• Prof. Dr.

SEN CHENG

  • Computational Neuroscience
Prodekan für Forschungsinfrastruktur

Ruhr-University Bochum
Faculty of Computer Science
Computational Neuroscience
Universitätsstr. 150
44801 Bochum

Room:  NB 3.33

Tel:      +49 (0)234 32-29486

E-Mail: sen.cheng@rub.de

Office hours: By Arrangement

Table of Contents

Courses

My research group investigates the neural mechanisms underlying learning and memory. We are primarily interested in the hippocampus, the brain region that is mainly involved in episodic memory, as well as in the learning and memory of sequences. Our research focuses on the dynamics of these processes, which has received relatively little attention to date.

We employ two complementary approaches. Our first approach is modeling, including mathematic models as well as computer simulation of complex networks. While all models are simplified, we aim to build biologically realistic models that capture the essence of the neural circuit mechanism underlying learning and memory. Our second approach is data-mining. We develop methods for model-based data analysis and apply such methods to experimental data. These data include electrophysiological and EEG recordings as well as behavioral data. We collaborate closely with neuroscientists on the RUB campus and at other universities in Germany.

Publications

2024

[1]
 linglin yang u. a., „Phase-precession-like Effects in the Anterior Insula Cortex during Reward Expectancy“, Feb. 2024. doi: 10.21203/rs.3.rs-3889969/v1.
[2]
R. Staadt, D. Jancke, und S. Cheng, „ Development of a system for high-volume multi-channel brain imaging of fluorescent voltage signals: optical data acquisition, signal processing and statistical analysis“, Universitätsbibliothek, Ruhr-Universität Bochum, Bochum, 2024. doi: 10.13154/294-11032.
[3]
S. Vijayabaskaran, S. Cheng, und S. Cheng, „Competition and integration of visual and goal vector signals for spatial navigation“, Mai 2024. doi: 10.1101/2024.05.14.594206.
[4]
J. Melchior, A. Altamimi, M. Bayati, S. Cheng, und L. Wiskott, „A neural network model for online one-shot storage of pattern sequences“, PLoS ONE, Bd. 19, Nr. 6, Art. Nr. e0304076, Juni 2024, doi: 10.1371/journal.pone.0304076.
[5]
B. Ghazinouri und S. Cheng, „The cost of behavioral flexibility : reversal learning driven by a spiking neural network“, in From Animals to Animats 17, Irvine, Sep. 2024, S. 39–50. doi: 10.1007/978-3-031-71533-4_23.
[6]
B. Ghazinouri und S. Cheng, „The cost of behavioral flexibility: reversal learning driven by a spiking neural network“, 17. Mai 2024.
[7]
R. Barzan u. a., „Gain control of sensory input across polysynaptic circuitries in mouse visual cortex by a single G protein-coupled receptor type (5-HT2A)“, Nature communications, Bd. 15, Art. Nr. 8078, Sep. 2024, doi: 10.1038/s41467-024-51861-1.
[8]
S. Cheng, „Distinct mechanisms and functions of episodic memory“, Philosophical transactions of the Royal Society of London B, Bd. 379, Nr. 1913, Art. Nr. 20230411, Sep. 2024, doi: 10.1098/rstb.2023.0411.
[1]
S. Cheng, „Gedächtnisverbesserung: Möglichkeiten und kritische Betrachtung“, in Die Grenze „Mensch“, Bd. 79, F. Hüttemann und K. Liggieri, Hrsg. Bielefeld: Transcript, 2023.
[2]
B. Ghazinouri, M. Mohagheghi Nejad, und S. Cheng, „Navigation and the efficiency of spatial coding: insights from closed-loop simulations“, 10. Januar 2023.
[3]
