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Robust and Highly Reproducible Generation of Cortical Brain Organoids for Modelling Brain Neuronal Senescence In Vitro

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The Generative Adversarial Brain.

Samuel J Gershman¹

¹Department of Psychology and Center for Brain Science, Harvard University, Cambridge, MA, United States.

Frontiers in Artificial Intelligence

|March 18, 2021

Summary

This study proposes an adversarial framework for brain-based generative models. It suggests implicit density models, trained via generative adversarial networks (GANs), offer a viable alternative to explicit density models for understanding brain computation and disorders.

Keywords:

bayesian inference consciousness delusions generative adversarial networks perception

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Area of Science:

Neuroscience
Computational Neuroscience
Artificial Intelligence

Background:

The brain is thought to learn generative models of the world.
Prior models often used explicit density estimation, which is computationally challenging.
Approximate inference in explicit models can lead to suboptimal solutions.

Purpose of the Study:

To propose an adversarial framework for probabilistic computation in the brain.
To explore generative adversarial algorithms (GANs) as a model for brain learning.
To investigate the implications of this framework for understanding mental disorders.

Main Methods:

Developing a computational framework based on generative adversarial principles.
Comparing the proposed implicit density model approach with traditional explicit density models.
Analyzing psychological and neural evidence supporting the adversarial framework.

Main Results:

Generative adversarial algorithms offer a solution to the difficulties of explicit density modeling.
Implicit density models can learn realistic generative models by using a discriminator.
The framework provides a novel perspective on brain computation and learning.

Conclusions:

Adversarial frameworks present a promising alternative for understanding how the brain learns generative models.
Dysfunction in generator and discriminator components may underlie delusions in mental disorders.
This approach offers new avenues for research in computational psychiatry.