J. DiCarlo

发表

Stimulus configuration, classical conditioning, and hippocampal function.

N. Schmajuk, J. DiCarlo, 1992, Psychological review.

Why is Real-World Visual Object Recognition Hard?

Nicolas Pinto, James J. DiCarlo, David D. Cox, 2008, PLoS Comput. Biol..

Opinion TRENDS in Cognitive Sciences Vol.11 No.8 Untangling invariant object recognition

David D. Cox, J. DiCarlo, D. Cox, 2022 .

Using goal-driven deep learning models to understand sensory cortex

J. DiCarlo, Daniel Yamins, 2016, Nature Neuroscience.

A High-Throughput Screening Approach to Discovering Good Forms of Biologically Inspired Visual Representation

David D. Cox, James J. DiCarlo, David Doukhan, 2009, PLoS Comput. Biol..

A High-Throughput Screening Approach to Discovering Good Forms of Visual Representation

B. Corda, J. DiCarlo, N. Pinto, 2007 .

A high-throughput screening approach to discovering good forms of inspired visual representation

J. DiCarlo, N. Pinto, D. Cox, 2009 .

'Breaking' position-invariant object recognition

J. DiCarlo, D. Cox, Philip Meier, 2005, Nature Neuroscience.

Establishing Good Benchmarks and Baselines for Face Recognition

Nicolas Pinto, James J. DiCarlo, David Cox, 2008 .

Object Selectivity of Local Field Potentials and Spikes in the Macaque Inferior Temporal Cortex

T. Poggio, R. Quiroga, G. Kreiman, 2006, Neuron.

Selectivity of Local Field Potentials in Macaque Inferior Temporal Cortex

T. Poggio, G. Kreiman, J. DiCarlo, 2004 .

Unsupervised Natural Visual Experience Rapidly Reshapes Size-Invariant Object Representation in Inferior Temporal Cortex

J. DiCarlo, Nuo Li, 2010, Neuron.

Neuronal Learning of Invariant Object Representation in the Ventral Visual Stream Is Not Dependent on Reward

J. DiCarlo, Nuo Li, 2012, The Journal of Neuroscience.

Structure of Receptive Fields in Area 3b of Primary Somatosensory Cortex in the Alert Monkey

J. DiCarlo, K. O. Johnson, S. Hsiao, 1998, The Journal of Neuroscience.

Form representation in monkey inferotemporal cortex is virtually unaltered by free viewing

J. Maunsell, J. DiCarlo, 2000, Nature Neuroscience.

Fast Readout of Object Identity from Macaque Inferior Temporal Cortex

Tomaso Poggio, Gabriel Kreiman, James J DiCarlo, 2005, Science.

How Does the Brain Solve Visual Object Recognition?

James J. DiCarlo, Davide Zoccolan, Nicole C. Rust, 2012, Neuron.

What response properties do individual neurons need to underlie position and clutter "invariant" object recognition?

David D. Cox, J. DiCarlo, D. Cox, 2009, Journal of neurophysiology.

Selectivity and Tolerance (“Invariance”) Both Increase as Visual Information Propagates from Cortical Area V4 to IT

Nicole C. Rust, N. Rust, J. DiCarlo, 2010, The Journal of Neuroscience.

Fine-Scale Spatial Organization of Face and Object Selectivity in the Temporal Lobe: Do Functional Magnetic Resonance Imaging, Optical Imaging, and Electrophysiology Agree?

Doris Y. Tsao, K. Grill-Spector, J. DiCarlo, 2008, The Journal of Neuroscience.

How far can you get with a modern face recognition test set using only simple features?

Nicolas Pinto, James J. DiCarlo, David D. Cox, 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition

Daniel L. K. Yamins, Ha Hong, Charles F. Cadieu, 2014, PLoS Comput. Biol..

How far can you get with a modern face recognition test set using only simple features?

Nicolas Pinto, James J. DiCarlo, David Cox, 2009, CVPR.

Comparing state-of-the-art visual features on invariant object recognition tasks

Nicolas Pinto, James J. DiCarlo, David D. Cox, 2011, 2011 IEEE Workshop on Applications of Computer Vision (WACV).

