finished new figure captions
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@ -1,10 +1,4 @@
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cell;species;size/cm;weight/g;celltype;structure;quality;eodf/Hz;nspikesbase;durationbase/s;ratebase/Hz;cvbase;vsbase;vsmode;serialcorr1;serialcorrnull;burstfrac;burstfracthresh;burstthresh/s;stimindex;contrast;stimulus;fcutoff/Hz;duration/s;trials;ratestim/Hz;cvstim;respmod1/Hz;respmod2/Hz;respmod4/Hz;transferfpeak/Hz;transferpeak/Hz;coherefpeak/Hz;coherepeak;nsegs;nlifpeak/Hz;nli;nlimedian;nsegs_single;nlifpeak_single/Hz;nli_single;nlimedian_single;nsegs_5s;nlifpeak_5s/Hz;nli_5s;nlimedian_5s
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2010-04-28-af;Apteronotus leptorhynchus;17;-;Ampullary;Nerve;good;892;4356;32.6146;133.619;0.132269;0.0572138;1;-0.262052;0.0493963;0;0;0;0;0.2;gwn100Hz10s0.3.dat;100;10;11;132.709;0.555356;89.3286;71.9568;53.3382;27.3438;522.369;27.3438;0.866904;418;148.438;1.3613;1.20051;38;175.781;2.09077;2.81892;18;175.781;2.86461;3.53128
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2010-04-28-at;Apteronotus leptorhynchus;17;-;Ampullary;Nerve;fair;885;3474;27.4183;126.73;0.115227;0.0410552;1;-0.325095;0.0519928;0;0;0;0;0.2;gwn100Hz10s0.3.dat;100;10;5;92.1224;0.658304;105.186;73.0515;51.219;39.0625;429.206;39.0625;0.63249;190;175.781;1.64147;1.54247;38;152.344;1.85215;1.75485;18;175.781;1.09077;1.93135
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2010-04-28-at;Apteronotus leptorhynchus;17;-;Ampullary;Nerve;fair;885;3474;27.4183;126.73;0.115227;0.0410552;1;-0.325095;0.0519928;0;0;0;1;0.2;gwn100Hz10s0.3.dat;100;10;5;83.1633;0.624523;101.084;66.6904;45.2904;31.25;361.963;39.0625;0.545143;190;113.281;1.67341;1.92267;38;113.281;1.59327;1.94575;18;175.781;1.27984;1.79457
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2010-04-28-at;Apteronotus leptorhynchus;17;-;Ampullary;Nerve;fair;885;3474;27.4183;126.73;0.115227;0.0410552;1;-0.325095;0.0519928;0;0;0;2;0.2;gwn100Hz10s0.3.dat;100;10;8;80.7653;0.575645;98.5909;63.8735;42.6526;39.0625;348.454;27.3438;0.618219;304;113.281;1.47452;1.8736;38;144.531;1.5704;1.90153;18;175.781;1.79957;2.34631
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2010-05-07-av;Apteronotus leptorhynchus;13;-;Ampullary;Nerve;good;723;3192;31.13;102.534;0.129037;0.122025;1;-0.381929;0.0542761;0;0;0;0;0.2;gwn100Hz10s0.3.dat;100;10;25;104.388;0.614711;86.4156;70.2956;52.2396;19.5312;499.882;11.7188;0.793711;950;54.6875;1.36459;1.90917;38;82.0312;1.4081;1.36934;18;132.812;1.4413;1.50664
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2010-05-07-av;Apteronotus leptorhynchus;13;-;Ampullary;Nerve;good;723;3192;31.13;102.534;0.129037;0.122025;1;-0.381929;0.0542761;0;0;0;2;0.2;gwn100Hz10s0.3.dat;100;10;50;104.039;0.604549;86.5408;70.8827;52.8477;19.5312;510.133;27.3438;0.849552;1900;54.6875;1.30522;1.81824;38;152.344;1.18372;1.5345;18;152.344;1.20295;1.56645
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2010-05-18-af;Apteronotus leptorhynchus;14;-;Ampullary;Nerve;good;635;2770;34.9956;79.1715;0.102739;0.0246739;1;0.0198069;0.0614926;0;0;0;0;0.2;gwn150Hz10s0.3.dat;150;10;22;78.4787;0.500701;57.7174;45.9132;36.058;15.625;454.473;11.7188;0.805778;836;35.1562;1.63285;2.11554;38;105.469;1.32812;1.52964;18;105.469;1.30443;1.49856
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2010-05-21-aj;Apteronotus leptorhynchus;15.5;-;Ampullary;Nerve;fair;793;5160;33.5363;153.888;0.148552;0.026761;1;-0.0471877;0.0476448;0;0;0;0;0.2;gwn150Hz10s0.3.dat;150;10;25;156.759;0.186892;39.3693;25.1679;18.0827;27.3438;210.939;27.3438;0.689643;950;156.25;1.68403;2.41387;38;152.344;1.56968;2.42894;18;152.344;1.67762;2.95679
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2010-05-21-aj;Apteronotus leptorhynchus;15.5;-;Ampullary;Nerve;fair;793;5160;33.5363;153.888;0.148552;0.026761;1;-0.0471877;0.0476448;0;0;0;1;0.1;gwn150Hz10s0.3.dat;150;10;11;135.232;0.232333;44.5768;18.5342;11.3723;136.719;252.304;27.3438;0.181645;418;152.344;3.91373;6.46129;38;152.344;3.36487;6.01846;18;152.344;3.39688;6.0765
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@ -60,57 +54,19 @@ cell;species;size/cm;weight/g;celltype;structure;quality;eodf/Hz;nspikesbase;dur
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2012-05-15-ac;Apteronotus leptorhynchus;12.5;5.1;Ampullary;Nerve;good;667;11806;83.3791;141.611;0.0481658;0.107312;1;-0.0545366;0.0288959;0;0;0;4;0.1;gwn150Hz10s0.3.dat;150;10;16;141.767;0.104929;41.4485;17.9908;10.1925;132.812;459.6;27.3438;0.585054;608;136.719;2.56081;5.5712;38;136.719;3.25441;5.55113;18;136.719;4.75285;7.28743
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2012-05-15-ac;Apteronotus leptorhynchus;12.5;5.1;Ampullary;Nerve;good;667;11806;83.3791;141.611;0.0481658;0.107312;1;-0.0545366;0.0288959;0;0;0;5;0.2;gwn150Hz10s0.3.dat;150;10;3;119.49;0.404033;88.6304;43.2043;25.6483;140.625;436.037;27.3438;0.313207;114;140.625;1.42244;2.33999;38;136.719;1.42761;2.65573;18;140.625;1.49888;2.92994
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2012-05-15-ac;Apteronotus leptorhynchus;12.5;5.1;Ampullary;Nerve;good;667;11806;83.3791;141.611;0.0481658;0.107312;1;-0.0545366;0.0288959;0;0;0;6;0.2;gwn150Hz10s0.3.dat;150;10;16;125.733;0.44698;57.9149;30.2131;18.4491;140.625;305.509;35.1562;0.461898;608;144.531;1.39374;2.65929;38;132.812;1.47686;2.09568;18;136.719;1.44734;2.05281
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2012-06-08-aj;Apteronotus leptorhynchus;14.5;10;Ampullary;Nerve;good;840;5026;30.464;164.96;0.101653;0.119912;1;-0.219405;0.0449507;0;0;0;0;0.025;gwn300Hz10s0.3short.dat;300;2;189;163.51;0.103695;15.216;8.52573;7.31872;164.062;4911.26;97.6562;0.0027973;1323;160.156;6.19991;7.42821;7;160.156;4.29183;4.91982;7;160.156;4.29183;4.91982
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2012-06-08-aj;Apteronotus leptorhynchus;14.5;10;Ampullary;Nerve;good;840;5026;30.464;164.96;0.101653;0.119912;1;-0.219405;0.0449507;0;0;0;1;0.05;gwn300Hz10s0.3short.dat;300;2;120;164.088;0.105995;15.2744;8.53854;7.2825;183.594;1168.71;27.3438;0.00624412;840;160.156;4.39603;4.77495;7;160.156;5.16386;6.15278;7;160.156;5.16386;6.15278
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2012-06-08-aj;Apteronotus leptorhynchus;14.5;10;Ampullary;Nerve;good;840;5026;30.464;164.96;0.101653;0.119912;1;-0.219405;0.0449507;0;0;0;2;0.1;gwn300Hz10s0.3short.dat;300;2;135;163.123;0.137737;14.2563;8.28482;7.18398;164.062;1142.69;203.125;0.00755612;945;160.156;4.99613;4.98378;7;164.062;2.84929;3.62476;7;164.062;2.84929;3.62476
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2012-07-12-al;Apteronotus leptorhynchus;12.5;-;Ampullary;Nerve;good;746;4243;30.5668;138.805;0.0755225;0.0068461;1;-0.222826;0.0479118;0;0;0;0;0.025;gwn300Hz10s0.3short.dat;300;2;49;137.562;0.0811087;16.7101;9.32536;7.22105;269.531;8658.31;31.25;0.105298;343;132.812;3.13309;3.15143;7;136.719;8.34537;6.68058;7;136.719;8.34537;6.68058
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2012-07-12-al;Apteronotus leptorhynchus;12.5;-;Ampullary;Nerve;good;746;4243;30.5668;138.805;0.0755225;0.0068461;1;-0.222826;0.0479118;0;0;0;1;0.05;gwn300Hz10s0.3short.dat;300;2;123;135.867;0.0932509;16.3089;10.7294;8.06282;140.625;3202;15.625;0.288209;861;132.812;3.79716;4.1151;7;132.812;7.21879;6.9329;7;132.812;7.21879;6.9329
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2012-07-12-al;Apteronotus leptorhynchus;12.5;-;Ampullary;Nerve;good;746;4243;30.5668;138.805;0.0755225;0.0068461;1;-0.222826;0.0479118;0;0;0;2;0.1;gwn300Hz10s0.3short.dat;300;2;114;136.672;0.130666;22.9208;16.6846;11.959;31.25;1418.35;15.625;0.593671;798;132.812;2.07958;2.73782;7;132.812;3.83435;4.06447;7;132.812;3.83435;4.06447
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2012-07-12-al;Apteronotus leptorhynchus;12.5;-;Ampullary;Nerve;good;746;4243;30.5668;138.805;0.0755225;0.0068461;1;-0.222826;0.0479118;0;0;0;3;0.025;gwn150Hz10s0.3short.dat;150;2;110;137.99;0.0842129;17.0328;9.94927;7.59422;140.625;2272.37;39.0625;0.158623;770;136.719;4.78018;6.52797;7;132.812;8.75118;11.0131;7;132.812;8.75118;11.0131
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2012-07-12-al;Apteronotus leptorhynchus;12.5;-;Ampullary;Nerve;good;746;4243;30.5668;138.805;0.0755225;0.0068461;1;-0.222826;0.0479118;0;0;0;4;0.05;gwn150Hz10s0.3short.dat;150;2;125;135.618;0.10256;18.8579;12.4904;9.09497;132.812;724.029;15.625;0.413126;875;132.812;2.95999;4.2622;7;132.812;4.04291;7.40859;7;132.812;4.04291;7.40859
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2012-07-12-al;Apteronotus leptorhynchus;12.5;-;Ampullary;Nerve;good;746;4243;30.5668;138.805;0.0755225;0.0068461;1;-0.222826;0.0479118;0;0;0;5;0.1;gwn150Hz10s0.3short.dat;150;2;122;137.336;0.156814;32.9157;21.9263;14.7499;132.812;495.864;15.625;0.703292;854;136.719;2.4388;3.62448;7;136.719;1.90433;3.21194;7;136.719;1.90433;3.21194
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2012-07-12-al;Apteronotus leptorhynchus;12.5;-;Ampullary;Nerve;good;746;4243;30.5668;138.805;0.0755225;0.0068461;1;-0.222826;0.0479118;0;0;0;6;0.025;gwn150Hz10s0.3.dat;150;10;20;137.01;0.0823391;31.1635;11.5256;4.87523;136.719;1402.18;27.3438;0.176036;760;132.812;6.08243;8.55906;38;132.812;7.106;8.96488;18;132.812;7.78783;10.6118
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2012-07-12-an;Apteronotus leptorhynchus;12.5;-;Ampullary;Nerve;good;766;3607;29.7472;121.237;0.057747;0.0301156;1;-0.208778;0.0515187;0;0;0;0;0.025;gwn300Hz10s0.3short.dat;300;2;88;120.979;0.0596767;16.232;9.23446;6.66715;242.188;7627.55;50.7812;0.0150735;616;117.188;8.60953;6.54486;7;117.188;9.00674;6.46307;7;117.188;9.00674;6.46307
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2012-07-12-an;Apteronotus leptorhynchus;12.5;-;Ampullary;Nerve;good;766;3607;29.7472;121.237;0.057747;0.0301156;1;-0.208778;0.0515187;0;0;0;1;0.05;gwn300Hz10s0.3short.dat;300;2;116;121.231;0.0623433;14.8498;8.76565;6.51384;121.094;4114.6;15.625;0.0767796;812;117.188;10.4532;7.99808;7;117.188;9.72651;6.44674;7;117.188;9.72651;6.44674
