SEE COMMENTARY

Performance-optimized hierarchical models predictneural responses in higher visual cortex
Daniel L. K. Yaminsa,1, Ha Honga,b,1, Charles F. Cadieua, Ethan A. Solomona, Darren Seiberta, and James J. DiCarloa,2aDepartment of Brain and Cognitive Sciences and McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139;
and bHarvard-MIT Division of Health Sciences and Technology, Institute for Medical Engineering and Science, Massachusetts Institute of Technology,
Cambridge, MA 02139

The ventral visual stream underlies key human visual object recognition abilities. However, neural encoding in the higher areas ofthe ventral stream remains poorly understood. Here, we describe
a modeling approach that yields a quantitatively accurate model ofinferior temporal (IT) cortex, the highest ventral cortical area. Using
high-throughput computational techniques, we discovered that,within a class of biologically plausible hierarchical neural networkmodels, there is a strong correlation between a model’s categorization performance and its ability to predict individual IT neural unit
response data. To pursue this idea, we then identified a high-performing neural network that matches human performance on
a range of recognition tasks. Critically, even though we did notconstrain this model to match neural data, its top output layer turns
out to be highly predictive of IT spiking responses to complex naturalistic images at both the single site and population levels. Moreover, the model’s intermediate layers are highly predictive of neural
responses in the V4 cortex, a midlevel visual area that provides thedominant cortical input to IT. These results show that performance
optimization—applied in a biologically appropriate model class—can be used to build quantitative predictive models of neuralprocessing.
computational neuroscience| computer vision | array electrophysiology

Retinal images of real-world objects vary drastically due tochanges in object pose, size, position, lighting, nonrigid deformation, occlusion, and many other sources of noise and variation. Humans effortlessly recognize objects rapidly and accurately
despite this enormous variation, an impressive computational feat(1). This ability is supported by a set of interconnected brain areas
collectively called the ventral visual stream (2, 3), with homologous
areas in nonhuman primates (4). The ventral stream is thought tofunction as a series of hierarchical processing stages (5–7) that
encode image content (e.g., object identity and category) increasingly explicitly in successive cortical areas (1, 8, 9). For example,
neurons in the lowest area, V1, are well described by Gabor-likeedge detectors that extract rough object outlines (10), although theV1 population does not show robust tolerance to complex imagetransformations (9). Conversely, rapidly evoked population activity
in top-level inferior temporal (IT) cortex can directly support realtime, invariant object categorization over a wide range of tasks (11,12). Midlevel ventral areas—such as V4, the dominant cortical
input to IT—exhibit intermediate levels of object selectivity andvariation tolerance (12–14).
Significant progress has been made in understanding lower
ventral areas such as V1, where conceptually compelling models
have been discovered (10). These models are also quantitatively
accurate and can predict response magnitudes of individual
neuronal units to novel image stimuli. Higher ventral corticalareas, especially V4 and IT, have been much more difficult tounderstand. Although first principles-based models of higher
ventral cortex have been proposed (15–20), these models fail tomatch important features of the higher ventral visual neuralrepresentation in both humans and macaques (4, 21). Moreover,attempts to fit V4 and IT neural tuning curves on general image
stimuli have shown only limited predictive success (22, 23).
www.pnas.org/cgi/doi/10.1073/pnas.1403112111

Explaining the neural encoding in these higher ventral areas thus
remains a fundamental open question in systems neuroscience.
As with V1, models of higher ventral areas should be neurally
predictive. However, because the higher ventral stream is also
believed to underlie sophisticated behavioral object recognition
capacities, models must also match IT on performance metrics,
equalling (or exceeding) the decoding capacity of IT neurons on
object recognition tasks. A model with perfect neural predictivity
in IT will necessarily exhibit high performance, because IT itself
does. Here we demonstrate that the converse is also true, withina biologically appropriate model class. Combining high-throughput
computational and electrophysiology techniques, we explore a wide
range of biologically plausible hierarchical neural network models
and then assess them against measured IT and V4 neural response
data. We show that there is a strong correlation between a model’s
performance on a challenging high-variation object recognition
task and its ability to predict individual IT neural unit responses.
Extending this idea, we used optimization methods to identify a neural network model that matches human performanceon a range of recognition tasks. We then show that even though
this model was never explicitly constrained to match neural data,
its output layer is highly predictive of neural responses in IT
cortex—providing a first quantitatively accurate model of thishighest ventral cortex area. Moreover, the middle layers of themodel are highly predictive of V4 neural responses, suggesting
top-down performance constraints directly shape intermediate
visual representations.
Significance
Humans and monkeys easily recognize objects in scenes. Thisability is known to be supported by a network of hierarchicallyinterconnected brain areas. However, understanding neurons
in higher levels of this hierarchy has long remained a major
challenge in visual systems neuroscience. We use computational techniques to identify a neural network model thatmatches human performance on challenging object categorization tasks. Although not explicitly constrained to matchneural data, this model turns out to be highly predictive ofneural responses in both the V4 and inferior temporal cortex,
the top two layers of the ventral visual hierarchy. In addition toyielding greatly improved models of visual cortex, these results
suggest that a process of biological performance optimization
directly shaped neural mechanisms.
Author contributions: D.L.K.Y., H.H., and J.J.D. designed research; D.L.K.Y., H.H., and
E.A.S. performed research; D.L.K.Y. contributed new reagents/analytic tools; D.L.K.Y.,
H.H., C.F.C., and D.S. analyzed data; and D.L.K.Y., H.H., and J.J.D. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
Freely available online through the PNAS open access option.
See Commentary on page 8327.
1

