i
RANGE g, e
}
INITIAL {}
PARAMETER {
g = 1
e = 0
}
BREAKPOINT {
i = g*(v - v_peer - e)
}
|
1bc838b696979f1dbd736fa477c0961d99baa5a7bef4a0aacdc27b7a086155f0 | NEURON | 167 | 19 | NEURON {
SUFFIX test6_nonlinear_diff
}
STATE {
p
}
BREAKPOINT {
SOLVE state METHOD sparse
}
DERIVATIVE state {
p' = sin(p)
}
INITIAL {
p = 1
}
|
6ff4fa1167467071b1350b3c70344a24133b1681b11eebef377dae17d6a3aaab | NEURON | 172 | 14 | NEURON {
SUFFIX hh03
NONSPECIFIC_CURRENT il
RANGE el, gl
}
PARAMETER {
gl = 0.0003 (S/cm2)
el = -54.3 (mV)
}
BREAKPOINT {
il = gl*(v - el)
}
|
d9af153f5a7ce388df74f933bad087d9408c667588c54768a159b82ff6b94811 | NEURON | 175 | 13 | NEURON {
VOLTAGE_PROCESS v_limit
GLOBAL v_low, v_high
}
PARAMETER {
v_high = 20 (mV)
v_low = -70 (mV)
}
BREAKPOINT {
v = max(min(v, v_high), v_low)
}
|
7e9efc7b056f9de7eb7735f826db6273439ec2199657b219beb58f704ff89e35 | NEURON | 189 | 27 | NEURON {
SUFFIX celsius_test
}
PARAMETER {
celsius
}
STATE {
c
}
ASSIGNED {
}
BREAKPOINT {
SOLVE states
}
DERIVATIVE states {
c = celsius
}
INITIAL {
c = 0
}
|
9ac606f3a192f8617ee577547f949daffc68264a670a3708b18e5fdcd2c97eac | NEURON | 193 | 18 | NEURON {
POINT_PROCESS net_rec_tst
NONSPECIFIC_CURRENT i
}
PARAMETER { foo e }
ASSIGNED { g }
INITIAL { g = 0 }
BREAKPOINT {
i = g*(v - e)
}
NET_RECEIVE(w, foo) {
g = g + foo
}
|
0183e513823a24e291c201cb4a731ab1910f1173c3bedbf21ccd96c20132f85b | NEURON | 196 | 24 | : Copy range parameter to state variable exactly.
NEURON {
SUFFIX param_as_state
RANGE p
}
PARAMETER {
p = 1
}
ASSIGNED {}
STATE {
s
}
INITIAL {
s = p
}
BREAKPOINT {
}
|
d550fb171663133c758612f03524cc02822d453d42b2730099bb51ff54faeb9c | NEURON | 202 | 25 | NEURON {
SUFFIX diam_test
}
PARAMETER { diam area }
STATE {
d
a
}
BREAKPOINT {
SOLVE state
}
DERIVATIVE state {
d = diam
a = area
}
INITIAL {
d = -23.0
a = -42.0
}
|
1bd681d065f4ef92bdf3f0a712919ee29ade9978281e6fe07bec41d90f5201b2 | NEURON | 209 | 28 | NEURON {
POINT_PROCESS example
}
STATE {
x
}
INITIAL {
x = 10
}
BREAKPOINT {
SOLVE state METHOD stochastic
}
WHITE_NOISE {
xi
}
DERIVATIVE state {
if (x > 2) {
}
else if (x > 1) {
}
}
|
6f6b6f1f4be806296eadf9828b655b82bde3e1dd2ec20fafa80f8771106da1ef | NEURON | 210 | 19 | : Test mechanism for verifying ionic valence
NEURON {
SUFFIX test_cl_valence
USEION cl WRITE icl VALENCE -1
}
PARAMETER {}
ASSIGNED {}
INITIAL {}
STATE {}
BREAKPOINT {
icl = 1.23*(v-4.56)
}
|
668b969e8ae7704faf36b0a9675b456f7c2798bb035389d241039930eda7415b | NEURON | 225 | 19 | : Test density mechanism for passive calcium current.
