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Related Concept Videos

Vision01:24

Vision

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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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Visual System01:26

Visual System

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Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...
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Anatomy of the Eyeball01:20

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The eye is a spherical, hollow structure composed of three tissue layers. The outer layer — the fibrous tunic, comprises the sclera — a white structure — and the cornea, which is transparent. The sclera encompasses some of the ocular surface, most of which is not visible. However, the 'white of the eye' is distinctively visible in humans compared to other species. The cornea, a clear covering at the front of the eye, enables light penetration. The eye's middle...
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Related Experiment Video

Updated: Jun 11, 2025

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss
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Learning to segment self-generated from externally caused optic flow through sensorimotor mismatch circuits.

Matthias Brucklacher1, Giovanni Pezzulo2, Francesco Mannella2

  • 1Cognitive and Systems Neuroscience, University of Amsterdam, 1098XH Amsterdam, Netherlands.

Neural Networks : the Official Journal of the International Neural Network Society
|October 9, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a neural network model that distinguishes self-generated optic flow from external motion, improving visual perception and object categorization. It extends predictive coding for active agents learning movement consequences.

Keywords:
Generative modelObject segmentationOptic flowPredictive codingSensorimotor

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Area of Science:

  • Computational Neuroscience
  • Systems Neuroscience
  • Visual Processing

Background:

  • Efficient sensory detection requires filtering irrelevant information, such as distinguishing self-motion optic flow from object-related visual input.
  • Predictive coding models with sensorimotor mismatch detection offer a framework for understanding this process, with evidence in early visual areas.
  • Integration of these mismatch signals into cortical networks for segmentation and categorization remains an open question.

Purpose of the Study:

  • To develop a biologically plausible computational model that extends predictive coding to differentiate self-generated from externally caused optic flow.
  • To investigate how sensorimotor mismatch signals are integrated within neural networks for visual input segmentation and object categorization.
  • To elucidate the role of feedback connections in maintaining generative models for optic flow processing.

Main Methods:

  • A three-neuron microcircuit was developed to model experience-dependent sensorimotor mismatch responses, validated against mouse calcium imaging data.
  • This microcircuit was integrated into a larger neural network featuring two generative streams: one for self-motion prediction and another for external optic flow modeling.
  • The model incorporated bidirectional connections between a motion-selective higher visual area (mHVA) and V1, emphasizing feedback connections for generative model maintenance.

Main Results:

  • The proposed three-neuron circuit successfully replicated sensorimotor mismatch responses observed in experimental data.
  • The two-stream neural network demonstrated the ability to distinguish self-generated from externally caused optic flow.
  • The model showed that the mHVA learns to segment moving objects and facilitates categorization, with its architecture mapping to primate visual cortex.

Conclusions:

  • The model provides a biologically plausible mechanism for separating self-motion from external visual stimuli within the visual system.
  • It extends Hebbian predictive coding principles to sensorimotor contexts, enabling agents to learn and predict the sensory consequences of their own movements.
  • The findings highlight the critical role of feedback connections in higher visual areas for maintaining generative models essential for visual perception and categorization.