Learning and neural plasticity in visual object recognition

J. DiCarlo, Z. Kourtzi, 2006, Current Opinion in Neurobiology.

Evidence that recurrent circuits are critical to the ventral stream’s execution of core object recognition behavior

Jonas Kubilius, James J DiCarlo, Elias B. Issa, 2019, Nature Neuroscience.

Comparison of Object Recognition Behavior in Human and Monkey

J. DiCarlo, Kailyn Schmidt, R. Rajalingham, 2014, The Journal of Neuroscience.

Performance-optimized hierarchical models predict neural responses in higher visual cortex

Ha Hong, James J DiCarlo, Ethan A Solomon, 2014, Proceedings of the National Academy of Sciences.

A High-throughput Screening Approach to Discovering Good Forms of Biologically-inspired Visual Representation. Text S2: Technical Details of the Computational Framework

J. DiCarlo, N. Pinto, D. Cox, 2009 .

Anterior inferotemporal neurons of monkeys engaged in object recognition can be highly sensitive to object retinal position.

J. Maunsell, J. DiCarlo, 2003, Journal of neurophysiology.

Neural dynamics at successive stages of the ventral visual stream are consistent with hierarchical error signals

Elias B. Issa, Elias B Issa, Charles F Cadieu, 2018, eLife.

High-resolution three-dimensional microelectrode brain mapping using stereo microfocal X-ray imaging.

David D. Cox, Benjamin B. Andken, J. DiCarlo, 2008, Journal of neurophysiology.

Neurophysiological Organization of the Middle Face Patch in Macaque Inferior Temporal Cortex

Elias B. Issa, J. DiCarlo, Paul Aparicio, 2016, The Journal of Neuroscience.

Precedence of the Eye Region in Neural Processing of Faces

Elias B. Issa, J. DiCarlo, 2012, The Journal of Neuroscience.

Large-Scale, High-Resolution Neurophysiological Maps Underlying fMRI of Macaque Temporal Lobe

Elias B. Issa, J. DiCarlo, A. Papanastassiou, 2013, The Journal of Neuroscience.

A neural network approach to hippocampal function in classical conditioning.

N. Schmajuk, J. DiCarlo, 1991, Behavioral neuroscience.

Reversible inactivation of different millimeter-scale regions of primate IT results in different patterns of core object recognition deficits

James J. DiCarlo, Rishi Rajalingham, J. DiCarlo, 2018, bioRxiv.

Velocity Invariance of Receptive Field Structure in Somatosensory Cortical Area 3b of the Alert Monkey

J. DiCarlo, K. O. Johnson, K. Johnson, 1999, The Journal of Neuroscience.

A neural network approach to hippocampal function in classical conditioning.

N. Schmajuk, J. DiCarlo, 1991, Behavioral neuroscience.

Form processing and attention effects in the somatosensory system

Kenneth O. Johnson, J. DiCarlo, S. Hsiao, 1996 .

Neuron Perspective How Does the Brain Solve Visual Object Recognition ?

Nicole C. Rust, N. Rust, J. DiCarlo, 2022 .

Optogenetic and pharmacological suppression of spatial clusters of face neurons reveal their causal role in face gender discrimination

J. DiCarlo, E. Boyden, Arash Afraz, 2015, Proceedings of the National Academy of Sciences.

Does Learned Shape Selectivity in Inferior Temporal Cortex Automatically Generalize Across Retinal Position?

J. DiCarlo, D. Cox, 2008, The Journal of Neuroscience.

A stable topography of selectivity for unfamiliar shape classes in monkey inferior temporal cortex.

N. Kanwisher, J. DiCarlo, W. Vanduffel, 2008, Cerebral cortex.

Discrimination Training Alters Object Representations in Human Extrastriate Cortex

N. Kanwisher, J. DiCarlo, C. Baker, 2006, The Journal of Neuroscience.

Balanced Increases in Selectivity and Tolerance Produce Constant Sparseness along the Ventral Visual Stream

James J. DiCarlo, Nicole C. Rust, N. Rust, 2012, The Journal of Neuroscience.

Multi-scale hierarchical neural network models that bridge from single neurons in the primate primary visual cortex to object recognition behavior

Martin Schrimpf, James J. DiCarlo, Tiago Marques, 2021, bioRxiv.