NEURON {
SUFFIX ca_linear
USEION ca WRITE ica VALENCE 2
RANGE g
}
PARAMETER {
g = .001 (S/cm2)
}
ASSIGNED {}
INITIAL {}
BREAKPOINT {
ica = g*v
}
|
2c3e43f54933acb6d9c18d39515c62c7f999f46053761f5f111b36e128531b55 | NEURON | 229 | 23 | : Test mechanism for consuming ionic reversal potential
NEURON {
SUFFIX read_eX
USEION x READ ex
}
PARAMETER {}
ASSIGNED {}
STATE {
record_ex
}
INITIAL {
record_ex = ex
}
BREAKPOINT {
record_ex = ex
}
|
de7a9ab8024777fca7db8b818cf451ce0cacdde2bb39c2219c42815f032c4480 | NEURON | 229 | 22 | : Test mechanism for checking ionic valence read
NEURON {
SUFFIX test_ca_read_valence
USEION ca READ ica VALENCE zca
}
PARAMETER {}
ASSIGNED {}
STATE {
record_z
}
INITIAL {
record_z = zca
}
BREAKPOINT {
}
|
a41b6065d9b8742a8261d4a143531a7e7a115cb3505bfbe04ed545684579402f | NEURON | 235 | 25 | NEURON {
SUFFIX test2_kin_diff
}
STATE {
a b c
}
BREAKPOINT {
SOLVE state METHOD sparse
}
KINETIC state {
LOCAL f, r
f = 2
r = 1
~ 2a + b <-> c (f, r)
}
INITIAL {
a = 0.2
b = 0.3
c = 0.5
}
|
33bec7122a80626f11c52d1d7e0489c3614554f345195a4ec8471df276182cf4 | NEURON | 239 | 20 | NEURON {
POINT_PROCESS inject
USEION x WRITE xd
RANGE alpha, beta
}
ASSIGNED { beta }
PARAMETER { alpha = 200 }
INITIAL { beta = 0 }
BREAKPOINT {
xd = xd + beta
beta = 0
}
NET_RECEIVE(weight) {
beta = alpha*weight
}
|
17e5eee01c108773e9d01495da577454e5abc653dc28fdef466d67f18dfd3e3b | NEURON | 240 | 21 | NEURON {THREADSAFE
POINT_PROCESS gap
NONSPECIFIC_CURRENT i
RANGE r, i
POINTER vgap
}
PARAMETER {
v (millivolt)
vgap (millivolt)
r = 100000(megohm)
}
ASSIGNED {
i (nanoamp)
}
BREAKPOINT {
i = (v - vgap)/r
}
|
5a7be8d86c52aa3e20d219ae5c3e073459d67523800fdca6a339132c60b2cd9a | NEURON | 248 | 22 | NEURON {
SUFFIX pas
NONSPECIFIC_CURRENT i
RANGE g
GLOBAL e
}
UNITS {
(mV) = (millivolt)
(S) = (siemens)
}
INITIAL {}
PARAMETER {
g = .001 (S/cm2)
e = -70 (mV) : Taken from nrn
}
BREAKPOINT {
i = g*(v - e)
}
|
e26c31b246be5bd982dacb0344b4976517f1f3e7d0e68bd0b4cc1c44070b6c25 | NEURON | 254 | 27 | : Test mechanism with linear response to ica.
NEURON {
SUFFIX read_cai_init
USEION ca READ cai VALENCE 2
}
PARAMETER {}
ASSIGNED {}
STATE {
s
}
INITIAL {
s = cai
}
BREAKPOINT {
SOLVE states
}
DERIVATIVE states {
s = cai
}
|
c78f715772567c98d70667dd40bb3e381a18515c6585fc97ac70bd67a1d0d76f | NEURON | 259 | 18 | NEURON { SUFFIX test_kinetic_alternating_differential }
STATE { A B C D }
BREAKPOINT { : NOTE we need a newline here :/
SOLVE foobar METHOD sparse
}
KINETIC foobar {
LOCAL x, y
x = 23*v
y = 42*v
~ A <-> B (x, y)
C' = 0.1
~ C <-> D (x, y)
}
|
ffc353835e7ad131aa86e27ccb21efacd887d1ea2645e9573c028d3909178872 | NEURON | 263 | 24 | : Test mechanism with linear response to ica.