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2012-07-12-an;Apteronotus leptorhynchus;12.5;-;Ampullary;Nerve;good;766;3607;29.7472;121.237;0.057747;0.0301156;1;-0.208778;0.0515187;0;0;0;2;0.1;gwn300Hz10s0.3short.dat;300;2;83;119.739;0.0761934;16.8761;10.381;7.5901;125;1539.8;35.1562;0.187141;581;117.188;4.59165;4.63272;7;117.188;10.8223;7.32657;7;117.188;10.8223;7.32657
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2012-12-13-ai;Apteronotus leptorhynchus;13.5;-;Ampullary;Nerve;good;659;3726;31.1565;119.619;0.0762188;0.0499267;1;-0.0366303;0.0498219;0;0;0;0;0.025;gwn300Hz10s0.3short.dat;300;2;48;117.465;0.0910177;21.4871;12.1595;8.09316;238.281;7947.85;19.5312;0.275028;336;117.188;3.61664;3.37879;7;113.281;3.72123;3.49761;7;113.281;3.72123;3.49761
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2012-12-13-ai;Apteronotus leptorhynchus;13.5;-;Ampullary;Nerve;good;659;3726;31.1565;119.619;0.0762188;0.0499267;1;-0.0366303;0.0498219;0;0;0;1;0.05;gwn300Hz10s0.3short.dat;300;2;34;117.206;0.135349;32.9434;19.4883;11.9358;121.094;5266.29;15.625;0.563418;238;117.188;2.46013;3.51194;7;113.281;2.30432;2.68436;7;113.281;2.30432;2.68436
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2012-12-18-ac;Apteronotus leptorhynchus;13.5;-;Ampullary;Nerve;good;629;4802;31.8472;150.788;0.146096;0.014558;1;0.0632353;0.0524178;0;0;0;0;0.025;gwn300Hz10s0.3short.dat;300;2;28;154.107;0.0830419;27.9514;10.4409;7.16917;156.25;11080.8;50.7812;0.0927743;196;148.438;2.62315;4.09521;7;144.531;2.69546;3.81528;7;144.531;2.69546;3.81528
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2012-12-18-ac;Apteronotus leptorhynchus;13.5;-;Ampullary;Nerve;good;629;4802;31.8472;150.788;0.146096;0.014558;1;0.0632353;0.0524178;0;0;0;1;0.05;gwn300Hz10s0.3short.dat;300;2;53;154.015;0.0898975;22.5454;11.1539;8.45333;160.156;4698.23;15.625;0.26807;371;152.344;2.94962;4.10697;7;156.25;3.50733;5.86238;7;156.25;3.50733;5.86238
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2012-12-18-ac;Apteronotus leptorhynchus;13.5;-;Ampullary;Nerve;good;629;4802;31.8472;150.788;0.146096;0.014558;1;0.0632353;0.0524178;0;0;0;2;0.1;gwn300Hz10s0.3short.dat;300;2;50;154.211;0.119666;25.4336;15.7951;11.8138;160.156;2558.94;39.0625;0.469134;350;152.344;2.31772;2.98929;7;148.438;3.16321;3.96013;7;148.438;3.16321;3.96013
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2012-12-18-ac;Apteronotus leptorhynchus;13.5;-;Ampullary;Nerve;good;629;4802;31.8472;150.788;0.146096;0.014558;1;0.0632353;0.0524178;0;0;0;3;0.025;gwn150Hz10s0.3short.dat;150;2;43;154.496;0.0869103;22.6864;10.444;7.83434;148.438;1959.94;15.625;0.166468;301;152.344;3.18552;6.31376;7;152.344;5.497;10.2219;7;152.344;5.497;10.2219
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2012-12-18-ac;Apteronotus leptorhynchus;13.5;-;Ampullary;Nerve;good;629;4802;31.8472;150.788;0.146096;0.014558;1;0.0632353;0.0524178;0;0;0;4;0.05;gwn150Hz10s0.3short.dat;150;2;39;154.031;0.103289;26.0697;13.5534;9.76368;148.438;943.078;15.625;0.416691;273;144.531;2.38246;4.63101;7;156.25;3.47182;7.02856;7;156.25;3.47182;7.02856
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2012-12-18-ac;Apteronotus leptorhynchus;13.5;-;Ampullary;Nerve;good;629;4802;31.8472;150.788;0.146096;0.014558;1;0.0632353;0.0524178;0;0;0;5;0.1;gwn150Hz10s0.3short.dat;150;2;34;154.069;0.156596;33.2365;21.6729;15.9511;140.625;384.881;39.0625;0.65497;238;156.25;1.54442;2.75087;7;156.25;1.944;4.07553;7;156.25;1.944;4.07553
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2012-12-18-ac;Apteronotus leptorhynchus;13.5;-;Ampullary;Nerve;good;629;4802;31.8472;150.788;0.146096;0.014558;1;0.0632353;0.0524178;0;0;0;6;0.025;gwn150Hz10s0.3.dat;150;10;19;156.176;0.0937916;33.1622;10.4479;5.42592;148.438;714.915;15.625;0.138218;722;152.344;2.07866;5.90619;38;148.438;2.41105;6.31729;18;148.438;3.35752;7.43257
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2012-12-18-ac;Apteronotus leptorhynchus;13.5;-;Ampullary;Nerve;good;629;4802;31.8472;150.788;0.146096;0.014558;1;0.0632353;0.0524178;0;0;0;7;0.05;gwn150Hz10s0.3.dat;150;10;19;157.084;0.108135;33.9416;13.7646;8.68919;148.438;614.434;27.3438;0.417872;722;156.25;2.78656;6.2252;38;152.344;2.41562;5.23298;18;160.156;2.01793;4.92892
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2012-12-18-ac;Apteronotus leptorhynchus;13.5;-;Ampullary;Nerve;good;629;4802;31.8472;150.788;0.146096;0.014558;1;0.0632353;0.0524178;0;0;0;12;0.025;blwn125Hz10s0.3.dat;125;10;18;155.754;0.0974115;32.8782;9.9014;4.43574;42.9688;2554.34;82.0312;0.0750057;684;152.344;2.35523;6.0588;38;148.438;2.26104;6.65742;18;148.438;2.66055;7.53206
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2012-12-18-ac;Apteronotus leptorhynchus;13.5;-;Ampullary;Nerve;good;629;4802;31.8472;150.788;0.146096;0.014558;1;0.0632353;0.0524178;0;0;0;13;0.05;blwn125Hz10s0.3.dat;125;10;17;158.571;0.100569;36.8134;11.3478;4.3966;42.9688;1585.58;78.125;0.220304;646;156.25;2.55341;6.32418;38;156.25;3.583;9.40472;18;156.25;4.38481;11.4592
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2012-12-18-af;Apteronotus leptorhynchus;13.5;-;Ampullary;Nerve;good;651;6206;36.7363;168.949;0.152686;0.0520582;1;0.222372;0.0424781;0;0;0;0;0.025;gwn300Hz10s0.3short.dat;300;2;95;169.333;0.133268;21.2381;14.2119;10.6057;175.781;5338.35;39.0625;0.458774;665;160.156;1.47941;2.28448;7;179.688;2.30005;3.83644;7;179.688;2.30005;3.83644
|
||||
2012-12-18-af;Apteronotus leptorhynchus;13.5;-;Ampullary;Nerve;good;651;6206;36.7363;168.949;0.152686;0.0520582;1;0.222372;0.0424781;0;0;0;1;0.05;gwn300Hz10s0.3short.dat;300;2;54;166.193;0.208838;33.8777;24.7291;17.5732;31.25;4214.72;39.0625;0.691694;378;179.688;1.46863;2.10692;7;171.875;1.6042;2.25387;7;171.875;1.6042;2.25387
|
||||
2012-12-19-aa;Apteronotus leptorhynchus;13.5;-;Ampullary;Nerve;good;766;4295;32.9982;130.187;0.10381;0.0678645;1;0.142469;0.0473172;0.00163018;0;0;0;0.025;gwn300Hz10s0.3short.dat;300;2;56;132.202;0.156586;19.0504;11.4753;8.98505;265.625;7197.53;15.625;0.0861497;392;117.188;1.99998;2.29908;7;125;3.95336;3.70615;7;125;3.95336;3.70615
|
||||
2012-12-20-af;Apteronotus leptorhynchus;13.5;-;Ampullary;Nerve;good;781;6139;33.7156;182.081;0.0765999;0.041354;1;-0.170063;0.0409603;0;0;0;0;0.025;gwn300Hz10s0.3short.dat;300;2;88;176.54;0.168192;32.9278;25.4127;19.6245;31.25;9440.11;15.625;0.815689;616;179.688;1.44982;2.66298;7;179.688;1.81392;3.30154;7;179.688;1.81392;3.30154
|
||||
2012-12-20-af;Apteronotus leptorhynchus;13.5;-;Ampullary;Nerve;good;781;6139;33.7156;182.081;0.0765999;0.041354;1;-0.170063;0.0409603;0;0;0;1;0.05;gwn300Hz10s0.3short.dat;300;2;94;172.89;0.304672;53.8993;46.1091;35.7967;31.25;8850.22;15.625;0.91231;658;207.031;1.73682;2.22773;7;222.656;1.67335;2.20819;7;222.656;1.67335;2.20819
|
||||
2012-12-20-af;Apteronotus leptorhynchus;13.5;-;Ampullary;Nerve;good;781;6139;33.7156;182.081;0.0765999;0.041354;1;-0.170063;0.0409603;0;0;0;3;0.025;gwn150Hz10s0.3short.dat;150;2;96;179.433;0.203859;37.8183;31.8988;24.7739;31.25;2555.1;15.625;0.850276;672;179.688;1.4531;1.91532;7;175.781;1.47878;2.51799;7;175.781;1.47878;2.51799
|
||||
2012-12-20-af;Apteronotus leptorhynchus;13.5;-;Ampullary;Nerve;good;781;6139;33.7156;182.081;0.0765999;0.041354;1;-0.170063;0.0409603;0;0;0;4;0.05;gwn150Hz10s0.3short.dat;150;2;94;182.784;0.388219;68.2983;59.5196;46.3678;27.3438;2363.18;15.625;0.918998;658;171.875;1.49438;1.26199;7;230.469;1.56285;2.98034;7;230.469;1.56285;2.98034
|
||||
2012-12-20-af;Apteronotus leptorhynchus;13.5;-;Ampullary;Nerve;good;781;6139;33.7156;182.081;0.0765999;0.041354;1;-0.170063;0.0409603;0;0;0;5;0.025;gwn150Hz10s0.3.dat;150;10;18;182.477;0.204827;47.2891;32.5837;24.8263;19.5312;2502.41;27.3438;0.853384;684;175.781;1.20305;2.11131;38;199.219;1.40132;2.97444;18;199.219;1.41973;3.19038
|
||||
2012-12-20-af;Apteronotus leptorhynchus;13.5;-;Ampullary;Nerve;good;781;6139;33.7156;182.081;0.0765999;0.041354;1;-0.170063;0.0409603;0;0;0;6;0.05;gwn150Hz10s0.3.dat;150;10;19;182.309;0.41787;74.2479;61.6598;47.7696;19.5312;2415.32;27.3438;0.833953;722;183.594;1.28397;1.2711;38;222.656;1.27877;2.36325;18;222.656;1.16916;2.15917
|
||||
2012-12-21-ac;Apteronotus leptorhynchus;13.5;-;Ampullary;Nerve;good;689;4469;31.898;140.121;0.0667173;0.0318861;1;0.356893;0.045462;0;0;0;0;0.025;gwn300Hz10s0.3short.dat;300;2;79;138.79;0.148865;17.4004;9.50749;7.58735;292.969;8171.05;19.5312;0.161845;553;140.625;2.33274;3.43181;7;136.719;2.78444;3.81426;7;136.719;2.78444;3.81426
|
||||
2012-12-21-ac;Apteronotus leptorhynchus;13.5;-;Ampullary;Nerve;good;689;4469;31.898;140.121;0.0667173;0.0318861;1;0.356893;0.045462;0;0;0;2;0.05;gwn300Hz10s0.3short.dat;300;2;29;140.996;0.115378;30.416;13.0813;8.95399;273.438;5947.73;15.625;0.168355;203;136.719;2.66014;3.90352;7;140.625;4.66881;4.67449;7;140.625;4.66881;4.67449
|
||||
2012-12-21-ad;Apteronotus leptorhynchus;13.5;-;Ampullary;Nerve;good;702;3011;21.632;139.235;0.0539231;0.0623144;1;-0.0432212;0.0595767;0;0;0;0;0.025;gwn300Hz10s0.3short.dat;300;2;60;135.806;0.212069;38.9364;29.3277;22.6563;31.25;10200.9;15.625;0.864882;420;144.531;1.32562;2.32323;7;140.625;1.46051;2.10497;7;140.625;1.46051;2.10497
|
||||
2012-12-21-ad;Apteronotus leptorhynchus;13.5;-;Ampullary;Nerve;good;702;3011;21.632;139.235;0.0539231;0.0623144;1;-0.0432212;0.0595767;0;0;0;1;0.025;gwn300Hz10s0.3short.dat;300;2;48;137.188;0.212023;39.0512;29.483;22.7907;31.25;10563.6;15.625;0.859242;336;132.812;1.61343;2.41872;7;152.344;1.4512;1.97743;7;152.344;1.4512;1.97743
|
||||
2012-12-21-ad;Apteronotus leptorhynchus;13.5;-;Ampullary;Nerve;good;702;3011;21.632;139.235;0.0539231;0.0623144;1;-0.0432212;0.0595767;0;0;0;3;0.05;gwn300Hz10s0.3short.dat;300;2;32;130.069;0.444894;73.0075;55.4261;42.6142;19.5312;9955.73;15.625;0.881572;224;183.594;1.75216;2.96043;7;140.625;1.59195;1.59219;7;140.625;1.59195;1.59219
