Statistical Mechanics for Neural Spike Data Analysis Using Log-Linear Model
スポンサーリンク
概要
- 論文の詳細を見る
Recently, we can simultaneously record spike data from many neurons in the field of electrophysiology, and thus it is required to develop mathematical framework for extracting higher-order correlation of neural firings. The joint probability of neural spike can be represented using the log-linear model. From statistical-mechanical point of view, the loglinear model can be regarded as a multi-body interacted Ising spin model or the Boltzman machine with higher-order interactions. The estimation of higher-order correlation of neural firing corresponds to that of higher-order interations in this Ising spin system, and to the hyper-parameter estimation in the Bayesian inference. In this paper, we apply maximization of marginal likelihood (MML) method to this problem, and discuss the properties of MML analytically using statistical-mechanical method.
- 2005-04-30
著者
-
Shouno H
Yamaguchi Univ. Yamaguchi
-
Wada Koji
Graduate School Of Science And Engineering Saitama University
-
SHOUNO Hayaru
Faculty of Engineering, Yamaguchi University
-
WADA Koji
Kochi National College of Technology
-
OKADA Masato
RIKEN BSI
-
Okada Masato
Riken Brain Sci. Inst. Saitama
-
Okada Masato
Graduate School Of Frontier Sciences The University Of Tokyo
-
Okada Masato
Riken Bsi:japan Scientific Technology Corp.:graduate School Of Frontier Science The University Of To
-
Shouno Hayaru
Faculty Of Engineering Yamaguchi University
関連論文
- Statistical Mechanics for Neural Spike Data Analysis Using Log-Linear Model
- Statistical Mechanical Study of Code-Division Multiple-Access Multiuser Detectors : Analysis of Replica Symmetric and One-Step Replica Symmetry Breaking Solutions(General)
- Statistical Mechanical Analysis of CDMA Multiuser Detectors : AT Stability and Entropy of the RS Solution, and 1RSB Solution
- Retrieval Property of Attractor Network with Synaptic Depression(General)
- Analysis of Ensemble Learning Using Simple Perceptrons Based on Online Learning Theory
- Residual Energies after Slow Quantum Annealing(General)
- Slow Dynamics Due to Singularities of Hierarchical Learning Machines
- On-Line Learning Dynamics of Multilayer Perceptrons with Unidentifiable Parameters
- Multiple Stability of a Sparsely Encoded Attractor Neural Network Model for the Inferior Temporal Cortex(General)
- Naive Mean Field Approximation for Image Restoration
- Statistical Mechanics of Mexican-Hat-Type Horizontal Connection
- Ensemble Learning of Linear Perceptrons : On-Line Learning Theory(General)
- Neural Network Model of Spatial Memory: Associative Recall of Maps
- Correlation of Firing in Layered Associative Neural Networks(Condensed Matter : Structure, Mechanical and Thermal Properties)
- Statistical Mechanics of Mutual Learning with a Latent Teacher(General)
- Controlling Synfire Chain by Inhibitory Synaptic Input(Cross-disciplinary physics and related areas of science and technology)
- Naive Mean Field Approximation for Image Restoration
- Statistical Mechanics for Neural Spike Data Analysis Using Log-Linear Model