Increased computational accuracy in multi-compartmental cable models (Lindsay et al. 2005)

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Accession:129149
Compartmental models of dendrites are the most widely used tool for investigating their electrical behaviour. Traditional models assign a single potential to a compartment. This potential is associated with the membrane potential at the centre of the segment represented by the compartment. All input to that segment, independent of its location on the segment, is assumed to act at the centre of the segment with the potential of the compartment. By contrast, the compartmental model introduced in this article assigns a potential to each end of a segment, and takes into account the location of input to a segment on the model solution by partitioning the effect of this input between the axial currents at the proximal and distal boundaries of segments. For a given neuron, the new and traditional approaches to compartmental modelling use the same number of locations at which the membrane potential is to be determined, and lead to ordinary differential equations that are structurally identical. However, the solution achieved by the new approach gives an order of magnitude better accuracy and precision than that achieved by the latter in the presence of point process input.
Reference:
1 . Lindsay AE, Lindsay KA, Rosenberg JR (2005) Increased computational accuracy in multi-compartmental cable models by a novel approach for precise point process localization. J Comput Neurosci 19:21-38 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism:
Cell Type(s):
Channel(s): I Na,t; I K;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON; C or C++ program;
Model Concept(s): Methods;
Implementer(s):
Search NeuronDB for information about:  I Na,t; I K;
/
LindsayEtAl2005
readme.txt
03-192.pdf
AnalyseResults.c
BitsAndPieces.c
CellData.dat
CompareSpikeTrain.c
Ed04.tex
ExactSolution.dat
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GenCom.c
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JaySpikeTrain.c
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LU.c
Mean50.dat
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mosinit.hoc
NC.pdf
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NCPics.tex
NeuronDriver.hoc
NewComExactSoln.c
NewComp.pdf
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NewComp.toc
NewComp1.tex
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NewComSpikeTrain.c
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OldComExactSoln.c
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principles_01.tex
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RelErr.dat
ReviewOfSpines.pdf
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testcellnew2.hoc
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8 	 0.023629
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1 	 0.003043
3 	 0.021404
7 	 0.276179
12 	 0.175546
13 	 0.936153
4 	 0.103033
3 	 0.455609
0 	 0.481242
14 	 0.957940
14 	 0.646275
9 	 0.993945
9 	 0.620220
4 	 0.427937
10 	 0.884084
1 	 0.741941
2 	 0.997048
13 	 0.666655
11 	 0.872205
14 	 0.576307
1 	 0.363024
12 	 0.679362
6 	 0.795997
9 	 0.257700
15 	 0.267631
0 	 0.130492
3 	 0.473699
6 	 0.423038
0 	 0.680609
14 	 0.231144
0 	 0.565457
9 	 0.645507
9 	 0.358982
3 	 0.416299
9 	 0.803037
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13 	 0.924104
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13 	 0.835617
12 	 0.089283
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5 	 0.866669
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15 	 0.140354
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11 	 0.504119
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12 	 0.707247
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12 	 0.501540
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11 	 0.297664
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14 	 0.245560
8 	 0.824435
1 	 0.695542
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14 	 0.192981
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10 	 0.725783
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14 	 0.340094
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11 	 0.134462
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14 	 0.054343
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11 	 0.671933
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14 	 0.028744
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15 	 0.498614
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14 	 0.382730