|
||||
2012-12-21-ae;Apteronotus leptorhynchus;13.5;-;Ampullary;Nerve;good;718;5389;36.1987;148.979;0.107008;0.0112806;1;0.04135;0.0423576;0;0;0;0;0.025;gwn300Hz10s0.3short.dat;300;2;39;142.934;0.115526;21.3654;9.99219;7.69452;289.062;7498.42;3.90625;0.090783;273;148.438;3.07068;3.23556;7;148.438;2.35089;3.42194;7;148.438;2.35089;3.42194
|
||||
2012-12-21-af;Apteronotus leptorhynchus;13.5;-;Ampullary;Nerve;good;723;4526;31.6671;142.954;0.0503564;0.0463125;1;-0.0238997;0.0457841;0;0;0;0;0.025;gwn300Hz10s0.3short.dat;300;2;38;137.208;0.162674;34.5896;23.1599;17.5734;144.531;9922.4;15.625;0.792977;266;136.719;2.02179;2.78505;7;136.719;1.59261;2.18129;7;136.719;1.59261;2.18129
|
||||
2012-12-21-af;Apteronotus leptorhynchus;13.5;-;Ampullary;Nerve;good;723;4526;31.6671;142.954;0.0503564;0.0463125;1;-0.0238997;0.0457841;0;0;0;1;0.025;gwn300Hz10s0.3short.dat;300;2;19;130.702;0.425661;37.1214;22.8798;16.8414;144.531;7602.64;15.625;0.723364;133;144.531;1.65814;2.19565;7;140.625;1.74963;2.28238;7;140.625;1.74963;2.28238
|
||||
2012-12-21-af;Apteronotus leptorhynchus;13.5;-;Ampullary;Nerve;good;723;4526;31.6671;142.954;0.0503564;0.0463125;1;-0.0238997;0.0457841;0;0;0;2;0.025;gwn300Hz10s0.3short.dat;300;2;27;126.379;0.444977;33.865;21.7709;16.2863;140.625;10116.1;15.625;0.762348;189;132.812;1.55912;2.40533;7;140.625;1.64703;2.41052;7;140.625;1.64703;2.41052
|
||||
2012-12-21-ah;Apteronotus leptorhynchus;13.5;-;Ampullary;Nerve;good;778;4713;31.591;149.17;0.0585815;0.0144477;1;-0.0667223;0.0454193;0;0;0;0;0.025;gwn300Hz10s0.3short.dat;300;2;105;148.026;0.0823988;19.69;11.6894;8.58732;296.875;6875.75;39.0625;0.404515;735;144.531;4.92418;6.05162;7;144.531;4.34987;4.64912;7;144.531;4.34987;4.64912
|
||||
2012-12-21-al;Apteronotus leptorhynchus;13.5;-;Ampullary;Nerve;good;804;12;31.2839;156.584;0.140215;0.122743;1;-0.116241;0.977609;0;0;0;0;0.025;gwn300Hz10s0.3short.dat;300;2;173;159.544;0.270519;55.4826;41.6305;29.5426;31.25;14753.3;15.625;0.886343;1211;191.406;1.59903;3.31809;7;152.344;1.47731;1.83604;7;152.344;1.47731;1.83604
|
||||
2012-12-21-al;Apteronotus leptorhynchus;13.5;-;Ampullary;Nerve;good;804;12;31.2839;156.584;0.140215;0.122743;1;-0.116241;0.977609;0;0;0;1;0.05;gwn300Hz10s0.3short.dat;300;2;26;147.97;0.769093;102.582;74.9655;53.061;27.3438;12653.3;15.625;0.852082;182;152.344;1.36267;1.56608;7;156.25;1.52955;1.27436;7;156.25;1.52955;1.27436
|
||||
2013-02-21-aa;Apteronotus leptorhynchus;18.9;-;Ampullary;Nerve;good;680;4115;31.898;129.033;0.0890287;0.00689996;1;-0.0844207;0.0476072;0.00097229;0;0;0;0.025;gwn300Hz10s0.3short.dat;300;2;24;123.75;0.216326;45.099;29.8771;19.9763;113.281;12088.5;15.625;0.818551;168;117.188;1.89885;2.25515;7;125;1.8454;2.24018;7;125;1.8454;2.24018
|
||||
2013-02-21-af;Apteronotus leptorhynchus;18.9;-;Ampullary;Nerve;good;663;5278;38.5536;136.911;0.102048;0.0429685;1;-0.151301;0.0442524;0;0;0;0;0.025;gwn300Hz10s0.3short.dat;300;2;97;134.977;0.102028;16.9388;9.5979;7.29954;136.719;7887.91;15.625;0.0751584;679;132.812;3.57322;4.66113;7;132.812;5.74085;6.17639;7;132.812;5.74085;6.17639
|
||||
2013-02-21-af;Apteronotus leptorhynchus;18.9;-;Ampullary;Nerve;good;663;5278;38.5536;136.911;0.102048;0.0429685;1;-0.151301;0.0442524;0;0;0;1;0.05;gwn300Hz10s0.3short.dat;300;2;56;134.107;0.114619;22.1633;12.319;8.47906;132.812;7871.61;15.625;0.156816;392;128.906;4.17131;4.46922;7;128.906;3.83779;4.29234;7;128.906;3.83779;4.29234
|
||||
2013-04-09-aa;Apteronotus leptorhynchus;18.9;-;Ampullary;Nerve;good;693;5152;32.0771;160.652;0.0896672;0.0480096;1;-0.210457;0.0439676;0;0;0;0;0.025;gwn300Hz10s0.3short.dat;300;2;64;154.01;0.194286;37.8169;27.1287;20.1745;156.25;9593.15;15.625;0.776714;448;156.25;2.1995;3.18294;7;160.156;1.4962;2.05285;7;160.156;1.4962;2.05285
|
||||
2013-04-09-aa;Apteronotus leptorhynchus;18.9;-;Ampullary;Nerve;good;693;5152;32.0771;160.652;0.0896672;0.0480096;1;-0.210457;0.0439676;0;0;0;1;0.025;gwn300Hz10s0.3short.dat;300;2;96;153.536;0.199485;36.7491;27.5569;20.7285;31.25;9409.9;15.625;0.802026;672;156.25;1.84149;3.10069;7;156.25;1.58809;2.37418;7;156.25;1.58809;2.37418
|
||||
2013-04-09-aa;Apteronotus leptorhynchus;18.9;-;Ampullary;Nerve;good;693;5152;32.0771;160.652;0.0896672;0.0480096;1;-0.210457;0.0439676;0;0;0;2;0.025;gwn150Hz10s0.3short.dat;150;2;126;160.926;0.270093;48.0063;38.802;29.4005;27.3438;2841.8;15.625;0.868614;882;179.688;1.563;1.80633;7;183.594;1.63625;2.60913;7;183.594;1.63625;2.60913
|
||||
2013-04-09-aa;Apteronotus leptorhynchus;18.9;-;Ampullary;Nerve;good;693;5152;32.0771;160.652;0.0896672;0.0480096;1;-0.210457;0.0439676;0;0;0;3;0.05;gwn150Hz10s0.3short.dat;150;2;121;163.618;0.526692;91.7656;74.0495;56.6222;23.4375;2762.54;15.625;0.929719;847;183.594;1.57083;1.55334;7;203.125;1.31755;1.71949;7;203.125;1.31755;1.71949
|
||||
2013-04-09-ab;Apteronotus leptorhynchus;18.9;-;Ampullary;Nerve;good;689;5341;38.3493;139.307;0.139216;0.0863535;1;-0.0981168;0.0611801;0;0;0;1;0.025;gwn150Hz10s0.3short.dat;150;2;26;141.603;0.199384;42.7906;26.8857;18.7824;140.625;2203.52;15.625;0.762956;182;144.531;1.48505;2.56134;7;132.812;1.58305;2.65824;7;132.812;1.58305;2.65824
|
||||
@ -132,5 +88,3 @@ cell;species;size/cm;weight/g;celltype;structure;quality;eodf/Hz;nspikesbase;dur
|
||||
2014-01-16-aj;Apteronotus leptorhynchus;16.5;11.4;Ampullary;Nerve;good;799;5332;32.4338;164.397;0.0811499;0.0703113;1;-0.0595718;0.044134;0;0;0;5;0.05;gwn150Hz50s0.3.dat;150;10;16;153.342;0.599727;84.762;72.6195;58.8329;11.7188;2696.71;7.8125;0.819015;608;203.125;1.22296;1.35702;38;195.312;1.26889;1.6106;18;183.594;1.26427;1.58779
|
||||
2014-01-16-aj;Apteronotus leptorhynchus;16.5;11.4;Ampullary;Nerve;good;799;5332;32.4338;164.397;0.0811499;0.0703113;1;-0.0595718;0.044134;0;0;0;6;0.025;gwn150Hz50s0.3.dat;150;2;119;164.197;0.309188;53.7162;44.7051;35.7388;15.625;3385.93;7.8125;0.937005;833;191.406;1.82802;1.95936;7;195.312;1.60111;2.93536;7;195.312;1.60111;2.93536
|
||||
2014-01-16-aj;Apteronotus leptorhynchus;16.5;11.4;Ampullary;Nerve;good;799;5332;32.4338;164.397;0.0811499;0.0703113;1;-0.0595718;0.044134;0;0;0;7;0.025;gwn150Hz50s0.3.dat;150;2;106;163.7;0.342671;50.149;44.012;35.2749;15.625;3347.19;7.8125;0.82756;742;207.031;1.53622;1.43503;7;199.219;1.36708;2.53912;7;199.219;1.36708;2.53912
|
||||
2017-08-15-ad-invivo-1;Apteronotus leptorhynchus;16;17.4;Ampullary;Brain;good;853.009;5800;32.0513;180.982;0.0748433;0.0261187;1;-0.0847882;0.0413143;0;0;0;2;0.05;gwn300Hz50s0.3.dat;300;5;11;179.527;0.249342;61.9918;40.4169;30.1865;31.25;1985.58;31.25;0.879003;198;199.219;1.23911;1.71587;18;210.938;1.2285;1.76261;18;210.938;1.2285;1.76261
|
||||
2017-10-25-am-invivo-1;Apteronotus leptorhynchus;17;15;Ampullary;Brain;good;895.009;4657;33.4337;139.349;0.0479628;0.0147115;1;-0.132162;0.0438107;0;0;0;1;0.05;gwn300Hz50s0.3.dat;300;10;4;140.153;0.0462193;73.5441;22.4287;3.53122;281.25;3056.42;207.031;0.0390586;152;136.719;11.7627;11.0595;38;136.719;11.8756;10.4106;18;136.719;11.3913;10.2854
|
||||
|
||||
|
@ -381,27 +381,3 @@ cell;species;size/cm;weight/g;celltype;structure;quality;eodf/Hz;nspikesbase;dur
|
||||
2021-12-17-ad-invivo-1;Apteronotus leptorhynchus;13;7.4;P-unit;Brain;awesome;515;21329;105.577;202.03;0.784883;0.892102;1;-0.630756;0.0228606;0.589366;0.589366;0.00291262;7;0.01;gwn300Hz10s0.3.dat;300;2.99998;128;199.636;0.795489;23.7668;17.2737;9.68446;82.0312;5367.22;7.8125;0.0200051;1280;156.25;1.35369;1.29993;10;203.125;1.55246;1.15671;10;203.125;1.55246;1.15671
|
||||
2021-12-17-ad-invivo-1;Apteronotus leptorhynchus;13;7.4;P-unit;Brain;awesome;515;21329;105.577;202.03;0.784883;0.892102;1;-0.630756;0.0228606;0.589366;0.589366;0.00291262;9;0.1;gwn300Hz10s0.3.dat;300;2.99998;128;208.632;0.852708;108.014;86.1085;49.9415;70.3125;2692.54;15.625;0.590848;1280;152.344;1.28036;2.08644;10;156.25;1.34483;1.15748;10;156.25;1.34483;1.15748
|
||||
2021-12-17-ad-invivo-1;Apteronotus leptorhynchus;13;7.4;P-unit;Brain;awesome;515;21329;105.577;202.03;0.784883;0.892102;1;-0.630756;0.0228606;0.589366;0.589366;0.00291262;12;0.001;gwn300Hz10s0.3.dat;300;2.99998;256;203.173;0.779194;14.7383;10.6011;6.82135;70.3125;20217.4;35.1562;0.00166669;2560;175.781;1.17253;1.06684;10;160.156;1.64755;1.4768;10;160.156;1.64755;1.4768
|
||||
2022-01-05-aa-invivo-1;Apteronotus leptorhynchus;15.7;17;P-unit;Nerve;good;558;1272;12.4685;102.103;0.197405;0.782115;1;-0.285104;0.0865205;0.000786782;0;0;0;0.2;InputArr_400hz_30s.dat;400;30;4;105.361;0.777905;102.089;82.7035;63.4736;19.5312;1239.37;19.5312;0.6499;464;54.6875;1.82612;2.24859;116;54.6875;1.61594;1.2798;18;144.531;1.42145;1.25172
|
||||
2022-01-05-ab-invivo-1;Apteronotus leptorhynchus;15.7;17;P-unit;Nerve;good;547;2803;24.508;114.486;0.727618;0.841286;1;-0.792328;0.0625113;0.383655;0.440757;0.00457038;0;0.2;InputArr_400hz_30s.dat;400;30;5;120.517;0.916766;135.742;105.305;65.2115;46.875;1465.99;19.5312;0.636699;580;70.3125;1.3792;4.01591;116;70.3125;1.40089;2.36945;18;66.4062;1.33967;1.62024
|
||||
2022-01-05-ab-invivo-1;Apteronotus leptorhynchus;15.7;17;P-unit;Nerve;good;547;2803;24.508;114.486;0.727618;0.841286;1;-0.792328;0.0625113;0.383655;0.440757;0.00457038;1;0.2;InputArr_400hz_30s.dat;400;30;5;108.342;1.08766;126.749;104.683;69.9059;35.1562;1617.31;19.5312;0.55982;580;70.3125;1.12842;3.42909;116;74.2188;1.42969;2.49517;18;78.125;1.37069;2.09999
|
||||
2022-01-05-ac-invivo-1;Apteronotus leptorhynchus;15.7;17;P-unit;Nerve;good;528;3014;20.8946;144.425;1.2446;0.786968;1;-0.341868;0.0552452;0.736143;0.736143;0.00284091;0;0.2;InputArr_400hz_30s.dat;400;30;3;102.573;1.24685;108.804;91.7133;61.7437;35.1562;645.161;19.5312;0.0406215;348;97.6562;1.06494;1.12292;116;97.6562;0.931386;0.98864;18;132.812;1.21531;0.923164
|
||||
2022-01-05-ae-invivo-1;Apteronotus leptorhynchus;15.7;17;P-unit;Nerve;good;516;3922;17.997;217.912;0.816962;0.758693;1;-0.594823;0.0509492;0.62331;0.62331;0.00290698;0;0.2;InputArr_400hz_30s.dat;400;30;5;211.967;1.04758;187.718;146.642;85.8938;54.6875;2469.97;35.1562;0.685574;580;175.781;1.06883;0.933609;116;175.781;1.23913;0.849658;18;222.656;1.31911;0.849699