D.L.K.Y. and H.H. contributed equally to this work.

2

To whom correspondence should be addressed. E-mail: dicarlo@mit.edu.

This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.
1073/pnas.1403112111/-/DCSupplemental.

PNAS | June 10, 2014 | vol. 111 | no. 23 | 8619–8624

NEUROSCIENCE

Edited by Terrence J. Sejnowski, Salk Institute for Biological Studies, La Jolla, CA, and approved April 8, 2014 (received for review March 3, 2014)

Results
Invariant Object Recognition Performance Strongly Correlates with IT
Neural Predictivity. We first measured IT neural responses on abenchmark testing image set that exposes key performance
characteristics of visual representations (24). This image set
consists of 5,760 images of photorealistic 3D objects drawn fromeight natural categories (animals, boats, cars, chairs, faces, fruits,
planes, and tables) and contains high levels of the object position, scale, and pose variation that make recognition difficult for
artificial vision systems, but to which humans are robustly tolerant (1, 25). The objects are placed on cluttered natural scenesthat are randomly selected to ensure background content is uncorrelated with object identity (Fig. S1A).
Using multiple electrode arrays, we collected responses from
168 IT neurons to each image. We then used high-throughput
computational methods to evaluate thousands of candidate
neural network models on these same images, measuring object
categorization performance as well as IT neural predictivity
for each model (Fig. 1A; each point represents a distinct model).
To measure categorization performance, we trained support
vector machine (SVM) linear classifiers on model output layer
units (11) and computed cross-validated testing accuracy forthese trained classifiers. To assess models’ neural predictivity, weused a standard linear regression methodology (10, 26, 27): foreach target IT neural site, we identified a synthetic neuron
composed of a linear weighting of model outputs that would best
match that site on fixed sample images and then tested response predictions against actual neural site’s output on novel
images (Materials and Methods and SI Text).
Models were drawn from a large parameter space of convolutional neural networks (CNNs) expressing an inclusive version of the hierarchical processing concept (17, 18, 20, 28). CNNs
approximate the general retinotopic organization of the ventral
stream via spatial convolution, with computations in any one
region of the visual field identical to those elsewhere. Eachconvolutional layer is composed of simple and neuronally plausible basic operations, including linear filtering, thresholding,pooling, and normalization (Fig. S2A). These layers are stacked
hierarchically to construct deep neural networks.
Each model is specified by a set of 57 parameters controllingthe number of layers and parameters at each layer, fan-in andfan-out, activation thresholds, pooling exponents, and local
receptive field sizes at each layer. Network depth ranged from
one to three layers, and filter weights for each layer were chosenrandomly from bounded uniform distributions whose bounds weremodel parameters (SI Text). These models are consistent with
the Hierarchical Linear-Nonlinear (HLN) hypothesis that higher
level neurons (e.g., IT) output a linear weighting of inputs fromintermediate-level (e.g., V4) neurons followed by simple additional nonlinearities (14, 16, 29).
Models were selected for evaluation by one of three procedures: (i) random sampling of the uniform distribution overparameter space (Fig. 1A; n = 2,016, green dots); (ii) optimization for performance on the high-variation eight-way categorization task (n = 2,043, blue dots); and (iii) optimization
directly for IT neural predictivity (n = 1,876, orange dots; also
see SI Text and Fig. S3). In each case, we observed significant
variation in both performance and IT predictivity across theparameter range. Thus, although the HLN hypothesis is consistent with a broad spectrum of particular neural network architectures, specific parameter choices have a large effect on a given
model’s recognition performance and neural predictivity.
Performance was significantly correlated with neural predictivity in all three selection regimes. Models that performed
better on the categorization task were also more likely to produce outputs more closely aligned to IT neural responses. Although the class of HLN-consistent architectures contains many
neurally inconsistent architectures with low IT predictivity, performance provides a meaningful way to a priori rule out manyof those inconsistent models. No individual model parameters
correlated nearly as strongly with IT predictivity as performance(Fig. S4), indicating that the performance/IT predictivity correlation cannot be explained by simpler mechanistic considerations(e.g., receptive field size of the top layer).
Critically, directed optimization for performance significantly
increased the correlation with IT predictivity compared with therandom selection regime (r = 0:78 vs. r = 0:55), even thoughneural data were not used in the optimization. Moreover, when
optimizing for performance, the best-performing models predicted neural output as well as those models directly selected forneural predictivity, although the reverse is not true. Together,
these results imply that, although the IT predictivity metric is
a complex function of the model parameter landscape, performance
optimization is an efficient means to identify regions in parameter
space containing IT-like models.
IT Cortex as a Neural Performance Target. Fig. 1A suggests a next
step toward improved encoding models of higher ventral cortex:
drive models further to the right along the x axis—if the correlation holds, the models will also climb on the y axis. Ideally, thiswould involve identifying hierarchical neural networks that perform at or near human object recognition performance levels
and validating them using rigorous tests against neural data
(Fig. 2A). However, the difficulty of meeting the performancechallenge itself can be seen in Fig. 2B. To obtain neural reference points on categorization performance, we trained linear

