Studies of the functional role of the primate ventral visual stream have traditionally focused on object categorization, often ignoring -- despite much prior evidence -- its role in estimating "spatial" latents such as object position and pose. Most leading ventral stream models are derived by optimizing networks for object categorization, which seems to imply that the ventral stream is also derived under such an objective. Here, we explore an alternative hypothesis: Might the ventral stream be optimized for estimating spatial latents? And a closely related question: How different -- if at all -- are representations learned from spatial latent estimation compared to categorization? To ask these questions, we leveraged synthetic image datasets generated by a 3D graphic engine and trained convolutional neural networks (CNNs) to estimate different combinations of spatial and category latents. We found that models trained to estimate just a few spatial latents achieve neural alignment scores comparable to those trained on hundreds of categories, and the spatial latent performance of models strongly correlates with their neural alignment. Spatial latent and category-trained models have very similar -- but not identical -- internal representations, especially in their early and middle layers. We provide evidence that this convergence is partly driven by non-target latent variability in the training data, which facilitates the implicit learning of representations of those non-target latents. Taken together, these results suggest that many training objectives, such as spatial latents, can lead to similar models aligned neurally with the ventral stream. Thus, one should not assume that the ventral stream is optimized for object categorization only. As a field, we need to continue to sharpen our measures of comparing models to brains to better understand the functional roles of the ventral stream.
Deep convolutional neural networks (CNNs) trained for object classification have a number of striking similarities with the primate ventral visual stream. In particular, activity in early, intermediate, and late layers is closely related to activity in V1, V4, and the inferotemporal cortex (IT). This study further compares activity in late layers of object-classification CNNs to activity patterns reported in the IT electrophysiology literature. There are a number of close similarities, including the distributions of population response sparseness across stimuli, and the distribution of size tu…
The illusory simplicity of the feedforward pass: evidence for the dynamical nature of stimulus encoding along the primate ventral stream
Institute of Cognitive Science, University of Osnabrück（奥斯纳布吕克大学认知科学研究所）；Department of Medicine and Surgery, Neuroscience Unit, University of Parma（帕尔马大学医学与外科系神经科学单元）；Department of Vision & Cognition, Netherlands Institute for Neuroscience (KNAW)（荷兰神经科学研究所（荷兰皇家科学院）视觉与认知系）
MRC Cognition and Brain Sciences Unit, University of Cambridge（英国医学研究委员会认知与脑科学单元，剑桥大学）；Radboud University（拉德堡德大学）；Donders Institute for Brain, Cognition and Behaviour（邓德斯脑、认知与行为研究所）；University of Amsterdam（阿姆斯特丹大学）；Freie Universität Berlin（柏林自由大学）；Columbia University（哥伦比亚大学）
The human visual system is an intricate network of brain regions that enables us to recognize the world around us. Despite its abundant lateral and feedback connections, object processing is commonly viewed and studied as a feedforward process. Here, we measure and model the rapid representational dynamics across multiple stages of the human ventral stream using time-resolved brain imaging and deep learning. We observe substantial representational transformations during the first 300 ms of processing within and across ventral-stream regions. Categorical divisions emerge in sequence, cascading …
Visual recognition takes a small fraction of a second and relies on the cascade of signals along the ventral visual stream. Given the rapid path through multiple processing steps between photoreceptors and higher visual areas, information must progress from stage to stage very quickly. This rapid progression of information suggests that fine temporal details of the neural response may be important to the how the brain encodes visual signals. We investigated how changes in the relative timing of incoming visual stimulation affect the representation of object information by recording intracrania…
Hearing Institute（听觉研究所）；Institut Pasteur（巴斯德研究所）；Nicolaus Copernicus University（尼古拉·哥白尼大学）；ISI Foundation（ISI基金会）；Politecnico di Torino（都灵理工大学）
Computational models of vision have traditionally been developed in a bottom-up fashion, by hierarchically composing a series of straightforward operations - i.e. convolution and pooling - with the aim of emulating simple and complex cells in the visual cortex, resulting in the introduction of deep convolutional neural networks (CNNs). Nevertheless, data obtained with recent neuronal recording techniques support that the nature of the computations carried out in the ventral visual stream is not completely captured by current deep CNN models. To fill the gap between the ventral visual stream an…
STSBench: A Large-Scale Dataset for Modeling Neuronal Activity in the Dorsal Stream of Primate Visual Cortex
