Scores on benchmarks

Model rank shown below is with respect to all public models.
.324 average_vision rank 82
81 benchmarks
.324
0
ceiling
best
median
.241 neural_vision rank 310
38 benchmarks
.241
0
ceiling
best
median
.358 V1 rank 69
24 benchmarks
.358
0
ceiling
best
median
.170 Coggan2024_fMRI.V1-rdm v1 rank 28
.170
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.681 Marques2020 [reference] rank 258
22 benchmarks
.681
0
ceiling
best
median
.834 V1-orientation rank 212
7 benchmarks
.834
0
ceiling
best
median
.860 Marques2020_Ringach2002-or_selective v1 rank 256
.860
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.747 Marques2020_Ringach2002-circular_variance v1 rank 231
.747
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.693 Marques2020_Ringach2002-orth_pref_ratio v1 rank 256
.693
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.959 Marques2020_Ringach2002-cv_bandwidth_ratio v1 rank 9
.959
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.982 Marques2020_DeValois1982-pref_or v1 rank 46
.982
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.802 Marques2020_Ringach2002-opr_cv_diff v1 rank 254
.802
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.794 Marques2020_Ringach2002-or_bandwidth v1 rank 237
.794
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.676 V1-spatial_frequency rank 280
3 benchmarks
.676
0
ceiling
best
median
.774 Marques2020_DeValois1982-peak_sf v1 rank 111
.774
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.712 Marques2020_Schiller1976-sf_bandwidth v1 [reference] rank 264
.712
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.540 Marques2020_Schiller1976-sf_selective v1 [reference] rank 316
.540
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.616 V1-response_selectivity rank 262
4 benchmarks
.616
0
ceiling
best
median
.667 Marques2020_FreemanZiemba2013-texture_selectivity v1 [reference] rank 257
.667
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.487 Marques2020_Ringach2002-modulation_ratio v1 rank 174
.487
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.463 Marques2020_FreemanZiemba2013-texture_variance_ratio v1 [reference] rank 333
.463
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.847 Marques2020_FreemanZiemba2013-texture_sparseness v1 [reference] rank 28
.847
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.465 V1-receptive_field_size rank 272
2 benchmarks
.465
0
ceiling
best
median
.479 Marques2020_Cavanaugh2002-grating_summation_field v1 [reference] rank 274
.479
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.451 Marques2020_Cavanaugh2002-surround_diameter v1 [reference] rank 193
.451
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.810 V1-surround_modulation rank 48
1 benchmark
.810
0
ceiling
best
median
.810 Marques2020_Cavanaugh2002-surround_suppression_index v1 [reference] rank 48
.810
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.517 V1-texture_modulation rank 286
2 benchmarks
.517
0
ceiling
best
median
.371 Marques2020_FreemanZiemba2013-abs_texture_modulation_index v1 [reference] rank 309
.371
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.662 Marques2020_FreemanZiemba2013-texture_modulation_index v1 [reference] rank 256
.662
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.846 V1-response_magnitude rank 202
3 benchmarks
.846
0
ceiling
best
median
.816 Marques2020_FreemanZiemba2013-max_texture v1 [reference] rank 215
.816
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.974 Marques2020_Ringach2002-max_dc v1 rank 83
.974
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.748 Marques2020_FreemanZiemba2013-max_noise v1 [reference] rank 213
.748
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.223 FreemanZiemba2013.V1-pls v2 [reference] rank 354
.223
0
ceiling
best
median
recordings from 102 sites in V1
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.229 V2 rank 41
2 benchmarks
.229
0
ceiling
best
median
.170 Coggan2024_fMRI.V2-rdm v1 rank 38
.170
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.287 FreemanZiemba2013.V2-pls v2 [reference] rank 270
.287
0
ceiling
best
median
recordings from 103 sites in V2
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.057 V4 rank 423
5 benchmarks
.057
0
ceiling
best
median
.053 Coggan2024_fMRI.V4-rdm v1 rank 65
.053
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.232 SanghaviMurty2020.V4-pls v1 [reference] rank 78
.232
0
ceiling
best
median
recordings from 46 sites in V4