S. Siestrup, B. Jainta, S. Cheng, und R. Schubotz, „Solidity meets surprise : cerebral and behavioral effects of learning from episodic prediction errors“, Journal of cognitive neuroscience, Bd. 35, Nr. 2, S. 291–313, Feb. 2023, doi: 10.1162/jocn_a_01948.
[4]
N. Diekmann, S. Vijayabaskaran, X. Zeng, D. Kappel, M. Chaves Menezes, und S. Cheng, „CoBeL-RL: A neuroscience-oriented simulation framework for complex behavior and learning“, Frontiers in neuroinformatics, Bd. 17, Art. Nr. 1134405, März 2023, doi: 10.3389/fninf.2023.1134405.
[5]
N. Diekmann und S. Cheng, „A model of hippocampal replay driven by experience and environmental structure facilitates spatial learning“, eLife, Bd. 12, Art. Nr. e82301, März 2023, doi: 10.7554/elife.82301.
[6]
B. Ghazinouri, M. Mohagheghi Nejad, und S. Cheng, „Navigation and the efficiency of spatial coding: insights from closed-loop simulations“, Brain structure & function, Bd. 2023, Art. Nr. 10.1007/s00429-023-02637–8, Apr. 2023, doi: 10.1007/s00429-023-02637-8.
[7]
X. Zeng, N. Diekmann, L. Wiskott, und S. Cheng, „Modeling the function of episodic memory in spatial learning“, Frontiers in psychology, Bd. 14, Art. Nr. 1160648, Apr. 2023, doi: 10.3389/fpsyg.2023.1160648.
[8]
L. Watkins de Jong, M. Mohagheghi Nejad, E. Yoon, S. Cheng, und K. Diba, „Optogenetics reveals paradoxical network stabilizations in hippocampal CA1 and CA3“, Current biology, Bd. 33, Nr. 9, S. 1689-1703.e5, Apr. 2023, doi: 10.1016/j.cub.2023.03.032.
[9]
E. Parra Barrero, S. Vijayabaskaran, E. Seabrook, L. Wiskott, und S. Cheng, „A map of spatial navigation for neuroscience“, Neuroscience & biobehavioral reviews, Bd. 152, Art. Nr. 105200, Mai 2023, doi: 10.1016/j.neubiorev.2023.105200.
[10]
E. Parra Barrero und S. Cheng, „Learning to predict future locations with internally generated theta sequences“, PLoS computational biology, Bd. 19, Nr. 5, Art. Nr. e1011101, Mai 2023, doi: 10.1371/journal.pcbi.1011101.
[11]
M. Menezes u. a., „A multisession SLAM approach for RatSLAM“, Journal of intelligent and robotic systems, Bd. 108, Nr. 4, Art. Nr. 61, Juli 2023, doi: 10.1007/s10846-023-01816-3.
[12]
J.-S. Jokeit, G. Schöner, und S. Cheng, „Primitives for generating human-like movement in a neuro-dynamic robotic architecture“, Universitätsbibliothek, Ruhr-Universität Bochum, Bochum, 2023. doi: 10.13154/294-9678.
[13]
L. Yang u. a., „Phase-Precession-Like Effects in the Anterior Insula Cortex during Reward Expectancy“, Sep. 2023. doi: 10.1101/2023.09.19.558205.
[14]
E. Parra Barrero, S. Cheng, D. Manahan-Vaughan, und M. Zugaro, „Spatial navigation and the hippocampal theta phase code“, Universitätsbibliothek, Ruhr-Universität Bochum, Bochum, 2023. doi: 10.13154/294-10610.
[15]
D. Kappel, S. Cheng, und S. Cheng, „Global remapping emerges as the mechanism for renewal of context-dependent behavior in a reinforcement learning model“, Nov. 2023. doi: 10.1101/2023.10.27.564433.
[16]
R. Pusch u. a., „Working memory performance is tied to stimulus complexity“, Communications biology, Bd. 6, Nr. 1, Art. Nr. 1119, Nov. 2023, doi: 10.1038/s42003-023-05486-7.
[17]
V. M. Caragea, D. Manahan-Vaughan, O. A. Masseck, M. Miquel, und S. Cheng, „Dopaminergic modulation of learning and memory in the hippocampus and cerebellum“, Universitätsbibliothek, Ruhr-Universität Bochum, Bochum, 2023. doi: 10.13154/294-12569.
[1]