Neuroscience Interdepartmental Program, Stanford University（斯坦福大学神经科学跨学科项目）；Department of Neurobiology, Stanford University（斯坦福大学神经生物学系）；Howard Hughes Medical Institute, Stanford University（斯坦福大学霍华德·霍夫曼医学研究所）
McGovern Institute for Brain Research, MIT（麻省理工学院麦戈文脑研究所）；KU Leuven（鲁汶大学）；Bay Labs Inc.（Bay Labs公司）；New York University（纽约大学）；Columbia University（哥伦比亚大学）；Stanford University（斯坦福大学）
Deep convolutional artificial neural networks (ANNs) are the leading class of candidate models of the mechanisms of visual processing in the primate ventral stream. While initially inspired by brain anatomy, over the past years, these ANNs have evolved from a simple eight-layer architecture in AlexNet to extremely deep and branching architectures, demonstrating increasingly better object categorization performance, yet bringing into question how brain-like they still are. In particular, typical deep models from the machine learning community are often hard to map onto the brain's anatomy due t…
Noninvasive precision modulation of high-level neural population activity via natural vision perturbations
McGovern Institute for Brain Research, Dept. of Brain and Cognitive Sciences, Massachusetts Institute of Technology（麦戈文脑科学研究所，脑科学与认知科学系，麻省理工学院）；Center for Brains, Minds, and Machines, MIT（脑、心智与机器中心，麻省理工学院）；MIT Quest for Intelligence（麻省理工学院智能探索计划）
Deep supervised neural networks trained to classify objects have emerged as popular models of computation in the primate ventral stream. These models represent information with a high-dimensional distributed population code, implying that inferotemporal (IT) responses are also too complex to interpret at the single-neuron level. We challenge this view by modelling neural responses to faces in the macaque IT with a deep unsupervised generative model, beta-VAE. Unlike deep classifiers, beta-VAE "disentangles" sensory data into interpretable latent factors, such as gender or hair length. We found…
The primate brain contains a hierarchy of visual areas, dubbed the ventral stream, which rapidly computes object representations that are both specific for object identity and relatively robust against identity-preserving transformations like depth-rotations. Current computational models of object recognition, including recent deep learning networks, generate these properties through a hierarchy of alternating selectivity-increasing filtering and tolerance-increasing pooling operations, similar to simple-complex cells operations. While simulations of these models recapitulate the ventral strea…
Self-organized MT Direction Maps Emerge from Spatiotemporal Contrastive Optimization
School of System Science, Beijing Normal University, Beijing 100875, China（北京师范大学系统科学学院，北京100875，中国）；State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China（北京师范大学认知神经科学与学习国家重点实验室，北京100875，中国）；Qiyuan Laboratory, Beijing 100095, China（启元实验室，北京100095，中国）
Convolutional neural network (CNN) driven by image recognition has been shown to be able to explain cortical responses to static pictures at ventral-stream areas. Here, we further showed that such CNN could reliably predict and decode functional magnetic resonance imaging data from humans watching natural movies, despite its lack of any mechanism to account for temporal dynamics or feedback processing. Using separate data, encoding and decoding models were developed and evaluated for describing the bi-directional relationships be-tween the CNN and the brain. Through the encoding models, the CN…
Recognition of objects from partial information presents a significant challenge for theories of vision because it requires spatial integration and extrapolation from prior knowledge. We combined neurophysiological recordings in human cortex with psychophysical measurements and computational modeling to investigate the mechanisms involved in object completion. We recorded intracranial field potentials from 1,699 electrodes in 18 epilepsy patients to measure the timing and selectivity of responses along human visual cortex to whole and partial objects. Responses along the ventral visual stream …
The Center for Brains, Minds and Machines（大脑、心智与机器中心）；Massachusetts Institute of Technology（麻省理工学院）
Modern high-scoring models of vision in the brain score competition do not stem from Vision Transformers. However, in this paper, we provide evidence against the unexpected trend of Vision Transformers (ViT) being not perceptually aligned with human visual representations by showing how a dual-stream Transformer, a CrossViT$~\textit{a la}$ Chen et al. (2021), under a joint rotationally-invariant and adversarial optimization procedure yields 2nd place in the aggregate Brain-Score 2022 competition(Schrimpf et al., 2020b) averaged across all visual categories, and at the time of the competition h…
There has been great progress in understanding of anatomical and functional microcircuitry of the primate cortex. However, the fundamental principles of cortical computation - the principles that allow the visual cortex to bind retinal spikes into representations of objects, scenes and scenarios - have so far remained elusive. In an attempt to come closer to understanding the fundamental principles of cortical computation, here we present a functional, phenomenological model of the primate visual cortex. The core part of the model describes four hierarchical cortical areas with feedforward, la…