NEURON {
SUFFIX write_cai_breakpoint
USEION ca WRITE cai READ ica VALENCE 2
}
PARAMETER {}
ASSIGNED {}
STATE {
cai
}
INITIAL {}
BREAKPOINT {
SOLVE states
}
DERIVATIVE states {
cai = 5.2e-4
} |
143ad2924dcc49bba836fac5de555746082323ff52aefd1afa3af6920bddedf7 | NEURON | 271 | 21 | NEURON { SUFFIX test_kinetic_under_conditional }
STATE { A B }
BREAKPOINT { : NOTE we need a newline here :/
SOLVE foobar METHOD sparse
}
KINETIC foobar {
LOCAL x, y
x = 23*v
y = 42*v
if (v<0) {
~ A <-> B (x, y)
}
else {
~ A <-> B (y, x)
}
}
|
31eb9643e4a9cde8b5c000cdecc24bfb1ceeb7ecaa0711b272d30780568c4ed2 | NEURON | 272 | 21 | NEURON { SUFFIX test_kinetic_alternating_reaction }
STATE { A B C D }
BREAKPOINT { : NOTE we need a newline here :/
SOLVE foobar METHOD sparse
}
KINETIC foobar {
LOCAL x, y
x = 23*v
y = 42*v
~ A <-> B (x, y)
x = sin(y)
y = cos(x)
~ C <-> D (x, y)
}
|
fec5eb567b2c2208e30f6403c0a329663d9de9027472bd785567f79ae522ef90 | NEURON | 272 | 22 | : Reversal potential writer that writes to a single ion
: as a simple function of internal concentration.
NEURON {
SUFFIX write_eX
USEION x READ xi WRITE ex
}
PARAMETER {}
ASSIGNED {}
STATE {}
INITIAL {
ex = 100 + xi
}
BREAKPOINT {
ex = 100 + xi
}
|
bed06b91a460f1385491812a56eb8cc6793d5276faa2848634fc6f63cd6c55e8 | NEURON | 273 | 15 | NEURON { SUFFIX test_misplaced_reaction }
STATE { A B }
BREAKPOINT { : NOTE we need a newline here :/
SOLVE foobar METHOD sparse
}
: NOTE this is OK to do, while alternating normal and reaction statements is not!
DERIVATIVE foobar {
LOCAL x
~ A <-> B (x, 1/x)
}
|
0a47b4b38993924d75983d6f7efa242eba071b02803e7753cbabd9d9e32e1af7 | NEURON | 276 | 28 | NEURON {
POINT_PROCESS bug_1893
}
INITIAL {
c = 0
rho = 0
theta_p = 0
}
STATE {
c
rho
theta_p
}
PARAMETER {
tau_c = 150 (ms)
}
BREAKPOINT {
SOLVE state METHOD cnexp
}
DERIVATIVE state {
c' = -c/tau_c
rho' = (c - theta_p) > 0
}
|
584ffbe21685415241afa2847073ca56dfc983f696c1048e8ed66766626162a5 | NEURON | 277 | 28 | NEURON {
POINT_PROCESS bug_1893
}
INITIAL {
c = 0
rho = 0
theta_p = 0
}
STATE {
c
rho
theta_p
}
PARAMETER {
tau_c = 150 (ms)
}
BREAKPOINT {
SOLVE state METHOD sparse
}
DERIVATIVE state {
c' = -c/tau_c
rho' = (c - theta_p) > 0
}
|
f3752597442118ceea38cc7ab56ac49ee303c3f39e11230fb3cfea47192edd08 | NEURON | 280 | 24 | : Test point mechanism generating a fixed ionic current
NEURON {
POINT_PROCESS point_ica_current
USEION ca WRITE ica VALENCE 2
}
ASSIGNED {}
STATE {
ica_nA
}
INITIAL {
ica_nA = 0
}
BREAKPOINT {
ica = ica_nA
}
NET_RECEIVE(weight) {
ica_nA = weight
}
|
150baaca11bf4fb84076b1cec39eaf57c9775e48dca37f06b2e5b98aace47113 | NEURON | 286 | 25 | UNITS {