|
||||
2022-01-06-ah-invivo-1;Apteronotus leptorhynchus;15.7;17;P-unit;Nerve;good;679;2058;11.112;185.34;1.01526;0.742714;1;-0.476177;0.0708686;0.631502;0.659212;0.00368189;0;0.2;InputArr_400hz_30s.dat;400;30;5;193.034;1.05855;149.647;114.552;61.489;54.6875;1765.14;31.25;0.38341;580;140.625;1.08028;1.01794;116;136.719;1.05448;0.992046;18;140.625;1.24709;1.10604
|
||||
2022-01-06-ai-invivo-1;Apteronotus leptorhynchus;15.7;17;P-unit;Nerve;good;657;3525;11.7313;300.485;0.953398;0.805977;1;-0.314176;0.0520852;0.745176;0.745176;0.00228311;0;0.2;InputArr_400hz_30s.dat;400;30;5;303.92;0.99732;195.842;145.994;69.9912;70.3125;2769.71;46.875;0.512263;580;339.844;1.18053;0.788881;116;339.844;1.17406;0.825191;18;339.844;1.21258;0.893669
|
||||
2022-01-06-ai-invivo-1;Apteronotus leptorhynchus;15.7;17;P-unit;Nerve;good;657;3525;11.7313;300.485;0.953398;0.805977;1;-0.314176;0.0520852;0.745176;0.745176;0.00228311;1;0.2;InputArr_400hz_30s.dat;400;30;5;298.41;0.971923;193.778;142.38;65.2662;70.3125;2796.52;46.875;0.494765;580;273.438;1.16795;0.700212;116;300.781;1.25474;0.792487;18;253.906;1.26334;0.805112
|
||||
2022-01-06-ai-invivo-1;Apteronotus leptorhynchus;15.7;17;P-unit;Nerve;good;657;3525;11.7313;300.485;0.953398;0.805977;1;-0.314176;0.0520852;0.745176;0.745176;0.00228311;2;0.2;InputArr_400hz_30s.dat;400;16.654;2;294.061;0.975171;165.879;126.051;75.7366;66.4062;1768.21;46.875;0.341511;128;343.75;1.06653;0.773348;64;343.75;1.00744;0.706679;18;339.844;1.16;0.862385
|
||||
2022-01-08-ad-invivo-1;Apteronotus leptorhynchus;15.7;17;P-unit;Nerve;good;734;1898;9.94953;190.949;0.998787;0.739642;1;-0.5075;0.0772921;0.606747;0.649446;0.00476839;0;0.2;InputArr_400hz_30s.dat;400;30;5;202.685;1.14756;198.2;149.245;80.4596;58.5938;2369.64;42.9688;0.527857;580;144.531;1.03608;1.82979;116;144.531;1.23671;1.31757;18;144.531;1.40276;1.48686
|
||||
2022-01-08-ad-invivo-1;Apteronotus leptorhynchus;15.7;17;P-unit;Nerve;good;734;1898;9.94953;190.949;0.998787;0.739642;1;-0.5075;0.0772921;0.606747;0.649446;0.00476839;1;0.2;InputArr_400hz_30s.dat;400;30;5;202.785;1.16133;195.912;149.141;80.9944;58.5938;2416.33;42.9688;0.491156;580;144.531;1.08387;1.89135;116;144.531;0.941063;1.0477;18;148.438;1.07361;1.05711
|
||||
2022-01-08-af-invivo-1;Apteronotus leptorhynchus;15.7;17;P-unit;Nerve;good;713;3669;21.0027;174.906;1.34726;0.710509;1;-0.313534;0.055758;0.749182;0.750273;0.00490884;0;0.2;InputArr_400hz_30s.dat;400;30;5;170.503;1.54278;207.049;174.774;117.533;35.1562;2764.11;19.5312;0.541786;580;125;0.99469;2.10482;116;125;1.04037;1.46753;18;125;1.05498;1.31076
|
||||
2022-01-27-aa-invivo-1;Apteronotus leptorhynchus;10.7;11;P-unit;Nerve;bad;626;4993;28.1087;177.685;0.873168;0.785724;1;-0.228023;0.0459643;0.385417;0.634615;0.00559105;0;0.2;InputArr_400hz_30s.dat;400;30;3;109.195;0.860215;113.562;86.7226;59.4107;42.9688;950.787;42.9688;0.342463;348;128.906;1.12658;1.17221;116;128.906;1.24101;1.21711;18;179.688;1.37152;1.22473
|
||||
2022-01-28-ab-invivo-1;Apteronotus leptorhynchus;10.7;11;P-unit;Nerve;good;761;2029;6.7742;299.5;0.890086;0.849786;1;-0.400537;0.0690345;0.66568;0.688363;0.00328515;0;0.2;InputArr_400hz_30s.dat;400;30;5;304.792;0.92168;228.991;155.387;80.6766;93.75;2406.43;42.9688;0.640329;580;250;1.20505;0.873663;116;250;1.51168;0.913428;18;296.875;1.2385;0.835487
|
||||
2022-01-28-ab-invivo-1;Apteronotus leptorhynchus;10.7;11;P-unit;Nerve;good;761;2029;6.7742;299.5;0.890086;0.849786;1;-0.400537;0.0690345;0.66568;0.688363;0.00328515;1;0.2;InputArr_400hz_30s.dat;400;26.2964;1;256.587;0.841758;234.2;150.871;76.5861;89.8438;2059.56;42.9688;0.567493;101;250;1.32771;1.02314;101;250;1.32771;1.02314;18;250;1.48823;1.02043
|
||||
2022-01-28-ab-invivo-1;Apteronotus leptorhynchus;10.7;11;P-unit;Nerve;good;761;2029;6.7742;299.5;0.890086;0.849786;1;-0.400537;0.0690345;0.66568;0.688363;0.00328515;2;0.2;InputArr_400hz_30s.dat;400;15.8295;2;221.121;0.6882;115.587;77.7136;47.7613;82.0312;1029.63;97.6562;0.293732;122;324.219;1.31508;1.184;61;324.219;1.29675;1.11515;18;332.031;1.2936;1.18297
|
||||
2022-01-28-ab-invivo-1;Apteronotus leptorhynchus;10.7;11;P-unit;Nerve;good;761;2029;6.7742;299.5;0.890086;0.849786;1;-0.400537;0.0690345;0.66568;0.688363;0.00328515;4;0.2;InputArr_400hz_30s.dat;400;30;3;205.694;0.878717;168.973;117.026;69.6077;82.0312;1483.59;66.4062;0.38325;348;250;1.32157;1.06247;116;285.156;1.26927;0.984763;18;304.688;1.21958;1.24385
|
||||
2022-01-28-ad-invivo-1;Apteronotus leptorhynchus;10.7;11;P-unit;Nerve;good;762;5580;21.7337;256.864;1.04767;0.817044;1;-0.316963;0.0410602;0.669475;0.730597;0.00459318;0;0.2;InputArr_400hz_30s.dat;400;30;4;237.995;1.22831;193.486;154.452;91.7969;54.6875;2042.52;19.5312;0.575148;464;261.719;1.11451;0.681172;116;285.156;1.4309;0.830054;18;285.156;1.47929;0.947566
|
||||
2022-01-28-af-invivo-1;Apteronotus leptorhynchus;10.7;11;P-unit;Nerve;good;760;2429;21.8774;111.18;0.96121;0.748431;1;-0.449343;0.063945;0.452224;0.520593;0.00328947;0;0.2;InputArr_400hz_30s.dat;400;30;5;150.832;1.23447;181.455;141.04;81.6907;42.9688;2303.76;42.9688;0.331232;580;74.2188;1.15906;6.04729;116;74.2188;1.18142;3.79887;18;62.5;1.05086;2.91075
|
||||
2022-01-28-af-invivo-1;Apteronotus leptorhynchus;10.7;11;P-unit;Nerve;good;760;2429;21.8774;111.18;0.96121;0.748431;1;-0.449343;0.063945;0.452224;0.520593;0.00328947;1;0.2;InputArr_400hz_30s.dat;400;30;2;138.96;1.0426;186.397;136.609;77.6986;42.9688;1883.66;42.9688;0.39765;232;66.4062;1.04485;3.07709;116;66.4062;1.13587;2.592;18;70.3125;1.27182;2.3737
|
||||
2022-01-28-af-invivo-1;Apteronotus leptorhynchus;10.7;11;P-unit;Nerve;good;760;2429;21.8774;111.18;0.96121;0.748431;1;-0.449343;0.063945;0.452224;0.520593;0.00328947;2;0.2;InputArr_400hz_30s.dat;400;30;4;97.8105;1.10007;135.224;102.799;60.8037;42.9688;1495.23;42.9688;0.313018;464;74.2188;1.51959;5.4281;116;74.2188;1.46223;3.5328;18;62.5;1.3184;2.58511
|
||||
2022-01-28-af-invivo-1;Apteronotus leptorhynchus;10.7;11;P-unit;Nerve;good;760;2429;21.8774;111.18;0.96121;0.748431;1;-0.449343;0.063945;0.452224;0.520593;0.00328947;3;0.2;InputArr_400hz_30s.dat;400;30;5;140.061;1.16742;168.327;128.608;73.5157;46.875;2060.43;42.9688;0.334177;580;62.5;1.12888;5.54363;116;74.2188;1.17827;3.16457;18;62.5;1.17605;3.25234
|
||||
2022-01-28-ah-invivo-1;Apteronotus leptorhynchus;10.7;11;P-unit;Nerve;good;759;4542;16.0602;282.909;0.882444;0.72099;1;-0.388297;0.0451745;0.609557;0.682889;0.00329381;0;0.2;InputArr_400hz_30s.dat;400;30;3;228.266;0.975535;191.569;139.537;83.8082;62.5;1897.63;42.9688;0.534334;348;312.5;1.1826;0.815446;116;332.031;1.1477;0.720368;18;308.594;1.36932;0.884038
|
||||
2022-01-28-ah-invivo-1;Apteronotus leptorhynchus;10.7;11;P-unit;Nerve;good;759;4542;16.0602;282.909;0.882444;0.72099;1;-0.388297;0.0451745;0.609557;0.682889;0.00329381;1;0.2;InputArr_400hz_30s.dat;400;30;4;225.99;0.95199;189.245;137.827;84.2536;66.4062;1721.13;42.9688;0.496521;464;273.438;1.22981;0.948748;116;238.281;1.25594;0.837559;18;332.031;1.36759;0.883603
|
||||
|
||||
|
153
dataoverview.py
153
dataoverview.py
@ -9,16 +9,27 @@ from plotstyle import plot_style, lighter, significance_str
|
||||
|
||||
data_path = Path('data')
|
||||
|
||||
|
||||
from noisesplit import model_cell as model_split_example
|
||||
from modelsusceptcontrasts import model_cells as model_contrast_examples
|
||||
from modelsusceptlown import model_cell as model_lown_example
|
||||
from punitexamplecell import example_cell as punit_example
|
||||
from punitexamplecell import example_cells as punit_examples
|
||||
from noisesplit import example_cell as punit_split_example
|
||||
from ampullaryexamplecell import example_cell as ampul_example
|
||||
from ampullaryexamplecell import example_cells as ampul_examples
|
||||
|
||||
model_examples = ([[model_lown_example, 0.01],
|
||||
[model_lown_example, 0.03],
|
||||
[model_lown_example, 0.1]],
|
||||
[[model_split_example, 0.01]],
|
||||
[[m, a] for m in model_contrast_examples for a in [0.01, 0.03, 0.1]])
|
||||
punit_examples = (punit_example, [punit_split_example], punit_examples)
|
||||
ampul_examples = (ampul_example, [], ampul_examples)
|
||||
|
||||
|
||||
def plot_corr(ax, data, xcol, ycol, zcol, zmin, zmax, xpdfmax, cmap, color,
|
||||
nli_thresh, example=[], examples=[]):
|
||||
ax.axhline(nli_thresh, color='k', ls=':', lw=0.5)
|
||||
si_thresh, example=[], split_example=[], examples=[]):
|
||||
ax.axhline(si_thresh, color='k', ls=':', lw=0.5)
|
||||
xmax = ax.get_xlim()[1]
|
||||
ymax = ax.get_ylim()[1]
|
||||
mask = (data[xcol] < xmax) & (data[ycol] < ymax)
|
||||
@ -31,13 +42,38 @@ def plot_corr(ax, data, xcol, ycol, zcol, zmin, zmax, xpdfmax, cmap, color,
|
||||
ax.scatter(data[mask, xcol], data[mask, ycol], c=data[mask, zcol],
|
||||
s=6, marker='^', linewidth=0.5, edgecolors='black',
|
||||
clip_on=False, cmap=cmap, vmin=zmin, vmax=zmax,
|
||||
zorder=20)
|
||||
for cell, run in split_example:
|
||||
mask = (data['cell'] == cell) & (data['stimindex'] == run)
|
||||
ax.scatter(data[mask, xcol], data[mask, ycol], c=data[mask, zcol],
|
||||
s=6, marker='s', linewidth=0.5, edgecolors='black',
|
||||
clip_on=False, cmap=cmap, vmin=zmin, vmax=zmax,
|
||||
zorder=20)
|
||||
for cell, run in examples:
|
||||
mask = (data['cell'] == cell) & (data['stimindex'] == run)
|
||||
ax.scatter(data[mask, xcol], data[mask, ycol], c=data[mask, zcol],
|
||||
s=5, marker='o', linewidth=0.5, edgecolors='black',
|
||||
clip_on=False, cmap=cmap, vmin=zmin, vmax=zmax,
|
||||
zorder=20)
|
||||
else:
|
||||
for cell, alpha in example:
|
||||
mask = (data['cell'] == cell) & (data['contrast'] == alpha)
|
||||
ax.scatter(data[mask, xcol], data[mask, ycol], c=data[mask, zcol],
|
||||
s=6, marker='^', linewidth=0.5, edgecolors='black',
|
||||
clip_on=False, cmap=cmap, vmin=zmin, vmax=zmax,
|
||||
zorder=20)
|
||||