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.322 IT rank 130
7 benchmarks
.322
0
ceiling
best
median
.256 Bracci2019.anteriorVTC-rdm v1 rank 103
.256
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.107 Coggan2024_fMRI.IT-rdm v1 rank 140
.107
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.385 SanghaviMurty2020.IT-pls v1 [reference] rank 91
.385
0
ceiling
best
median
recordings from 29 sites in IT
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.534 Sanghavi2020.IT-pls v1 [reference] rank 110
.534
0
ceiling
best
median
recordings from 88 sites in IT
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.472 SanghaviJozwik2020.IT-pls v1 [reference] rank 217
.472
0
ceiling
best
median
recordings from 26 sites in IT
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.499 MajajHong2015.IT-pls v3 [reference] rank 242
.499
0
ceiling
best
median
recordings from 168 sites in IT
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
X Kar2019-ost v2 [reference] rank X
X
0
ceiling
best
median
recordings from 424 sites in IT
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.407 behavior_vision rank 54
43 benchmarks
.407
0
ceiling
best
median
.584 Rajalingham2018-i2n v2 [reference] rank 19
.584
0
ceiling
best
median
match-to-sample task
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.565 Geirhos2021-error_consistency [reference] rank 24
17 benchmarks
.565
0
ceiling
best
median
.324 Geirhos2021colour-error_consistency v1 [reference] rank 141
.324
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.102 Geirhos2021contrast-error_consistency v1 [reference] rank 206
.102
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.780 Geirhos2021cueconflict-error_consistency v1 [reference] rank 7
.780
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.979 Geirhos2021edge-error_consistency v1 [reference] rank 6
.979
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.567 Geirhos2021eidolonI-error_consistency v1 [reference] rank 42
.567
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.512 Geirhos2021eidolonII-error_consistency v1 [reference] rank 63
.512
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.286 Geirhos2021eidolonIII-error_consistency v1 [reference] rank 147
.286
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.219 Geirhos2021falsecolour-error_consistency v1 [reference] rank 166
.219
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.652 Geirhos2021highpass-error_consistency v1 [reference] rank 12
.652
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.463 Geirhos2021lowpass-error_consistency v1 [reference] rank 34
.463
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.521 Geirhos2021phasescrambling-error_consistency v1 [reference] rank 18
.521
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.486 Geirhos2021powerequalisation-error_consistency v1 [reference] rank 28
.486
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.571 Geirhos2021rotation-error_consistency v1 [reference] rank 6
.571
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
1.2 Geirhos2021silhouette-error_consistency v1 [reference] rank 1
1.2
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.663 Geirhos2021sketch-error_consistency v1 [reference] rank 12
.663
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.737 Geirhos2021stylized-error_consistency v1 [reference] rank 10
.737
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.577 Geirhos2021uniformnoise-error_consistency v1 [reference] rank 34
.577
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.605 Baker2022 rank 36
3 benchmarks
.605
0
ceiling
best
median
.721 Baker2022fragmented-accuracy_delta v1 [reference] rank 56
.721
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.111 Baker2022frankenstein-accuracy_delta v1 [reference] rank 134
.111
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.982 Baker2022inverted-accuracy_delta v1 [reference] rank 13
.982
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.654 Ferguson2024 [reference] rank 9
14 benchmarks
.654
0
ceiling
best
median
1.0 Ferguson2024half-value_delta v1 [reference] rank 1
1.0
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.942 Ferguson2024gray_hard-value_delta v1 [reference] rank 19
.942
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.747 Ferguson2024lle-value_delta v1 [reference] rank 54
.747
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.118 Ferguson2024juncture-value_delta v1 [reference] rank 108
.118