(mV) = (millivolt)
(S) = (siemens)
}
NEURON {
SUFFIX pas
NONSPECIFIC_CURRENT i
RANGE g, e
}
INITIAL {}
PARAMETER {
g = .001 (S/cm2)
e = -65 (mV) : we use -65 for the ball and stick model, instead of Neuron default of -70
}
ASSIGNED {
v (mV)
}
BREAKPOINT {
i = g*(v - e)
}
|
ea671b5bf7fe72830f970372180f03b216af0ff160c46f81857d43d6cbe1a3b4 | NEURON | 286 | 24 | : Test mechanism generating a fixed ionic current
NEURON {
SUFFIX fixed_ica_current
USEION ca WRITE ica
RANGE current_density
}
PARAMETER {
current_density = 0
}
ASSIGNED {}
INITIAL {
ica = current_density
}
STATE {}
BREAKPOINT {
ica = current_density
}
|
7eda8f22f82a475d907249b3a796354cfe6f9bab82a6174084d79b4e7aee6b82 | NEURON | 298 | 28 | NEURON {
SUFFIX test3_kin_diff
}
STATE {
a b c
}
BREAKPOINT {
SOLVE state METHOD sparse
}
KINETIC state {
LOCAL f0, f1, r0, r1
f0 = 2
r0 = 1
f1 = 3
r1 = 0
~ a + b <-> c (f0, r0)
~ c <-> b (f1, r1)
}
INITIAL {
a = 0.2
b = 0.3
c = 0.5
}
|
5afc141e45d6db37b95fba4f7901297624956b06acacb5bac4736251ee6ce968 | NEURON | 305 | 21 | NEURON { SUFFIX test_derivative_alternating }
STATE { A B }
BREAKPOINT { : NOTE we need a newline here :/
SOLVE foobar METHOD sparse
}
: NOTE this is OK to do, while alternating normal and reaction statements is not!
DERIVATIVE foobar {
LOCAL x
x = 23*v
A' = B*x
x = sin(x)
B' = x*A
}
|
220dc92cda73e0acd0ef40fbb9d7e33d69e7ac7e0dec196c13bbc19e41e9df93 | NEURON | 311 | 31 | NEURON {
POINT_PROCESS OrnsteinUhlenbeck
GLOBAL tau, sigma
}
PARAMETER {
tau = 1000
sigma = 0.1
}
ASSIGNED {}
STATE {
y
}
INITIAL {
y=1
}
BREAKPOINT {
SOLVE state METHOD stochastic
}
WHITE_NOISE {
zeta zeta2
}
DERIVATIVE state {
y' = -y/tau + (2*sigma*sigma/tau)^0.5*zeta
}
|
c720b22ce2016c22ea28f866b1af9823055386274d80551b89df31a378fd6674 | NEURON | 312 | 35 | NEURON {
SUFFIX SABR
GLOBAL alpha, beta
}
PARAMETER {
alpha = 1
beta = 0.1
}
ASSIGNED {}
STATE {
F
sigma
}
INITIAL {
F=1
sigma=0.2
}
BREAKPOINT {
SOLVE state METHOD stochastic
}
WHITE_NOISE {
W Z
}
DERIVATIVE state {
F' = sigma*F^beta*W
sigma' = alpha*sigma*Z
}
|
3d9fe86cb2f7c217f69c9d48026b078e3fbccb23ff3175e7bff3630f4ab064c6 | NEURON | 313 | 26 | NEURON {
SUFFIX mean_reverting_stochastic_density_process
GLOBAL kappa, mu, sigma
}
PARAMETER {
kappa = 0.1
mu = 1
sigma = 0.1
}
STATE { s }
WHITE_NOISE { w }
BREAKPOINT {
SOLVE states METHOD stochastic
}
INITIAL {
s = 2
}
DERIVATIVE states {
s' = kappa*(mu - s) + sigma*w
}
|
ca6612d350b102c8a0b42c928fc9e06d14599a62839d713483baf1ab1625a41a | NEURON | 322 | 30 | : Test mechanism with linear response to ica.