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13 	 0.477679
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2 	 0.258127
15 	 0.455824
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14 	 0.372333
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12 	 0.977404
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14 	 0.516315
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1 	 0.325165
10 	 0.282938
14 	 0.138918
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14 	 0.367074
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14 	 0.461023
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15 	 0.241792
1 	 0.358876
14 	 0.588259
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14 	 0.198358
11 	 0.580115
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15 	 0.618394
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14 	 0.787504
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14 	 0.981498
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14 	 0.993871
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14 	 0.199741
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1 	 0.423589
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15 	 0.182388
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14 	 0.518305
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13 	 0.819646
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11 	 0.671308
14 	 0.211817
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14 	 0.692107
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15 	 0.828634
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13 	 0.593108
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14 	 0.910201
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14 	 0.736411
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14 	 0.941384
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13 	 0.281386
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11 	 0.039558
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8 	 0.602635
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14 	 0.858087
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0 	 0.405697
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8 	 0.373693
9 	 0.672767
12 	 0.467185
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6 	 0.012956
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14 	 0.889374
10 	 0.627275
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5 	 0.240835
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11 	 0.001914
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9 	 0.530905
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11 	 0.253042
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9 	 0.951544
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14 	 0.380703
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5 	 0.499423
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14 	 0.465386
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14 	 0.933057
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15 	 0.691983
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11 	 0.288310
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14 	 0.104820
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15 	 0.241698
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14 	 0.198965
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14 	 0.512209
11 	 0.985279
13 	 0.451255
15 	 0.653590
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15 	 0.598642
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12 	 0.105736
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9 	 0.794964
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11 	 0.038826
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14 	 0.796101
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1 	 0.476625
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12 	 0.608788
6 	 0.383394
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9 	 0.315654
1 	 0.925449
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4 	 0.788112
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14 	 0.232438
0 	 0.019325
8 	 0.214590
8 	 0.451617
8 	 0.157548
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12 	 0.457209
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10 	 0.390449
13 	 0.801719
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14 	 0.912415
9 	 0.071223
0 	 0.545680
10 	 0.343218
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14 	 0.505917
13 	 0.991685
6 	 0.750993
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5 	 0.098617
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12 	 0.413849
