Joachim Hass

Affiliations: 
Central Institute of Mental Health, Mannheim, Mannheim, Baden-Württemberg, Germany 
Area:
Computational Neuroscience, PFC, time perception
Website:
http://www.chaos.gwdg.de/nldpeoplefolder.2007-07-17.7548289764/nldperson.2007-12-17.6423769378
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"Joachim Hass"
Cross-listing: Neurotree

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Publications

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Hass J, Ardid S, Sherfey J, et al. (2022) Constraints on Persistent Activity in a Biologically Detailed Network Model of the Prefrontal Cortex with Heterogeneities. Progress in Neurobiology. 102287
Sadeghi S, Mier D, Gerchen MF, et al. (2020) Dynamic Causal Modeling for fMRI With Wilson-Cowan-Based Neuronal Equations. Frontiers in Neuroscience. 14: 593867
Schmidt SNL, Sojer CA, Hass J, et al. (2020) fMRI adaptation reveals: The human mirror neuron system discriminates emotional valence. Cortex; a Journal Devoted to the Study of the Nervous System and Behavior. 128: 270-280
Ardid S, Sherfey JS, McCarthy MM, et al. (2019) Biased competition in the absence of input bias revealed through corticostriatal computation. Proceedings of the National Academy of Sciences of the United States of America
Sherfey JS, Ardid S, Hass J, et al. (2018) Flexible resonance in prefrontal networks with strong feedback inhibition. Plos Computational Biology. 14: e1006357
Hass J, Hertäg L, Durstewitz D. (2016) A Detailed Data-Driven Network Model of Prefrontal Cortex Reproduces Key Features of In Vivo Activity. Plos Computational Biology. 12: e1004930
Ardid S, Sherfey J, McCarthy MM, et al. (2016) Unraveling action selection and inhibitory control mechanisms in a striatal microcircuit model International Journal of Psychophysiology. 108: 18
Hass J, Durstewitz D. (2016) Time at the center, or time at the side? Assessing current models of time perception Current Opinion in Behavioral Sciences. 8: 238-244
Hass J, Durstewitz D. (2014) Neurocomputational models of time perception. Advances in Experimental Medicine and Biology. 829: 49-71
Quiroga-Lombard CS, Hass J, Durstewitz D. (2013) Method for stationarity-segmentation of spike train data with application to the Pearson cross-correlation. Journal of Neurophysiology. 110: 562-72
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