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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: Jan 2, 2026

Inducing Long-Term Plasticity of Intrinsic Neuronal Excitability in Neurons of the Dorsal Lateral Geniculate Nucleus

Inducing Long-Term Plasticity of Intrinsic Neuronal Excitability in Neurons of the Dorsal Lateral Geniculate Nucleus

Published on: September 20, 2024

Short-Term Attractive Tilt Aftereffects Predicted by a Recurrent Network Model of Primary Visual Cortex.

Maria Del Mar Quiroga^1,2, Adam P Morris^1,3, Bart Krekelberg¹

¹Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ, United States.

Frontiers in Systems Neuroscience

|November 30, 2019

Summary

The Tilt Aftereffect (TAE) typically repulses perceived orientation. New research shows it can attract orientation on short timescales, revealing insights into neural processing and recurrent network dynamics.

Keywords:

V1 adaptation model orientation perception recurrent connections tilt aftereffect

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Last Updated: Jan 2, 2026

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Published on: April 11, 2025

Area of Science:

Neuroscience
Computational Neuroscience
Visual Perception

Background:

Adaptation is crucial for understanding neural processing.
The Tilt Aftereffect (TAE) traditionally demonstrates repulsive visual perception after adapter exposure.
Recurrent network dynamics are increasingly recognized for their role in neural adaptation.

Purpose of the Study:

To investigate the role of recurrent network dynamics in short-term visual adaptation.
To test the novel prediction of an attractive TAE on a millisecond timescale.
To explore how network connectivity influences visual information processing.

Main Methods:

Extended a network model to predict behavioral outcomes.
Designed a novel adaptation protocol targeting short timescales.
Conducted behavioral experiments to measure the TAE under specific conditions.

Main Results:

Confirmed the prediction of an attractive TAE on a timescale of a few hundred milliseconds.
Demonstrated that recurrent network dynamics significantly contribute to short-term adaptation.
Provided evidence for the role of network dynamics in visual processing during typical fixation durations.

Conclusions:

Recurrent network dynamics are integral to short-term visual adaptation.
Understanding visual processing requires analyzing both neuronal properties and network dynamics.
Simple recurrent networks can support complex functions like memory and integration.