5 	 0.289517
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0 	 0.679917
11 	 0.531622
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12 	 0.697206
0 	 0.034075
13 	 0.546225
15 	 0.396266
9 	 0.911835
0 	 0.920703
0 	 0.738920
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14 	 0.056613
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14 	 0.091669
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15 	 0.754676
3 	 0.532064
11 	 0.325024
3 	 0.137953
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14 	 0.658784
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8 	 0.981059
3 	 0.537599
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14 	 0.838505
6 	 0.524288
6 	 0.018650
8 	 0.455663
2 	 0.578899
5 	 0.654050
1 	 0.157829
1 	 0.576887
7 	 0.480322
8 	 0.529577
9 	 0.955026
4 	 0.444403
13 	 0.474393
10 	 0.327762
11 	 0.422879
8 	 0.408767
14 	 0.069797
11 	 0.572333
1 	 0.330597
1 	 0.200413
14 	 0.838107
14 	 0.185703
9 	 0.269186
8 	 0.402707
13 	 0.505274
7 	 0.598857
12 	 0.487824
13 	 0.138048
14 	 0.468068
0 	 0.940807
6 	 0.854832
0 	 0.508092
2 	 0.531406
4 	 0.951824
2 	 0.018315
3 	 0.433251
15 	 0.021551
1 	 0.720475
0 	 0.444788
0 	 0.792685
9 	 0.830561
14 	 0.471174
10 	 0.989950
6 	 0.856497
10 	 0.660075
1 	 0.653011
2 	 0.511109
12 	 0.566905
5 	 0.342357
1 	 0.868286
4 	 0.029607
7 	 0.989642
7 	 0.797127
6 	 0.797056
2 	 0.317006
3 	 0.875000
1 	 0.852605
0 	 0.064789
3 	 0.705881
4 	 0.612593
11 	 0.074211
9 	 0.792728
13 	 0.483178
15 	 0.053665
14 	 0.530387
8 	 0.303078
9 	 0.246272
13 	 0.365788
15 	 0.265455
2 	 0.695951
8 	 0.025025
1 	 0.725953
0 	 0.848692
4 	 0.466155
1 	 0.788444
9 	 0.392595
5 	 0.225844
8 	 0.881626
9 	 0.180823
3 	 0.512878
7 	 0.197500
12 	 0.448090
1 	 0.358132
4 	 0.358349
6 	 0.283992
2 	 0.047602
3 	 0.616038
1 	 0.063276
3 	 0.566395
5 	 0.788822
8 	 0.844174
1 	 0.495581
0 	 0.398260
10 	 0.106104
9 	 0.894436
7 	 0.206019
4 	 0.114859
5 	 0.549161
4 	 0.887841
5 	 0.487567
11 	 0.438124
0 	 0.972595
3 	 0.130007
8 	 0.909736
4 	 0.692942
0 	 0.077742
6 	 0.245513
4 	 0.557253
3 	 0.056898
2 	 0.662865
8 	 0.183171
12 	 0.683327
9 	 0.921011
2 	 0.205982
12 	 0.412797
12 	 0.836918
15 	 0.354925
13 	 0.361457
3 	 0.057194
14 	 0.392624
5 	 0.143828
12 	 0.936857
1 	 0.802296
2 	 0.685946
8 	 0.297073
3 	 0.723829
10 	 0.446290
1 	 0.501039
4 	 0.124955
8 	 0.391365
10 	 0.097069
1 	 0.764495
9 	 0.377227
3 	 0.838987
2 	 0.565855
10 	 0.645327
4 	 0.943937
10 	 0.053192
9 	 0.318978
8 	 0.618199
10 	 0.240654
4 	 0.761832
9 	 0.633857
9 	 0.853307
12 	 0.035372
12 	 0.283691
1 	 0.895560
1 	 0.913672
4 	 0.875644
13 	 0.723338
8 	 0.883897
4 	 0.341201
1 	 0.151200
6 	 0.854423
0 	 0.357182
0 	 0.032772
9 	 0.696876
2 	 0.338972
0 	 0.397529
0 	 0.407647
6 	 0.062043
6 	 0.890118
3 	 0.543727
3 	 0.062663
3 	 0.871599
12 	 0.560037
14 	 0.930443
3 	 0.922499
1 	 0.436928
10 	 0.542004
12 	 0.763200
4 	 0.047498
8 	 0.784861
2 	 0.397224
5 	 0.871599
10 	 0.035746
11 	 0.262880
6 	 0.183788
4 	 0.909379
8 	 0.008901
2 	 0.308646
14 	 0.070977
11 	 0.692159
13 	 0.066798
2 	 0.724874
4 	 0.215206
14 	 0.649331
14 	 0.744276
10 	 0.981641
5 	 0.558616
4 	 0.565847
14 	 0.701391
9 	 0.573767
14 	 0.067352
11 	 0.926449
8 	 0.597095
4 	 0.784165
9 	 0.949086
3 	 0.555057
0 	 0.445449
13 	 0.442672
0 	 0.969402
12 	 0.182978
14 	 0.300188
4 	 0.826413
13 	 0.121927
11 	 0.847308
8 	 0.015650
14 	 0.292424
0 	 0.181470
9 	 0.571350
13 	 0.820646
5 	 0.971251
6 	 0.675416
1 	 0.063609
13 	 0.768549
3 	 0.487910
0 	 0.678606
6 	 0.782994
3 	 0.029835
9 	 0.956779
11 	 0.617934
3 	 0.258935
2 	 0.695153
14 	 0.389065
4 	 0.791238
0 	 0.397447
14 	 0.395304
13 	 0.945252
14 	 0.678138
14 	 0.469120
12 	 0.422929
15 	 0.568486
2 	 0.468851
11 	 0.821223
13 	 0.714428
13 	 0.342972
14 	 0.667075
11 	 0.679344
4 	 0.936942
3 	 0.131217
11 	 0.371160
11 	 0.940586
0 	 0.250976
10 	 0.710788
6 	 0.725411
4 	 0.532021
1 	 0.329068
3 	 0.714886
9 	 0.747090
15 	 0.914930
13 	 0.464288
0 	 0.723933
2 	 0.402967
14 	 0.104451
2 	 0.718752
8 	 0.721166
5 	 0.879337
9 	 0.835791
4 	 0.017477
4 	 0.906641
13 	 0.465436
9 	 0.908398
13 	 0.276274
3 	 0.637455
3 	 0.282581
2 	 0.943456
1 	 0.591986
0 	 0.873370
10 	 0.164197
13 	 0.426764
4 	 0.769966
15 	 0.809055
6 	 0.753060
9 	 0.496791
3 	 0.372019
14 	 0.783627
12 	 0.515615
1 	 0.655581
2 	 0.694300
7 	 0.650114
3 	 0.450500
6 	 0.152273
9 	 0.310082
2 	 0.381267
1 	 0.303259
0 	 0.044277
10 	 0.344273
7 	 0.982430
0 	 0.956581
4 	 0.562453
14 	 0.051998

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