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Vortrag

Implications of correlated neuronal noise in decision making circuits for physiology and behavior

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons83951

Häfner,  R
Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83931

Gerwinn,  S
Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84066

Macke,  J
Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83805

Bethge,  M
Research Group Computational Vision and Neuroscience, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Häfner, R., Gerwinn, S., Macke, J., & Bethge, M. (2010). Implications of correlated neuronal noise in decision making circuits for physiology and behavior. Talk presented at Computational and Systems Neuroscience Meeting (COSYNE 2010). Salt Lake City, UT, USA.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-C14E-4
Zusammenfassung
Understanding how the activity of sensory neurons contribute to perceptual decision making is one of the major questions in neuroscience. In the current standard model, the output of opposing pools of noisy, correlated sensory neurons is integrated by downstream neurons whose activity elicits a decision-dependent behavior [1][2]. The predictions of the standard model for empirical measurements like choice probability (CP), psychophysical kernel (PK) and reaction time distribution crucially depend on the spatial and temporal correlations within the pools of sensory neurons. This dependency has so far only been investigated numerically and for time-invariant correlations and variances. However, it has recently been shown that the noise variance undergoes significant changes over the course of the stimulus presentation [3]. The same is true for inter-neuronal correlations that have been shown to change with task and attentional state [4][5]. In the first part of our work we compute analytically the time course of CPs and PKs in the presence of arbitrary noise correlations and variances for the case of non-leaky integration and Gaussian noise. This allows general insights and is especially needed in the light of the experimental transition from single-cell to multi-cell recordings. Then we simulate the implications of realistic noise in several variants of the standard model (leaky and non-leaky integration, integration over the entire stimulus presentation or until a bound, with and without urgency signal) and compare them to physiological data. We find that in the case of non-leaky integration over the entire stimulus duration, the PK only depends on the overall level of noise variance, not its time course. That means that the PK remains constant regardless of the temporal changes in the noise. This finding supports an earlier conclusion that an observed decreasing PK suggests that the brain is not integrating over the entire stimulus duration but only until it has accumulated sufficient evidence, even in the case of no urgency [6]. The time course of the CP, on the other hand, strongly depends on the time course of the noise variances and on the temporal and interneuronal correlations. If noise variance or interneuronal correlation increases, CPs increase as well. This dissociation of PK and CP allows an alternative solution to the puzzle recently posed by [7] in a bottom-up framework by combining integration to a bound with an increase in noise variance/correlation. In addition, we derive how the distribution of reaction times depends on noise variance and correlation, further constraining the model using empirical observations.