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
1.0 Ferguson2024color-value_delta v1 [reference] rank 1
1.0
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
1.0 Ferguson2024round_v-value_delta v1 [reference] rank 1
1.0
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.486 Ferguson2024eighth-value_delta v1 [reference] rank 34
.486
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.510 Ferguson2024quarter-value_delta v1 [reference] rank 67
.510
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.691 Ferguson2024convergence-value_delta v1 [reference] rank 39
.691
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.544 Ferguson2024round_f-value_delta v1 [reference] rank 67
.544
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.303 Ferguson2024llh-value_delta v1 [reference] rank 140
.303
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.320 Ferguson2024circle_line-value_delta v1 [reference] rank 103
.320
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.845 Ferguson2024gray_easy-value_delta v1 [reference] rank 17
.845
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.657 Ferguson2024tilted_line-value_delta v1 [reference] rank 77
.657
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.256 Hebart2023-match v1 rank 115
.256
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.594 BMD2024 rank 9
4 benchmarks
.594
0
ceiling
best
median
.478 BMD2024.dotted_1Behavioral-accuracy_distance v1 rank 13
.478
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.858 BMD2024.texture_1Behavioral-accuracy_distance v1 rank 7
.858
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.672 BMD2024.texture_2Behavioral-accuracy_distance v1 rank 9
.672
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.369 BMD2024.dotted_2Behavioral-accuracy_distance v1 rank 10
.369
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.237 engineering_vision rank 219
25 benchmarks
.237
0
ceiling
best
median
.803 Geirhos2021-top1 [reference] rank 6
17 benchmarks
.803
0
ceiling
best
median
.997 Geirhos2021colour-top1 v1 [reference] rank 6
.997
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.996 Geirhos2021contrast-top1 v1 [reference] rank 3
.996
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.386 Geirhos2021cueconflict-top1 v1 [reference] rank 21
.386
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.925 Geirhos2021edge-top1 v1 [reference] rank 3
.925
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.576 Geirhos2021eidolonI-top1 v1 [reference] rank 28
.576
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.636 Geirhos2021eidolonII-top1 v1 [reference] rank 10
.636
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.715 Geirhos2021eidolonIII-top1 v1 [reference] rank 5
.715
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.996 Geirhos2021falsecolour-top1 v1 [reference] rank 2
.996
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.947 Geirhos2021highpass-top1 v1 [reference] rank 7
.947
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.615 Geirhos2021lowpass-top1 v1 [reference] rank 13
.615
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.880 Geirhos2021phasescrambling-top1 v1 [reference] rank 5
.880
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.963 Geirhos2021powerequalisation-top1 v1 [reference] rank 6
.963
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.966 Geirhos2021rotation-top1 v1 [reference] rank 3
.966
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.700 Geirhos2021silhouette-top1 v1 [reference] rank 10
.700
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.945 Geirhos2021sketch-top1 v1 [reference] rank 6
.945
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.657 Geirhos2021stylized-top1 v1 [reference] rank 7
.657
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.757 Geirhos2021uniformnoise-top1 v1 [reference] rank 11
.757
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.384 Hermann2020 [reference] rank 34
2 benchmarks
.384
0
ceiling
best
median
.347 Hermann2020cueconflict-shape_match v1 [reference] rank 24
.347
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9
.420 Hermann2020cueconflict-shape_bias v1 [reference] rank 62
.420
0
ceiling
best
median
sample 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 sample 7 sample 8 sample 9

How to use

from brainscore_vision import load_model
model = load_model("vit_huge_patch14_clip_336:laion2b_ft_in12k_in1k")
model.start_task(...)
model.start_recording(...)
model.look_at(...)

