Publications
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Filters: Author is Daniel Harnack [Clear All Filters]
Unexpectedly strong attentional modulation of V1/V2 activity implements a robust, contrastinvariant control mechanism for selective information processing. Bernstein Conference (2019). doi:10.12751/nncn.bc2019.0060
bccn_Schuenemann_2019.pdf (251.85 KB)

Performance-optimization guided distribution of attentional resources. BMC Neuroscience 2017 (2017).
HarnackErnst_CNS2017.pdf (448.23 KB)

Topological causality in dynamical systems. Physical Review Letters 119, 098301, (2017).
PhysRevLett.119.098301.pdf (899.59 KB)
topological_causality_KP5_DH11_MS5_EL2_supplement_alt.pdf (731.89 KB)


On Causality in Dynamical Systems. arXiv (2016). at <https://arxiv.org/abs/1605.02570v1>
Harnack_2016.pdf (969.54 KB)

Self-organization and control of flexible information routing in cortical networks. Bernstein Conference 2016 (2016).
abstract_harnack_BCCN2016.pdf (15.88 KB)
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A model for attentional information routing through coherence predicts biased competition and multistable perception. Journal of Neurophysiology 114, 1593-1605 (2015).
harnack_2015_attentional_information_routing.pdf (1.07 MB)

Probing communicationa through coherence via phase-dependent analysis. Society for Neuroscience (SfN) Annual Meeting (2015).
Probing information routing mechanisms by precisely-timed electrical stimulation pulses: a modelling study. Computational Neuroscience Conference (CNS) 2015, BMC Neuroscience (2015).
A simplified model for oscillatory population dynamics in visual cortex. Annual Meeting of the German Neuroscience Society 2015 Poster T26-2A (2015).
Stability of neuronal networks with homeostatic regulation. PLoS Computational Biology (2015). doi:10.1371/journal.pcbi.1004357
harnack_2015_stability_homeostasis.pdf (881.73 KB)

Guiding attention: phase-response curve analysis of a communication through coherence model. Bernstein Conference 2014, Frontiers (2014). doi:10.12751/nncn.bc2014.003
BCCN14posterLisitsyn.pdf (1.86 MB)

Multistable network dynamics through lateral inhibition: an efficient mechanism for selective information routing. BMC Neuroscience 2014 15 (Suppl1), P165 (2014).
A dynamic model for selective visual attention predicts information routing. 10th Göttingen Meeting of the German Neuroscience Society T26-6C (2013).
A dynamic model for selective visual attention predicts information routing. Göttingen Meeting of the German Neuroscience Society 2013 T26-6C (2013).
A model for selective visual attention predicts information gating and biased competition. Bernstein Conference 2013 (2013). doi:10.12751/nncn.bc2013.0062
A model for selective visual attention predicts information routing. BMC Neuroscience 2013 14(Suppl.1), P310 (2013).
A model of selective visual attention predicts biased competition and information routing. ECVP 2013 152 (2013).
A model for selective visual attention predicts information routing through coherence. Neuroscience New Orleans 724.02 (2012).
Transient activation of MT neurons to stimulus velocity changes: experiments and modelling. BC11 : Computational Neuroscience & Neurotechnology Bernstein Conference & Neurex Annual Meeting 2011 (2011). doi:doi: 10.3389/conf.fncom.2011.53.00054