@booklet {185, title = {How does the primate brain combine generative and discriminative computations in vision?}, year = {2024}, abstract = {

Vision is widely understood as an inference problem. However, two contrasting conceptions of the inference process have each been influential in research on biological vision as well as the engineering of machine vision. The first emphasizes bottom-up signal flow, describing vision as a largely feedforward, discriminative inference process that filters and transforms the visual information to remove irrelevant variation and represent behaviorally relevant information in a format suitable for downstream functions of cognition and behavioral control. In this conception, vision is driven by the sensory data, and perception is direct because the processing proceeds from the data to the latent variables of interest. The notion of \"inference\" in this conception is that of the engineering literature on neural networks, where feedforward convolutional neural networks processing images are said to perform inference. The alternative conception is that of vision as an inference process in Helmholtz\&$\#$39;s sense, where the sensory evidence is evaluated in the context of a generative model of the causal processes giving rise to it. In this conception, vision inverts a generative model through an interrogation of the evidence in a process often thought to involve top-down predictions of sensory data to evaluate the likelihood of alternative hypotheses. The authors include scientists rooted in roughly equal numbers in each of the conceptions and motivated to overcome what might be a false dichotomy between them and engage the other perspective in the realm of theory and experiment. The primate brain employs an unknown algorithm that may combine the advantages of both conceptions. We explain and clarify the terminology, review the key empirical evidence, and propose an empirical research program that transcends the dichotomy and sets the stage for revealing the mysterious hybrid algorithm of primate vision.

}, keywords = {discriminative model, generativemodel, Primatevision, visual inference}, doi = { https://doi.org/10.48550/arXiv.2401.06005}, url = {https://arxiv.org/pdf/2401.06005.pdf}, author = {Benjamin Peters and James J. DiCarlo and Todd Gureckis and Ralf Haefner and Leyla Isik and Joshua Tenenbaum and Talia Konkle and Thomas Naselaris and Kimberly Stachenfeld and Zenna Tavares and Doris Tsao and Ilker Yildirim and Nikolaus Kriegeskorte} } @article {184, title = {How well do rudimentary plasticity rules predict adult visual object learning?}, journal = {PLOS Computational Biology}, volume = {19}, year = {2023}, month = {Nov-12-2023}, pages = {e1011713}, abstract = {

A core problem in visual object learning is using a finite number of images of a new object to accurately identify that object in future, novel images.

One longstanding, conceptual hypothesis asserts that this core problem is solved by adult brains through two connected mechanisms: 1) the re-representation of incoming retinal images as points in a fixed, multidimensional neural space, and 2) the optimization of linear decision boundaries in that space, via simple plasticity rules applied to a single downstream layer.

Though this scheme is biologically plausible, the extent to which it explains learning behavior in humans has been unclear\—in part because of a historical lack of image-computable models of the putative neural space, and in part because of a lack of measurements of human learning behaviors in difficult, naturalistic settings.

Here, we addressed these gaps by 1) drawing from contemporary, image-computable models of the primate ventral visual stream to create a large set of testable learning models (n = 2,408 models), and 2) using online psychophysics to measure human learning trajectories over a varied set of tasks involving novel 3D objects (n = 371,000 trials), which we then used to develop (and\ publicly release) empirical benchmarks for comparing learning models to humans.

We evaluated each learning model on these benchmarks, and found those based on deep, high-level representations from neural networks were surprisingly aligned with human behavior. While no tested model explained the entirety of replicable human behavior, these results establish that rudimentary plasticity rules, when combined with appropriate visual representations, have high explanatory power in predicting human behavior with respect to this core object learning problem.

}, issn = {1553-734X}, doi = {10.1371/journal.pcbi.1011713}, url = {https://dx.plos.org/10.1371/journal.pcbi.1011713}, author = {Lee, Michael J. and DiCarlo, James J.}, editor = {Kietzmann, Tim Christian} } @article {183, title = {How well do rudimentary plasticity rules predict adult visual object learning?}, journal = {PLOS Computational Biology}, volume = {19}, year = {2023}, month = {Nov-12-2023}, pages = {e1011713}, abstract = {

A core problem in visual object learning is using a finite number of images of a new object to accurately identify that object in future, novel images.

One longstanding, conceptual hypothesis asserts that this core problem is solved by adult brains through two connected mechanisms: 1) the re-representation of incoming retinal images as points in a fixed, multidimensional neural space, and 2) the optimization of linear decision boundaries in that space, via simple plasticity rules applied to a single downstream layer.

