	A	B	C	D
1	Question/comparison	Values	Statistical test and n numbers	stat results
2
3	All responses
4	# reliable responses	stat/run/both	McNemar test
5	does proportion of reliable responses vary between states?	Total responses = 9,498 (per state)	(+stat+run, +stat-run -stat+run, -stat-run)	p=0
6		Stat reliable resps = 2,077 (21.87%)	1807 270 995 6426
7		Run reliable resps = 2,802 (29.5%)
8		Both reliable resps = 1,807 (19.03%)
9
10
11	onset/offset features	Proportions (Decay, Rise, Peak, Trough, Flat)	Binomial GLMEs
12	does proportion of features vary between states?	propStatOn = 0.0896 0.1247 0.6856 0.0515 0.0486	independent GLME for each feature, equation:	pVal_onset = 0.1291 0.0000 0.0000 0.1222 0.0986
13		propRunOn = 0.0774 0.4336 0.4079 0.0421 0.0389	fet ~ state + (1\|session)	pVal_offset = 0.0000 0.0037 0.0996 0.0000 0.0000
14		propStatOff =0.4612 0.0558 0.0823 0.1223 0.2783	n numbers are stat/run reliable responses (see above)
15		propRunOff =0.5496 0.0771 0.0960 0.0846 0.1927
16
17	sustainedness		LME	pValue for state = 5.3278e-103
18	does sustainedness of responses vary between states?	Quantiles (median and IQR)	n numbers are both reliable responses
19		Stat: 0.1829 0.3193 0.4846	sustainedness ~ state + (1\|unit) + (1\|session)
20		Run: 0.3229 0.4826 0.6161
21
22	Mean and peak firing rate changes
23	Do mean and peak firing rates change by the same fractional amount?	Quantiles (median and IQR)	signrank(log2(meanFR_fracChange), log2(peakFR_fracChange))	p = 1.5994e-138
24		Mean rate (baseline corrected)	n numbers are both reliable responses
25		Stat: 1.4219 3.2061 6.2320
26		Run: 1.2405 5.4136 12.1279
27
28		Peak rate (baseline corrected)
29		Stat: 5.5777 10.0790 19.2957
30		Run: 5.3432 12.9791 25.7037
31
32		Fractional change in mean: 1.0999 2.0281 4.0721
33		Fractional change in peak: 0.7297 1.1714 1.7186
34
35	Visual speeds (Fig S1)
36	Response features	Proportions (Decay, Rise, Peak, Trough, Flat)	Binomial GLMEs as above, separate for each speed
37	0	statProp_onset: [0.0994 0.0426 0.7983 0.0284 0.0312]	# reliable responses	pVal_onset: [0.0064 8.1154e-17 9.5748e-13 0.0490 0.1644]
38		runProp_onset: [0.0495 0.3237 0.5526 0.0577 0.0165]	nStat: 352	pVal_offset: [2.8582e-13 0.5581 0.0158 0.0633 1.1693e-19]
39		statProp_offset: [0.2188 0.0540 0.0852 0.0284 0.6136]	nRun:485
40		runProp_offset: [0.4660 0.0454 0.1402 0.0557 0.2928]
41
42	16	statProp_onset: [0.0628 0.2120 0.6283 0.0524 0.0445]	nStat:382	pVal_onset: [0.7352 3.8472e-13 2.5451e-11 0.1012 0.6849]
43		runProp_onset: [0.0685 0.4476 0.4032 0.0302 0.0504]	nRun: 496	pVal_offset: [0.2535 0.7394 0.2914 3.2750e-04 0.7611]