G. Batsikadze u. a., „The cerebellum contributes to context-effects during fear extinction learning: a 7T fMRI study“, NeuroImage, Bd. 253, Art. Nr. 119080, 2022, doi: 10.1016/j.neuroimage.2022.119080.
[2]
O. Hakobyan, S. Cheng, und N. Axmacher, „Modeling recognition memory as a decision process based on generic memory modules“, Universitätsbibliothek, Ruhr-Universität Bochum, Bochum, 2022. doi: 10.13154/294-8895.
[3]
S. Vijayabaskaran und S. Cheng, „Navigation task and action space drive the emergence of egocentric and allocentric spatial representations“, 23. Juni 2022.
[4]
X. Zeng, L. Wiskott, und S. Cheng, „The functional role of episodic memory in spatial learning“, 12. April 2022.
[5]
B. Jainta u. a., „Seeing what I did (not): cerebral and behavioral effects of agency and perspective on episodic memory Re-activation“, Frontiers in behavioral neuroscience, Bd. 15, Art. Nr. 793115, Jan. 2022, doi: 10.3389/fnbeh.2021.793115.
[6]
S. Siestrup u. a., „What happened when?: Cerebral processing of modified structure and content in episodic cueing“, Journal of cognitive neuroscience, Bd. 34, Nr. 7, S. 1287–1305, Juni 2022, doi: 10.1162/jocn_a_01862.
[7]
A. Rayan, J. R. Donoso, M. Mendez‐Couz, L. Dolón Vera, S. Cheng, und D. Manahan-Vaughan, „Learning shifts the preferred theta phase of gamma oscillations in CA1“, Hippocampus, Bd. 32, Nr. 9, S. 695–704, Aug. 2022, doi: 10.1002/hipo.23460.
[8]
C. Zöllner, N. Klein, S. Cheng, R. I. Schubotz, N. Axmacher, und O. T. Wolf, „Where was the toaster?:  A systematic investigation of semantic construction in a new virtual episodic memory paradigm“, Quarterly journal of experimental psychology, Bd. 2022, Art. Nr. 174702182211166, Juli 2022, doi: 10.1177/17470218221116610.
[9]
S. Vijayabaskaran und S. Cheng, „Navigation task and action space drive the emergence of egocentric and allocentric spatial representations“, PLoS computational biology, Bd. 18, Nr. 10, Art. Nr. e1010320, Okt. 2022, doi: 10.1371/journal.pcbi.1010320.
[10]
M. E. de Souza Muñoz u. a., „xRatSLAM: an extensible RatSLAM computational framework“, Sensors, Bd. 22, Nr. 21, Art. Nr. 8305, Okt. 2022, doi: 10.3390/s22218305.
[11]
N. Diekmann und S. Cheng, „A model of hippocampal replay driven by experience and environmental structure facilitates spatial learning“, 28. Juli 2022.
[12]
N. Diekmann, S. Vijayabaskaran, X. Zeng, D. Kappel, M. C. Menezes, und S. Cheng, „CoBeL-RL: A neuroscience-oriented simulation framework for complex behavior and learning“, 28. Dezember 2022.
[13]
B. Jainta u. a., „Corrigendum: Seeing what i did (not): cerebral and behavioral effects of agency and perspective on episodic memory re-activation“, Frontiers in behavioral neuroscience, Bd. 16, März 2022, doi: 10.3389/fnbeh.2022.887395.
[14]
Z. Fayyaz u. a., „A model of semantic completion in generative episodic memory“, Neural computation, Bd. 34, Nr. 9, S. 1841–1870, 2022, doi: 10.1162/neco_a_01520.
[15]
R. Heinen, N. Axmacher, und S. Cheng, „Understanding memory representations through deep neural networks“, Universitätsbibliothek, Ruhr-Universität Bochum, Bochum, 2022. doi: 10.13154/294-9356.
[16]
M. Pacharra u. a., „Research Data Management Policy of the collaborative research centre SFB 1280 ‚Extinction Learning‘“, 28. September 2022.
[17]
C. Hummert, G. Schöner, und S. Cheng, „Moving to visual targets reflects both attentional processes and processes of motor control“, Universitätsbibliothek, Ruhr-Universität Bochum, Bochum, 2022. doi: 10.13154/294-9844.
 