Though this scheme is biologically plausible, the extent to which it explains learning behavior in humans has been unclear\—in part because of a historical lack of image-computable models of the putative neural space, and in part because of a lack of measurements of human learning behaviors in difficult, naturalistic settings.

Here, we addressed these gaps by 1) drawing from contemporary, image-computable models of the primate ventral visual stream to create a large set of testable learning models (n = 2,408 models), and 2) using online psychophysics to measure human learning trajectories over a varied set of tasks involving novel 3D objects (n = 371,000 trials), which we then used to develop (and\ publicly release) empirical benchmarks for comparing learning models to humans.

We evaluated each learning model on these benchmarks, and found those based on deep, high-level representations from neural networks were surprisingly aligned with human behavior. While no tested model explained the entirety of replicable human behavior, these results establish that rudimentary plasticity rules, when combined with appropriate visual representations, have high explanatory power in predicting human behavior with respect to this core object learning problem.

}, issn = {1553-734X}, doi = {10.1371/journal.pcbi.1011713}, url = {https://dx.plos.org/10.1371/journal.pcbi.1011713}, author = {Lee, Michael J. and DiCarlo, James J.}, editor = {Kietzmann, Tim Christian} } @conference {37, title = {Hierarchical Modular Optimization of Convolutional Networks Achieves Representations Similar to Macaque IT and Human Ventral Stream}, booktitle = {Advances in Neural Information Processing Systems}, year = {2013}, month = {12/2013}, address = {Lake Tahoe, Nevada, United States.}, abstract = {

Humans recognize visually-presented objects rapidly and accurately. To understand this ability, we seek to construct models of the ventral stream, the series of cortical areas thought to subserve object recognition. One tool to assess the quality of a model of the ventral stream is the Representational Dissimilarity Matrix (RDM), which uses a set of visual stimuli and measures the distances produced in either the brain (i.e. fMRI voxel responses, neural firing rates) or in models (features). Previous work has shown that all known models of the ventral stream fail to capture the RDM pattern observed in either IT cortex, the highest ventral area, or in the human ventral stream. In this work, we construct models of the ventral stream using a novel optimization procedure for category-level object recognition problems, and produce RDMs resembling both macaque IT and human ventral stream. The model, while novel in the optimization procedure, further develops a long-standing functional hypothesis that the ventral visual stream is a hierarchically arranged series of processing stages optimized for visual object recognition.

}, doi = {https://papers.nips.cc/paper/4991-hierarchical-modular-optimization-of-convolutional-networks-achieves-representations-similar-to-macaque-it-and-human-ventral-stream}, url = {https://papers.nips.cc/paper/4991-hierarchical-modular-optimization-of-convolutional-networks-achieves-representations-similar-to-macaque-it-and-human-ventral-stream.pdf}, author = {Daniel L. K. Yamins and Ha Hong and Cadieu, Charles F and DiCarlo, James J.} } @article {41, title = {How Does the Brain Solve Visual Object Recognition?}, journal = {Neuron}, volume = {73}, year = {2012}, month = {01/2012}, pages = {415 - 434}, abstract = {

Mounting evidence suggests that \&$\#$39;core object recognition,\&$\#$39; the ability to rapidly recognize objects despite substantial appearance variation, is solved in the brain via a cascade of reflexive, largely feedforward computations that culminate in a powerful neuronal representation in the inferior temporal cortex. However, the algorithm that produces this solution remains poorly understood. Here we review evidence ranging from\ individual neurons and neuronal populations to behavior and computational models. We propose that understanding this algorithm will require using neuronal and psychophysical data to sift through many computational models, each based on building blocks of small, canonical subnetworks with a common functional goal.

