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Related Concept Videos

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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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.
53.4K
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  5. Information And Computing Sciences
  7. Computer Vision And Multimedia Computation
  9. Video Processing
  11. May I See What You See? Predicting Visual Features From Neuronal Activity

May I see what you see? Predicting visual features from neuronal activity

Vikram Ravindra1, Chih-Hao Fang2, Ananth Grama2

  • 1University of Cincinnati, Cincinnati, OH, USA.

Iscience
|February 2, 2024

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Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
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Targeted Labeling of Neurons in a Specific Functional Micro-domain of the Neocortex by Combining Intrinsic Signal and Two-photon Imaging
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Targeted Labeling of Neurons in a Specific Functional Micro-domain of the Neocortex by Combining Intrinsic Signal and Two-photon Imaging

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Topographical Estimation of Visual Population Receptive Fields by fMRI
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Topographical Estimation of Visual Population Receptive Fields by fMRI

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View abstract on PubMed

Summary
This summary is machine-generated.

Researchers reconstructed video frames from functional MRI (fMRI) data, demonstrating that brain activity can predict visual objects and reconstruct images, including faces.

Area of Science:

  • Neuroscience
  • Computer Vision
  • Machine Learning

Background:

  • Understanding brain responses to audiovisual stimuli is crucial for deciphering neuronal processes.
  • Functional magnetic resonance imaging (fMRI) measures brain activity by detecting associated changes in blood flow.

Purpose of the Study:

  • To reconstruct video frames from fMRI data.
  • To demonstrate the model's ability to predict visual objects from fMRI signals.
  • To investigate the relationship between brain activity and visual perception.

Main Methods:

  • An autoencoder model was trained on video segments to create latent representations of video streams.
  • A mapping was learned between fMRI responses and the corresponding latent video frame representations.
  • fMRI data was used to compute latent vectors, which were then passed through a decoder to reconstruct predicted images.
Keywords:
Machine learningMedical imagingSignal processingSignal reconstruction

Related Experiment Videos

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches
10:50

Computational Modeling of Retinal Neurons for Visual Prosthesis Research - Fundamental Approaches

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Targeted Labeling of Neurons in a Specific Functional Micro-domain of the Neocortex by Combining Intrinsic Signal and Two-photon Imaging
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Targeted Labeling of Neurons in a Specific Functional Micro-domain of the Neocortex by Combining Intrinsic Signal and Two-photon Imaging

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Topographical Estimation of Visual Population Receptive Fields by fMRI
06:02

Topographical Estimation of Visual Population Receptive Fields by fMRI

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Main Results:

  • Representations derived from video frames and corresponding fMRI images showed high clustering.
  • The model successfully predicted objects in video frames using only fMRI data.
  • fMRI responses enabled the reconstruction of inputs, predicting the presence of faces.

Conclusions:

  • The study successfully reconstructed visual information from fMRI data, bridging neuroscience and computer vision.
  • This approach demonstrates the potential for decoding visual content directly from brain activity.
  • The findings open avenues for advanced brain-computer interfaces and understanding visual processing.
Systems neuroscience