Fig. 1. Performance/IT-predictivity correlation. (A)Object categorization performance vs. IT neuralHMO
IT Fittingr = 0.87 ± 0.15
50
30
Optimization
explained variance percentage (IT-predictivity) for(r =0.80)
CNN models in three independent high-throughput
40
computational experiments (each point is a distinct
15
neural network architecture). The x axis shows
30
performance (balanced accuracy, chance is 0.5) ofV2-like
HMAX
Categorization
the model output features on a high-variation catSIFT
Performanceegorization task; the y axis shows the median single
Optimization0
20
PLOS09(r =0.78)
site IT explained variance percentage (n = 168 sites)of that model. Each dot corresponds to a distinct
Category
10
RandomIdeal
model selected from a large family of convolutional
Selection
V1-likeObserverPixels–15(r =0.55)
neural network architectures. Models were selected0
by random draws from parameter space (green
0.5
0.6
0.7
0.6
0.8
1.0
dots), object categorization performance-optimiCategorization Performance (balanced accuracy)
zation (blue dots), or explicit IT predictivity optimization (orange dots). (B) Pursuing the correlation identified in A, a high-performing neural network was identified that matches human performance on
a range of recognition tasks, the HMO model. The object categorization performance vs. IT neural predictivity correlation extends across a variety of models
exhibiting a wide range of performance levels. Black circles include controls and published models; red squares are models produced during the HMO optimization procedure. The category ideal observer (purple square) lies significantly off the main trend, but is not an actual image-computable model. The r
value is computed over red and black points. For reference, light blue circles indicate performance optimized models (blue dots) from A.

B

IT Explained Variance (%)

A

8620 | www.pnas.org/cgi/doi/10.1073/pnas.1403112111

Yamins et al.

A

Behavioral Tasks

Operations in Linear-Nonlinear Layer⊗Φ⊗Φe.g. Trees vs non-Trees

1
2

...
⊗ Φk

FilterThreshold

Pool

LN

LN

Spatial Convolution
LN
over Image Input

LN

...

...

Normalize

1. Optimize Model for Task Performance
LN
LN
...

...
LN

...

...

LN

LN

layer 3layer 4LN

layer 1layer 2

2. Test Per-Site Neural Predictions

100ms
VisualPresentation

V1

V4

V2

...

...

Neural Recordings from IT and V4
100

80

60

40

20

PixelsSIFT
V1-likeV2-like
HMAX
PLOS09
V4 Population
IT Population
HumansHMO

PerformanceB

IT

Low Variation Tasks

High-variation
V4-to-IT Gap

Medium Variation Tasks

High Variation Tasks

Fig. 2. Neural-like models via performance optimization. (A) We (1) usedhigh-throughput computational methods to optimize the parameters ofa hierarchical CNN with linear-nonlinear (LN) layers for performance on achallenging invariant object recognition task. Using new test images distinct
from those used to optimize the model, we then (2) compared output of eachof the model’s layers to IT neural responses and the output of intermediate
layers to V4 neural responses. To obtain neural data for comparison, we usedchronically implanted multielectrode arrays to record the responses of multiunit sites in IT and V4, obtaining the mean visually evoked response of eachof 296 neural sites to ∼6,000 complex images. (B) Object categorization
performance results on the test images for eight-way object categorization at
three increasing levels of object view variation (y axis units are 8-way categorization percent-correct, chance is 12.5%). IT (green bars) and V4 (hatchedgreen bars) neural responses, and computational models (gray and red bars)
were collected on the same image set and used to train support vector machine (SVM) linear classifiers from which population performance accuracy
was evaluated. Error bars are computed over train/test image splits. Humansubject responses on the same tasks were collected via psychophysics experiments (black bars); error bars are due to intersubject variation.

Yamins et al.