Benchmarks bibtex

@inproceedings{santurkar2019computer,
    title={Computer Vision with a Single (Robust) Classifier},
    author={Shibani Santurkar and Dimitris Tsipras and Brandon Tran and Andrew Ilyas and Logan Engstrom and Aleksander Madry},
    booktitle={ArXiv preprint arXiv:1906.09453},
    year={2019}
}
        @article {Marques2021.03.01.433495,
	author = {Marques, Tiago and Schrimpf, Martin and DiCarlo, James J.},
	title = {Multi-scale hierarchical neural network models that bridge from single neurons in the primate primary visual cortex to object recognition behavior},
	elocation-id = {2021.03.01.433495},
	year = {2021},
	doi = {10.1101/2021.03.01.433495},
	publisher = {Cold Spring Harbor Laboratory},
	abstract = {Primate visual object recognition relies on the representations in cortical areas at the top of the ventral stream that are computed by a complex, hierarchical network of neural populations. While recent work has created reasonably accurate image-computable hierarchical neural network models of those neural stages, those models do not yet bridge between the properties of individual neurons and the overall emergent behavior of the ventral stream. One reason we cannot yet do this is that individual artificial neurons in multi-stage models have not been shown to be functionally similar to individual biological neurons. Here, we took an important first step by building and evaluating hundreds of hierarchical neural network models in how well their artificial single neurons approximate macaque primary visual cortical (V1) neurons. We found that single neurons in certain models are surprisingly similar to their biological counterparts and that the distributions of single neuron properties, such as those related to orientation and spatial frequency tuning, approximately match those in macaque V1. Critically, we observed that hierarchical models with V1 stages that better match macaque V1 at the single neuron level are also more aligned with human object recognition behavior. Finally, we show that an optimized classical neuroscientific model of V1 is more functionally similar to primate V1 than all of the tested multi-stage models, suggesting room for further model improvements with tangible payoffs in closer alignment to human behavior. These results provide the first multi-stage, multi-scale models that allow our field to ask precisely how the specific properties of individual V1 neurons relate to recognition behavior.HighlightsImage-computable hierarchical neural network models can be naturally extended to create hierarchical {\textquotedblleft}brain models{\textquotedblright} that allow direct comparison with biological neural networks at multiple scales {\textendash} from single neurons, to population of neurons, to behavior.Single neurons in some of these hierarchical brain models are functionally similar to single neurons in macaque primate visual cortex (V1)Some hierarchical brain models have processing stages in which the entire distribution of artificial neuron properties closely matches the biological distributions of those same properties in macaque V1Hierarchical brain models whose V1 processing stages better match the macaque V1 stage also tend to be more aligned with human object recognition behavior at their output stageCompeting Interest StatementThe authors have declared no competing interest.},
	URL = {https://www.biorxiv.org/content/early/2021/08/13/2021.03.01.433495},
	eprint = {https://www.biorxiv.org/content/early/2021/08/13/2021.03.01.433495.full.pdf},
	journal = {bioRxiv}
}
        @article{Schiller1976,
            author = {Schiller, P. H. and Finlay, B. L. and Volman, S. F.},
            doi = {10.1152/jn.1976.39.6.1352},
            issn = {0022-3077},
            journal = {Journal of neurophysiology},
            number = {6},
            pages = {1334--1351},
            pmid = {825624},
            title = {{Quantitative studies of single-cell properties in monkey striate cortex. III. Spatial Frequency}},
            url = {http://www.ncbi.nlm.nih.gov/pubmed/825624},
            volume = {39},
            year = {1976}
            }
        @article{Freeman2013,
            author = {Freeman, Jeremy and Ziemba, Corey M. and Heeger, David J. and Simoncelli, E. P. and Movshon, J. A.},
            doi = {10.1038/nn.3402},
            issn = {10976256},
            journal = {Nature Neuroscience},
            number = {7},
            pages = {974--981},
            pmid = {23685719},
            publisher = {Nature Publishing Group},
            title = {{A functional and perceptual signature of the second visual area in primates}},
            url = {http://dx.doi.org/10.1038/nn.3402},
            volume = {16},
            year = {2013}
            }
        @article{Cavanaugh2002,
            author = {Cavanaugh, James R. and Bair, Wyeth and Movshon, J. A.},
            doi = {10.1152/jn.00692.2001},
            isbn = {0022-3077 (Print) 0022-3077 (Linking)},
            issn = {0022-3077},
            journal = {Journal of Neurophysiology},
            mendeley-groups = {Benchmark effects/Done,Benchmark effects/*Surround Suppression},
            number = {5},
            pages = {2530--2546},
            pmid = {12424292},
            title = {{Nature and Interaction of Signals From the Receptive Field Center and Surround in Macaque V1 Neurons}},
            url = {http://www.physiology.org/doi/10.1152/jn.00692.2001},
            volume = {88},
            year = {2002}
            }
        @Article{Freeman2013,
                author={Freeman, Jeremy
                and Ziemba, Corey M.
