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Updated: Dec 21, 2025

Concurrent Recording of Co-localized Electroencephalography and Local Field Potential in Rodent

Concurrent Recording of Co-localized Electroencephalography and Local Field Potential in Rodent

Published on: November 30, 2017

Time-resolved correspondences between deep neural network layers and EEG measurements in object processing.

Nathan C L Kong¹, Blair Kaneshiro², Daniel L K Yamins³

¹Department of Psychology, Stanford University, United States; Department of Electrical Engineering, Stanford University, United States.

Vision Research

|May 11, 2020

Summary

Convolutional neural networks (CNNs) mimic the brain's visual hierarchy. This study compares CNNs to human brain responses, finding some hierarchical alignment but also deviations, especially in early visual processing stages.

Keywords:

Convolutional neural network EEG Object recognition Representational similarity analysis

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Last Updated: Dec 21, 2025

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Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

Area of Science:

Neuroscience
Computer Science
Computational Neuroscience

Background:

The ventral visual stream exhibits hierarchical organization, processing simple features in early areas and complex features in higher areas.
Hierarchical convolutional neural networks (CNNs) are inspired by this brain organization and effectively model neural responses.
Understanding the temporal dynamics of human object processing through CNNs remains an active area of research.

Purpose of the Study:

To investigate the correspondence between CNN representations and the temporal dynamics of human object processing.
To compare various similarity metrics and datasets for evaluating CNN model performance against brain activity.
To explore hierarchical relationships between CNN layers and brain responses over time.

Main Methods:

Representational Similarity Analysis (RSA) was employed to compare CNN model representations with electroencephalography (EEG) data.
Two EEG datasets, comprising responses to 72 images, were analyzed.
Novel methods for optimally weighting CNN features were developed to enhance correlation with brain responses.

Main Results:

A hierarchical relationship was observed between CNN layer depth and peak correlation time with brain responses for specific similarity metrics.
Correlation onset times did not strictly follow a hierarchical pattern across CNN layers.
Deeper CNN layers showed better correspondence with later EEG responses, while shallow layers did not consistently outperform deeper layers for early EEG responses.

Conclusions:

CNNs partially capture the hierarchical temporal dynamics of human object processing, but deviations from strict hierarchy exist.
The findings suggest that while deeper CNN layers may model later stages of visual processing, early visual processing dynamics are not fully represented hierarchically.
This study provides a comprehensive comparison of CNNs against neural data, offering insights into the temporal aspects of visual object recognition.