44		statProp_offset: [0.4791 0.0707 0.0890 0.1309 0.2304]
45		runProp_offset: [0.5101 0.0766 0.1109 0.0605 0.2419]
46
47	32	statProp_onset: [0.0765 0.1831 0.6311 0.0710 0.0383]	nStat:366	pVal_onset: [0.6839 3.4120e-20 3.2140e-17 0.0584 0.8352]
48		runProp_onset: [0.0842 0.4928 0.3409 0.0411 0.0411]	nRun: 487	pVal_offset: [0.0057 0.4587 0.6270 0.2657 0.0052]
49		statProp_offset: [0.5000 0.0628 0.0984 0.1148 0.2240]
50		runProp_offset: [0.5893 0.0760 0.0883 0.0924 0.1540]
51
52	64	statProp_onset: [0.0978 0.1201 0.6704 0.0447 0.0670]	nStat:358	pVal_onset: [0.2743 4.1914e-23 9.8567e-18 0.3337 0.5595]
53		runProp_onset: [0.0763 0.4746 0.3602 0.0318 0.0572]	nRun: 472	pVal_offset: [0.3132 0.0016 0.4402 0.0425 0.2519]
54		statProp_offset: [0.5251 0.0335 0.0866 0.1564 0.1983]
55		runProp_offset: [0.5593 0.0911 0.0720 0.1102 0.1674]
56
57	128	statProp_onset: [0.1061 0.0772 0.6913 0.0450 0.0804]	nStat:311	pVal_onset: [0.7309 5.6576e-21 3.4326e-16 0.8140 0.0176]
58		runProp_onset: [0.0985 0.4420 0.3786 0.0416 0.0394]	nRun: 457	pVal_offset: [0.2423 0.0397 0.8032 0.0577 0.1400]
59		statProp_offset: [0.5305 0.0514 0.0675 0.1640 0.1865]
60		runProp_offset: [0.5733 0.0919 0.0722 0.1160 0.1466]
61
62	256	statProp_onset: [0.1006 0.0942 0.7045 0.0682 0.0325]	nStat:308	pVal_onset: [0.6759 3.9831e-18 5.3267e-14 0.3371 0.6785]
63		runProp_onset: [0.0914 0.4198 0.4099 0.0519 0.0272]	nRun: 405	pVal_offset: [0.0199 0.2782 0.2020 0.0026 0.0244]
64		statProp_offset: [0.5260 0.0617 0.0617 0.1461 0.2045]
65		runProp_offset: [0.6123 0.0840 0.0889 0.0741 0.1407]
66
67	Sustainedness Index	Quantiles: top = stat, bottom = run		1.0e-16 *
68	0	0.1198 0.2115 0.3377	nBoth: 311	0.4935
69		0.2344 0.3855 0.5134
70	16	0.1987 0.3361 0.4998	nBoth: 339	0.2928
71		0.3165 0.4800 0.6271
72	32	0.2047 0.3577 0.4876	nBoth: 328	0
73		0.3535 0.5162 0.6473
74	64	0.2016 0.3587 0.5261	nBoth: 309	0.9516
75		0.3609 0.5023 0.6249
76	128	0.2105 0.3632 0.5104	nBoth: 266	0.0035
77		0.3606 0.5148 0.6430
78	256	0.2039 0.3541 0.4806	nBoth: 254	0.1038
79		0.3482 0.5001 0.6054
80
81
82
83	Cell types (Fig S2)
84	Response features	Proportions (Decay, Rise, Peak, Trough, Flat)	Binomial GLMEs as above, separate for each cell-type
85	Pyramidal	statProp_onset: [0.1039 0.1419 0.6503 0.0390 0.0649]	# reliable responses	pVal_onset: [0.3719 7.2090e-41 1.3987e-29 0.5967 0.1653]
86		runProp_onset: [0.0929 0.4093 0.4115 0.0348 0.0515]	nStat: 1001	pVal_offset: [7.4961e-05 0.0880 0.6359 7.4715e-04 9.7084e-04]
87		statProp_offset: [0.4625 0.0569 0.1009 0.0959 0.2837]	nRun: 1378
88		runProp_offset: [0.5450 0.0747 0.0951 0.0595 0.2257]
89