Reversible Inactivation of Different Millimeter-Scale Regions of Primate IT Results in Different Patterns of Core Object Recognition Deficits

James J. DiCarlo, Rishi Rajalingham, J. DiCarlo, 2018, Neuron.

Object Recognition? Need to Underlie Position and Clutter ''Invariant'' What Response Properties Do Individual Neurons

David D. Cox, N. P. Bichot, Ethan M. Meyers, 2011 .

Evidence that the ventral stream codes the errors used in hierarchical 1 inference and learning 2 Running title : Error coding in the ventral stream 3 4

Elias B. Issa, J. DiCarlo, C. Cadieu, 2016 .

The inferior temporal cortex is a potential cortical precursor of orthographic processing in untrained monkeys

J. DiCarlo, S. Dehaene, Kohitij Kar, 2020, Nature Communications.

39th Annual European Brain and Behaviour Society Abstracts

Maria V. Sanchez-Vives, R. C. Honey, J. L. Moore, 2007, Neural Plasticity.

The Emergence of Face-Selective Units in a Model that Has Never Seen a Face.

Nancy Kanwisher, Ha Hong, Daniel Yamins, 2015, Journal of vision.

Integrative Benchmarking to Advance Neurally Mechanistic Models of Human Intelligence

Jonas Kubilius, Martin Schrimpf, James J. DiCarlo, 2020, Neuron.

Adversarially trained neural representations may already be as robust as corresponding biological neural representations

A. Madry, J. DiCarlo, Joel Dapello, 2022, ArXiv.

Neural Population Geometry Reveals the Role of Stochasticity in Robust Perception pdf

James J. DiCarlo, David D. Cox, Josh H. McDermott, 2021, NeurIPS.

Unsupervised neural network models of the ventral visual stream

Michael C. Frank, Martin Schrimpf, Chengxu Zhuang, 2020, Proceedings of the National Academy of Sciences.

Supporting Online Material for Fast Readout of Object Identity from Macaque Inferior Temporal Cortex

Tomaso Poggio, Gabriel Kreiman, James J. DiCarlo, 2005 .

Aligning Model and Macaque Inferior Temporal Cortex Representations Improves Model-to-Human Behavioral Alignment and Adversarial Robustness

David D. Cox, J. DiCarlo, D. Cox, 2022, bioRxiv.

Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution

Eero P. Simoncelli, Doris Y. Tsao, Adam H. Marblestone, 2022, ArXiv.

Wiring Up Vision: Minimizing Supervised Synaptic Updates Needed to Produce a Primate Ventral Stream

Martin Schrimpf, James J. DiCarlo, Franziska Geiger, 2020, bioRxiv.

Large-scale Characterization of a Universal and Compact Visual Perceptual Space

Ha Hong, James J. DiCarlo, Daniel Yamins, 2014 .

Author response: Unsupervised changes in core object recognition behavior are predicted by neural plasticity in inferior temporal cortex

Ha Hong, James J DiCarlo, Xiaoxuan Jia, 2021 .

Comparison of Object Recognition Behavior in Human and Monkey

James J. DiCarlo, Rishi Rajalingham, Kailyn Schmidt, 2014, Journal of Neuroscience.

An empirical assay of view-invariant object learning in humans and comparison with baseline image-computable models

J. DiCarlo, Michael J. Lee, 2022 .

Feedforward deep neural networks diverge from humans and monkeys on core visual object recognition behavior

J. DiCarlo, R. Rajalingham, 2017 .

ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation

Josh H. McDermott, Daniel L. K. Yamins, Julian De Freitas, 2020, NeurIPS Datasets and Benchmarks.

An empirical assay of visual object learning in humans and baseline image-computable models

J. DiCarlo, Michael J. Lee, 2023, bioRxiv.

A hippocampal theory of schizophrenia

N. Schmajuk, J. DiCarlo, 1991, Behavioral and Brain Sciences.

Topographic deep artificial neural networks reproduce the hallmarks of the primate inferior temporal cortex face processing network

Nancy Kanwisher, Kamila M. Jozwik, Michael A. Cohen, 2020, bioRxiv.

A potential cortical precursor of visual word form recognition in untrained monkeys

James J. DiCarlo, Stanislas Dehaene, Rishi Rajalingham, 2019, bioRxiv.