[1]
J. Packheiser, J. R. Donoso, S. Cheng, O. Güntürkün, und R. Pusch, „Trial-by-trial dynamics of reward prediction error-associated signals during extinction learning and renewal“, Progress in neurobiology, Bd. 197, Art. Nr. 101901, 2021, doi: 10.1016/j.pneurobio.2020.101901.
[2]
T. Walther u. a., „Context-dependent extinction learning emerging from raw sensory inputs: a reinforcement learning approach“, Scientific reports, Bd. 11, Art. Nr. 2713, 2021, doi: 10.1038/s41598-021-81157-z.
[3]
J. R. Donoso u. a., „Emergence of complex dynamics of choice due to repeated exposures to extinction learning“, 18. April 2021.
[4]
E. Parra Barrero, K. Diba, und S. Cheng, „Neuronal sequences during theta rely on behavior-dependent spatial maps“, eLife, Bd. 10, Art. Nr. e70296, Okt. 2021, doi: 10.7554/elife.70296.
[5]
B. Jainta u. a., „Seeing what I did (not): cerebral and behavioral effects of agency and perspective on episodic memory re-activation“, 24. November 2021.
[6]
J. R. Donoso u. a., „Emergence of complex dynamics of choice due to repeated exposures to extinction learning“, Animal cognition, Bd. 24, Nr. 6, S. 1279–1297, Mai 2021, doi: 10.1007/s10071-021-01521-4.
[7]
C. Zöllner, N. Klein, S. Cheng, R. Schubotz, N. Axmacher, und O. T. Wolf, „Where was the Toaster?: A systematic investigation of semantic construction in a new virtual episodic memory paradigm“, 10. November 2021.
[8]
R. Pusch u. a., „Working memory performance is tied to stimulus complexity“, 11. September 2021.
[9]
E. Parra Barrero, K. Diba, und S. Cheng, „Behavior-dependent spatial maps enable efficient theta phase coding“, 5. Mai 2021.
[10]
J. Wang, H. Otgaar, M. L. Howe, und S. Cheng, „Self-referential false associations: a self-enhanced constructive effect for verbal but not pictorial stimuli“, Quarterly journal of experimental psychology, Bd. 74, Nr. 9, S. 1512–1524, Apr. 2021, doi: 10.1177/17470218211009772.
[11]
O. Hakobyan und S. Cheng, „Recognition receiver operating characteristic curves: the complex influence of input statistics, memory, and decision-making “, Journal of cognitive neuroscience, Bd. 33, Nr. 6, S. 1032–1055, 2021, doi: 10.1162/jocn_a_01697.
[12]
O. Hakobyan und S. Cheng, „A multistage retrieval account of associative recognition ROC curves“, Learning & memory, Bd. 28, Nr. 11, S. 400–404, 2021, doi: 10.1101/lm.053432.121.
[13]
Z. Fayyaz, A. Altamimi, S. Cheng, und L. Wiskott, „A model of semantic completion in generative episodic memory“, 26. November 2021.
[14]
S. Siestrup u. a., „What happened when?: Brain and behavioral responses to modified structure and content in episodic cueing“, 6. Dezember 2021.
[15]
X. Zeng, L. Wiskott, und S. Cheng, „The functional role of episodic memory in spatial learning“, 29. November 2021.
[1]
O. Hakobyan und S. Cheng, „How do memory modules differentially contribute to familiarity and recollection?: In response to: An integrative memory model of recollection and familiarity to understand memory deficits“, Behavioral and brain sciences, Bd. 42, Art. Nr. e288, 2020, doi: 10.1017/s0140525x19001833.
[2]
D. Zhao, Z. Zhang, H. Lu, S. Cheng, B. Si, und X. Feng, „Learning cognitive map representations for navigation by sensory–motor integration“, IEEE transactions on cybernetics, Bd. 52, Nr. 1, S. 508–521, Apr. 2020, doi: 10.1109/tcyb.2020.2977999.
[3]
R. Görler, L. Wiskott, und S. Cheng, „Cover Image, Volume 30, Issue 6“, Hippocampus, Bd. 30, Nr. 6, S. C1, Juni 2020, doi: 10.1002/hipo.22789.
[4]
T. Walther u. a., „Context-dependent extinction learning emerging from raw sensory inputs: a reinforcement learning approach“, 28. April 2020.
[5]
M. C. Menezes u. a., „Automatic tuning of RatSLAM’s parameters by irace and iterative closest point“, in IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society, Singapur, Okt. 2020, S. 568–562. doi: 10.1109/iecon43393.2020.9254718.
[1]
B. Giri, H. Miyawaki, K. Mizuseki, S. Cheng, und K. Diba, „Hippocampal reactivation extends for several hours following novel experience“, The journal of neuroscience, Bd. 39, Nr. 5, S. 866–875, Jan. 2019, doi: 10.1523/jneurosci.1950-18.2018.
[2]
M. E. de Souza Muñoz u. a., „A parallel RatSlam C++ library implementation“, in Computational neuroscience, São João Del-Rei, Brazil, 2019, Bd. 1068, S. 173–183. doi: 10.1007/978-3-030-36636-0_13.
[3]
A. H. Azizi u. a., „Emerging category representation in the visual forebrain hierarchy of pigeons (Columba livia)“, Behavioural brain research, Bd. 356, S. 423–434, 2019, doi: 10.1016/j.bbr.2018.05.014.
[4]
R. Görler, L. Wiskott, und S. Cheng, „Improving sensory representations using episodic memory“, Hippocampus, Bd. 30, Nr. 6, S. 638–656, Jan. 2019, doi: 10.1002/hipo.23186.
[5]
J. Melchior, M. Bayati, A. H. Azizi, S. Cheng, und L. Wiskott, „A Hippocampus Model for Online One-Shot Storage of Pattern Sequences“, 30. Mai 2019.
 