                and Heeger, David J.
                and Simoncelli, Eero P.
                and Movshon, J. Anthony},
                title={A functional and perceptual signature of the second visual area in primates},
                journal={Nature Neuroscience},
                year={2013},
                month={Jul},
                day={01},
                volume={16},
                number={7},
                pages={974-981},
                abstract={The authors examined neuronal responses in V1 and V2 to synthetic texture stimuli that replicate higher-order statistical dependencies found in natural images. V2, but not V1, responded differentially to these textures, in both macaque (single neurons) and human (fMRI). Human detection of naturalistic structure in the same images was predicted by V2 responses, suggesting a role for V2 in representing natural image structure.},
                issn={1546-1726},
                doi={10.1038/nn.3402},
                url={https://doi.org/10.1038/nn.3402}
                }
        @misc{Sanghavi_Murty_DiCarlo_2021,
  title={SanghaviMurty2020},
  url={osf.io/fchme},
  DOI={10.17605/OSF.IO/FCHME},
  publisher={OSF},
  author={Sanghavi, Sachi and Murty, N A R and DiCarlo, James J},
  year={2021},
  month={Nov}
}
        @misc{Sanghavi_DiCarlo_2021,
  title={Sanghavi2020},
  url={osf.io/chwdk},
  DOI={10.17605/OSF.IO/CHWDK},
  publisher={OSF},
  author={Sanghavi, Sachi and DiCarlo, James J},
  year={2021},
  month={Nov}
}
        @misc{Sanghavi_Jozwik_DiCarlo_2021,
  title={SanghaviJozwik2020},
  url={osf.io/fhy36},
  DOI={10.17605/OSF.IO/FHY36},
  publisher={OSF},
  author={Sanghavi, Sachi and Jozwik, Kamila M and DiCarlo, James J},
  year={2021},
  month={Nov}
}
        @article {Majaj13402,
            author = {Majaj, Najib J. and Hong, Ha and Solomon, Ethan A. and DiCarlo, James J.},
            title = {Simple Learned Weighted Sums of Inferior Temporal Neuronal Firing Rates Accurately Predict Human Core Object Recognition Performance},
            volume = {35},
            number = {39},
            pages = {13402--13418},
            year = {2015},
            doi = {10.1523/JNEUROSCI.5181-14.2015},
            publisher = {Society for Neuroscience},
            abstract = {To go beyond qualitative models of the biological substrate of object recognition, we ask: can a single ventral stream neuronal linking hypothesis quantitatively account for core object recognition performance over a broad range of tasks? We measured human performance in 64 object recognition tests using thousands of challenging images that explore shape similarity and identity preserving object variation. We then used multielectrode arrays to measure neuronal population responses to those same images in visual areas V4 and inferior temporal (IT) cortex of monkeys and simulated V1 population responses. We tested leading candidate linking hypotheses and control hypotheses, each postulating how ventral stream neuronal responses underlie object recognition behavior. Specifically, for each hypothesis, we computed the predicted performance on the 64 tests and compared it with the measured pattern of human performance. All tested hypotheses based on low- and mid-level visually evoked activity (pixels, V1, and V4) were very poor predictors of the human behavioral pattern. However, simple learned weighted sums of distributed average IT firing rates exactly predicted the behavioral pattern. More elaborate linking hypotheses relying on IT trial-by-trial correlational structure, finer IT temporal codes, or ones that strictly respect the known spatial substructures of IT ({	extquotedblleft}face patches{	extquotedblright}) did not improve predictive power. Although these results do not reject those more elaborate hypotheses, they suggest a simple, sufficient quantitative model: each object recognition task is learned from the spatially distributed mean firing rates (100 ms) of \~{}60,000 IT neurons and is executed as a simple weighted sum of those firing rates.SIGNIFICANCE STATEMENT We sought to go beyond qualitative models of visual object recognition and determine whether a single neuronal linking hypothesis can quantitatively account for core object recognition behavior. To achieve this, we designed a database of images for evaluating object recognition performance. We used multielectrode arrays to characterize hundreds of neurons in the visual ventral stream of nonhuman primates and measured the object recognition performance of \>100 human observers. Remarkably, we found that simple learned weighted sums of firing rates of neurons in monkey inferior temporal (IT) cortex accurately predicted human performance. Although previous work led us to expect that IT would outperform V4, we were surprised by the quantitative precision with which simple IT-based linking hypotheses accounted for human behavior.},
            issn = {0270-6474},
            URL = {https://www.jneurosci.org/content/35/39/13402},
            eprint = {https://www.jneurosci.org/content/35/39/13402.full.pdf},
            journal = {Journal of Neuroscience}}
        @Article{Kar2019,
                                                    author={Kar, Kohitij
                                                    and Kubilius, Jonas
                                                    and Schmidt, Kailyn
                                                    and Issa, Elias B.