90	Narrow Interneurons	statProp_onset: [0.0452 0.1036 0.7495 0.0640 0.0377]	nStat: 531	pVal_onset: [0.4061 3.1015e-33 1.1981e-24 0.0076 0.4231]
91		runProp_onset: [0.0360 0.4453 0.4569 0.0322 0.0296]	nRun: 777	pVal_offset: [1.1451e-06 0.0886 0.8000 0.9661 3.7003e-10]
92		statProp_offset: [0.4388 0.0433 0.0734 0.0734 0.3710]
93		runProp_offset: [0.5663 0.0656 0.0772 0.0734 0.2175]
94
95	Wide Internuerons	statProp_onset: [0.1064 0.1138 0.6881 0.0624 0.0294]	nStat: 545	pVal_onset: [0.4636 3.0446e-34 5.5207e-30 0.6826 0.5057]
96		runProp_onset: [0.0943 0.4714 0.3416 0.0696 0.0232]	nRun: 647	pVal_offset: [0.0347 0.0714 2.2970e-04 0.0023 2.1178e-05]
97		statProp_offset: [0.4807 0.0661 0.0569 0.2183 0.1780]
98		runProp_offset: [0.5394 0.0958 0.1206 0.1515 0.0927]
99
100	Sustainedness Index	Quantiles: top = stat, bottom = run	LME, as above, separetly for cell-types
101	Pyramidal	susIndexQuant_stat: [0.1924 0.3153 0.4752]	nBoth:844	p = 2.2913e-50
102		susIndexQuant_run: [0.3146 0.4850 0.6197]
103
104	Narrow Interneurons	susIndexQuant_stat: [0.1403 0.2985 0.4698]	nBoth: 480	p =1.8159e-27
105		susIndexQuant_run: [0.3042 0.4510 0.5957]
106
107	Wide Internuerons	susIndexQuant_stat: [0.2153 0.3623 0.5013]	nBoth: 483	p = 1.1020e-29
108		susIndexQuant_run: [0.3579 0.5023 0.6363]
109
110
111
112
113	Visual areas (Fig S3)
114	Response features	Proportions (Decay, Rise, Peak, Trough, Flat)	Binomial GLMEs as above, separate for each speed
115	VISp	statProp_onset: [0.1235 0.3019 0.4503 0.0620 0.0623]	# reliable responses (from n sesh)
116		runProp_onset: [0.1233 0.4024 0.3467 0.0665 0.0611]	nStat: 3611 (13)	pVal_onset: [0.6773 7.2633e-11 2.5590e-08 0.5902 0.8318]
117		statProp_offset: [0.1235 0.3019 0.4503 0.0620 0.0623]	nRun: 3698 (11)	pVal_offset: [0.1293 0.0073 0.0198 4.5861e-05 0.0154]
118		runProp_offset: [0.1233 0.4024 0.3467 0.0665 0.0611]
119
120	VISl	statProp_onset: [0.1463 0.2781 0.4113 0.0882 0.0760]	nStat: 3513 (11)	pVal_onset: [0.9149 1.7852e-10 1.5883e-07 3.0080e-06 0.9085]
121		runProp_onset: [0.1437 0.4929 0.2557 0.0344 0.0732]	nRun: 1830 (8)	pVal_offset: [0.3187 0.1767 0.3108 1.0557e-07 0.9406]
122		statProp_offset: [0.1463 0.2781 0.4113 0.0882 0.0760]
123		runProp_offset: [0.1437 0.4929 0.2557 0.0344 0.0732]
124
125	VISal	statProp_onset: [0.1261 0.3125 0.4060 0.0923 0.0631]	nStat: 1776 (11)	pVal_onset: [0.1202 0.1161 0.0243 0.9438 0.3464]
126		runProp_onset: [0.1874 0.3248 0.3208 0.0829 0.0841]	nRun: 3485 (9)	pVal_offset: [3.6379e-04 0.2411 8.7358e-07 5.3875e-05 0.7041]
127		statProp_offset: [0.1261 0.3125 0.4060 0.0923 0.0631]
128		runProp_offset: [0.1874 0.3248 0.3208 0.0829 0.0841]
129
130	VISrl	statProp_onset: [0.1581 0.3874 0.2940 0.0663 0.0942]	nStat: 1221 (11)	pVal_onset: [0.7756 0.0031 0.1617 0.0685 0.4392]