}, issn = {08966273}, doi = {10.1016/j.neuron.2012.01.010}, url = {https://www.sciencedirect.com/science/article/pii/S089662731200092X}, author = {DiCarlo, ~J. and Zoccolan, Davide and Rust, ~C.} } @proceedings {52, title = {A High-Throughput Screening Approach to Biologically-Inspired Object Recognition}, journal = {Learning Workshop-Computation and Systems Neuroscience (COSYNE)}, year = {2010}, month = {03/2010}, address = {Snow Bird, Utah, USA}, author = {Pinto, Nicolas and DiCarlo, James J. and Cox, David D.} } @proceedings {51, title = {Human versus machine: comparing visual object recognition systems on a level playing field}, journal = {Computation and Systems Neuroscience (COSYNE)}, year = {2010}, month = {02/2010}, address = {Salt Lake City, Utah, USA}, abstract = {

It is received wisdom that biological visual systems easily outmatch current artificial systems at complex visual tasks like object recognition. But have the appropriate comparisons been made? Because artificial systems are improving every day, they may surpass human performance some day. We must understand our progress toward reaching that day, because that success is one of several necessary requirements for \"understanding\" visual object recognition. How large (or small) is the difference in performance between current state-of-the-art object recognition systems and the primate visual system? In practice, the performance comparison of any two object recognition systems requires a focus on the computational crux of the problem and sets of images that engage it. Although it is widely believed that tolerance (\"invariance\") to identity-preserving image variation (e.g. variation in object position, scale, pose, illumination) is critical, systematic comparisons of state-of-the-art artificial visual representations almost always rely on \"natural\" image databases that can fail to probe the ability of a recognition system to solve the invariance problem [Pinto et al PLoS08, COSYNE08, ECCV08, CVPR09]. Thus, to understand how well current state-of-the-art visual representations perform relative to each other, relative to low-level neuronal representations (e.g. retinal-like and V1-like), and relative to high-level representations (e.g. human performance), we tested all of these representations on a common set of visual object recognition tasks that directly engage the invariance problem. Specifically, we used a synthetic testing approach that allows direct engagement of the invariance problem, as well as knowledge and control of all the key parameters that make object recognition challenging. We successfully re-implemented a variety of state-of-the-art visual representations, and we confirmed the high published performance of all of these state-of-the-art representations on large, complex \"natural\â\€ image benchmarks. Surprisingly, we found that most of these representations were weak on our simple synthetic tests of invariant recognition, and only high-level biologically-inspired representations showed performance gains above the neuroscience \"null\" representation (V1-like). While in aggregate, we found that the performance of these state-of-the-art representations pales in comparison to human performance, humans and computers seem to fail in different and potentially enlightening ways when faced with the problem of invariance. We also show how our synthetic testing approach can more deeply illuminate the strengths and weaknesses of different visual representations and thus guide progress on invariant object recognition.

}, doi = {10.3389/conf.fnins.2010.03.00283}, url = {https://www.frontiersin.org/Community/AbstractDetails.aspx?ABS_DOI=10.3389/conf.fnins.2010.03.00283\&eid=770\&sname=Computational_and_Systems_Neuroscience_2010}, author = {Pinto, Nicolas and Majaj, Najib J and Barhomi, Y and Solomon, E. A. and Cox, David D. and DiCarlo, James J.} } @article {56, title = {A High-Throughput Screening Approach to Discovering Good Forms of Biologically Inspired Visual Representation}, journal = {PLoS Computational Biology}, volume = {5}, year = {2009}, month = {2009}, pages = {e1000579}, abstract = {

While many models of biological object recognition share a common set of \"broad-stroke\" properties, the performance of any one model depends strongly on the choice of parameters in a particular instantiation of that model\–e.g., the number of units per layer, the size of pooling kernels, exponents in normalization operations, etc. Since the number of such parameters (explicit or implicit) is typically large and the computational cost of evaluating one particular parameter set is high, the space of possible model instantiations goes largely unexplored. Thus, when a model fails to approach the abilities of biological visual systems, we are left uncertain whether this failure is because we are missing a fundamental idea or because the correct \"parts\" have not been tuned correctly, assembled at sufficient scale, or provided with enough training. Here, we present a high-throughput approach to the exploration of such parameter sets, leveraging recent advances in stream processing hardware (high-end {NVIDIA} graphic cards and the {PlayStation} 3\&$\#$39;s {IBM} Cell Processor). In analogy to high-throughput screening approaches in molecular biology and genetics, we explored thousands of potential network architectures and parameter instantiations, screening those that show promising object recognition performance for further analysis. We show that this approach can yield significant, reproducible gains in performance across an array of basic object recognition tasks, consistently outperforming a variety of state-of-the-art purpose-built vision systems from the literature. As the scale of available computational power continues to expand, we argue that this approach has the potential to greatly accelerate progress in both artificial vision and our understanding of the computational underpinning of biological vision.

