A. Hanuschkin

发表

A Hebbian learning rule gives rise to mirror neurons and links them to control theoretic inverse models

Richard Hans Robert Hahnloser, S. Ganguli, A. Hanuschkin, 2013, Front. Neural Circuits.

A Network Model Reveals That the Experimentally Observed Switch of the Granule Cell Phenotype During Epilepsy Can Maintain the Pattern Separation Function of the Dentate Gyrus

J. Wolfart, A. Hanuschkin, M. Y. Yim, 2018 .

Rhythmic Continuous-Time Coding in the Songbird Analog of Vocal Motor Cortex

Alexander Hanuschkin, Tatsuo S. Okubo, Michale S. Fee, 2016, Neuron.

Adaptive intrinsic plasticity in human dentate gyrus granule cells during temporal lobe epilepsy.

J. Wolfart, A. Hanuschkin, O. Staszewski, 2012, Cerebral cortex.

Intrinsic rescaling of granule cells restores pattern separation ability of a dentate gyrus network model during epileptic hyperexcitability

J. Wolfart, A. Hanuschkin, M. Y. Yim, 2015, Hippocampus.

Image potential and field states at Ag(100) and Fe(110) surfaces

A. Hanuschkin, S. Blügel, D. Wortmann, 2007 .

Deep feature learning of in-cylinder flow fields to analyze cycle-to-cycle variations in an SI engine

Steven Peters, Benjamin Böhm, Andreas Dreizler, 2020, International Journal of Engine Research.

Investigation of cycle-to-cycle variations in a spark-ignition engine based on a machine learning analysis of the early flame kernel

Benjamin Böhm, Andreas Dreizler, Marius Schmidt, 2020 .

Machine learning–based analysis of in-cylinder flow fields to predict combustion engine performance

Steffen Schober, Steven Peters, Alexander Hanuschkin, 2021, International Journal of Engine Research.

Songbirds work around computational complexity by learning song vocabulary independently of sequence

Gary F. Marcus, Ofer Tchernichovski, Richard H. R. Hahnloser, 2017, Nature Communications.

With Time After Kainate Treatment Excitatory Synaptic Input to Granule Cells Increases

F. Dudek, Wei Zhang, J. Huguenard, 2015 .

Modular composition and dynamics of native GABAB receptors identified by high-resolution proteomics

E. Pérez-Garci, A. Hanuschkin, B. Fakler, 2015, Nature Neuroscience.

A General and Efficient Method for Incorporating Precise Spike Times in Globally Time-Driven Simulations

Moritz Helias, Markus Diesmann, Abigail Morrison, 2010, Front. Neuroinform..

Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance Methodology

A. Hanuschkin, Klaus-Robert Müller, T. Bui, 2020, Mach. Learn. Knowl. Extr..

Compositionality of arm movements can be realized by propagating synchrony

Markus Diesmann, J. Michael Herrmann, Abigail Morrison, 2010, Journal of Computational Neuroscience.

A reafferent and feed-forward model of song syntax generation in the Bengalese finch

Markus Diesmann, Abigail Morrison, Alexander Hanuschkin, 2011, Journal of Computational Neuroscience.

A model of free monkey scribbling based on the propagation of cell assembly activity

Markus Diesmann, Abigail Morrison, Alexander Hanuschkin, 2009, BMC Neuroscience.

CUNY Academic Works

Anja T. Zai, Richard Hans Robert Hahnloser, O. Tchernichovski, 2022 .