Constructing a High-Performing Model. Although simple threelayer hierarchical CNNs can be effective at low-variation object
recognition tasks, recent work has shown that they may be limited in their performance capacity for higher-variation tasks (9).
For this reason, we extended our model class to contain combinations (e.g., mixtures) of deeper CNN networks (Fig. S2B),
which correspond intuitively to architecturally specialized subregions like those observed in the ventral visual stream (13, 32).
To address the significant computational challenge of finding especially high-performing architectures within this large space ofpossible networks, we used hierarchical modular optimization(HMO). The HMO procedure embodies a conceptually simple
hypothesis for how high-performing combinations of functionallyspecialized hierarchical architectures can be efficiently discovered and hierarchically combined, without needing to prespecify
the subtasks ahead of time. Algorithmically, HMO is analogous
to an adaptive boosting procedure (33) interleaved with hyperparameter optimization (see SI Text and Fig. S2C).
As a pretraining step, we applied the HMO selection procedure on a screening task (Fig. S1B). Like the testing set, thescreening set contained images of objects placed on randomly
selected backgrounds, but used entirely different objects in totally nonoverlapping semantic categories, with none of the samebackgrounds and widely divergent lighting conditions and noiselevels. Like any two samples of naturalistic images, the screening
and testing images have high-level commonalities but quite different semantic content. For this reason, performance increases
that transfer between them are likely to also transfer to othernaturalistic image sets. Via this pretraining, the HMO procedure
identified a four-layer CNN with 1,250 top-level outputs (Figs.
S2B and S5), which we will refer to as the HMO model.
Using the same classifier training protocol as with the neuraldata and control models, we then tested the HMO model todetermine whether its performance transferred from the screening
to the testing image set. In fact, the HMO model matched theobject recognition performance of the IT neural sample (Fig. 2B,
red bars), even when faced with large amounts of variation—a hallmark of human object recognition ability (1). These performance results are robust to the number of training examples
and number of sampled model neurons, across a variety of distinct
recognition tasks (Figs. S6 and S7).
Predicting Neural Responses in Individual IT Neural Sites. Given thatthe HMO model had plausible performance characteristics, wethen measured its IT predictivity, both for the top layer and eachof the three intermediate layers (Fig. 3, red lines/bars). We foundthat each successive layer predicted IT units increasingly well,demonstrating that the trend identified in Fig. 1A continues tohold in higher performance regimes and across a wide range ofmodel complexities (Fig. 1B). Qualitatively examining the specific predictions for individual images, the model layers showthat category selectivity and tolerance to more drastic imagetransformations emerges gradually along the hierarchy (Fig. 3A,
top four rows). At lower layers, model units predict IT responses
only at a limited range of object poses and positions. At higher
layers, variation tolerance grows while category selectivity develops,
suggesting that as more explicit “untangled” object recognition
PNAS | June 10, 2014 | vol. 111 | no. 23 | 8621

SEE COMMENTARY

similar to those observed in IT. The HMAX model can be trainedin a domain-specific fashion, and to give it the best chance of
success, we performed this training using the benchmark images
themselves (see SI Text for more information on the comparison
models). Like V4, the control models that we tested approach IT
and human performance levels in the low-variation condition, but
in the high-variation condition, all of them fail to match the performance of IT units by a large margin. It is not surprising that V1
and V2 models are not nearly as effective as IT, but it is instructive
to note that the task is sufficiently difficult that the HMAX model
performs less well than the V4 population sample, even whenpretrained directly on the test dataset.

NEUROSCIENCE

classifiers on the IT neural population (Fig. 2B, green bars) andthe V4 neural population (n = 128, hatched green bars). To expose a key axis of recognition difficulty, we computed performance results at three levels of object view variation, from low(fixed orientation, size, and position) to high (180° rotations on
all axes, 2.5× dilation, and full-frame translations; Fig. S1A). Asa behavioral reference point, we also measured human performance on these tasks using web-based crowdsourcing methods(black bars). A crucial observation is that at all levels of variation,
the IT population tracks human performance levels, consistent withknown results about IT’s high category decoding abilities (11, 12).
The V4 population matches IT and human performance at low
levels of variation, but performance drops quickly at higher variation levels. (This V4-to-IT performance gap remains nearly as large
even for images with no object translation variation, showing thatthe performance gap is not due just to IT’s larger receptive fields.)
As a computational reference, we used the same procedure toevaluate a variety of published ventral stream models targetingseveral levels of the ventral hierarchy. To control for low-level
confounds, we tested the (trivial) pixel model, as well as SIFT,
a simple baseline computer vision model (30). We also evaluateda V1-like Gabor-based model (25), a V2-like conjunction-ofGabors model (31), and HMAX (17, 28), a model targeted at
explaining higher ventral cortex and that has receptive field sizes