NEURON {
SUFFIX linear_ca_conc
USEION ca READ ica WRITE cai VALENCE 2
RANGE coeff
}
PARAMETER {
coeff = 0
}
ASSIGNED {}
STATE {
cai
}
INITIAL {
cai = 0
}
BREAKPOINT {
SOLVE update METHOD cnexp
}
DERIVATIVE update {
cai' = -coeff*ica
}... |
8bdc7d05bb73292ed2be6230f022d72fc058ebd6d15574f4a282b82aae7048fb | NEURON | 339 | 28 | NEURON {
POINT_PROCESS inject_norm_amount
USEION x WRITE xd
RANGE alpha, beta
}
ASSIGNED {
beta
}
PARAMETER {
alpha = 200
area
diam
}
INITIAL {
beta = 0
}
BREAKPOINT {
xd = xd + beta * area / 1000 : considering particle amount -> normalization by area
beta = 0
}
NET_RECEIVE(weight) {
bet... |
b54648f7b96c17f0fd4c12d648e6a58d282c3355c3e658ccbc796b0da479c37d | NEURON | 340 | 28 | NEURON {
SUFFIX test0_kin_diff
}
STATE {
s d h
}
BREAKPOINT {
SOLVE state METHOD sparse
}
KINETIC state {
LOCAL alpha1, beta1, alpha2, beta2
alpha1 = 2
beta1 = 0.6
alpha2 = 3
beta2 = 0.7
~ s <-> h (alpha1, beta1)
~ d <-> s (alpha2, beta2)
}
INITIAL {
h = 0.2
d = ... |
526b63eeb028015ae6790aed614e914d091ee434aa09ddc3016d2b54cb67c568 | NEURON | 343 | 29 | NEURON {
SUFFIX test1_kin_diff
}
STATE {
a b x y
}
BREAKPOINT {
SOLVE state METHOD sparse
}
KINETIC state {
LOCAL alpha, beta, gamma, delta
alpha = 2
beta = 0.6
gamma = 3
delta = 0.7
~ a <-> b (alpha, beta)
~ x <-> y (gamma, delta)
}
INITIAL {
a = 0.2
b = 1 - a
x... |
315db91f4f6c16cf0f9dd1e824def4f4d4efaebf4fc05fcb4bb644d21f3961f3 | NEURON | 345 | 38 | NEURON {
POINT_PROCESS expsyn
RANGE tau, e
NONSPECIFIC_CURRENT i
}
UNITS {
(mV) = (millivolt)
}
PARAMETER {
tau = 2.0 (ms) : the default for Neuron is 0.1
e = 0 (mV)
}
ASSIGNED {}
STATE {
g
}
INITIAL {
g=0
}
BREAKPOINT {
SOLVE state METHOD cnexp
i = g*(v - e)
}
DERIVATIVE state {
g' = -g/tau
}
NET_RECEIVE(wei... |
d5e813315213db9eda60d1aef366e1a7d2ac84026c7cdf1e4a7c311dd8d0c172 | NEURON | 345 | 29 | NEURON {
SUFFIX test5_nonlinear_diff
}
STATE {
a b c
}
BREAKPOINT {
SOLVE state METHOD sparse
}
DERIVATIVE state {
LOCAL f0, f1, r0, r1
f0 = 2
r0 = 1
f1 = 3
r1 = 0
a' = -f0*a*b + r0*c
b' = -f0*a*b -r1*b + (r0+f1)*c
c' = f0*a*b +r1*b - (r0+f1)*c
}
INITIAL {
a = 0.2
... |
2f5e734944a5b5a5a9d7bb00a63bac2b94894e68cbddaba98c6681a42abf9057 | NEURON | 349 | 38 | NEURON {
POINT_PROCESS expsyn
RANGE tau, e
NONSPECIFIC_CURRENT i
}
UNITS {
(mV) = (millivolt)
}
PARAMETER {
tau = 2.0 (ms) : the default for Neuron is 0.1
e = 0 (mV)
}
ASSIGNED {}
STATE {
g
}
INITIAL {
g=0
}
BREAKPOINT {
SOLVE state METHOD cnexp
i = g*(v - e) + 2
}
DERIVATIVE state {
g' = -g/tau