Evidence that recurrent circuits are critical to the ventral stream’s execution of core object recognition behavior

James J. DiCarlo, Elias B. Issa, Kubilius Jonas, 2018, Nature Neuroscience.

Simple Learned Weighted Sums of Inferior Temporal Neuronal Firing Rates Accurately Predict Human Core Object Recognition Performance

Ha Hong, Najib J Majaj, James J DiCarlo, 2015, The Journal of Neuroscience.

A rodent model for the study of invariant visual object recognition

James J DiCarlo, David D. Cox, Davide Zoccolan, 2009, Proceedings of the National Academy of Sciences.

Recurrent Connections in the Primate Ventral Visual Stream Mediate a Tradeoff Between Task Performance and Network Size During Core Object Recognition

Daniel L. K. Yamins, J. DiCarlo, David Sussillo, 2022, bioRxiv.

Explicit information for category-orthogonal object properties increases along the ventral stream

Ha Hong, Daniel L K Yamins, Najib J Majaj, 2016, Nature Neuroscience.

Fast recurrent processing via ventral prefrontal cortex is needed by the primate ventral stream for robust core visual object recognition

Kohitij Kar, James J DiCarlo, J. DiCarlo, 2020, bioRxiv.

The Neural Representation Benchmark and its Evaluation on Brain and Machine pdf

Ha Hong, Nicolas Pinto, James J. DiCarlo, 2013, ICLR.

Unsupervised changes in core object recognition behavior are predicted by neural plasticity in inferior temporal cortex

Ha Hong, James J. DiCarlo, Xiaoxuan Jia, 2020, bioRxiv.

Unsupervised Natural Experience Rapidly Alters Invariant Object Representation in Visual Cortex

James J. DiCarlo, Nuo Li, J. DiCarlo, 2008, Science.

Simulating a Primary Visual Cortex at the Front of CNNs Improves Robustness to Image Perturbations

Martin Schrimpf, David D. Cox, James J. DiCarlo, 2020, bioRxiv.

Are Topographic Deep Convolutional Neural Networks Better Models of the Ventral Visual Stream?

Nancy Kanwisher, Kamila M. Jozwik, Hyodong Lee, 2019, 2019 Conference on Cognitive Computational Neuroscience.

Topographic Deep Artificial Neural Networks (TDANNs) predict face selectivity topography in primate inferior temporal (IT) cortex

James J. DiCarlo, Hyodong Lee, J. DiCarlo, 2019, 1909.09847.

Eight open questions in the computational modeling of higher sensory cortex

James J DiCarlo, Daniel L. K. Yamins, Daniel LK Yamins, 2016, Current Opinion in Neurobiology.

Spatial and Temporal Structure of Receptive Fields in Primate Somatosensory Area 3b: Effects of Stimulus Scanning Direction and Orientation

J. DiCarlo, K O Johnson, J J DiCarlo, 2000, The Journal of Neuroscience.

Integrative Benchmarking to Advance Neurally Mechanistic Models of Human Intelligence

Jonas Kubilius, Martin Schrimpf, James J. DiCarlo, 2020, Neuron.

A performance-optimized model of neural responses across the ventral visual stream

Ha Hong, James J. DiCarlo, Justin L. Gardner, 2016, bioRxiv.

Computational models of category-selective brain regions enable high-throughput tests of selectivity

N. Kanwisher, J. DiCarlo, P. Bashivan, 2021, Nature Communications.

Neural population control via deep image synthesis

James J DiCarlo, Pouya Bashivan, Kohitij Kar, 2018, Science.

Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like?

Jonas Kubilius, Martin Schrimpf, Ha Hong, 2018, bioRxiv.

Large-Scale, High-Resolution Comparison of the Core Visual Object Recognition Behavior of Humans, Monkeys, and State-of-the-Art Deep Artificial Neural Networks

James J DiCarlo, Pouya Bashivan, Rishi Rajalingham, 2018, The Journal of Neuroscience.

CORnet: Modeling the Neural Mechanisms of Core Object Recognition

Aran Nayebi, Jonas Kubilius, Martin Schrimpf, 2018, bioRxiv.

