Knowledge Vault 5 /21 - CVPR 2017
The Science of Natural intelligence (NI): Reverse Engineering Primate Visual Perception
James J. DiCarlo
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graph LR classDef perception fill:#f9d4d4, font-weight:bold, font-size:14px classDef neuroscience fill:#d4f9d4, font-weight:bold, font-size:14px classDef modeling fill:#d4d4f9, font-weight:bold, font-size:14px classDef learning fill:#f9f9d4, font-weight:bold, font-size:14px classDef future fill:#f9d4f9, font-weight:bold, font-size:14px A[The Science of
Natural intelligence NI:
Reverse Engineering Primate
Visual Perception] --> B[Humans excel at object perception. 1] A --> C[Neuroscientists study brain for
perception mechanisms. 2] C --> D[Reverse engineering: Models based
on brain constraints. 3] C --> E[Ventral stream critical for
object recognition. 4] E --> F[IT neurons respond to
complex object features. 5] E --> G[Linear classifiers on IT
predict human-level performance. 6] G --> H[~500 IT features sufficient
for human-level performance. 7] C --> I[CNNs fit ventral stream data. 8] I --> J[Task-optimized CNNs predict higher
visual areas better. 9] I --> K[Very deep ImageNet CNNs
dont necessarily explain IT. 10] C --> L[Goal: End-to-end models predicting
responses and behavior. 11] C --> M[Large-scale recordings enable extensive
awake animal data. 12] A --> N[Deep learning and biological
vision models converge. 13] N --> O[CNN-primate performance gap on
large image set. 14] O --> P[Unsolved images take primates
~30ms longer than solved. 15] O --> Q[Performance gap may be
recurrent/feedback connections. 16] Q --> R[Disrupting activity could test
feedbacks causal role. 17] A --> S[Open questions on ventral
development and task learning. 18] S --> T[Neural data constrains models,
granularity unclear. 19] S --> U[Natural image responses can
inform robust vision models. 20] A --> V[Deep learning predicts auditory,
somatosensory responses. 21] A --> W[Brain achieves convolution without
weight sharing, via learning. 22] A --> X[Neuroscience-computer vision collaboration improves
brain models, engineering. 23] X --> Y[Anatomical measurements could inform
bio-constrained architectures. 24] X --> Z[Deep learning + neuroscience advances
BMIs, neurological treatments. 25] class B perception class C,D,E,F,G,H,I,J,K,L,M,O,P,Q,R,S,T,U neuroscience class N,V,W modeling class X,Y,Z future


1.- Humans excel at object perception, identifying objects in scenes despite variations in appearance.

2.- Neuroscientists study the brain to understand the neural mechanisms underlying object perception.

3.- Reverse engineering approach: Build models based on brain constraints and compare with neural data.

4.- The ventral visual stream, including areas V1, V2, V4, and IT, is critical for object recognition.

5.- IT cortex neurons respond to complex object features, but their selectivity is not easily interpreted.

6.- Linear classifiers applied to IT neural population data can predict human-level object recognition performance.

7.- Approximately 500 IT features are sufficient to achieve human-level performance on core object recognition tasks.

8.- Deep convolutional neural networks (CNNs) have shown significant progress in fitting neural data from the ventral stream.

9.- Task-optimized CNNs tend to better predict neural responses in higher visual areas compared to shallower models.

10.- Recent very deep CNNs optimized for ImageNet performance do not necessarily better explain IT neural data.

11.- A goal is to build end-to-end models that accurately predict neural responses and match behavior.

12.- Large-scale chronic array recordings enable collecting extensive neural data from awake behaving animals.

13.- Advances in deep learning have led to a convergence between artificial and biological vision models.

14.- Comparing CNN and primate behavioral patterns on a large image set reveals a performance gap.

15.- "Unsolved" images for CNNs take primates around 30ms longer to accurately decode than "solved" images.

16.- The performance gap may be due to recurrent and feedback connections present in the brain but not CNNs.

17.- Disrupting neural activity could test the causal role of feedback in processing challenging images.

18.- Open questions remain on how the ventral stream develops and supports task learning.

19.- Neural data can constrain models, but the appropriate level of granularity (e.g., spike timing) is unclear.

20.- Analyzing neural responses to natural images (e.g., MS COCO dataset) can inform more robust vision models.

21.- Deep learning models have also shown promise in predicting responses in the auditory and somatosensory systems.

22.- The brain likely achieves a form of convolution without explicit weight sharing, possibly through learning and development.

23.- Continued collaboration between neuroscience and computer vision could lead to improved models of brain function and engineering applications.

24.- Detailed anatomical measurements (e.g., connectomics) could further inform biologically-constrained neural network architectures.

25.- Integrating deep learning and neuroscience can potentially advance brain-machine interfaces and neurological treatments.

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