131		runProp_onset: [0.1658 0.4413 0.2467 0.0503 0.0960]	nRun: 1532 (11)	pVal_offset: [0.0583 0.6608 0.0156 0.0028 0.1116]
132		statProp_offset: [0.1581 0.3874 0.2940 0.0663 0.0942]
133		runProp_offset: [0.1658 0.4413 0.2467 0.0503 0.0960]
134
135	VISam	statProp_onset: [0.1267 0.3265 0.3840 0.0755 0.0873]	nStat: 2383 (14)	pVal_onset: [0.7754 6.9551e-04 0.0753 0.0438 0.3652]
136		runProp_onset: [0.1271 0.4105 0.3235 0.0558 0.0832]	nRun: 2597 (10)	pVal_offset: [0.6530 0.9734 0.0043 1.7540e-08 0.2023]
137		statProp_offset: [0.1267 0.3265 0.3840 0.0755 0.0873]
138		runProp_offset: [0.1271 0.4105 0.3235 0.0558 0.0832]
139
140	VISpm	statProp_onset: [0.0826 0.2860 0.4818 0.0826 0.0670]	nStat: 1731 (9)	pVal_onset: [0.1504 0.0062 0.0160 0.7503 0.3942]
141		runProp_onset: [0.1600 0.3754 0.2795 0.1018 0.0833]	nRun: 1356 (7)	pVal_offset: [0.6385 5.5162e-05 0.0125 2.2659e-16 0.8762]
142		statProp_offset: [0.0826 0.2860 0.4818 0.0826 0.0670]
143		runProp_offset: [0.1600 0.3754 0.2795 0.1018 0.0833]
144
145	LGN	statProp_onset: [0.1851 0.2645 0.3521 0.1091 0.0893]	nStat: 605 (4)	pVal_onset: [0.7827 0.2583 0.0156 0.8497 0.8767]
146		runProp_onset: [0.2261 0.3768 0.2283 0.0855 0.0832]	nRun: 889 (5)	pVal_offset: [0.8813 0.8005 0.0520 0.1396 8.1621e-04]
147		statProp_offset: [0.1851 0.2645 0.3521 0.1091 0.0893]
148		runProp_offset: [0.2261 0.3768 0.2283 0.0855 0.0832]
149
150	LP	statProp_onset: [0.2238 0.2885 0.2489 0.0874 0.1515]	nStat: 1591 (12)	pVal_onset: [0.3870 0.0686 0.5577 0.9370 6.7671e-04]
151		runProp_onset: [0.2047 0.4871 0.1647 0.0612 0.0824]	nRun: 850 (9)	pVal_offset: [0.0190 0.3285 0.1012 0.1127 0.0080]
152		statProp_offset: [0.2238 0.2885 0.2489 0.0874 0.1515]
153		runProp_offset: [0.2047 0.4871 0.1647 0.0612 0.0824]
154
155
156
157	Pupil area (Fig S5)
158	Sustainedness index	Quantiles: top = small pupil, bottom = big pupil	# reliable responses
159		0.2006 0.3288 0.4774	nBoth = 1977	p = 7.7341e-16
160		0.2207 0.3637 0.5120
161
162	Response features
163		propSmallOn = 0.0843 0.1486 0.6492 0.0802 0.0378		pVal_onset = 0.0000 0.0020 0.0000 0.6480 0.8409
164		propBigOn = 0.1216 0.1739 0.5895 0.0762 0.0389		pVal_offset = 0.0138 0.4145 0.3429 0.0000 0.0007
165		propSmallOff = 0.4717 0.0739 0.0688 0.1246 0.2610
166		propBigOff = 0.4363 0.0800 0.0758 0.1874 0.2205
167
168			Compare big pupil to locomotion	pVal_onset = 0.0000 0.0000 0.0000 0.0000 0.9940
169				pVal_offset = 0.0000 0.6879 0.0084 0.0000 0.0099
170
171			Compare small pupil to locomotion	pVal_onset = 0.3631 0.0000 0.0000 0.0000 0.8324
172				pVal_offset = 0.0000 0.6610 0.0005 0.0000 0.0000