for cell, alpha in split_example:
|
||||
mask = (data['cell'] == cell) & (data['contrast'] == alpha)
|
||||
ax.scatter(data[mask, xcol], data[mask, ycol], c=data[mask, zcol],
|
||||
s=6, marker='s', linewidth=0.5, edgecolors='black',
|
||||
clip_on=False, cmap=cmap, vmin=zmin, vmax=zmax,
|
||||
zorder=20)
|
||||
for cell, alpha in examples:
|
||||
mask = (data['cell'] == cell) & (data['contrast'] == alpha)
|
||||
ax.scatter(data[mask, xcol], data[mask, ycol], c=data[mask, zcol],
|
||||
s=5, marker='o', linewidth=0.5, edgecolors='black',
|
||||
clip_on=False, cmap=cmap, vmin=zmin, vmax=zmax,
|
||||
zorder=20)
|
||||
# color bar:
|
||||
fig = ax.get_figure()
|
||||
cax = ax.inset_axes([1.3, 0, 0.04, 1])
|
||||
@ -66,9 +102,9 @@ def plot_corr(ax, data, xcol, ycol, zcol, zmin, zmax, xpdfmax, cmap, color,
|
||||
yax.set_xlim(left=0)
|
||||
# threshold:
|
||||
if 'cvbase' in xcol:
|
||||
ax.text(xmax, 0.4*ymax, f'{100*np.sum(data[ycol] > nli_thresh)/len(data):.0f}\\%',
|
||||
ax.text(xmax, 0.4*ymax, f'{100*np.sum(data[ycol] > si_thresh)/len(data):.0f}\\%',
|
||||
ha='right', va='bottom', fontsize='small')
|
||||
ax.text(xmax, 0.3, f'{100*np.sum(data[ycol] < nli_thresh)/len(data):.0f}\\%',
|
||||
ax.text(xmax, 0.3, f'{100*np.sum(data[ycol] < si_thresh)/len(data):.0f}\\%',
|
||||
ha='right', va='center', fontsize='small')
|
||||
# statistics:
|
||||
r, p = pearsonr(data[xcol], data[ycol])
|
||||
@ -83,76 +119,80 @@ def plot_corr(ax, data, xcol, ycol, zcol, zmin, zmax, xpdfmax, cmap, color,
|
||||
return cax
|
||||
|
||||
|
||||
def nli_stats(title, data, column, nli_thresh):
|
||||
def si_stats(title, data, column, si_thresh):
|
||||
print(title)
|
||||
print(f' nli threshold: {nli_thresh:.1f}')
|
||||
cells = np.unique(data['cell'])
|
||||
ncells = len(cells)
|
||||
nrecs = len(data)
|
||||
print(f' cells: {ncells}')
|
||||
print(f' recordings: {nrecs}')
|
||||
hcells = np.unique(data[data(column) > nli_thresh, 'cell'])
|
||||
print(f' high nli cells: n={len(hcells):3d}, {100*len(hcells)/ncells:4.1f}%')
|
||||
print(f' high nli recordings: n={np.sum(data(column) > nli_thresh):3d}, '
|
||||
f'{100*np.sum(data(column) > nli_thresh)/nrecs:4.1f}%')
|
||||
print(f' cells: {ncells}')
|
||||
print(f' recordings: {nrecs}')
|
||||
print(f' SI threshold: {si_thresh:.1f}')
|
||||
hcells = np.unique(data[data(column) > si_thresh, 'cell'])
|
||||
print(f' high SI cells: n={len(hcells):3d}, {100*len(hcells)/ncells:4.1f}%')
|
||||
print(f' high SI recordings: n={np.sum(data(column) > si_thresh):3d}, '
|
||||
f'{100*np.sum(data(column) > si_thresh)/nrecs:4.1f}%')
|
||||
nsegs = data['nsegs']
|
||||
print(f' number of segments: {np.min(nsegs):4.0f} - {np.max(nsegs):4.0f}, median={np.median(nsegs):4.0f}, mean={np.mean(nsegs):4.0f}, std={np.std(nsegs):4.0f}')
|
||||
nrecs = []
|
||||
for cell in cells:
|
||||
nrecs.append(len(data[data["cell"] == cell, :]))
|
||||
print(f' number of recordings per cell: {np.min(nrecs):4.0f} - {np.max(nrecs):4.0f}, median={np.median(nrecs):4.0f}, mean={np.mean(nrecs):4.0f}, std={np.std(nrecs):4.0f}')
|
||||
fcutoff = data['fcutoff']
|
||||
print(' cutoff frequencies:', ' '.join([f'{f:3.0f}Hz' for f in np.unique(fcutoff)]))
|
||||
print(' cutoff frequencies:', ' '.join([f'{np.sum(fcutoff == f):3d}' for f in np.unique(fcutoff)]))
|
||||
print(f' cutoff frequencies: {np.min(fcutoff):.0f}Hz - {np.max(fcutoff):.0f}Hz, median={np.median(fcutoff):.0f}Hz, mean={np.mean(fcutoff):.0f}Hz, std={np.std(fcutoff):.0f}Hz')
|
||||
contrasts = 100*data['contrast']
|
||||
print(' contrasts: ', ' '.join([f'{c:.2g}%' for c in np.unique(contrasts)]))
|
||||
print(f' contrasts: {np.min(contrasts):.2g}% - {np.max(contrasts):.2g}%, median={np.median(contrasts):.2g}%, mean={np.mean(contrasts):.2g}%, std={np.std(contrasts):.2g}%')
|
||||
|
||||
|
||||
def plot_cvbase_nli_punit(ax, data, ycol, nli_thresh, color):
|
||||
def plot_cvbase_si_punit(ax, data, ycol, si_thresh, color):
|
||||
ax.set_xlabel('CV$_{\\rm base}$')
|
||||
ax.set_ylabel('SI($r$)')
|
||||
ax.set_xlim(0, 1.5)
|
||||
ax.set_ylim(0, 7.2)
|
||||
ax.set_yticks_delta(2)
|
||||
examples = punit_examples if 'stimindex' in data else model_examples
|
||||
cax = plot_corr(ax, data, 'cvbase', ycol, 'respmod2', 0, 250, 3,
|
||||
'coolwarm', color, nli_thresh,
|
||||
punit_example, punit_examples)
|
||||
'coolwarm', color, si_thresh, *examples)
|
||||
cax.set_ylabel('Response mod.', 'Hz')
|
||||
|
||||
|
||||
def plot_cvstim_nli_punit(ax, data, ycol, nli_thresh, color):
|
||||
def plot_cvstim_si_punit(ax, data, ycol, si_thresh, color):
|
||||
ax.set_xlabel('CV$_{\\rm stim}$')
|
||||
ax.set_ylabel('SI($r$)')
|
||||
ax.set_xlim(0, 1.6)
|
||||
ax.set_ylim(0, 7.2)
|
||||
ax.set_xticks_delta(0.5)
|
||||
ax.set_yticks_delta(2)
|
||||
examples = punit_examples if 'stimindex' in data else model_examples
|
||||
#cax = plot_corr(ax, data, 'cvstim', ycol, 'respmod2', 0, 250, 2,
|
||||
# 'coolwarm', color, nli_thresh,
|
||||
# punit_example, punit_examples)
|
||||
# 'coolwarm', color, si_thresh, *examples)
|
||||
#cax.set_ylabel('Response mod.', 'Hz')
|
||||
cax = plot_corr(ax, data, 'cvstim', ycol, 'cvbase', 0, 1.5, 2,
|
||||
'coolwarm', color, nli_thresh,
|
||||
punit_example, punit_examples)
|
||||
'coolwarm', color, si_thresh, *examples)
|
||||
cax.set_ylabel('CV$_{\\rm base}$')
|
||||
#cax = plot_corr(ax, data, 'cvstim', ycol, 'ratebase', 50, 450, 2,
|
||||
# 'coolwarm', color, nli_thresh,
|
||||
# punit_example, punit_examples)
|
||||
# 'coolwarm', color, si_thresh, *examples)
|
||||
#cax.set_ylabel('$r$', 'Hz')
|
||||
#cax = plot_corr(ax, data, 'cvstim', ycol, 'serialcorr1', -0.6, 0, 2,
|
||||
# 'coolwarm', color, nli_thresh,
|
||||
# punit_example, punit_examples)
|
||||
# 'coolwarm', color, si_thresh, *examples)
|
||||
#cax.set_ylabel('$\\rho_1$')
|
||||
|
||||
|
||||
def plot_mod_nli_punit(ax, data, ycol, nli_thresh, color):
|
||||
def plot_rmod_si_punit(ax, data, ycol, si_thresh, color):
|
||||
ax.set_xlabel('Response modulation', 'Hz')
|
||||
ax.set_ylabel('SI($r$)')
|
||||
ax.set_xlim(0, 250)
|
||||
ax.set_ylim(0, 7.2)
|
||||
ax.set_yticks_delta(2)
|
||||
examples = punit_examples if 'stimindex' in data else model_examples
|
||||
cax = plot_corr(ax, data, 'respmod2', ycol, 'cvbase', 0, 1.5, 0.016,
|
||||
'coolwarm', color, nli_thresh,
|
||||
punit_example, punit_examples)
|
||||
'coolwarm', color, si_thresh, *examples)
|
||||
cax.set_ylabel('CV$_{\\rm base}$')
|
||||
|
||||
|
||||
def plot_cvbase_nli_ampul(ax, data, ycol, nli_thresh, color):
|
||||
def plot_cvbase_si_ampul(ax, data, ycol, si_thresh, color):
|
||||
ax.set_xlabel('CV$_{\\rm base}$')
|
||||
ax.set_ylabel('SI($r$)')
|
||||
ax.set_xlim(0, 0.2)
|
||||
@ -160,12 +200,11 @@ def plot_cvbase_nli_ampul(ax, data, ycol, nli_thresh, color):
|
||||
ax.set_xticks_delta(0.1)
|
||||
ax.set_yticks_delta(5)
|
||||
cax = plot_corr(ax, data, 'cvbase', ycol, 'respmod2', 0, 80, 20,
|
||||
'coolwarm', color, nli_thresh,
|
||||
ampul_example, ampul_examples)
|
||||
'coolwarm', color, si_thresh, *ampul_examples)
|
||||
cax.set_ylabel('Response mod.', 'Hz')
|
||||
|
||||
|
||||
def plot_cvstim_nli_ampul(ax, data, ycol, nli_thresh, color):
|
||||
def plot_cvstim_si_ampul(ax, data, ycol, si_thresh, color):
|
||||
ax.set_xlabel('CV$_{\\rm stim}$')
|
||||
ax.set_ylabel('SI($r$)')
|
||||
ax.set_xlim(0, 0.85)
|
||||
@ -173,22 +212,19 @@ def plot_cvstim_nli_ampul(ax, data, ycol, nli_thresh, color):
|
||||
ax.set_xticks_delta(0.2)
|
||||
ax.set_yticks_delta(5)
|
||||
#cax = plot_corr(ax, data, 'cvstim', ycol, 'respmod2', 0, 80, 6,
|
||||
# 'coolwarm', color, nli_thresh,
|
||||
# ampul_example, ampul_examples)
|
||||
# 'coolwarm', color, si_thresh, *ampul_examples)
|
||||
#cax.set_ylabel('Response mod.', 'Hz')
|
||||
cax = plot_corr(ax, data, 'cvstim', ycol, 'cvbase', 0, 0.2, 6,
|
||||
'coolwarm', color, nli_thresh,
|
||||
ampul_example, ampul_examples)
|
||||
'coolwarm', color, si_thresh, *ampul_examples)
|
||||
cax.set_ylabel('CV$_{\\rm base}$')
|
||||
cax.set_yticks_delta(0.1)
|
||||
#cax = plot_corr(ax, data, 'cvstim', ycol, 'ratebase', 90, 180, 6,
|
||||
# 'coolwarm', color, nli_thresh,
|
||||
# ampul_example, ampul_examples)
|
||||
# 'coolwarm', color, si_thresh, *ampul_examples)
|
||||
#cax.set_ylabel('$r$', 'Hz')
|
||||
#cax.set_yticks_delta(30)
|
||||
|
||||
|
||||
def plot_mod_nli_ampul(ax, data, ycol, nli_thresh, color):
|
||||
def plot_rmod_si_ampul(ax, data, ycol, si_thresh, color):
|
||||
ax.set_xlabel('Response modulation', 'Hz')
|
||||
ax.set_ylabel('SI($r$)')
|
||||
ax.set_xlim(0, 80)
|
||||
@ -196,8 +232,7 @@ def plot_mod_nli_ampul(ax, data, ycol, nli_thresh, color):
|
||||
ax.set_xticks_delta(20)
|
||||
ax.set_yticks_delta(5)
|
||||
cax = plot_corr(ax, data, 'respmod2', ycol, 'cvbase', 0, 0.2, 0.06,
|
||||
'coolwarm', color, nli_thresh,
|
||||
ampul_example, ampul_examples)
|
||||
'coolwarm', color, si_thresh, *ampul_examples)
|
||||
cax.set_ylabel('CV$_{\\rm base}$')
|
||||
cax.set_yticks_delta(0.1)
|
||||
|
||||
@ -213,25 +248,25 @@ if __name__ == '__main__':
|
||||
ampul_data = TableData(data_path /
|
||||
'Apteronotus_leptorhynchus-Ampullary-data.csv',
|
||||
sep=';')
|
||||
nli_thresh = 1.8
|
||||
si_thresh = 1.8
|
||||
|
||||
u, p = mannwhitneyu(punit_model['cvbase'], punit_data['cvbase'])
|
||||
print('CV differs between P-unit models and data:')
|
||||
print(f' U={u:g}, p={p:.2g}')
|
||||
print(f' median model: {np.median(punit_model["cvbase"]):.2f}')
|
||||
print(f' median data: {np.median(punit_data["cvbase"]):.2f}')
|
||||
print(f' median data: {np.median(punit_data["cvbase"]):.2f}')
|
||||
print()
|
||||
u, p = mannwhitneyu(punit_model['respmod2'], punit_data['respmod2'])
|
||||
print('Response modulation differs between P-unit models and data:')
|
||||
print(f' U={u:g}, p={p:.2g}')
|
||||
print(f' median model: {np.median(punit_model["respmod2"]):.2f}')
|
||||
print(f' median data: {np.median(punit_data["respmod2"]):.2f}')
