Related Experiment Video
Updated: Sep 5, 2025
07:08
Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings
Published on: August 1, 2018
8.4K
Recurrent Connections in the Primate Ventral Visual Stream Mediate a Trade-Off Between Task Performance and Network
Aran Nayebi1, Javier Sagastuy-Brena2, Daniel M Bear3
1Stanford University, Stanford, CA 94305, U.S.A. anayebi@stanford.edu.
Neural Computation
|July 7, 2022
Summary
Recurrent convolutional neural networks (ConvRNNs) better explain visual processing in the brain than feedforward networks. These models match primate behavior and neural activity, suggesting temporal complexity drives computational power in the ventral visual stream.
Area of Science:
- Computational neuroscience
- Computer vision
- Neuroscience
Background:
- The function of feedback connections in the ventral visual stream for object recognition remains unclear.
- Previous models often lack direct comparability to standard feedforward networks.
Purpose of the Study:
- To develop and evaluate task-optimized convolutional recurrent (ConvRNN) network models that mimic the ventral pathway's timing and neuroanatomy.
- To compare ConvRNNs and convolutional neural networks (CNNs) against detailed primate behavioral and neural data.
Main Methods:
- Developed task-optimized ConvRNNs with feedforward bypassing and recurrent gating.
- Compared ConvRNNs and CNNs to fine-grained primate categorization behavior and neural response trajectories.
- Utilized thousands of stimuli for comprehensive model evaluation.
Main Results:
- High-performing ConvRNNs matched primate behavioral decoding timings.
- ConvRNNs accurately predicted neural dynamics in V4 and IT.
- Best-performing ConvRNNs showed an optimal trade-off between task performance and network size.
Conclusions:
- ConvRNNs provide a superior model for visual processing compared to feedforward networks.
- Recurrence in the ventral pathway likely enables computational power through temporal, not spatial, complexity.
- These findings offer insights into the neural basis of object recognition.
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