                                                    and DiCarlo, James J.},
                                                    title={Evidence that recurrent circuits are critical to the ventral stream's execution of core object recognition behavior},
                                                    journal={Nature Neuroscience},
                                                    year={2019},
                                                    month={Jun},
                                                    day={01},
                                                    volume={22},
                                                    number={6},
                                                    pages={974-983},
                                                    abstract={Non-recurrent deep convolutional neural networks (CNNs) are currently the best at modeling core object recognition, a behavior that is supported by the densely recurrent primate ventral stream, culminating in the inferior temporal (IT) cortex. If recurrence is critical to this behavior, then primates should outperform feedforward-only deep CNNs for images that require additional recurrent processing beyond the feedforward IT response. Here we first used behavioral methods to discover hundreds of these `challenge' images. Second, using large-scale electrophysiology, we observed that behaviorally sufficient object identity solutions emerged {	extasciitilde}30{	hinspace}ms later in the IT cortex for challenge images compared with primate performance-matched `control' images. Third, these behaviorally critical late-phase IT response patterns were poorly predicted by feedforward deep CNN activations. Notably, very-deep CNNs and shallower recurrent CNNs better predicted these late IT responses, suggesting that there is a functional equivalence between additional nonlinear transformations and recurrence. Beyond arguing that recurrent circuits are critical for rapid object identification, our results provide strong constraints for future recurrent model development.},
                                                    issn={1546-1726},
                                                    doi={10.1038/s41593-019-0392-5},
                                                    url={https://doi.org/10.1038/s41593-019-0392-5}
                                                    }
        @article {Rajalingham240614,
                author = {Rajalingham, Rishi and Issa, Elias B. and Bashivan, Pouya and Kar, Kohitij and Schmidt, Kailyn and DiCarlo, James J.},
                title = {Large-scale, high-resolution comparison of the core visual object recognition behavior of humans, monkeys, and state-of-the-art deep artificial neural networks},
                elocation-id = {240614},
                year = {2018},
                doi = {10.1101/240614},
                publisher = {Cold Spring Harbor Laboratory},
                abstract = {Primates{	extemdash}including humans{	extemdash}can typically recognize objects in visual images at a glance even in the face of naturally occurring identity-preserving image transformations (e.g. changes in viewpoint). A primary neuroscience goal is to uncover neuron-level mechanistic models that quantitatively explain this behavior by predicting primate performance for each and every image. Here, we applied this stringent behavioral prediction test to the leading mechanistic models of primate vision (specifically, deep, convolutional, artificial neural networks; ANNs) by directly comparing their behavioral signatures against those of humans and rhesus macaque monkeys. Using high-throughput data collection systems for human and monkey psychophysics, we collected over one million behavioral trials for 2400 images over 276 binary object discrimination tasks. Consistent with previous work, we observed that state-of-the-art deep, feed-forward convolutional ANNs trained for visual categorization (termed DCNNIC models) accurately predicted primate patterns of object-level confusion. However, when we examined behavioral performance for individual images within each object discrimination task, we found that all tested DCNNIC models were significantly non-predictive of primate performance, and that this prediction failure was not accounted for by simple image attributes, nor rescued by simple model modifications. These results show that current DCNNIC models cannot account for the image-level behavioral patterns of primates, and that new ANN models are needed to more precisely