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Updated: May 8, 2026

Knowing What Counts: Unbiased Stereology in the Non-human Primate Brain

Knowing What Counts: Unbiased Stereology in the Non-human Primate Brain

Published on: May 14, 2009

Probabilistic brains: knowns and unknowns.

Alexandre Pouget¹, Jeffrey M Beck, Wei Ji Ma

¹Department of Brain and Cognitive Sciences, University of Rochester, Rochester, New York, USA. alexandre.pouget@unige.ch

Nature Neuroscience

|August 20, 2013

Summary

This summary is machine-generated.

The brain uses probability distributions and probabilistic inference for various tasks. Future research will focus on applying these computational neuroscience theories to complex, real-life problems like learning and inference.

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Perspectives on Neuroscience

Perspectives on Neuroscience

Published on: July 31, 2007

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Last Updated: May 8, 2026

Knowing What Counts: Unbiased Stereology in the Non-human Primate Brain

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Published on: May 14, 2009

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

Perspectives on Neuroscience

Perspectives on Neuroscience

Published on: July 31, 2007

Area of Science:

Computational neuroscience
Cognitive neuroscience
Neuroscience

Background:

The brain demonstrably represents probability distributions and performs probabilistic inference.
Computational neuroscience offers theories for neural implementations of these probabilistic computations.
The generality of these theories allows modeling diverse tasks from sensory to cognitive levels.

Purpose of the Study:

To discuss the challenges in applying probabilistic theories to complex, real-life computations.
To highlight the need for advancements in modeling probabilistic learning, structural learning, and approximate inference.

Main Methods:

Review of current computational neuroscience theories on probabilistic brain function.
Discussion of challenges and future directions for applying these theories to complex tasks.

Main Results:

Current theories are primarily applied to simplified tasks, limiting their real-world applicability.
Significant challenges exist in extending these models to handle probabilistic learning, structural learning, and approximate inference.

Conclusions:

Bridging the gap between simple task models and real-life computations is crucial for advancing computational neuroscience.
Future research must address the complexities of learning and inference in more realistic scenarios.