}, doi = {10.1371/journal.pcbi.1000579}, url = {https://dx.plos.org/10.1371/journal.pcbi.1000579}, author = {Pinto, Nicolas and Doukhan, David and DiCarlo, James J. and Cox, David D.}, editor = {Friston, Karl J.} } @conference {57, title = {How far can you get with a modern face recognition test set using only simple features?}, booktitle = {IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR Workshops)}, year = {2009}, month = {06/2009}, publisher = {IEEE}, organization = {IEEE}, address = {Miami, FL}, abstract = {

In recent years, large databases of natural images have become increasingly popular in the evaluation of face and object recognition algorithms. However, Pinto et al. previously illustrated an inherent danger in using such sets, showing that an extremely basic recognition system, built on a trivial feature set, was able to take advantage of low-level regularities in popular object and face recognition sets, performing on par with many state-of-the-art systems. Recently, several groups have raised the performance \“bar\” for these sets, using more advanced classification tools. However, it is difficult to know whether these improvements are due to progress towards solving the core computational problem, or are due to further improvements in the exploitation of low-level regularities. Here, we show that even modest optimization of the simple model introduced by Pinto et al. using modern multiple kernel learning (MKL) techniques once again yields \“state-of-the-art\” performance levels on a standard face recognition set (\“labeled faces in the wild\”). However, at the same time, even with the inclusion of MKL techniques, systems based on these simple features still fail on a synthetic face recognition test that includes more \“realistic\” view variation by design. These results underscore the importance of building test sets focussed on capturing the central computational challenges of real-world face recognition.

}, isbn = {978-1-4244-3992-8}, doi = {10.1109/CVPR.2009.5206605}, url = {https://ieeexplore.ieee.org/document/5206605}, author = {Pinto, Nicolas and DiCarlo, James J. and Cox, David D.} } @article {64, title = {High-Resolution Three-Dimensional Microelectrode Brain Mapping Using Stereo Microfocal X-ray Imaging}, journal = {Journal of Neurophysiology}, volume = {100}, year = {2008}, month = {01/2008}, pages = {2966 - 2976}, abstract = {

Much of our knowledge of brain function has been gleaned from studies using microelectrodes to characterize the response properties of individual neurons in vivo. However, because it is difficult to accurately determine the location of a microelectrode tip within the brain, it is impossible to systematically map the fine three-dimensional spatial organization of many brain areas, especially in deep structures. Here, we present a practical method based on digital stereo microfocal X-ray imaging that makes it possible to estimate the three-dimensional position of each and every microelectrode recording site in \"real time\" during experimental sessions. We determined the system\&$\#$39;s ex vivo localization accuracy to be better than 50 microm, and we show how we have used this method to coregister hundreds of deep-brain microelectrode recordings in monkeys to a common frame of reference with median error of {\textless}150 microm. We further show how we can coregister those sites with magnetic resonance images {(MRIs),} allowing for comparison with anatomy, and laying the groundwork for more detailed electrophysiology/functional {MRI} comparison. Minimally, this method allows one to marry the single-cell specificity of microelectrode recording with the spatial mapping abilities of imaging techniques; furthermore, it has the potential of yielding fundamentally new kinds of high-resolution maps of brain function.

}, issn = {0022-3077}, doi = {10.1152/jn.90672.2008}, url = {https://www.physiology.org/doi/10.1152/jn.90672.2008}, author = {Cox, David D. and Papanastassiou, Alexander M. and Oreper, Daniel and Andken, Benjamin B. and DiCarlo, James J.} } @proceedings {71, title = {A high-throughput screening approach to discovering good forms of visual representation}, journal = {Computation and Systems Neuroscience (COSYNE)}, year = {2008}, month = {03/2008}, address = {Salt Lake City, Utah, USA}, author = {Cox, David D. and Pinto, Nicolas and Doukhan, David and Corda, B and DiCarlo, James J.} } @article {120, title = {A hippocampal theory of schizophrenia}, journal = {Behavioral and Brain Sciences}, volume = {14}, year = {1991}, month = {01/1991}, pages = {47-49}, issn = {0140-525X}, doi = {10.1017/S0140525X00065353}, url = {https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/article/hippocampal-theory-of-schizophrenia/D6F6C7FD873BCA595607226B4BD349B1}, author = {Schmajuk, Nestor A. and DiCarlo, James J.} }