capture the neural mechanisms underlying primate object vision. To this end, large-scale, high-resolution primate behavioral benchmarks{	extemdash}such as those obtained here{	extemdash}could serve as direct guides for discovering such models.SIGNIFICANCE STATEMENT Recently, specific feed-forward deep convolutional artificial neural networks (ANNs) models have dramatically advanced our quantitative understanding of the neural mechanisms underlying primate core object recognition. In this work, we tested the limits of those ANNs by systematically comparing the behavioral responses of these models with the behavioral responses of humans and monkeys, at the resolution of individual images. Using these high-resolution metrics, we found that all tested ANN models significantly diverged from primate behavior. Going forward, these high-resolution, large-scale primate behavioral benchmarks could serve as direct guides for discovering better ANN models of the primate visual system.},
                URL = {https://www.biorxiv.org/content/early/2018/02/12/240614},
                eprint = {https://www.biorxiv.org/content/early/2018/02/12/240614.full.pdf},
                journal = {bioRxiv}
            }
        @article{geirhos2021partial,
              title={Partial success in closing the gap between human and machine vision},
              author={Geirhos, Robert and Narayanappa, Kantharaju and Mitzkus, Benjamin and Thieringer, Tizian and Bethge, Matthias and Wichmann, Felix A and Brendel, Wieland},
              journal={Advances in Neural Information Processing Systems},
              volume={34},
              year={2021},
              url={https://openreview.net/forum?id=QkljT4mrfs}
        }
        @article{BAKER2022104913,
                title = {Deep learning models fail to capture the configural nature of human shape perception},
                journal = {iScience},
                volume = {25},
                number = {9},
                pages = {104913},
                year = {2022},
                issn = {2589-0042},
                doi = {https://doi.org/10.1016/j.isci.2022.104913},
                url = {https://www.sciencedirect.com/science/article/pii/S2589004222011853},
                author = {Nicholas Baker and James H. Elder},
                keywords = {Biological sciences, Neuroscience, Sensory neuroscience},
                abstract = {Summary
                A hallmark of human object perception is sensitivity to the holistic configuration of the local shape features of an object. Deep convolutional neural networks (DCNNs) are currently the dominant models for object recognition processing in the visual cortex, but do they capture this configural sensitivity? To answer this question, we employed a dataset of animal silhouettes and created a variant of this dataset that disrupts the configuration of each object while preserving local features. While human performance was impacted by this manipulation, DCNN performance was not, indicating insensitivity to object configuration. Modifications to training and architecture to make networks more brain-like did not lead to configural processing, and none of the networks were able to accurately predict trial-by-trial human object judgements. We speculate that to match human configural sensitivity, networks must be trained to solve a broader range of object tasks beyond category recognition.}
        }
        @misc{ferguson_ngo_lee_dicarlo_schrimpf_2024,
         title={How Well is Visual Search Asymmetry predicted by a Binary-Choice, Rapid, Accuracy-based Visual-search, Oddball-detection (BRAVO) task?},
         url={osf.io/5ba3n},
         DOI={10.17605/OSF.IO/5BA3N},
         publisher={OSF},
         author={Ferguson, Michael E, Jr and Ngo, Jerry and Lee, Michael and DiCarlo, James and Schrimpf, Martin},
         year={2024},
         month={Jun}
}
        @article{hermann2020origins,
              title={The origins and prevalence of texture bias in convolutional neural networks},
              author={Hermann, Katherine and Chen, Ting and Kornblith, Simon},
              journal={Advances in Neural Information Processing Systems},
              volume={33},
              pages={19000--19015},
              year={2020},
              url={https://proceedings.neurips.cc/paper/2020/hash/db5f9f42a7157abe65bb145000b5871a-Abstract.html}
        }
        

Layer Commitment

Region Layer
V1 blocks.6.attn
V2 blocks.6.attn
V4 blocks.7.attn.qkv
IT blocks.11.attn

Visual Angle

None degrees