}
NET_RECEIVE... |
43f8031972367ff3f79a6588792c59a2f2922ff6f4afc17f1d55a75ced8111ef | NEURON | 351 | 22 | NEURON {
POINT_PROCESS synapse_with_diffusion
USEION s WRITE sd
}
PARAMETER {
diam : CV diameter (in µm, internal variable)
area : CV lateral surface (µm2)
}
STATE { alpha }
INITIAL { alpha = 0 }
BREAKPOINT {
LOCAL volume
volume = 0.25*area*diam
sd = sd + alpha / volume
alpha = 0
}
NET_RECEIVE(weight... |
dc61ef5cc31124c758c8867a4c5757450a99ad8eb6a3f85ca8942d7b6bc1db8a | NEURON | 351 | 38 | NEURON {
POINT_PROCESS expsyn
RANGE tau, e
NONSPECIFIC_CURRENT i
}
UNITS {
(mV) = (millivolt)
}
PARAMETER {
tau = 2.0 (ms) : the default for Neuron is 0.1
e = 0 (mV)
}
ASSIGNED {}
STATE {
g
}
INITIAL {
g=0
}
BREAKPOINT {
SOLVE state METHOD cnexp
i = g*(v - e)
}
DERIVATIVE state {
g' = -g/tau + tau
}
NET_RECEI... |
eaec522dcaeba0983b13a587ccd7468d89d46c43a9fdba6838c64a89683e1516 | NEURON | 351 | 38 | NEURON {
POINT_PROCESS expsyn
RANGE tau, e
NONSPECIFIC_CURRENT i
}
UNITS {
(mV) = (millivolt)
}
PARAMETER {
tau = 2.0 (ms) : the default for Neuron is 0.1
e = 0 (mV)
}
ASSIGNED {}
STATE {
g
}
INITIAL {
g=0
}
BREAKPOINT {
SOLVE state METHOD cnexp
i = g*(v - e)
}
DERIVATIVE state {
g' = -g/tau + tau
}
NET_RECEI... |
0141fef361ac8a531965a7e8b1d0b3a8245353e4cd6fded2f6bc73d86c3018c5 | NEURON | 353 | 25 | : Reversal potential writer that writes to two different ions
: as a simple function of internal concentration.
NEURON {
SUFFIX write_multiple_eX
USEION x READ xi WRITE ex
USEION y READ yi WRITE ey
}
PARAMETER {}
ASSIGNED {}
STATE {}
INITIAL {
ex = 100 + xi
ey = 200 + yi
}
BREAKPOINT {
ex... |
49e5f396fbbc367491336de90b9f12ae0cdafc7d56437936132f2e272862f0b6 | NEURON | 355 | 32 | NEURON {
SUFFIX test_linear_init_shuffle
RANGE a0, a1, a3, a3
}
STATE {
s d h
}
PARAMETER {
a4 = 7.3
}
ASSIGNED { a0 a1 a2 a3 }
BREAKPOINT {
s = a1
}
INITIAL {
a0 = 2.5
a1 = 0.5
a2 = 3
a3 = 2.3
SOLVE sinit
}
LINEAR sinit {
~ a4*d - a3*d - a2*h = 0
~ a0*s - a0*d = - ... |
68949394daaaf1f812552f3fc5c5e6ac297a58e7679038f25e55dd083cb04e9d | NEURON | 357 | 29 | NEURON {
SUFFIX mean_reverting_stochastic_density_process2
GLOBAL kappa, mu, sigma
}
PARAMETER {
kappa = 0.1
mu = 1
sigma = 0.1
}
STATE { x y }
WHITE_NOISE { q b }
BREAKPOINT {
SOLVE states METHOD stochastic
}
INITIAL {
x = 2
y = 1
}
DERIVATIVE states {
x' = kappa*(mu - x) + sigma*q
... |
1fadd16efdc28a4801583742fbdcd7e1ddb2aea851e567b9eee6a0f920149ede | NEURON | 364 | 33 | : Ion concentration writer, with state variable.
