An Algorithm Board in Neural Decoding
Understanding the mechanisms of neural encoding and decoding has always been a highly interesting research topic in fields such as neuroscience and cognitive intelligence. In prior studies, some researchers identified a symmetry in neural data decoded by unsupervised methods in motor scenarios and constructed a cognitive learning system based on this pattern (i.e., symmetry). Nevertheless, the distribution state of the data flow that significantly influences neural decoding positions still remains a mystery within the system, which further restricts the enhancement of the system's interpretability. Based on this, this paper mainly explores changes in the distribution state within the system from the machine learning and mathematical statistics perspectives. In the experiment, we assessed the correctness of this symmetry using various tools and indicators commonly utilized in mathematics and statistics. According to the experimental results, the normal distribution (or Gaussian distribution) plays a crucial role in the decoding of prediction positions within the system. Eventually, an algorithm board similar to the Galton board was built to serve as the mathematical foundation of the discovered symmetry.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Multistage Spatial Context Models for Learned Image Compression
Recent state-of-the-art Learned Image Compression methods feature spatial context models, achieving great rate-distortion improvements over hyperprior methods. However, the autoregressive context model requires serial de…
Image CompressionMobile Keyboard Input Decoding with Finite-State Transducers
We propose a finite-state transducer (FST) representation for the models used to decode keyboard inputs on mobile devices. Drawing from learnings from the field of speech recognition, we describe a decoding framework tha…
Decoderspeech-recognitionSpeech RecognitionAn investigation of phone-based subword units for end-to-end speech recognition
Phones and their context-dependent variants have been the standard modeling units for conventional speech recognition systems, while characters and subwords have demonstrated their effectiveness for end-to-end recognitio…
DecoderLanguage ModelingLanguage Modellingspeech-recognition+1PuzzleBoard: A New Camera Calibration Pattern with Position Encoding
Accurate camera calibration is a well-known and widely used task in computer vision that has been researched for decades. However, the standard approach based on checkerboard calibration patterns has some drawbacks that …
Camera CalibrationCamera Pose EstimationObject LocalizationPose Estimation+1Checkerboard Context Model for Efficient Learned Image Compression
For learned image compression, the autoregressive context model is proved effective in improving the rate-distortion (RD) performance. Because it helps remove spatial redundancies among latent representations. However, t…
Computational EfficiencyImage Compressionmodel