features are generated at each stage, the representations become increasingly IT-like (9).
Critically, we found that the top layer of the high-performing
HMO model achieves high predictivity for individual IT neuralsites, predicting 48:5 ± 1:3% of the explainable IT neuronalvariance (Fig. 3 B and C). This represents a nearly 100% improvement over the best comparison models and is comparable
to the prediction accuracy of state-of-the-art models of lowerlevel ventral areas such as V1 on complex stimuli (10). In comparison, although the HMAX model was better at predicting IT
responses than baseline V1 or SIFT, it was not significantly
different from the V2-like model.
To control for how much neural predictivity should be
expected from any algorithm with high categorization performance, we assessed semantic ideal observers (34), including
a hypothetical model that has perfect access to all categorylabels. The ideal observers do predict IT units above chance level(Fig. 3C, left two bars), consistent with the observation that ITneurons are partially categorical. However, the ideal observers
are significantly less predictive than the HMO model, showingthat high IT predictivity does not automatically follow fromcategory selectivity and that there is significant noncategoricalstructure in IT responses attributable to intrinsic aspects of hierarchical network structure (Fig. 3A, last row). These results
suggest that high categorization performance and the hierarchical model architecture class work in concert to produce ITlike populations, and neither of these constraints is sufficient on
its own to do so.

C

50
40
30
20

10

HMO L2HMO L3
HMO Top

IT Site 42

HMO L1IT Site 56

Predicting Responses in V4 from Intermediate Model Layers. Corticalarea V4 is the dominant cortical input to IT, and the neuralrepresentation in V4 is known to be significantly less categorical

Pixels
V1-Like
SIFT
PLOS09HMAX
V2-Like

IT Site 150

Category
All VariablesA

IT Explained Variance (%)

Population Representation Similarity. Characterizing the IT neuralrepresentation at the population level may be equally importantfor understanding object visual representation as individual IT
neural sites. The representation dissimilarity matrix (RDM) is aconvenient tool comparing two representations on a common
stimulus set in a task-independent manner (4, 35). Each entry in
the RDM corresponds to one stimulus pair, with high/low values
indicating that the population as a whole treats the pair stimuli
as very different/similar. Taken over the whole stimulus set, theRDM characterizes the layout of the images in the highdimensional neural population space. When images are orderedby category, the RDM for the measured IT neural population(Fig. 4A) exhibits clear block-diagonal structure—associatedwith IT’s exceptionally high categorization performance—as well
as off-diagonal structure that characterizes the IT neural representation more finely than any single performance metric (Fig.
4A and Fig. S8). We found that the neural population predictedby the output layer of the HMO model had very high similarity to
the actual IT population structure, close to the split-half noise
ceiling of the IT population (Fig. 4B). This implies that much of
the residual variance unexplained at the single-site level may not
be relevant for object recognition in the IT population level code.
We also performed two stronger tests of generalization: (i)object-level generalization, in which the regressor training set
contained images of only 32 object exemplars (four in each ofeight categories), with RDMs assessed only on the remaining 32
objects, and (ii) category-level generalization, in which the regressor sample set contained images of only half the categories
but RDMs were assessed only on images of the other categories(see Figs. S8 and S9). We found that the prediction generalizes
robustly, capturing the IT population’s layout for images ofcompletely novel objects and categories (Fig. 4 B and C and
Fig. S8).

0

B

Ideal
Observers

Control
ModelsHMO
Layers(n=168)HMO
Top Layer(48%)

HMO Layers

HMO
Layer 3(36%)

Response Magnifude

Binned Site Counts

HMO
Layer 2
(21%)

HMO
Layer 1(4%)

V2-Like
Model
(26%)

Control Models

HMAX
Model
(25%)

V1-LikeModel(16%)
Category
Ideal
Observer(15%)
0

Animals Boats Cars Chairs Faces Fruits PlanesTables

8622 | www.pnas.org/cgi/doi/10.1073/pnas.1403112111

25

50

75

100

Single Site Explained Variance (%)

Fig. 3. IT neural predictions. (A) Actual neural response (black trace) vs. model predictions (colored
trace) for three individual IT neural sites. The x axis
in each plot shows 1,600 test images sorted first by
category identity and then by variation amount,with more drastic image transformations toward theright within each category block. The y axis represents the prediction/response magnitude of theneural site for each test image (those not used to fit
the model). Two of the units show selectivity forspecific classes of objects, namely chairs (Left) andfaces (Center), whereas the third (Right) exhibitsa wider variety of image preferences. The four top
rows show neural predictions using the visual feature set (i.e., units sampled) from each of the four
layers of the HMO model, whereas the lower rows
show the those of control models. (B) Distributionsof model explained variance percentage, over the
population of all measured IT sites (n = 168). Yellow
dotted line indicates distribution median. (C)Comparison of IT neural explained variance percentage for various models. Bar height shows median explained variance, taken over all predicted IT
units. Error bars are computed over image splits.
Colored bars are those shown in A and B, whereas
gray bars are additional comparisons.

Yamins et al.

