Kernel density compression for real-time Bayesian encoding/decoding of unsorted hippocampal spikes
نویسندگان
چکیده
منابع مشابه
Kernel density compression for real-time Bayesian encoding/decoding of unsorted hippocampal spikes
To gain a better understanding of how neural ensembles communicate and process information, neural decoding algorithms are used to extract information encoded in their spiking activity. Bayesian decoding is one of the most used neural population decoding approaches to extract information from the ensemble spiking activity of rat hippocampal neurons. Recently it has been shown how Bayesian decod...
متن کاملBayesian decoding using unsorted spikes in the rat hippocampus.
A fundamental task in neuroscience is to understand how neural ensembles represent information. Population decoding is a useful tool to extract information from neuronal populations based on the ensemble spiking activity. We propose a novel Bayesian decoding paradigm to decode unsorted spikes in the rat hippocampus. Our approach uses a direct mapping between spike waveform features and covariat...
متن کاملKernel density estimation-based real-time prediction for respiratory motion.
Effective delivery of adaptive radiotherapy requires locating the target with high precision in real time. System latency caused by data acquisition, streaming, processing and delivery control necessitates prediction. Prediction is particularly challenging for highly mobile targets such as thoracic and abdominal tumors undergoing respiration-induced motion. The complexity of the respiratory mot...
متن کاملReal - Time Kernel for
D’nia is a real-time system and cross-development environment based on Oberon. It is used for the programming and control of embedded, mechatronic systems. Until now, it was available for various VME boards based on the MC680x0 processor. The main goal of this work was the development of a real-time kernel for the PowerPC family of RISC processors, allowing D’nia to run on this new hardware. Th...
متن کاملInnovative Methodology Bayesian decoding using unsorted spikes in the rat hippocampus
Fabian Kloosterman, Stuart P. Layton, Zhe Chen, and Matthew A. Wilson Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts; Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, Massachusetts; NERF, Leuven, Belgium; imec, Leuven, Belgium; Laboratory of Biological Psychology, Department of Psychology, K...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Knowledge-Based Systems
سال: 2016
ISSN: 0950-7051
DOI: 10.1016/j.knosys.2015.09.013