Encoder Decoder Network
Encoder Decoder Network - 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.
11:46
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...
11:18
Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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Cited by 9 •
2015
In this paper, an experimental framework to perform closed-loop experiments is presented, in which information processing (i.e., coding and decoding) and learning of neuronal assemblies are studied during the continuous interaction with a robotic body.
FAQs
The latent representation acts as an intermediate summary of the input. The encoder progressively extracts features while reducing the input to this compressed form, and the decoder uses it to expand information into the requested output. This arrangement allows the network to represent complex input-output relationships while retaining a compact internal form for prediction or reconstruction.
Skip connections and attention address information loss and alignment in different ways. Skip connections help carry important details from earlier processing stages toward the decoder, whereas attention mechanisms help focus on relevant relationships between input and output. Their inclusion can improve preservation of fine information or correspondence when straightforward compression would make those aspects harder to recover.
These tasks differ in what the output represents, but both rely on a learned mapping between data domains. For prediction, the generated output expresses a target inferred from the input; for reconstruction, it represents a recovered version of information in that input. The shared architecture can therefore support different engineering objectives by changing the desired output.
Engineers need to identify the input data, the useful output, and the relationship the system should learn between them. They can then determine how feature extraction, latent compression, and output expansion should serve that mapping, with skip connections or attention added when detail preservation or input-output alignment is important. This frames architecture selection around the engineering task.
Applications include machine translation, image segmentation, signal denoising, and anomaly detection. These examples span language, visual data, signals, and system monitoring, showing that the approach is not restricted to one data type. In each case, the useful outcome comes from transforming a complex input into an output that supports automated analysis or interpretation.
They can learn mappings in which an available data representation is used to generate a desired engineering result. This is especially relevant to inverse problems, where the practical goal is to connect data with a useful reconstructed output. More broadly, the architecture supports system modeling and data-driven design by converting complex relationships into automated computational workflows.