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Vision

Vision

Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.

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Related Experiment Video

Updated: May 11, 2026

Topographical Estimation of Visual Population Receptive Fields by fMRI

Topographical Estimation of Visual Population Receptive Fields by fMRI

Published on: February 3, 2015

A new method for estimating population receptive field topography in visual cortex.

Sangkyun Lee¹, Amalia Papanikolaou¹, Nikos K Logothetis²

¹Max Planck Institute for Biological Cybernetics, Spemannstr. 38, 72076 Tübingen, Germany.

|May 21, 2013

Summary

We developed a new, unbiased method to measure visual population receptive fields (pRF) using functional magnetic resonance imaging (fMRI). This approach accurately models pRF structure and outperforms previous methods, especially for visual pathway lesions.

Keywords:

Population receptive field Retinotopic mapping Visual field mapping fMRI

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Last Updated: May 11, 2026

Topographical Estimation of Visual Population Receptive Fields by fMRI

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Published on: February 3, 2015

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Published on: February 15, 2017

Area of Science:

Neuroscience
Computational Neuroscience
Visual Neuroscience

Background:

Visual population receptive fields (pRF) map visual information processing in the brain.
Existing methods often assume specific pRF shapes, limiting unbiased analysis.
Functional magnetic resonance imaging (fMRI) is a key tool for studying brain activity.

Purpose of the Study:

Introduce a novel, flexible method for measuring visual pRFs using fMRI.
Overcome limitations of existing pRF modeling techniques.
Provide an unbiased tool for exploring pRF structure and properties.

Main Methods:

Model pRF structure using a linear model to predict the Blood Oxygen-Level-Dependent (BOLD) signal.
Utilize stimulus protocols and canonical hemodynamic response function for prediction.
Avoid a priori assumptions about pRF shape, allowing for unbiased estimation.

Main Results:

The new method demonstrates higher accuracy and explained variance in BOLD signal prediction compared to direct-fit models.
Direct-fit models (Gaussian, difference of Gaussians) showed limitations in capturing true pRF shape and localization.
The proposed method successfully characterized various pRF properties like surround suppression and elongation.

Conclusions:

This linear modeling approach offers a more accurate and unbiased estimation of visual pRFs.
The method is particularly valuable for studying pRFs in individuals with visual pathway lesions.
The computational efficiency and flexibility make it a powerful tool for visual neuroscience research.