: Used for testing mechanism and ion probes.
NEURON {
SUFFIX write_Xi_Xo
USEION x WRITE xi, xo
GLOBAL xi0, xo0, s0
}
PARAMETER {
xi0 = 1
xo0 = 2
s0 = 3
}
ASSIGNED {}
STATE {
s
}
INITIAL {
s = s0
xi = xi0
xo = xo0
}
BREAKPOINT... |
28bc9ed2c1d795221cf2a867498e47a8f63470796a54f86c0fbed308051a17c1 | NEURON | 368 | 30 | NEURON {
SUFFIX test0_kin_conserve
}
STATE {
s d h
}
BREAKPOINT {
SOLVE state METHOD sparse
}
KINETIC state {
LOCAL alpha1, beta1, alpha2, beta2
alpha1 = 2
beta1 = 0.6
alpha2 = 3
beta2 = 0.7
~ s <-> h (alpha1, beta1)
~ d <-> s (alpha2, beta2)
CONSERVE s + d + h = 1
}
IN... |
c4cd37688a281cd0d6c898205c0a349e34d3e06c3609a98fbe5b9a7d96acf03f | NEURON | 368 | 32 | NEURON {
POINT_PROCESS non_linear
RANGE tau, e
NONSPECIFIC_CURRENT i
}
PARAMETER {
tau = 2.0 (ms) : the default for Neuron is 0.1
e = 0 (mV)
}
STATE {
g
}
INITIAL {
g=0
}
BREAKPOINT {
SOLVE state METHOD cnexp
i = g*(v - e)
}
DERIVATIVE state {
g' = -g/tau
}
NET_RECEIVE(we... |
d799bfba8efa9c3d0229f1460c34517aa8d630202a11114876ddf61893105c1c | NEURON | 372 | 35 | NEURON {
SUFFIX test_kin1
NONSPECIFIC_CURRENT il
}
UNITS {
(mV) = (millivolt)
(mA) = (milliamp)
(S) = (siemens)
}
PARAMETER {
tau = 10 (ms)
v (mV)
}
STATE {
a (mA/cm2)
b (mA/cm2)
}
BREAKPOINT {
SOLVE states METHOD sparse
il = 0*v+a
}
INITIAL {
a = 0.01
b = 0
}
K... |
0cec44a7b9b206584ec20e7c8ec78ead1f440ff4bcab713edaae0f1ee3731db0 | NEURON | 376 | 30 | NEURON {
SUFFIX test0_kin_steadystate
}
STATE {
s d h
}
BREAKPOINT {
SOLVE state STEADYSTATE sparse
}
KINETIC state {
LOCAL alpha1, beta1, alpha2, beta2
alpha1 = 2
beta1 = 0.6
alpha2 = 3
beta2 = 0.7
~ s <-> h (alpha1, beta1)
~ d <-> s (alpha2, beta2)
CONSERVE s + d + h =... |
5e317e0c4dc477e9a7b6c0357aae06cf71771f4366d987833df7e628ce286145 | NEURON | 381 | 36 | NEURON {
POINT_PROCESS mean_reverting_stochastic_process2
GLOBAL kappa, mu, sigma
}
PARAMETER {
kappa = 0.1
mu = 1
sigma = 0.1
}
ASSIGNED {}
STATE {
X
Y
}
INITIAL {
X=2
Y=1
}
BREAKPOINT {
SOLVE state METHOD stochastic
}
WHITE_NOISE {
Q Z
}
DERIVATIVE state {
X' = k... |
37e0d07557e7bcc5810cb64db0b9ced169ff88d6be6c1bfe11c46f0536c02cd2 | NEURON | 392 | 35 | NEURON {