|
||||
print(f' median data: {np.median(punit_data["respmod2"]):.2f}')
|
||||
print()
|
||||
u, p = mannwhitneyu(punit_model['dnli100'], punit_data['nli'])
|
||||
print('NLI does not differ between P-unit models and data:')
|
||||
print('SI does not differ between P-unit models and data:')
|
||||
print(f' U={u:g}, p={p:.2g}')
|
||||
print(f' median model: {np.median(punit_model["dnli100"]):.1f}')
|
||||
print(f' median data: {np.median(punit_data["nli"]):.1f}')
|
||||
print(f' median data: {np.median(punit_data["nli"]):.1f}')
|
||||
print()
|
||||
|
||||
s = plot_style()
|
||||
@ -240,28 +275,28 @@ if __name__ == '__main__':
|
||||
fig.subplots_adjust(leftm=6.5, rightm=13.5, topm=4.5, bottomm=4,
|
||||
wspace=1.1, hspace=0.6)
|
||||
|
||||
nli_stats('P-unit model:', punit_model, 'dnli100', nli_thresh)
|
||||
si_stats('P-unit model:', punit_model, 'dnli100', si_thresh)
|
||||
axs[0, 0].text(0, 1.35, 'P-unit models',
|
||||
transform=axs[0, 0].transAxes, color=s.model_color1)
|
||||
plot_cvbase_nli_punit(axs[0, 0], punit_model, 'dnli100', nli_thresh, s.model_color2)
|
||||
plot_mod_nli_punit(axs[0, 1], punit_model, 'dnli100', nli_thresh, s.model_color2)
|
||||
plot_cvstim_nli_punit(axs[0, 2], punit_model, 'dnli100', nli_thresh, s.model_color2)
|
||||
plot_cvbase_si_punit(axs[0, 0], punit_model, 'dnli100', si_thresh, s.model_color2)
|
||||
plot_rmod_si_punit(axs[0, 1], punit_model, 'dnli100', si_thresh, s.model_color2)
|
||||
plot_cvstim_si_punit(axs[0, 2], punit_model, 'dnli100', si_thresh, s.model_color2)
|
||||
print()
|
||||
|
||||
nli_stats('P-unit data:', punit_data, 'nli', nli_thresh)
|
||||
si_stats('P-unit data:', punit_data, 'nli', si_thresh)
|
||||
axs[1, 0].text(0, 1.35, 'P-unit data',
|
||||
transform=axs[1, 0].transAxes, color=s.punit_color1)
|
||||
plot_cvbase_nli_punit(axs[1, 0], punit_data, 'nli', nli_thresh, s.punit_color2)
|
||||
plot_mod_nli_punit(axs[1, 1], punit_data, 'nli', nli_thresh, s.punit_color2)
|
||||
plot_cvstim_nli_punit(axs[1, 2], punit_data, 'nli', nli_thresh, s.punit_color2)
|
||||
plot_cvbase_si_punit(axs[1, 0], punit_data, 'nli', si_thresh, s.punit_color2)
|
||||
plot_rmod_si_punit(axs[1, 1], punit_data, 'nli', si_thresh, s.punit_color2)
|
||||
plot_cvstim_si_punit(axs[1, 2], punit_data, 'nli', si_thresh, s.punit_color2)
|
||||
print()
|
||||
|
||||
nli_stats('Ampullary data:', ampul_data, 'nli', nli_thresh)
|
||||
si_stats('Ampullary data:', ampul_data, 'nli', si_thresh)
|
||||
axs[2, 0].text(0, 1.35, 'Ampullary data',
|
||||
transform=axs[2, 0].transAxes, color=s.ampul_color1)
|
||||
plot_cvbase_nli_ampul(axs[2, 0], ampul_data, 'nli', nli_thresh, s.ampul_color2)
|
||||
plot_mod_nli_ampul(axs[2, 1], ampul_data, 'nli', nli_thresh, s.ampul_color2)
|
||||
plot_cvstim_nli_ampul(axs[2, 2], ampul_data, 'nli', nli_thresh, s.ampul_color2)
|
||||
plot_cvbase_si_ampul(axs[2, 0], ampul_data, 'nli', si_thresh, s.ampul_color2)
|
||||
plot_rmod_si_ampul(axs[2, 1], ampul_data, 'nli', si_thresh, s.ampul_color2)
|
||||
plot_cvstim_si_ampul(axs[2, 2], ampul_data, 'nli', si_thresh, s.ampul_color2)
|
||||
print()
|
||||
|
||||
fig.common_xticks(axs[:2, 0])
|
||||
@ -270,6 +305,6 @@ if __name__ == '__main__':
|
||||
fig.common_yticks(axs[0, :])
|
||||
fig.common_yticks(axs[1, :])
|
||||
fig.common_yticks(axs[2, :])
|
||||
fig.tag(xoffs=-3.5, yoffs=2)
|
||||
fig.tag(axs, xoffs=-3.5, yoffs=2)
|
||||
fig.savefig()
|
||||
print()
|
||||
|
||||
@ -189,10 +189,10 @@ if __name__ == '__main__':
|
||||
xthresh = 1.2
|
||||
ythresh = 1.8
|
||||
s = plot_style()
|
||||
fig, axs = plt.subplots(4, 4, cmsize=(s.plot_width, 0.85*s.plot_width),
|
||||
height_ratios=[1, 1, 0, 1, 0, 1])
|
||||
fig, axs = plt.subplots(3, 4, cmsize=(s.plot_width, 0.6*s.plot_width),
|
||||
height_ratios=[1, 1, 0, 1])
|
||||
fig.subplots_adjust(leftm=7, rightm=9, topm=2, bottomm=4,
|
||||
wspace=1, hspace=0.8)
|
||||
wspace=1, hspace=0.6)
|
||||
for ax in axs.flat:
|
||||
ax.set_visible(False)
|
||||
print('Example cells:')
|
||||
@ -204,9 +204,9 @@ if __name__ == '__main__':
|
||||
fig.common_xticks(axs[:2, k])
|
||||
print()
|
||||
plot_summary_contrasts(axs[2], s, xthresh, ythresh, model_cell)
|
||||
plot_summary_diags(axs[3], s, xthresh, ythresh, model_cell)
|
||||
fig.common_yticks(axs[2, 1:])
|
||||
fig.common_yticks(axs[3, 1:])
|
||||
#plot_summary_diags(axs[3], s, xthresh, ythresh, model_cell)
|
||||
#fig.common_yticks(axs[3, 1:])
|
||||
fig.tag(axs, xoffs=-4.5, yoffs=1.8)
|
||||
axs[1, 0].set_visible(False)
|
||||
fig.savefig()
|
||||
|
||||
@ -5,9 +5,8 @@ from spectral import whitenoise, diag_projection, peakedness
|
||||
from plotstyle import plot_style
|
||||
|
||||
|
||||
#example_cell = ['2012-07-03-ak-invivo-1', 0]
|
||||
example_cell = ['2017-07-18-ai-invivo-1', 1] # Take this! at 3% model, 5% data
|
||||
model_cell = example_cell
|
||||
example_cell = ['2017-07-18-ai-invivo-1', 1]
|
||||
model_cell = example_cell[0]
|
||||
|
||||
base_path = Path('data')
|
||||
data_path = base_path / 'cells'
|
||||
@ -275,8 +274,8 @@ if __name__ == '__main__':
|
||||
|
||||
# model 5%:
|
||||
axss = axs[1]
|
||||
data_files = sims_path.glob(f'chi2-noisen-{example_cell[0]}-{1000*data_contrast:03.0f}-*.npz')
|
||||
files, nums = sort_files(example_cell[0], data_files, 2)
|
||||
data_files = sims_path.glob(f'chi2-noisen-{model_cell}-{1000*data_contrast:03.0f}-*.npz')
|
||||
files, nums = sort_files(model_cell, data_files, 2)
|
||||
axss[1].text(xt, yt, 'P-unit model', fontsize='large',
|
||||
transform=axs[1, 1].transAxes, color=s.model_color1)
|
||||
plot_chi2_contrast(axss[1], axss[2], s, files, nums, nsmall, nlarge, ratebase)
|
||||
@ -287,16 +286,16 @@ if __name__ == '__main__':
|
||||
|
||||
# model 1%:
|
||||
axss = axs[2]
|
||||
data_files = sims_path.glob(f'chi2-noisen-{example_cell[0]}-{1000*contrast:03.0f}-*.npz')
|
||||
files, nums = sort_files(example_cell[0], data_files, 2)
|
||||
data_files = sims_path.glob(f'chi2-noisen-{model_cell}-{1000*contrast:03.0f}-*.npz')
|
||||
files, nums = sort_files(model_cell, data_files, 2)
|
||||
plot_chi2_contrast(axss[1], axss[2], s, files, nums, nsmall, nlarge, ratebase)
|
||||
axr2 = plot_noise_split(axss[0], contrast, 0, 1, wtime, wnoise)
|
||||
plot_overn(axss[3], s, files, nmax=1e6)
|
||||
|
||||
# model noise split:
|
||||
axss = axs[3]
|
||||
data_files = sims_path.glob(f'chi2-split-{example_cell[0]}-*.npz')
|
||||
files, nums = sort_files(example_cell[0], data_files, 1)
|
||||
data_files = sims_path.glob(f'chi2-split-{model_cell}-*.npz')
|
||||
files, nums = sort_files(model_cell, data_files, 1)
|
||||
axss[1].text(xt, yt, 'P-unit model', fontsize='large',
|
||||
transform=axss[1].transAxes, color=s.model_color1)
|
||||
axss[1].text(xt + 0.9, yt, f'(noise split)', fontsize='large',
|
||||
|
||||
@ -420,7 +420,7 @@ We here analyze nonlinear responses in two types of primary electroreceptor affe
|
||||
|
||||
\begin{figure*}[t]
|
||||
\includegraphics[width=\columnwidth]{lifsuscept}
|
||||
\caption{\label{fig:lifresponse} First- (linear) and second-order response functions of the leaky integrate-and-fire model. \figitem{A} Magnitude of the first-order response function $|\chi_1(f)|$, also known as the ``gain'' function, quantifies the response amplitude relative to the stimulus amplitude, both measured at the same stimulus frequency. \figitem{B} Magnitude of the second-order response function $|\chi_2(f_1, f_2)|$ quantifies the response at the sum of two stimulus frequencies. For linear systems, the second-order response function is zero, because linear systems do not create new frequencies and thus there is no response at the sum of the two frequencies. The plots show the analytical solutions from \citet{Lindner2001} and \citet{Voronenko2017} with $\mu = 1.1$ and $D = 0.001$. Note that the leaky integrate-and-fire model is formulated without dimensions, frequencies are given in multiples of the inverse membrane time constant.}
|
||||
\caption{\label{fig:lifresponse} First- and second-order response functions of the leaky integrate-and-fire model. \figitem{A} Magnitude of the first-order (linear) response function $|\chi_1(f)|$, also known as the ``gain'' function, quantifies the response amplitude relative to the stimulus amplitude, both measured at the same stimulus frequency. \figitem{B} Magnitude of the second-order (non-linear) response function $|\chi_2(f_1, f_2)|$ quantifies the response at the sum of two stimulus frequencies. For linear systems, the second-order response function is zero, because linear systems do not create new frequencies and thus there is no response at the sum of the two frequencies. The plots show the analytical solutions from \citet{Lindner2001} and \citet{Voronenko2017} with $\mu = 1.1$ and $D = 0.001$. Note that the leaky integrate-and-fire model is formulated without dimensions, frequencies are given in multiples of the inverse membrane time constant.}
|
||||
\end{figure*}
|
||||
We like to think about signal encoding in terms of linear relations with unique mapping of a given input value to a certain output of the system under consideration. Indeed, such linear methods, for example the transfer function or first-oder susceptibility shown in figure~\ref{fig:lifresponse}, have been widely and successfully applied to describe and predict neuronal responses and are an invaluable tool to characterize neural systems \citep{Borst1999}. Nonlinear mechanisms, on the other hand, are key on different levels of neural processing. Deciding for one action over another is a nonlinear process on the systemic level. On the cellular level, spiking neurons are inherently nonlinear. Whether an action potential is elicited depends on the membrane potential to exceed a threshold \citep{Hodgkin1952, Koch1995}. Because of such nonlinearities, understanding and predicting neuronal responses to sensory stimuli is in general a difficult task.
