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Related Experiment Video

Updated: May 30, 2026

Continuous Theta Burst Stimulation of the Posterior Medial Frontal Cortex to Experimentally Reduce Ideological Threat Responses

Continuous Theta Burst Stimulation of the Posterior Medial Frontal Cortex to Experimentally Reduce Ideological Threat Responses

Published on: September 28, 2018

Dependency reduction with divisive normalization: justification and effectiveness.

¹Computer Science Department, University at Albany, State University of New York, Albany, NY 12222, USA. lsw@cs.albany.edu

Neural Computation

|August 20, 2011

Summary

This summary is machine-generated.

Divisive normalization (DN) is an effective efficient coding transform for sensory signals. For optimal performance, DN requires pooling over a sufficient number of inputs to reduce statistical dependencies.

Related Experiment Videos

Last Updated: May 30, 2026

Continuous Theta Burst Stimulation of the Posterior Medial Frontal Cortex to Experimentally Reduce Ideological Threat Responses

Continuous Theta Burst Stimulation of the Posterior Medial Frontal Cortex to Experimentally Reduce Ideological Threat Responses

Published on: September 28, 2018

Area of Science:

Computational Neuroscience
Information Theory
Signal Processing

Background:

Efficient coding transforms are crucial for processing natural sensory signals in biological and engineering systems.
Divisive normalization (DN) has emerged as a promising nonlinear transform for efficient coding.
Understanding the theoretical underpinnings and practical performance of DN is essential.

Purpose of the Study:

To provide a theoretical justification for DN as an efficient coding transform.
To demonstrate the equivalence of various DN forms in efficient coding.
To quantitatively evaluate DN's dependency reduction capabilities.

Main Methods:

Utilizing the multivariate t model to capture natural sensory signal properties.
Analyzing DN's approximation to optimal dependency-eliminating transforms.
Comparing different DN implementations for their efficient coding effects.
Quantitatively assessing DN's performance on both model and natural data.

Main Results:

DN approximates optimal transforms for eliminating statistical dependencies in the multivariate t model.
Multiple DN formulations exhibit equivalent efficient coding outcomes.
DN effectively reduces statistical dependencies in sensory signals, particularly with higher input dimensions.
Low-input dimensions can paradoxically increase statistical dependencies with DN.

Conclusions:

DN is theoretically justified as an efficient coding transform, approximating optimal solutions.
The effectiveness of DN as an efficient coding transform is contingent upon pooling over a sufficiently large number of inputs.
DN's performance is robust across different formulations, but dimensionality plays a critical role.