A joint maximum-entropy model for binary neural population patterns and 
continuous signals

Gerwinn, S; Berens, P; Bethge, M

A joint maximum-entropy model for binary neural population patterns and continuous signals

Gerwinn, S., Berens, P., & Bethge, M. (2010). A joint maximum-entropy model for binary neural population patterns and continuous signals. In Y., Bengio, D., Schuurmans, J., Lafferty, C., Williams, & A., Culotta (Eds.), Advances in Neural Information Processing Systems 22 (pp. 620-628). Red Hook, NY, USA: Curran.

Item is 公開

表示: 全項目非表示: 全項目

基本情報

表示: 非表示:

アイテムのパーマリンク: https://hdl.handle.net/11858/00-001M-0000-0013-C0BE-0 版のパーマリンク: https://hdl.handle.net/21.11116/0000-0002-93C9-5

資料種別: 会議論文

ファイル

表示: ファイル

作成者

表示:

非表示:

作成者:
Gerwinn, S^{1, 2}, 著者
Berens, P^{1, 2}, 著者
Bethge, M^{1, 2}, 著者

所属:
1Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497805
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794

内容説明

表示:

非表示:

キーワード: -

要旨: Second-order maximum-entropy models have recently gained much interest for describing the statistics of binary spike trains. Here, we extend this approach to take continuous stimuli into account as well. By constraining on the joint secondorder statistics, we obtain a joint Gaussian-Boltzmann distribution of continuous stimuli and binary neural firing patterns, for which we also compute marginal and conditional distributions. This model has the same computational complexity as pure binary models and fitting it to data is a convex problem. We show that the model can be seen as an extension to the classical spike-triggered average and can be used as a non-linear method for extracting features which a neural population is sensitive to. Further, by calculating the posterior distribution of stimuli given an observed neural response, the model can be used to decode stimuli and yields a natural spike-train metric. Therefore, extending the framework of maximumentropy
models to continuous variables allows us to gain novel insights into the relationship between the firing patterns of neural ensembles and the stimuli they are processing.

資料詳細

表示:

非表示:

言語:

日付: 出版: 2010-04

出版の状態: 出版

ページ: -

出版情報: -

目次: -

査読: -

識別子（DOI, ISBNなど）: BibTex参照ID: 6075

学位: -

訴訟

表示:

Project information

表示:

出版物 1

表示:

非表示:

出版物名: Advances in Neural Information Processing Systems 22

種別: 会議論文集

著者・編者:
Bengio, Y, 編集者
Schuurmans, D, 編集者
Lafferty, J, 編集者
Williams, C, 編集者
Culotta, A, 編集者

所属:
-

出版社, 出版地: Red Hook, NY, USA : Curran

ページ: - 巻号: - 通巻号: - 開始・終了ページ: 620 - 628 識別子（ISBN, ISSN, DOIなど）: ISBN: 978-1-615-67911-9

アイテム詳細

基本情報

ファイル

関連URL

作成者

内容説明

資料詳細

関連イベント

訴訟

Project information

出版物 1