Hierarchical Bayesian Reservoir Memory
عنوان مقاله: Hierarchical Bayesian Reservoir Memory
شناسه ملی مقاله: CSICC14_082
منتشر شده در چهاردهمین کنفرانس بین المللی سالانه انجمن کامپیوتر ایران در سال 1388
شناسه ملی مقاله: CSICC14_082
منتشر شده در چهاردهمین کنفرانس بین المللی سالانه انجمن کامپیوتر ایران در سال 1388
مشخصات نویسندگان مقاله:
Ali Nouri - Bu-Ali Sina University/Computer Engineering Department, Hamedan, Iran
Hooman Nikmehr - Bu-Ali Sina University/Computer Engineering Department, Hamedan, Iran.
خلاصه مقاله:
Ali Nouri - Bu-Ali Sina University/Computer Engineering Department, Hamedan, Iran
Hooman Nikmehr - Bu-Ali Sina University/Computer Engineering Department, Hamedan, Iran.
In a quest for modeling human brain, we are going to introduce a brain model based on a general framework for brain called Memory-Prediction Framework. The model is a hierarchical Bayesian structure that uses Reservoir Computing methods as the state-of-the-art and the most biological plausible Temporal Sequence Processing method for online and unsupervised learning. So, the model is called Hierarchical Bayesian Reservoir Memory (HBRM). HBRM uses a simple stochastic gradient descent learning algorithm to learn and organize common multi-scale spatio-temporal patterns/features of the input signals in a hierarchical structure in an unsupervised manner to provide robust and real-time prediction of future inputs. We suggest HBRM as a real-time high-dimensional stream processing model for the basic brain computations. In this paper we will describe the model and assess its prediction accuracy in a simulated real-world environment.
کلمات کلیدی: Brian Theory, Bayesian Networks, Memory-Prediction Framework, Stochastic Time-Series Prediction, Reservoir Computing
صفحه اختصاصی مقاله و دریافت فایل کامل: https://civilica.com/doc/73047/