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Type: Conference paper
Title: Hierarchical Bayesian Reservoir Memory
Author: Nouri, A.
Nikmehr, H.
Citation: Proceedings of the 14th International CSI Computer Conference (CSICC 2009), 2009, pp.582-587
Publisher: IEEE
Publisher Place: Piscataway, NJ
Issue Date: 2009
ISBN: 1424442613
Conference Name: 14th International CSI Computer Conference (CSICC 2009) (20 Oct 2009 - 21 Oct 2009 : Tehran, Iran)
Statement of
Ali Nouri and Hooman Nikmehr
Abstract: 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-theart 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.
Keywords: Brian theory; Bayesian networks, Memory- Prediction Framework; stochastic time-series prediction; Reservoir Computing
Rights: ©2009 IEEE
DOI: 10.1109/CSICC.2009.5349642
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Appears in Collections:Aurora harvest 3
Computer Science publications

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