|
||||
|
||||
@ -498,7 +498,7 @@ In the example recordings shown above (\figsrefb{fig:punit} and \fref{fig:ampull
|
||||
|
||||
\begin{figure*}[p]
|
||||
\includegraphics[width=\columnwidth]{noisesplit}
|
||||
\caption{\label{fig:noisesplit} Estimation of second-order susceptibilities. \figitem{A} \suscept{} (right) estimated from $N=198$ 256\,ms long FFT segments of an electrophysiological recording of another P-unit (cell ``2017-07-18-ai'', $r=78$\,Hz, CV$_{\text{base}}=0.22$) driven with a RAM stimulus with contrast 5\,\% (left). \figitem[i]{B} \textit{Standard condition} of model simulations with intrinsic noise (bottom) and a RAM stimulus (top). \figitem[ii]{B} \suscept{} estimated from simulations of the cell's LIF model counterpart (cell ``2017-07-18-ai'', table~\ref{modelparams}) based on a similar number of $N=100$ FFT segments. As in the electrophysiological recording only a weak anti-diagonal is visible. \figitem[iii]{B} Same as \panel[ii]{B} but using $10^6$ FFT segments. Now, the expected triangular structure is revealed. \figitem[iv]{B} Convergence of the \suscept{} estimate as a function of FFTsegements. \figitem{C} At a lower stimulus contrast of 1\,\% the estimate did not converge yet even for $10^6$ FFT segments. \figitem[i]{D} Same as in \panel[i]{B} but in the \textit{noise split} condition: there is no external RAM signal (red) driving the model. Instead, a large part (90\,\%) of the total intrinsic noise is treated as a signal and is presented as an equivalent amplitude modulation ($s_{\xi}(t)$, orange), while the intrinsic noise is reduced to 10\,\% of its original strength (bottom, see methods for details). \figitem[i]{D} 100 FFT segements are still not sufficient for estimating \suscept{}. \figitem[iii]{D} Simulating one million segments reveals the full expected trangular structure of the second-order susceptibility. \figitem[iv]{D} In the noise-split condition, the \suscept{} estimate converges already at about $10^{4}$ FFT Segements.}
|
||||
\caption{\label{fig:noisesplit} Estimation of second-order susceptibilities. \figitem{A} \suscept{} (right) estimated from $N=198$ 256\,ms long FFT segments of an electrophysiological recording of another P-unit (cell ``2017-07-18-ai'', $r=78$\,Hz, CV$_{\text{base}}=0.22$) driven with a RAM stimulus with contrast 5\,\% (left). \figitem[i]{B} \textit{Standard condition} of model simulations with intrinsic noise (bottom) and a RAM stimulus (top). \figitem[ii]{B} \suscept{} estimated from simulations of the cell's LIF model counterpart (cell ``2017-07-18-ai'', table~\ref{modelparams}) based on a similar number of $N=100$ FFT segments. As in the electrophysiological recording only a weak anti-diagonal is visible. \figitem[iii]{B} Same as \panel[ii]{B} but using $10^6$ FFT segments. Now, the expected triangular structure is revealed. \figitem[iv]{B} Convergence of the \suscept{} estimate as a function of FFT segments. \figitem{C} At a lower stimulus contrast of 1\,\% the estimate did not converge yet even for $10^6$ FFT segments. \figitem[i]{D} Same as in \panel[i]{B} but in the \textit{noise split} condition: there is no external RAM signal (red) driving the model. Instead, a large part (90\,\%) of the total intrinsic noise is treated as a signal and is presented as an equivalent amplitude modulation ($s_{\xi}(t)$, orange), while the intrinsic noise is reduced to 10\,\% of its original strength (bottom, see methods for details). \figitem[i]{D} 100 FFT segments are still not sufficient for estimating \suscept{}. \figitem[iii]{D} Simulating one million segments reveals the full expected trangular structure of the second-order susceptibility. \figitem[iv]{D} In the noise-split condition, the \suscept{} estimate converges already at about $10^{4}$ FFT segments.}
|
||||
\end{figure*}
|
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|
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%\notejb{Since the model overestimated the sensitivity of the real P-unit, we adjusted the RAM contrast to 0.9\,\%, such that the resulting spike trains had the same CV as the electrophysiological recorded P-unit during the 2.5\,\% contrast stimulation (see table~\ref{modelparams} for model parameters).} \notejb{chi2 scale is higher than in real cell}
|
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@ -509,12 +509,12 @@ In model simulations we can increase the number of FFT segments beyond what woul
|
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|
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Using a broadband stimulus increases the effective input-noise level. This may linearize signal transmission and suppress potential nonlinear responses \citep{Longtin1993, Chialvo1997, Roddey2000, Voronenko2017}. Assuming that the intrinsic noise level in this P-unit is small enough, the full expected structure of the second-order susceptibility should appear in the limit of weak AMs. Again, this cannot be done experimentally, because the problem of insufficient averaging becomes even more severe for weak AMs (low contrast). In the model, however, we know the time course of the intrinsic noise and can use this knowledge to determine the susceptibilities by input-output correlations via the Furutsu-Novikov theorem \citep{Furutsu1963, Novikov1965}. This theorem, in its simplest form, states that the cross-spectrum $S_{x\eta}(\omega)$ of a Gaussian noise $\eta(t)$ driving a nonlinear system and the system's output $x(t)$ is proportional to the linear susceptibility according to $S_{x\eta}(\omega)=\chi(\omega)S_{\eta\eta}(\omega)$. Here $\chi(\omega)$ characterizes the linear response to an infinitely weak signal $s(t)$ in the presence of the background noise $\eta(t)$. Likewise, the nonlinear susceptibility can be determined in an analogous fashion from higher-order input-output cross-spectra (see methods, equations \eqref{eq:crosshigh} and \eqref{eq:susceptibility}) \citep{Egerland2020}. In line with an alternative derivation of the Furutsu-Novikov theorem \citep{Lindner2022}, we can split the total noise and consider a fraction of it as a stimulus. This allows us to calculate the susceptibility from the cross-spectrum between the output and this stimulus fraction of the noise. Adapting this approach to our P-unit model (see methods), we replace the intrinsic noise by an approximately equivalent RAM stimulus $s_{\xi}(t)$ and a weak remaining intrinsic noise $\sqrt{2D \, c_{\rm{noise}}}\;\xi(t)$ with $c_\text{noise} = 0.1$ (see methods, equations \eqref{eq:ram_split}, \eqref{eq:Noise_split_intrinsic}, \eqref{eq:Noise_split_intrinsic_dendrite}, \subfigrefb{fig:noisesplit}\,\panel[i]{C}). We tune the amplitude of the RAM stimulus $s_{\xi}(t)$ such that the output firing rate and variability (CV) are the same as in the baseline activity (i.e. full intrinsic noise $\sqrt{2D}\;\xi(t)$ in the voltage equation but no RAM) and compute the cross-spectra between the RAM part of the noise $s_{\xi}(t)$ and the output spike train. This procedure has two consequences: (i) by means of the cross-spectrum between the output and \signalnoise, which is a large fraction of the noise, the signal-to-noise ratio of the measured susceptibilities is drastically improved; (ii) the total noise in the system has been reduced (by what was before the external RAM stimulus $s(t)$), which makes the system more nonlinear. For both reasons we now see the expected nonlinear features in the second-order susceptibility for a sufficient number of segments (\subfigrefb{fig:noisesplit}\,\panel[iii]{C}), but not for a number of segments comparable to the experiment (\subfigrefb{fig:noisesplit}\,\panel[ii]{C}). In addition to the strong response for \fsumb{}, we now also observe pronounced nonlinear responses at \foneb{} and \ftwob{} (vertical and horizontal lines, \subfigrefb{fig:noisesplit}\,\panel[iii]{C}).
|
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|
||||
Note, that the increased number of segments goes along with a substantial reduction of second-order susceptibility values (\subfigrefb{fig:noisesplit}\,\panel[iii]{C}). Only for more than about $10^5$ segments does the estimate of the second-order susceptibility converge for most of the model cells (\subfigrefb{fig:trialnr}{A--D}).
|
||||
Note, that the increased number of segments goes along with a substantial reduction of second-order susceptibility values (\subfigrefb{fig:noisesplit}\,\panel[iii]{C}). Only for more than about $10^5$ segments does the estimate of the second-order susceptibility converge for most of the model cells.
|
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|
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|
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\begin{figure}[p]
|
||||
\includegraphics[width=\columnwidth]{modelsusceptcontrasts}
|
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\caption{\label{fig:modelsusceptcontrasts}Dependence of second order susceptibility on stimulus contrast. \figitem{A} Second-order susceptibilities estimated for increasing stimulus contrasts $c=0, 1, 3$ and $10$\,\% as indicated ($N=10^7$ FFT segments for $c=1$\,\%, $N=10^6$ segments for all other contrasts). $c=0$\,\% refers to the noise-split configuration (limit to vanishing external RAM signal, see \subfigrefb{fig:noisesplit}{D}). Shown are simulations of the P-unit model cell ``2017-07-18-ai'' (table~\ref{modelparams}) with a baseline firing rate of $82$\,Hz and CV$=0.23$. The cell shows a clear triangular pattern in its second-order susceptibility even up to a contrast of $10$\,\%. Note, that for $c=1$\,\% (\panel[ii]{D}), the estimate did not converge yet. \figitem{B} Cell ``2012-12-13-ao'' (baseline firing rate of $146$\,Hz, CV$=0.23$) also has strong interactions at its baseline firing rate that survive up to a stimulus contrast of $3$\,\%. \figitem{C} Model cell ``2012-12-20-ac'' (baseline firing rate of $212$\,Hz, CV$=0.26$) shows a weak triangular structure in the second-order susceptibility that vanishes for stimulus contrasts larger than $1$\,\%. \figitem{D} Cell ``2013-01-08-ab'' (baseline firing rate of $218$\,Hz, CV$=0.55$) does not show any triangular pattern in its second-order susceptibility. Nevertheless, interactions between low stimulus frequencies become substantial at higher contrasts. \figitem{E} The presence of an elevated second-order susceptibility where the stimulus frequency add up to the neuron's baseline frequency, can be identified by the susceptibility index (SI($r$), \eqnref{eq:nli_equation2}) greater than one (horizontal black line). The SI($r$) (density to the right) is plotted as a function of the model neuron's baseline CV for all $39$ model cells (table~\ref{modelparams}). Model cells have been visually categorized based on the presence of a triangular pattern in their second-order susceptibility estimated in the noise-split configuration (legend). The cells from \panel{A--D} are marked by a black circle. Pearson's correlation coefficients $r$, the corresponding significance level $p$ and regression line (dashed gray line) are indicated. The higher the stimulus contrast, the less cells show weakly nonlinear interactions as expressed by the triangular structure in the second-order susceptibility.}
|
||||
\caption{\label{fig:modelsusceptcontrasts}Dependence of second order susceptibility on stimulus contrast. \figitem{A} Second-order susceptibilities estimated for increasing stimulus contrasts of $c=0, 1, 3$ and $10$\,\% as indicated ($N=10^7$ FFT segments for $c=1$\,\%, $N=10^6$ segments for all other contrasts). $c=0$\,\% refers to the noise-split configuration (limit to vanishing external RAM signal, see \subfigrefb{fig:noisesplit}{D}). Shown are simulations of the P-unit model cell ``2017-07-18-ai'' (table~\ref{modelparams}) with a baseline firing rate of $82$\,Hz and CV$_{\text{base}}=0.23$. The cell shows a clear triangular pattern in its second-order susceptibility even up to a contrast of $10$\,\%. Note, that for $c=1$\,\% (\panel[ii]{D}), the estimate did not converge yet. \figitem{B} Cell ``2012-12-13-ao'' (baseline firing rate of $146$\,Hz, CV$=0.23$) also has strong interactions at its baseline firing rate that survive up to a stimulus contrast of $3$\,\%. \figitem{C} Model cell ``2012-12-20-ac'' (baseline firing rate of $212$\,Hz, CV$=0.26$) shows a weak triangular structure in the second-order susceptibility that vanishes for stimulus contrasts larger than $1$\,\%. \figitem{D} Cell ``2013-01-08-ab'' (baseline firing rate of $218$\,Hz, CV$=0.55$) does not show any triangular pattern in its second-order susceptibility. Nevertheless, interactions between low stimulus frequencies become substantial at higher contrasts. \figitem{E} The presence of an elevated second-order susceptibility where the stimulus frequency add up to the neuron's baseline frequency, can be identified by the susceptibility index (SI($r$), \eqnref{eq:nli_equation2}) greater than one (horizontal black line). The SI($r$) (density to the right) is plotted as a function of the model neuron's baseline CV for all $39$ model cells (table~\ref{modelparams}). Model cells have been visually categorized based on the presence of a triangular pattern in their second-order susceptibility estimated in the noise-split configuration (legend). The cells from \panel{A--D} are marked by black circles. Pearson's correlation coefficients $r$, the corresponding significance level $p$ and regression line (dashed gray line) are indicated. The higher the stimulus contrast, the less cells show weakly nonlinear interactions as expressed by the triangular structure in the second-order susceptibility.}
|
||||
\end{figure}
|
||||
|
||||
\subsection{Weakly nonlinear interactions in many model cells}
|
||||
@ -534,14 +534,13 @@ At a RAM contrast of 3\,\% the \nli{} values become smaller (\figrefb{fig:models
|
||||
|
||||
\begin{figure}[tp]
|
||||
\includegraphics[width=\columnwidth]{modelsusceptlown}
|
||||
\notejb{We could remove D?}
|
||||