0.6

Image
generalization

Fruits (8)Planes (8)Tables (8)Objectgeneralization

Category
generalization

Pixels
SIFT

0.0

HMAX
V2-like
HMO
V4 sites
IT sites split-half

Faces (8)0.3Category
generalization

Fig. 4. Population-level similarity. (A) Object-level representation dissimilarity matrices (RDMs) visualized via rank-normalized color plots (blue = 0th
distance percentile, red = 100th percentile). (B) IT population and the HMObased IT model population, for image, object, and category generalizations(SI Text). (C) Quantification of model population representation similarity to
IT. Bar height indicates the spearman correlation value of a given model’s
RDM to the RDM for the IT neural population. The IT bar represents theSpearman-Brown corrected consistency of the IT RDM for split-halves overthe IT units, establishing a noise-limited upper bound. Error bars are takenover cross-validated regression splits in the case of models and over image
and unit splits in the case of neural data.

than that of IT (12). Comparing a performance-optimized modelto these data would provide a strong test both of its ability to
predict the internal structure of the ventral stream, as well as togo beyond the direct consequences of category selectivity. We
thus measured the HMO model’s neural predictivity for the V4neural population (Fig. 5). We found that the HMO model’spenultimate layer is highly predictive of V4 neural responses(51:7 ± 2:3% explained V4 variance), providing a significantly
better match to V4 than either the model’s top or bottom layers.
These results are strong evidence for the hypothesis that V4
corresponds to an intermediate layer in a hierarchical model
whose top layer is an effective model of IT. Of the control
models that we tested, the V2-like model predicts the most V4
variation ð34:1 ± 2:4%Þ. Unlike the case of IT, semantic models
explain effectively no variance in V4, consistent with V4’s lack of
category selectivity. Together these results suggest that performance optimization not only drives top-level output model layers
to resemble IT, but also imposes biologically consistent constraints on the intermediate feature representations that can
support downstream performance.
Discussion
Here, we demonstrate a principled method for achieving greatly
improved predictive models of neural responses in higher
ventral cortex. Our approach operationalizes a hypothesis for
how two biological constraints together shaped visual cortex:(i) the functional constraint of recognition performance and(ii) the structural constraint imposed by the hierarchical network architecture.

A

C
50
40
30
20
10
0

Ideal

Control
ModelsB Observers
V4
Site
60

HMO
Layers(n=128)

Yamins et al.

HMO
Top Layer
(33%)

HMO
Layer 3(52%)

HMO
Layer 2(48%)

HMO
Layer 1(8%)

V2-Like
Model
(34%)

HMAX
Model
(24%)

V1-LikeModel(11%)
Category
Ideal
Observer(8%)

Generative Basis for Higher Visual Cortical Areas. Our modelingapproach has common ground with existing work on neural response prediction (27), e.g., the HLN hypothesis. However, ina departure from that line of work, we do not tune model
parameters (the nonlinearities or the model filters) separately
for each neural unit to be predicted. In fact, with the exceptionof the final linear weighting, we do not tune parameters usingneural data at all. Instead, the parameters of our model wereindependently selected to optimize functional performance atthe top level, and these choices create fixed bases from which any
individual IT or V4 unit can be composed. This yields a generative model that allows the sampling of an arbitrary number of

SEE COMMENTARY

What Features Do Good Models Share? Although the highest-performing models had certain commonalities (e.g., more hierarchical layers), many poor models also exhibited these features,and no one architectural parameter dominated performancevariability (Fig. S3). To gain further insight, we performed an
exploratory analysis of the parameters of the learned HMO
model, evaluating each parameter both for how sensitively it was
tuned and how diverse it was between model mixture components.
Two classes of model parameters were especially sensitive anddiverse (SI Text and Figs. S10 and S11): (i) filter statistics, including filter mean and spread, and (ii) the exponent trading off
between max-pooling and average-pooling (16). This observation
hints at a computationally rigorous explanation for experimentally

0

25

50

75

100

Single Site Explained Variance (%)

Fig. 5. V4 neural predictions. (A) Actual vs. predicted response magnitudes
for a typical V4 site. V4 sites are highly visually driven, but unlike IT sites
show very little categorical preference, manifesting in more abrupt changes
in the image-by-image plots shown here. Red highlight indicates the bestmatching model (viz., HMO layer 3). (B) Distributions of explained variances
percentage for each model, over the population of all measured V4 sites
ðn = 128Þ. (C) Comparison of V4 neural explained variance percentage forvarious models. Conventions follow those used in Fig. 3.

PNAS | June 10, 2014 | vol. 111 | no. 23 | 8623

NEUROSCIENCE

Objectgeneralization

Animals (4)Boats (4)Cars (4)Chairs (4)Faces (4)Fruits (4)Planes (4)Tables (4)HMO L2HMO L3C

0.9

V 1 -like

Population similaritty to IT

Bneurally consistent units. As a result, the size of the model does
not scale with the number of neural sites to be predicted—and
because the prediction results were assessed for a random
sample of IT and V4 units, they are likely to generalize withsimilar levels of predictivity to any new sites that are measured.