[1]
M. Werning und S. Cheng, „Doing without metarepresentation: scenario construction explains the epistemic generativity and privileged status of episodic memory“, Behavioral and brain sciences, Bd. 41, Art. Nr. e34, Jan. 2018, doi: 10.1017/s0140525x17001534.
[2]
M. Chaves Menezes, E. Pignaton de Freitas, S. Cheng, A. C. Muniz de Oliveira, und P. R. de Almeida Ribeiro, „A neuro-inspired approach to solve a simultaneous location and mapping task using shared information in multiple robots systems“, in 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV), Singapur, 2018, S. 1753–1758. doi: 10.1109/icarcv.2018.8581270.
[3]
R. Gomes Santos, E. Pignaton de Freitas, S. Cheng, P. R. de Almeida Ribeiro, und A. C. Muniz de Oliveira, „Autonomous exploration guided by optimisation metaheuristic“, in 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV), Singapur, 2018, S. 1759–1764. doi: 10.1109/icarcv.2018.8581136.
[4]
M. Bayati, S. Cheng, und N. Axmacher, „Storage fidelity for sequence memory in the hippocampal circuit“, Universitätsbibliothek, Ruhr-Universität Bochum, Bochum, 2018.
[5]
J. Lins, G. Schöner, und S. Cheng, „An embodied account of spatial language grounding“, Universitätsbibliothek, Ruhr-Universität Bochum, Bochum, 2018.
[6]
J. Fang, S. Demic, und S. Cheng, „The reduction of adult neurogenesis in depression impairs the retrieval of new as well as remote episodic memory“, PLoS ONE, Bd. 13, Nr. 6, Art. Nr. e0198406, 2018, doi: 10.1371/journal.pone.0198406.
[7]
M. Bayati, T. Neher, J. Melchior, K. Diba, L. Wiskott, und S. Cheng, „Storage fidelity for sequence memory in the hippocampal circuit“, PLoS ONE, Bd. 13, Nr. 10, Art. Nr. e0204685, 2018, doi: 10.1371/journal.pone.0204685.
[8]
J. Fang, N. N. Rüther, C. Bellebaum, L. Wiskott, und S. Cheng, „The interaction between semantic representation and episodic memory“, Neural computation, Bd. 30, Nr. 2, S. 293–332, 2018, doi: 10.1162/neco_a_01044.
[1]
M. Bayati, J. Melchior, L. Wiskott, und S. Cheng, „Generating sequences in recurrent neural networks for storing and retrieving episodic memories“, BMC neuroscience, Bd. 18, Nr. Suppl. 1. BioMed Central, London, S. 30–31, 2017. doi: 10.1186/s12868-017-0371-2.
[2]
T. Neher, A. H. Azizi, und S. Cheng, „From grid cells to place cells with realistic field sizes“, PLoS ONE, Bd. 12, Nr. 7, Art. Nr. e0181618, 2017, doi: 10.1371/journal.pone.0181618.
[3]
M. Werning und S. Cheng, „Taxonomy and unity of memory“, in The Routledge handbook of philosophy of memory, S. Bernecker und K. Michaelian, Hrsg. London: Routledge, 2017, S. 7–21. doi: 10.4324/9781315687315-2.
[4]
M. Stacho, O. Güntürkün, und S. Cheng, „The canonical circuit of the avian forebrain“, Universitätsbibliothek, Ruhr-Universität Bochum, Bochum, 2017.
[5]
J. Fang, S. Cheng, und B. Suchan, „Function and dysfunction of episodic memory: an algorithmic model“, Universitätsbibliothek, Ruhr-Universität Bochum, Bochum, 2017.
