Encoder Decoder
Encoder Decoder - Related Videos
08:00
Decoding Natural Behavior from Neuroethological Embedding
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2025
This protocol gives an integrated framework based on advanced computational neuroethological methods to understand brain coding in naturalistic contexts.
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Decoding Auditory Imagery with Multivoxel Pattern Analysis
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2023
Source: Laboratories of Jonas T. Kaplan and Sarah I. Gimbel—University of Southern California Imagine the sound of a bell ringing. What is happening in the brain when we conjure up a sound like this in the "mind's ear?" There is growing evidence that the brain uses the same mechanisms for imagination that it uses for perception.1 For example, when imagining visual images, the visual cortex becomes activated, and when imagining sounds, the auditory cortex is engaged. However, to what extent are...
05:28
Incidental Encoding
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2023
Source: Laboratory of Jonathan Flombaum—Johns Hopkins University Long-term memory is a critical feature of human cognition, and it has been a prominent focus of research in experimental psychology. Many paradigms designed to tap long-term memory rely on asking participants to learn or study content, then test memory about that content. This is a good approach if one wants to understand how memory supports educational achievement, for example, where explicit study is part of the process. But,...
01:19
Encoding
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2025
Information enters the brain through encoding, which is the input of information into the memory system. Once sensory information is received from the environment, the brain labels or codes it. The information is then organized with similar information and connected to existing concepts. Encoding occurs through automatic processing and effortful processing. Automatic processing involves the encoding of details like time, space, frequency, and the meaning of words, usually done without conscious...
03:39
Generation of an Immunostimulatory Protein-Encoding Recombinant Measles Virus
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2026
Source: Heidbuechel, J. P., Engeland, C. E. Paramyxoviruses for Tumor-targeted Immunomodulation: Design and Evaluation Ex Vivo. J. Vis. Exp. (2019)This video demonstrates the generation of recombinant measles viruses using liposomes to deliver plasmids encoding the viral antigenome and essential helper proteins into producer cells. The transcribed and replicated RNA leads to the formation of viral particles expressing immunostimulatory and fluorescent reporter proteins. Fluorescence microscopy...
FAQs
The latent representation acts as an intermediate summary of the input, preserving information needed for the target task while reducing the complexity of the original data. Its quality affects whether the decoder can reconstruct, classify, translate, or generate useful results. In medicine, this representation can organize meaningful structure from images, signals, or clinical text for subsequent analysis.
Training compares the decoder's predicted output with a reference output and minimizes their difference through a loss function. The resulting error guides adjustments to the network weights, gradually improving the relationship between the input representation and the desired result. This optimization provides a consistent way to tailor the model to a selected medical task.
The decoder determines how information in the latent representation becomes an output suited to the task. It may reconstruct an input, assign a classification, translate clinical text, or generate another target representation. Training against the corresponding reference output encourages the model to learn the transformation required for that specific purpose rather than a single universal response.
Images, signals, and clinical text contain different forms of patient information, so the encoder must convert each into a latent representation that supports the intended analysis. The output objective then determines how that representation is used. Matching the input modality with an appropriate target helps the model extract information relevant to reconstruction, interpretation, or quantitative evaluation.
A typical workflow begins by providing medical images, signals, or clinical text to the encoder. The resulting latent representation passes to the decoder, which produces the selected target output. Training then compares that output with a reference and adjusts network weights to reduce the difference. The trained system can subsequently support the same type of analysis on relevant patient data.
Medical researchers apply these models to image segmentation, denoising, reconstruction, and clinical language processing. Segmentation can help separate or delineate structures in images, while denoising and reconstruction focus on recovering useful information. Language-oriented applications process clinical text. Together, these uses support extracting structured information from patient data and performing more quantitative analyses.
By converting complex patient data into task-specific outputs, these models can make information easier to measure and compare. Image segmentation may provide delineated regions, while reconstruction or denoising can produce cleaner data for further evaluation. Clinical language processing can also extract information from text, supporting systematic analysis rather than relying only on unstructured observations.