HMO Top

Image
generalization

Animals (8)Boats (8)Cars (8)Chairs (8)Faces (8)Fruits (8)Planes (8)Tables (8)HMO L1

HMO model

Pixels
V1-Like
SIFT
PLOS09HMAX
V2-Like

IT neuronal units

Category
All Variables

V4 neuronal units

V4 Explained Variance (%)

HMAX Model

Binned Site Counts

V1-like Model

Response MagnifudeAobserved heterogeneities in higher ventral cortex areas (13,32), but much work remains to be done to confirm such ahypothesis.

that the overall approach may be applicable to other corticalareas and task domains.

Top-Down Approach to Understanding Cortical Circuits. A common

Array Electrophysiology. Neural data were collected in two awake behaving
rhesus macaques (Macaca mulatta, 7 and 9 kg) using parallel multielectrodearray electrophysiology systems (Cerebus System; BlackRock Microsystems).
All procedures were done in accordance with National Institutes of Healthguidelines and approved by the Massachusetts Institute of Technology (MIT)Committee on Animal Care. 296 neural sites (168 in IT and 128 in V4) wereselected as being visually driven. Fixating animals were presented withtesting images for 100 ms, and scalar firing rates were obtained from spiketrains by averaging spike counts in the period 70–170 ms after stimulus
presentation. See SI Text for additional details.

assumption in visual neuroscience is that understanding thetuning curves of neurons in lower cortical areas will be a necessary precursor to explaining higher visual cortex. For example,
significant work has gone into assessing the extent to which V4
neurons can be understood as a curvature-selective shape representation (27). Our results indicate that it is useful to complement this bottom-up approach with a top-down perspective
characterizing IT as the product of an evolutionary/developmental
process that selected for high performance on recognition on tasks
like those used in our optimization. V4 may in turn be characterized as having been selected precisely to support the downstream computation in IT. This type of explanation is qualitatively
different from more traditional approaches that seek explicit
descriptions of neural responses in terms of particular geometrical primitives. However, our results show functionally
relevant constraints can be used to obtain quantitatively predictive models even when such explicit bottom-up primitives
have not been identified.
Going forward, we will bridge these bottom-up and top-down
explanations by building links to lower and intermediate visualcortex, especially in V1 and V2. We will also explore recent highperforming computer vision systems with architectures inspiredby the ventral stream (36). Our results show that behaviorally
driven computational approaches have an important role inunderstanding the details of visual processing (37) and suggest
1. DiCarlo JJ, Cox DD (2007) Untangling invariant object recognition. Trends Cogn Sci
11(8):333–341.
2. Grill-Spector K, Kourtzi Z, Kanwisher N (2001) The lateral occipital complex and its
role in object recognition. Vision Res 41(10-11):1409–1422.
3. Malach R, Levy I, Hasson U (2002) The topography of high-order human object areas.
Trends Cogn Sci 6(4):176–184.
4. Kriegeskorte N, et al. (2008) Matching categorical object representations in inferior
temporal cortex of man and monkey. Neuron 60(6):1126–1141.
5. Tanaka K (1996) Inferotemporal cortex and object vision. Annu Rev Neurosci 19:
109–139.
6. Logothetis NK, Sheinberg DL (1996) Visual object recognition. Annu Rev Neurosci 19:
577–621.
7. Felleman DJ, Van Essen DC (1991) Distributed hierarchical processing in the primate
cerebral cortex. Cereb Cortex 1(1):1–47.
8. Vogels R, Orban GA (1994) Activity of inferior temporal neurons during orientation
discrimination with successively presented gratings. J Neurophysiol 71(4):1428–1451.
9. DiCarlo JJ, Zoccolan D, Rust NC (2012) How does the brain solve visual object recognition? Neuron 73(3):415–434.
10. Carandini M, et al. (2005) Do we know what the early visual system does? J Neurosci
25(46):10577–10597.
11. Hung CP, Kreiman G, Poggio T, DiCarlo JJ (2005) Fast readout of object identity from
macaque inferior temporal cortex. Science 310(5749):863–866.
12. Rust NC, DiCarlo JJ (2010) Selectivity and tolerance (“invariance”) both increase
as visual information propagates from cortical area V4 to IT. J Neurosci 30(39):12978–12995.
13. Freiwald WA, Tsao DY (2010) Functional compartmentalization and viewpoint generalization within the macaque face-processing system. Science 330(6005):845–851.
14. Connor CE, Brincat SL, Pasupathy A (2007) Transformation of shape information in
the ventral pathway. Curr Opin Neurobiol 17(2):140–147.
15. Fukushima K (1980) Neocognitron: A self organizing neural network model for amechanism of pattern recognition unaffected by shift in position. Biol Cybern 36(4):
193–202.
16. Riesenhuber M, Poggio T (2000) Models of object recognition. Nat Neurosci 3(Suppl):
1199–1204.
17. Serre T, Oliva A, Poggio T (2007) A feedforward architecture accounts for rapid categorization. Proc Natl Acad Sci USA 104(15):6424–6429.
18. Lecun Y, Huang F-J, Bottou L (2004) Learning methods for generic object recognition
with invariance to pose and lighting. Proceedings of the 2004 IEEE Computer Society
Conference on Computer Vision and Pattern Recognition (IEEE Computer Society,
Washington, DC), Vol 2, pp 97–104.
19. Bengio Y (2009) Learning deep architectures for AI. Foundations and Trends in Machine Learning (Now Publishers, Hanover, MA), Vol 2.

