日本語
 
Help Privacy Policy ポリシー/免責事項
  詳細検索ブラウズ

アイテム詳細

  Can serial dependencies in choices and neural activity explain choice probabilities?

Lueckmann, J.-M., Macke, J., & Nienborg, H. (2017). Can serial dependencies in choices and neural activity explain choice probabilities?. Poster presented at Computational and Systems Neuroscience Meeting (COSYNE 2017), Salt Lake City, UT, USA.

Item is

基本情報

表示: 非表示:
アイテムのパーマリンク: https://hdl.handle.net/21.11116/0000-0000-C505-C 版のパーマリンク: https://hdl.handle.net/21.11116/0000-0000-C506-B
資料種別: ポスター

ファイル

表示: ファイル

関連URL

表示:
非表示:
URL:
Link (全文テキスト(全般))
説明:
-
OA-Status:

作成者

表示:
非表示:
 作成者:
Lueckmann, J-M, 著者
Macke, J1, 2, 3, 著者           
Nienborg, H, 著者
所属:
1Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              
2Center of Advanced European Studies and Research (caesar), Max Planck Society, ou_2173675              
3Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              

内容説明

表示:
非表示:
キーワード: -
 要旨: The activity of sensory neurons co-varies with choice during perceptual decisions, commonly quantified as “choice probability”. Moreover, choices are influenced by a subject’s previous choice (serial dependencies) and neuronal activity often shows temporal correlations on long (seconds) timescales. Here, we ask whether these findings are linked, specifically: How are choice probabilities in sensory neurons influenced by serial dependencies in choices and neuronal activity? Do serial dependencies in choices and neural activity reflect the same underlying process? Using generalized linear models (GLMs) we analyze simultaneous measurements of behavior and V2 neural activity in macaques performing a visual discrimination task. We observe that past decisions are substantially more predictive of the current choice than the current spike count. Moreover, spiking activity exhibits strong correlations from trial to trial. We dissect temporal correlations by systematically varying the order of predictors in the GLM, and find that these correlations reflect two largely separate processes: There is neither a direct effect of the previous-trial spike count on choice, nor a direct effect of preceding choices on the spike count. Additionally, variability in spike counts can largely be explained by slow fluctuations across multiple trials (using a Gaussian Process latent modulator within the GLM). Is choice-probability explained by history effects, i.e. how big is the residual choice probability after correcting for temporal correlations? We compute semi-partial correlations between choices and neural activity, which constitute a lower bound on the residual choice probability. We find that removing history effects by using semi-partial correlations does not systematically change the magnitude of choice probabilities. We therefore conclude that despite the substantial serial dependencies in choices and neural activity these do not explain the observed choice probability. Rather, the serial dependencies in choices and spiking activity reflect two parallel processes which are correlated by instantaneous co-variations between choices and activity.

資料詳細

表示:
非表示:
言語:
 日付: 2017-02-24
 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: LueckmannMN2017
 学位: -

関連イベント

表示:
非表示:
イベント名: Computational and Systems Neuroscience Meeting (COSYNE 2017)
開催地: Salt Lake City, UT, USA
開始日・終了日: -

訴訟

表示:

Project information

表示:

出版物 1

表示:
非表示:
出版物名: Computational and Systems Neuroscience Meeting (COSYNE 2017)
種別: 会議論文集
 著者・編者:
所属:
出版社, 出版地: -
ページ: - 巻号: - 通巻号: - 開始・終了ページ: 153 - 153 識別子(ISBN, ISSN, DOIなど): -