[6]
S. Cheng, „Consolidation of episodic memory: an epiphenomenon of semantic learning“, in Cognitive neuroscience of memory consolidation, N. Axmacher und B. Rasch, Hrsg. Cham: Springer, 2017, S. 57–72. doi: 10.1007/978-3-319-45066-7_4.
[1]
A. N. Kalweit, D. Manahan-Vaughan, und S. Cheng, „Investigations of the role of neuronal oscillations in information processing in the hippocampus“, Universitätsbibliothek, Ruhr-Universität Bochum, Bochum, 2016. [Online]. Verfügbar unter: https://hss-opus.ub.ruhr-uni-bochum.de/opus4/frontdoor/index/index/docId/5569
[2]
S. Cheng und M. Werning, „What is episodic memory if it is a natural kind?“, Synthese, Bd. 193, Nr. 5, S. 1345–1385, 2016, doi: 10.1007/s11229-014-0628-6.
[3]
A. Babichev, S. Cheng, und Y. A. Dabaghian, „Topological schemas of cognitive maps and spatial learning“, Frontiers in computational neuroscience, Bd. 10, Art. Nr. 18, 2016, doi: 10.3389/fncom.2016.00018.
[4]
S. Cheng, M. Werning, und T. Suddendorf, „Dissociating memory traces and scenario construction in mental time travel“, Neuroscience & biobehavioral reviews, Bd. 60, S. 82–89, Jan. 2016, doi: 10.1016/j.neubiorev.2015.11.011.
[1]
A. H. Azizi, S. Cheng, und L. Wiskott, „The generation of sequential activitiy and spatial responses in the hippocampus: computational studies of network mechanisms and their robustness“, Universitätsbibliothek, Ruhr-Universität Bochum, Bochum, 2015.
[2]
M. Bayati, A. Valizadeh, A. Abbassian, und S. Cheng, „Self-organization of synchronous activity propagation in neuronal networks driven by local excitation“, Frontiers in computational neuroscience, Bd. 9, Art. Nr. 69, 2015, doi: 10.3389/fncom.2015.00069.
[3]
T. Neher, S. Cheng, und L. Wiskott, „Memory storage fidelity in the hippocampal circuit: the role of subregions and input statistics“, PLoS computational biology, Bd. 11, Nr. 5, Art. Nr. e1004250, 2015, doi: 10.1371/journal.pcbi.1004250.
[4]
A. Babichev, S. Cheng, und Y. A. Dabaghian, „Topological schemas of cognitive maps and spatial learning in the hippocampus“, 1. September 2015.
[1]
N. Wang, L. Wiskott, und S. Cheng, „Learning natural image statistics with variants of restricted Boltzmann machines“, Universitätsbibliothek, Ruhr-Universität Bochum, Bochum, 2014. [Online]. Verfügbar unter: http://hss-opus.ub.ruhr-uni-bochum.de/opus4/files/4619/diss.pdf
[2]
A. H. Azizi und S. Cheng, „The transformation of grid to place cells is robust to noise in the grid pattern“, BMC neuroscience, Bd. 15, Nr. Suppl 1, S. P188, Juli 2014, doi: 10.1186/1471-2202-15-s1-p188.
[3]
S. Demic und S. Cheng, „Modeling the dynamics of disease states in depression“, PLoS ONE, Bd. 9, Nr. 10, Art. Nr. e110358, 2014, doi: 10.1371/journal.pone.0110358.
[4]