8624 | www.pnas.org/cgi/doi/10.1073/pnas.1403112111

Materials and Methods

Neural Predictivity Metric. For each IT neural site, we used linear regression to
identify a linear weighting of model output units (from the top or intermediate layers) that is most predictive of that site’s actual output ona fixed set of sample images (10, 26, 27). Using this “synthetic neuron,” wethen produced per-image response predictions on novel images not used in
the regression training and compared them to the actual neural site’s output
for those images (Figs. 3A and 5A). We computed the goodness-of-fit r 2
value, normalized by the neural site’s trial-by-trial variability, to obtain the
explained variance percentage for that site. The overall area predictivity ofa model is the median explained variance over all measured sites in that area
(Figs. 3 B and C and 5 B and C and see SI Text).
ACKNOWLEDGMENTS. We thank Diego Ardila, Najib Majaj, and Nancy
Kanwisher for useful conversations. This work was partially supported by
National Science Foundation Grant IS 0964269 and National Eye Institute
Grant R01-EY014970.

20. Pinto N, Doukhan D, DiCarlo JJ, Cox DD (2009) A high-throughput screening approachto discovering good forms of biologically inspired visual representation. PLOS Comput
Biol 5(11):e1000579.
21. Kiani R, Esteky H, Mirpour K, Tanaka K (2007) Object category structure in response
patterns of neuronal population in monkey inferior temporal cortex. J Neurophysiol
97(6):4296–4309.
22. Rust NC, Mante V, Simoncelli EP, Movshon JA (2006) How MT cells analyze the motionof visual patterns. Nat Neurosci 9(11):1421–1431.
23. Gallant JL, Connor CE, Rakshit S, Lewis JW, Van Essen DC (1996) Neural responses
to polar, hyperbolic, and Cartesian gratings in area V4 of the macaque monkey.
J Neurophysiol 76(4):2718–2739.
24. Cadieu C, et al. (2013) The neural representation benchmark and its evaluation on brain
and machine. International Conference on Learning Representations 2013. arXiv:
1301.3530.
25. Pinto N, Cox DD, DiCarlo JJ (2008) Why is real-world visual object recognition hard?
PLOS Comput Biol 4(1):e27.
26. Cadieu C, et al. (2007) A model of V4 shape selectivity and invariance. J Neurophysiol
98(3):1733–1750.
27. Sharpee TO, Kouh M, Reynolds JH (2013) Trade-off between curvature tuning andposition invariance in visual area V4. Proc Natl Acad Sci USA 110(28):11618–11623.
28. Mutch J, Lowe DG (2008) Object class recognition and localization using sparse features with limited receptive fields. International Journal of Computer Vision 80(1):45–57.
29. Brincat SL, Connor CE (2004) Underlying principles of visual shape selectivity in posterior inferotemporal cortex. Nat Neurosci 7(8):880–886.
30. Lowe DG (2004) Distinctive Image Features from Scale-Invariant Keypoints (IJCV).
31. Freeman J, Simoncelli EP (2011) Metamers of the ventral stream. Nat Neurosci 14(9):1195–1201.
32. Downing PE, Chan AW, Peelen MV, Dodds CM, Kanwisher N (2006) Domain specificity
in visual cortex. Cereb Cortex 16(10):1453–1461.
33. Schapire RE (1999) Theoretical Views of Boosting and Applications, Lecture Notes in
Computer Science (Springer, Berlin), Vol 1720, pp 13–25.
34. Geisler WS (2003) Ideal observer analysis. The Visual Neurosciences, eds, Chalupa L,
Werner J (MIT Press, Boston), pp 825–837.
35. Pasupathy A, Connor CE (2002) Population coding of shape in area V4. Nat Neurosci
5(12):1332–1338.
36. Krizhevsky A, Sutskever I, Hinton G (2012) ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems 25:
1097–1105.
37. Marr D, Poggio T, Ullman S (2010) Vision. A Computational Investigation Into the Human Representation and Processing of Visual Information (MIT Press, Cambridge, MA).

Yamins et al.