Goal-Driven Recurrent Neural Network Models of the Ventral Visual Stream

Daniel L. K. Yamins, J. DiCarlo, David Sussillo, 2021 .

Evidence that recurrent circuits are critical to the ventral stream’s execution of core object recognition behavior

Elias B. Issa, J. DiCarlo, Kohitij Kar, 2018, Nature Neuroscience.

Natural and Artificial Intelligence: A brief introduction to the interplay between AI and neuroscience research

Terry Sejnowski, Yukiyasu Kamitani, Takatoshi Hikida, 2021, Neural Networks.

Carlo Baldassi, Riccardo Zecchina, James J. DiCarlo, 2013, PLoS Comput. Biol..

Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs

Elias B. Issa, Daniel L. K. Yamins, J. DiCarlo, 2019, NeurIPS.

Recurrent Connections in the Primate Ventral Visual Stream Mediate a Tradeoff Between Task Performance and Network Size During Core Object Recognition

Daniel L. K. Yamins, J. DiCarlo, David Sussillo, 2021, bioRxiv.

Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution

Eero P. Simoncelli, Doris Y. Tsao, Adam H. Marblestone, 2022, ArXiv.

Task-Driven Convolutional Recurrent Models of the Visual System

Surya Ganguli, Aran Nayebi, Jonas Kubilius, 2018, NeurIPS.

Trade-Off between Object Selectivity and Tolerance in Monkey Inferotemporal Cortex

Tomaso Poggio, James J DiCarlo, Davide Zoccolan, 2007, The Journal of Neuroscience.

Marking microelectrode penetrations with fluorescent dyes

John W. Lane, J. DiCarlo, S. Hsiao, 1996, Journal of Neuroscience Methods.

The ThreeDWorld Transport Challenge: A Visually Guided Task-and-Motion Planning Benchmark Towards Physically Realistic Embodied AI pdf

Joshua B. Tenenbaum, Antonio Torralba, James J DiCarlo, 2021, 2022 International Conference on Robotics and Automation (ICRA).

Multiple Object Response Normalization in Monkey Inferotemporal Cortex

James J DiCarlo, David D. Cox, Davide Zoccolan, 2005, The Journal of Neuroscience.

A Unifying Principle for the Functional Organization of Visual Cortex

K. Grill-Spector, J. DiCarlo, Daniel Yamins, 2023, bioRxiv.

Catalyzing next-generation Artificial Intelligence through NeuroAI

Eero P. Simoncelli, Konrad Paul Kording, Blake A. Richards, 2023, Nature Communications.

Neural Mechanisms Underlying Visual Object Recognition.

Daniel L K Yamins, James J DiCarlo, Daniel L. K. Yamins, 2014, Cold Spring Harbor symposia on quantitative biology.

Adversarially trained neural representations are already as robust as biological neural representations

A. Madry, J. DiCarlo, Joel Dapello, 2022, ICML.

Hierarchical Modular Optimization of Convolutional Networks Achieves Representations Similar to Macaque IT and Human Ventral Stream

Ha Hong, James J. DiCarlo, Charles F. Cadieu, 2013, NIPS.

Selective behavioral deficits from focal inactivation of primate inferior temporal (IT) cortex: a new quantitative constraint for models of core object recognition

James J. DiCarlo, Rishi Rajalingham, Hyodong Lee, 2018 .

Unsupervised changes in core object recognition behavior are predicted by unsupervised neural plasticity in inferior temporal cortex

J. DiCarlo, Ha Hong, Xiaoxuan Jia, 2020 .

Unsupervised changes in core object recognition behavioral performance are accurately predicted by unsupervised neural plasticity in inferior temporal cortex

J. DiCarlo, Ha Hong, Xiaoxuan Jia, 2020 .

Functional Properties of Circuits, Cellular Populations, and Areas

Gordon Pipa, Wolf Singer, John H. Reynolds, 2019, The Neocortex.

Winning Faces: Model of basic primate visual processing predicts elections

J. DiCarlo, N. Pinto, Darren Schreiber, 2012 .

Can Deep Neural Networks Rival Human Ability to Generalize in Core Object Recognition

Jonas Kubilius, James J. DiCarlo, Kohitij Kar, 2018 .