POINT_PROCESS post_events_syn
RANGE tau, e
NONSPECIFIC_CURRENT i
}
PARAMETER {
tau = 2.0 (ms) : the default for Neuron is 0.1
e = 0 (mV)
}
STATE {
g
}
INITIAL {
g=0
}
BREAKPOINT {
SOLVE state METHOD cnexp
i = g*(v - e)
}
DERIVATIVE state {
g' = -g/tau
}
NET_RECEI... |
887774b09f4235ac0b39287511a1ea702e683cbc842ed0fb9854835ee80eace9 | NEURON | 394 | 31 | NEURON {
SUFFIX test_linear_state
RANGE a0, a1, a3, a3
}
STATE {
s d h
}
PARAMETER {
a4 = 7.3
}
ASSIGNED { a0 a1 a2 a3 }
INITIAL {
a0 = 2.5
a1 = 0.5
a2 = 3
a3 = 2.3
}
BREAKPOINT {
SOLVE sinit
}
LINEAR sinit {
~ (a4 - a3)*d - a2*h = 0
~ (a0 + a1)*s - (-... |
d73fe275163451c3144b03db3e209855925d3c3420b0c5b6a50cbdd514369f64 | NEURON | 394 | 42 | NEURON {
POINT_PROCESS expsyn
RANGE tau, e
NONSPECIFIC_CURRENT i
}
UNITS {
(mV) = (millivolt)
}
PARAMETER {
tau = 2.0 (ms) : the default for Neuron is 0.1
e = 0 (mV)
}
ASSIGNED {}
STATE {
g
}
INITIAL {
g=0
}
BREAKPOINT {
SOLVE state METHOD cnexp
i = foo(g)*(v - e)
}
DERIVATIVE state {
g' = -g/tau + tau
}
NET_... |
0aea039c5475cd060909610fadcf4baa88be3a0bd0c8e517775377665a3909bd | NEURON | 396 | 41 | NEURON {
SUFFIX test4_kin_compartment
}
STATE {
A
B
C
d
e
}
PARAMETER {
x = 4
y = 0.4
z = 1.8
w = 21
s0 = 27
s1 = 5.9
}
BREAKPOINT {
SOLVE state METHOD sparse
}
INITIAL {
A = 4.5
B = 6.6
C = 0.28
d = 2
e = 0
}
KINETIC state {
COMPARTMENT s0 {A B C}
COMPA... |
bc1a61e5bc0968a64b79d6b089c53ebeed81de239698539c7bfbcae0d8023e9c | NEURON | 403 | 32 | NEURON {
SUFFIX test1_kin_conserve
}
STATE {
a b x y
}
BREAKPOINT {
SOLVE state METHOD sparse
}
KINETIC state {
LOCAL alpha1, beta1, alpha2, beta2
alpha1 = 2
beta1 = 0.6
alpha2 = 3
beta2 = 0.7
~ a <-> b (alpha1, beta1)
~ x <-> y (alpha2, beta2)
CONSERVE a + b = 1
CON... |
bfab622b06e4eeac7e980b12c0becd14ddfd9e2a5c69e4a172cfb2aef07e8b93 | NEURON | 404 | 32 | NEURON {
SUFFIX test_linear_init
RANGE a0, a1, a3, a3
}
STATE {
s d h
}
PARAMETER {
a4 = 7.3
}
ASSIGNED { a0 a1 a2 a3 }
BREAKPOINT {
s = a1
}
INITIAL {
a0 = 2.5
a1 = 0.5
a2 = 3
a3 = 2.3
SOLVE sinit
}
LINEAR sinit {
~ (a4 - a3)*d - a2*h = 0
~ (a0 + ... |
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