30 Questions
What do the top layers of deeper CNNs predict more accurately than 'regular-deep' models?
Late phases of IT neural responses
What has been observed for challenge images when using deeper CNNs?
A reduction in the number of challenge images
What is unique about the images that remain unsolved by deeper CNNs?
They have longer OSTs in the IT cortex
What does CORnet model implement?
Within-area recurrent connections with shared weights
What is unique about Pass 4 of the CORnet model?
It is a better predictor of late phases of IT responses
What do the results of CORnet model further argue for?
Recurrent computations in the ventral stream
What do the data not yet explain?
The exact nature of the computational problem solved by recurrent circuits
What is the number of time-steps implemented in the CORnet model?
Five
What happens when an image is rapidly followed by a spatially overlapping mask?
The image is interrupted from further processing
What is a limitation of standard feed-forward models like AlexNet?
They are not robust to occlusion
What is the purpose of adding recurrent connections to the fc7 layer of AlexNet?
To allow the model to perform pattern completion
What is a characteristic of attractor networks like the Hopfield network?
They have all-to-all connections with fixed attractor points
What was observed when evaluating the performance of feed-forward models on partially visible objects?
Performance was comparable to humans at full visibility, but declined at limited visibility
What was found when comparing the latency of neural response and computational distance of each partial object to its whole object category mean?
A modest but significant correlation
What is the name of the model that was developed by adding recurrent connections to the fc7 layer of AlexNet?
RNNh
What is shown in the temporal evolution of the feature representation for RNNh as visualized with stochastic neighborhood embedding?
The iterative refinement of the model's representation
What is the primary advantage of deeper CNNs like Inception-v3 and ResNet-50 compared to shallower networks like AlexNet?
They introduce more nonlinear transformations to the image pixels
What is the primary function of recurrent computations in the primate brain during core object recognition?
To act as additional nonlinear transformations of the initial feedforward
What is the primary purpose of pattern completion in perception?
To enable recognition of poorly visible or occluded objects
What is the result of backward masking on visual object recognition?
It disrupts recognition of partially visible objects
What can be inferred from the representation of whole objects and partial objects in the study?
Partial objects are more similar to each other than to their whole object counterparts.
What is the minimum percentage of object visibility required for the visual system to make inferences?
10%
What happens to the representation of partial objects over time in the clusters of whole images?
They approach the correct category.
What is the primary finding of Tang et al.'s (2018) study on pattern completion?
Pattern completion is implemented by recurrent computations
What is the timing of the saturating performance and correlation with humans in the RNNh model?
Around 10-20 time steps.
What is the result of visual categorization of objects under limited visibility?
Recognition is robust to limited visibility
What is the effect of backward masking on the RNN model's performance?
It impairs the performance.
What is the primary difference between the visual system and current computer vision models?
The visual system has a more efficient implementation of recurrent circuits
What cognitive process is critical for recognition of poorly visible or occluded objects?
Pattern completion.
What is the timing of the physiological responses to heavily occluded objects?
Around 200 ms.
Study Notes
Deeper CNNs and IT Neural Responses
- Deeper CNNs, such as Inception-v3 and ResNet-50, predicted IT neural responses at late phases (150-250 ms) more accurately than shallower models like AlexNet.
- This suggests that deeper CNNs might be approximating 'unrolled' versions of recurrent circuits in the ventral stream.
- Deeper CNNs had fewer challenge images, and the remaining challenge images showed longer OSTs (object-selective tolerance) in the IT cortex.
CORnet Model
- CORnet is a four-layered recurrent neural network model that better predicts IT responses, especially in the late phase.
- The top layer of CORnet has within-area recurrent connections with shared weights and implements five time-steps.
- Pass 1 and pass 2 of the network are better predictors of early time bins, while late passes (especially pass 4) are better at predicting late phases of IT responses.
Recurrent Computations in the Ventral Stream
- Deeper CNNs partially approximate recurrent computations in the ventral stream, which are more efficiently built into the primate brain architecture.
- During core object recognition, recurrent computations act as additional nonlinear transformations of the initial feedforward process.
Image Completion and RNN
- Recurrent computations are necessary for visual pattern completion, enabling recognition of poorly visible or occluded objects.
- The visual system can make inferences even when only 10-20% of the object is visible.
Backward Masking
- Backward masking disrupts recognition of partially visible objects by interrupting processing of the visual stimulus.
- Behavioral performance declined at limited visibility, and standard feed-forward models were not robust to occlusion.
RNNh Model
- The RNNh model, which added recurrent connections to the fc7 layer, demonstrated a significant improvement over the standard AlexNet architecture.
- The RNNh model's performance and correlation with humans saturated at around 10-20 time steps.
- The model's performance was impaired by backward masking, reducing it from 58 ± 2% to 37 ± 2%.
This quiz explores the relationship between deep convolutional neural networks (CNNs) and neural responses in the ventral stream, including the performance of deeper CNNs and their ability to approximate recurrent circuits.
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