Measuring the Alignment of ANNs and Primate V1 on Luminance and Contrast Response Characteristics

J. DiCarlo, Tiago Marques, Stephanie Olaiya, 2022 .

NeuronsInteractions in Macaque Inferior Temporal

G. Orban, R. Vogels, J. DiCarlo, 2015 .

Receptive field structure in cortical area 3b of the alert monkey

James J DiCarlo, J. DiCarlo, Kenneth O Johnson, 2002, Behavioural Brain Research.

Using neuronal latency to determine sensory-motor processing pathways in reaction time tasks.

James J DiCarlo, J. Maunsell, J. DiCarlo, 2005, Journal of neurophysiology.

Steven Hsiao: In Memoriam

J. DiCarlo, S. Bensmaia, 2015, Neuron.

Combining Different V1 Brain Model Variants to Improve Robustness to Image Corruptions in CNNs pdf

James J. DiCarlo, Tiago Marques, Joel Dapello, 2021, ArXiv.

How well do rudimentary plasticity rules predict adult visual object learning?

J. DiCarlo, Michael J. Lee, 2023, bioRxiv.

CORnet: Modeling the Neural Mechanisms of Core Object Recognition

J. DiCarlo, Daniel Yamins, Aran Nayebi, 2018, bioRxiv.

Evidence that the ventral stream codes the errors used in hierarchical inference and learning

James J. DiCarlo, Charles F. Cadieu, Elias B. Issa, 2016, bioRxiv.

Neural dynamics at successive stages of the ventral visual stream are 1 consistent with hierarchical error signals 2 Running title : Error signaling in the ventral stream 3

James J. DiCarlo, Charles F. Cadieu, Elias B. Issa, 2018 .

To find better neural network models of human vision, find better neural network models of primate vision

Nancy Kanwisher, Martin Schrimpf, James J. DiCarlo, 2019, bioRxiv.

Unsupervised changes in core object recognition behavioral performance are accurately predicted by unsupervised neural plasticity in inferior temporal cortex

J. DiCarlo, Ha Hong, Xiaoxuan Jia, 2020 .

Teacher Guided Architecture Search

Pouya Bashivan, James J. DiCarlo, Mark Tensen, 2018, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

Linking image-by-image population dynamics in the macaque inferior temporal cortex to core object recognition behavior

James J. DiCarlo, Kohitij Kar, Kailyn Schmidt, 2018 .

Robustified ANNs Reveal Wormholes Between Human Category Percepts

J. DiCarlo, Michael J. Lee, Guy Gaziv, 2023, arXiv.org.

Primate Inferotemporal Cortex Neurons Generalize Better to Novel Image Distributions Than Corresponding Deep Neural Networks Units

J. DiCarlo, Tiago Marques, Martin Schrimpf, 2023 .

Implementing Divisive Normalization in CNNs Improves Robustness to Common Image Corruptions

Lawrence C. Udeigwe, J. DiCarlo, Tiago Marques, 2022 .

Progress in a Moscow children's burn unit: a joint Russian-American collaboration.

J. DiCarlo, D. Featherston, J. Remensnyder, 1995, Burns : journal of the International Society for Burn Injuries.

How Does the Brain Solve Visual Object Recognition?

Nicole C. Rust, J. DiCarlo, D. Zoccolan, 2012, Neuron.

Precedence of the Eye Region in Neural Processing of Faces

Elias B. Issa, J. DiCarlo, 2012, The Journal of Neuroscience.

Ultra-fast Object Recognition from Few Spikes

Gabriel Kreiman, Tomaso Poggio, James J. DiCarlo, 2005 .

Neuroscience: Making faces in the brain

J. DiCarlo, 2006, Nature.

Unsupervised Natural Experience Rapidly Alters Invariant Object Representation in Visual Cortex

J. DiCarlo, Nuo Li, 2008, Science.

MIT Open Access Articles Comparing state-of-the-art visual features on invariant object recognition tasks

J. DiCarlo, Youssef Barhomi, Nicolas Pinto, 2022 .

Do Topographic Deep ANN Models of the Primate Ventral Stream Predict the Perceptual Effects of Direct IT Cortical Interventions?

J. DiCarlo, Martin Schrimpf, Eshed Margalit, 2024, bioRxiv.