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Poster

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

MPG-Autoren

Lueckmann,  J-M
Center of Advanced European Studies and Research (caesar), Max Planck Society;

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Macke,  J
Center of Advanced European Studies and Research (caesar), Max Planck Society;

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Zitation

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.


Zitierlink: http://hdl.handle.net/21.11116/0000-0000-C505-C
Zusammenfassung
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.