Explore: Neural Decoding
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Source: The Open Library
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1A Discriminative Approach to Bayesian Filtering with Applications to Human Neural Decoding
By Michael Craig Burkhart
“A Discriminative Approach to Bayesian Filtering with Applications to Human Neural Decoding” Metadata:
- Title: ➤ A Discriminative Approach to Bayesian Filtering with Applications to Human Neural Decoding
- Author: Michael Craig Burkhart
- Language: English
- Number of Pages: Median: 112
- Publisher: Brown University
- Publish Date: 2019
- Publish Location: Providence, USA
“A Discriminative Approach to Bayesian Filtering with Applications to Human Neural Decoding” Subjects and Themes:
- Subjects: ➤ Machine Learning - Brain-computer interfaces - Gaussian processes - Bayesian filtering - Supervised learning (Machine learning) - Neural decoding - Hidden Markov models - State-space models - Neural networks (Computer science) - Computational neuroscience - Discriminative probabilistic modeling - Nonparametric statistics
Edition Identifiers:
- The Open Library ID: OL32035375M
- All ISBNs: 9798664701937
Author's Alternative Names:
"Michael Burkhart" and "Michael C. Burkhart"Access and General Info:
- First Year Published: 2019
- Is Full Text Available: Yes
- Is The Book Public: Yes
- Access Status: Public
Online Access
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Neural decoding
Neural decoding is a neuroscience field concerned with the hypothetical reconstruction of sensory and other stimuli from information that has already
Transformer (deep learning architecture)
low-dimensional KV vector needs to be cached. Speculative decoding is a method to accelerate token decoding. Similarly to speculative execution in CPUs, future
Recurrent neural network
In artificial neural networks, recurrent neural networks (RNNs) are designed for processing sequential data, such as text, speech, and time series, where
Michael J. Black
Shaikhouni, A.; Donoghue, J.P. (2003). "Neural decoding of cursor motion using a Kalman filter". Advances in Neural Information Processing Systems 15 (NIPS)
Homomorphic filtering
Orcioni, A. Paffi, F. Camera, F. Apollonio, and M. Liberti, “Automatic decoding of input sinusoidal signal in a neuron model: Improved SNR spectrum by
Neural field
In machine learning, a neural field (also known as implicit neural representation, neural implicit, or coordinate-based neural network), is a mathematical
Neural coding
the development of large-scale neural recording and decoding technologies, researchers have begun to crack the neural code and have already provided the
Neural correlates of consciousness
(cognitive architecture) Models of neural computation Multiple drafts model Münchhausen trilemma Neural coding Neural decoding Neural substrate Philosophy of mind
Neural machine translation
outputs during, this way of decoding is called auto-regressive. In 1987, Robert B. Allen demonstrated the use of feed-forward neural networks for translating
Mental representation
ways to approach this: restricted decoding and unrestricted decoding. Here's how they differ: Restricted decoding is when scientists focus on brain activity