\caption{\label{fig:modelsusceptlown}Inferring the triangular structure of the second-order susceptibility from limited data. \figitem{A} Reliably estimating the structure of the second-order susceptibility requires a high number of FFT segements $N$ in the order of one or even ten millions. As an example, susceptibilities of the model cell ``2012-12-21-ak-invivo-1'' (baseline firing rate of 157\,Hz, CV=0.15) are shown for the noise-split configuration ($c=0$\,\%) and RAM stimulus contrasts of $c=1$, $3$, and $10$\,\% as indicated. For contrasts below $10$\,\% this cell shows a nice triangular pattern in its susceptibilities, quite similar to the introductory example of a LIF in \figrefb{fig:lifresponse}. \figitem{B} However, with limited data of $N=100$ trials the susceptibility estimates are noisy and show much less structure, except for the anti-diagonal at the cell's baseline firing rate. \figitem{C} Correlating the estimates of SI($r$), that quantify the height of the ridge where the two stimulus frequencies add up to the neuron's baseline firing rate, based on 100 FFT segments (density to the right) with the converged ones based on one or ten million segments at a given stimulus contrast. The black diagonal line is the identity line and the dashed line is a linear regression. The correlation coefficient and corresponding significance level are indicated in the top left corner. The thin vertical line is a threshold at 1.2, the thin horizontal line a threshold at 1.8. The number of cells within each of the resulting four quadrants denote the false positives (top left), true positives (top right), true negatives (bottom left), and false negatives (bottom right) for predicting a triangular structure in the converged susceptibility estimate. \figitem{D} Relation between the estimates based on 100 trials with the one in the noise-split configuration based on one million trials. Cells are categorized as in \subfigref{fig:modelsusceptcontrasts}{E}.}
|
||||
\caption{\label{fig:modelsusceptlown}Inferring the triangular structure of the second-order susceptibility from limited data. \figitem{A} Reliably estimating the structure of the second-order susceptibility requires a high number of FFT segments $N$ in the order of one or even ten millions. As an example, susceptibilities of the model cell ``2012-12-21-ak-invivo-1'' (baseline firing rate of 157\,Hz, CV=0.15) are shown for the noise-split configuration ($c=0$\,\%) and RAM stimulus contrasts of $c=1$, $3$, and $10$\,\% as indicated. For contrasts below $10$\,\% this cell shows a nice triangular pattern in its susceptibilities, quite similar to the introductory example of a LIF in \figrefb{fig:lifresponse}. \figitem{B} However, with limited data of $N=100$ trials the susceptibility estimates are noisy and show much less structure, except for the anti-diagonal at the cell's baseline firing rate. The SI($r$) quantifies the height of this ridge where the two stimulus frequencies add up to the neuron's baseline firing rate. \figitem{C} Correlations between the estimates of SI($r$) based on 100 FFT segments (density to the right) with the converged ones based on one or ten million segments at a given stimulus contrast for all $n=39$ model cells. The black circle marks the model cell shown in \panel{A} and \panel{B}. The black diagonal line is the identity line and the dashed line is a linear regression. The correlation coefficient and corresponding significance level are indicated in the top left corner. The thin vertical line is a threshold at 1.2, the thin horizontal line a threshold at 1.8. The number of cells within each of the resulting four quadrants denote the false positives (top left), true positives (top right), true negatives (bottom left), and false negatives (bottom right) for predicting a triangular structure in the converged susceptibility estimate from the estimates based on only 100 segments.}
|
||||
\end{figure}
|
||||
|
||||
\subsection{Weakly nonlinear interactions can be deduced from limited data}
|
||||
Estimating second-order susceptibilities reliably requires large numbers (millions) of FFT segments (\figrefb{fig:trialnr}). Electrophysiological measurements, however, suffer from limited recording durations and hence limited numbers of available FFT segments and estimating weakly nonlinear interactions from just ten segments appears futile. The question arises, to what extend such limited-data estimates are still informative?
|
||||
|
||||
The second-order susceptibility matrices that are based on only 10 segements look flat and noisy, lacking the triangular structure \subfigref{fig:modelsusceptcontrasts}{B}. The anti-diagonal ridge, however, when the sum of the stimulus frequencies matches the neuron's baseline firing rate seems to be present whenever the converged estimate shows a clear triangular structure (compare \subfigref{fig:modelsusceptcontrasts}{B} and \subfigref{fig:modelsusceptcontrasts}{a}).
|
||||
The second-order susceptibility matrices that are based on only 10 sgements look flat and noisy, lacking the triangular structure \subfigref{fig:modelsusceptcontrasts}{B}. The anti-diagonal ridge, however, when the sum of the stimulus frequencies matches the neuron's baseline firing rate seems to be present whenever the converged estimate shows a clear triangular structure (compare \subfigref{fig:modelsusceptcontrasts}{B} and \subfigref{fig:modelsusceptcontrasts}{a}).
|
||||
|
||||
%A comparison of well converged estimates based on millions of segments with estimates based on just ten segments as a worst-case scenario (\subfigrefb{fig:modelsusceptlown}{A\&B}) seems hopeless on a first glance. These estimates using just ten segments look flat and noisy, no triangular structure is visible. However, the anti-diagonal ridge where the stimulus frequencies add up to the neuron's baseline firing rate seems to be present when the converged estimate shows a clear triangular structure.
|
||||
|
||||
@ -549,16 +548,12 @@ The \nli{} characterizes the ridgeness of the second-oder susceptibility plane.
|
||||
|
||||
Trying to predict whether there is a triangular structure in the noise-split configuration is more difficult (\subfigrefb{fig:modelsusceptlown}{D}). The correlations are weaker and at a stimulus contrast of 10\,\% the correlation is no longer significant. One false positive arises at a contrast of 1\,\%, and false positives are absent for higher contrasts. False negatives increase with increasing contrast and at a contrast of 10\,\% all \nli{} values based on 10 segments are so low that just one triangular pattern can be predicted. This makes sense given the absent correlation between \nli{} values estimated at 10\,\% stimulus contrast and the noise-split configuration described above.
|
||||
|
||||
Overall, observing \nli{} values greater than at least 1.6, even for a number of FFT segments as low as ten, seems to be a reliable indication for a triangular structure in the second-order susceptibility at the corresponding stimulus contrast. Small stimulus contrasts of 1\,\% are less informative, because of the bad signal-to-noise ratio. Intermediate stimulus contrasts around 3\,\% seem to be optimal, because there, most cells still have a triangular structure in their susceptibility and the signal-to-noise ratio is better. At RAM stimulus contrasts of 10\,\% or higher the signal-to-noise ratio is even better, but only few cells remain with weak triangularly shaped susceptibilities that might be missed as a false positives. Note that increasing the number of segements used for estimating the susceptibilities to 100 or 1000 improves the situation only marginally (not shown).
|
||||
Overall, observing \nli{} values greater than at least 1.6, even for a number of FFT segments as low as ten, seems to be a reliable indication for a triangular structure in the second-order susceptibility at the corresponding stimulus contrast. Small stimulus contrasts of 1\,\% are less informative, because of the bad signal-to-noise ratio. Intermediate stimulus contrasts around 3\,\% seem to be optimal, because there, most cells still have a triangular structure in their susceptibility and the signal-to-noise ratio is better. At RAM stimulus contrasts of 10\,\% or higher the signal-to-noise ratio is even better, but only few cells remain with weak triangularly shaped susceptibilities that might be missed as a false positives. Note that increasing the number of segments used for estimating the susceptibilities to 100 or 1000 improves the situation only marginally (not shown).
|
||||
|
||||
|
||||
\begin{figure*}[tp]
|
||||
\includegraphics[width=\columnwidth]{dataoverview}
|
||||
\caption{\label{fig:dataoverview} Nonlinear responses in P-units and ampullary afferents. The second-order susceptibility is condensed into the susceptibility index, SI($r$) \eqnref{eq:nli_equation}, that quantifies the relative amplitude of the projected susceptibility at a cell's baseline firing rate (see \subfigrefb{fig:punit}{G}). Each of the recorded neurons contributes on average two stimulus contrasts. Black squares and circles highlight recordings conducted in a single cell. Squares in \panel{A, C, E} correspond to the cell in \figrefb{fig:punit} and circles to the cell in \figrefb{fig:punithighcv}. Squares in \panel{B, D, F} correspond to the cell in \figrefb{fig:ampullary}. \figitem{A, B} There is a negative correlation between the CV during baseline and \nli. \figitem{C, D} There is a negative correlation between the CV during stimulation and \nli. \figitem{E, F} \nli{} is plotted against the response modulation, (see methods), an indicator of the subjective stimulus strength for a cell. There is a negative correlation between response modulation and \nli. Restricting the analysis to the weakest stimulus that was presented to each unique neuron, does not change the results. The number of unique neurons is 221 for P-units and 45 for ampullary cells.
|
||||
% The two example P-units shown before are highlighted with dark markers in \subfigrefb{fig:dataoverview}{A, C, E} (squares -- \figrefb{fig:punit}, circles -- \figrefb{fig:punithighcv}).
|
||||
% Several of the recorded neurons contribute with two samples to the population analysis as their responses have been recorded to two different contrasts of the same RAM stimulus. Higher stimulus contrasts lead to a stronger drive and thus stronger response modulations (see color code bar in \subfigref{fig:dataoverview}{A}, see methods).
|
||||
% The example cell shown above (\figref{fig:ampullary}) was recorded at two different stimulus intensities and the \nli{} values are highlighted with black squares.
|
||||
}
|
||||
\caption{\label{fig:dataoverview} Nonlinear responses in P-units and ampullary afferents. The second-order susceptibility is condensed into the susceptibility index, SI($r$) \eqnref{eq:nli_equation}, that quantifies the relative amplitude of the ridge where the two stimulus frequencies add up to the cell's baseline firing rate (see \subfigrefb{fig:punit}{G}). The SI($r$) is plotted against the cells' CV of its baseline interspike intervals (left column), the response modulation (the standard deviation of firing rate evoked by the band-limited white-noise stimulus) --- a measure of effective stimulus strenght (center column), and the CV of the interspike intervals during stimulation with the white-noise stimulus (right column). Pearson's correlation coefficient $R$ and the number of data points $n$ are indicated; all correlations are significant at a level below $p=0.0002$. Kernel-density estimates of the distributions of the displayed quantities are plotted on top and right. Data points are color coded by a third quantity as indicated by the color bars. The horizontal dashed line marks a threshold for SI($r$) values at 1.8 and the percentages to the right denote the fractions above and below this threshold. \figitem{A} The SI($r$) of all 39 model P-units (table~\ref{modelparams}) measured at contrasts of 1, 3, and 10\,\% of RAM stimuli with a cutoff frequency of 300\,Hz. The SI($r$) was estimated based on 100 FFT segments. The black square marks the cell from \subfigrefb{fig:noisesplit}{C}, the circles the four cells shown in \subfigref{fig:modelsusceptcontrasts}{A--D}, and the triangle the cell from \subfigref{fig:modelsusceptlown}{A--B}. \figitem{B} Electrophysiological data from 159 P-units. Each cell contributes on average with 2 (min. 1, max. 10) RAM stimulus presentations to the $n=382$ data points. The RAMs with cutoff frequency of 300\,Hz were presented at contrasts ranging from 0.1\,\% to 20\,\% (median 5\,\%). The number of available FFT segements ranged from 105 to 2560 (median 235). The two black triangles mark the responses of the example P-unit from \subfigrefb{fig:punit}{E,F}, the circles the other four examples from \subfigrefb{fig:punit}{H}, and the triangle the unit from \subfigrefb{fig:noisesplit}{A}. \figitem{C} Recordings from 30 ampullary afferents, each contributing on average 3 RAM stimulus presentations to $n=89$ data points. Stimuli had a cutoff frequency of 150\,Hz and their contrasts ranged from 2.5\,\% to 20\,\% (median 5\,\%). 105 to 3648 FFT segements were available per stimulus (median 722). The two black triangles mark the responses of the example ampullary afferent from \subfigrefb{fig:ampullary}{E,F}, and the circles the other four examples from \subfigrefb{fig:ampullary}{H}.}
|
||||
\end{figure*}
|
||||
|
||||
\subsection{Low CVs and weak stimuli are associated with distinct nonlinearity in recorded electroreceptive neurons}
|
||||
@ -830,6 +825,7 @@ The P-unit models were integrated by the Euler forward method with a time-step o
|
||||
%\paragraph{Fitting the model to recorded P-units}
|
||||
The eight free parameters of the P-unit model $\beta$, $\tau_m$, $\mu$, $D$, $\tau_A$, $\Delta_A$, $\tau_d$, and $t_{ref}$, were fitted to both the baseline activity (baseline firing rate, CV of ISIs, serial correlation of ISIs at lag one, and vector strength of spike coupling to EOD) and the responses to step increases and decreases in EOD amplitude (onset and steady-state responses, effective adaptation time constant, \citealp{Benda2005}) of recorded P-units (table~\ref{modelparams}).
|
||||
|
||||
\notejb{add table with all 39 cells}
|
||||
\begin{table*}[tp]
|
||||
\caption{\label{modelparams} Model parameters of LIF models, fitted to 3 electrophysiologically recorded P-units \citep{Ott2020}.}
|
||||
\begin{tabular}{lrrrrrrrr}
|
||||
|
||||
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Reference in New Issue
Block a user