M. Pyka, S. Klatt, und S. Cheng, „Parametric anatomical modeling: a method for modeling the anatomical layout of neurons and their projections“, Frontiers in neuroanatomy, Bd. 8, Art. Nr. 91, 2014, doi: 10.3389/fnana.2014.00091.
[5]
A. H. Azizi, N. Schieferstein, und S. Cheng, „The transformation from grid cells to place cells is robust to noise in the grid pattern“, Hippocampus, Bd. 24, Nr. 8, S. 912–919, 2014, doi: 10.1002/hipo.22306.
[6]
M. Werning und S. Cheng, „Is episodic memory a natural kind?: A defense of the sequence analysis“, in Cognitive science meets artificial intelligence: human and artificial agents in interactive contexts, Québec City, 2014, S. 964–969. [Online]. Verfügbar unter: https://mindmodeling.org/cogsci2014/cogsci2014.zip
[7]
M. Pyka, S. Cheng, und T. Wennekers, „Pattern association and consolidation emerges from connectivity properties between cortex and hippocampus“, PLoS ONE, Bd. 9, Nr. 1, Art. Nr. e85016, 2014, doi: 10.1371/journal.pone.0085016.
[1]
S. Zhang, D. Manahan-Vaughan, und S. Cheng, „Investigation of place cell activity during sensory learning in rats“, Universitätsbibliothek, Ruhr-Universität Bochum, Bochum, 2013. [Online]. Verfügbar unter: http://www-brs.ub.ruhr-uni-bochum.de/netahtml/HSS/Diss/ZhangSijie/diss.pdf
[2]
S. Cheng, „Gehirnaktivität im Ruhezustand und im Schlaf“, 18. September 2013, Publiziert.
[3]
S. Helduser, S. Cheng, und O. Güntürkün, „Identification of two forebrain structures that mediate execution of memorized sequences in the pigeon“, Journal of neurophysiology, Bd. 109, Nr. 4, S. 958–968, 2013, doi: 10.1152/jn.00763.2012.
[4]
S. Cheng, „The CRISP theory of hippocampal function in episodic memory“, Frontiers in neural circuits, Bd. 7, Nr. May, Art. Nr. 88, 2013, doi: 10.3389/fncir.2013.00088.
[5]
S. Cheng und M. Werning, „Composition and replay of mnemonic sequences: the contributions of REM and slow-wave sleep to episodic memory“, Behavioral and brain sciences, Bd. 36, Nr. 6, S. 610–611, 2013, doi: 10.1017/s0140525x13001234.
[6]
A. H. Azizi, L. Wiskott, und S. Cheng, „A computational model for preplay in the hippocampus“, Frontiers in computational neuroscience, Bd. 7, Art. Nr. 161, 2013, doi: 10.3389/fncom.2013.00161.
[7]
T. Neher, S. Cheng, und L. Wiskott, „Are memories really stored in the hippocampal CA3 region?“, 10th Göttingen Meeting of the German Neuroscience Society. S. 34, 2013.
[1]
S. Cheng, P. Crotty, und E. Lasker, „Synchronization of entorhinal cortex stellate cells“, BMC neuroscience, Bd. 13, Nr. 1, S. P167, 2012, doi: 10.1186/1471-2202-13-s1-p167.
[2]
P. Crotty, E. Lasker, und S. Cheng, „Constraints on the synchronization of entorhinal cortex stellate cells“, Physical review E, Bd. 86, Nr. 1, Art. Nr. 011908, 2012, doi: 10.1103/physreve.86.011908.
[1]
S. Cheng und L. M. Frank, „The structure of networks that produce the transformation from grid cells to place cells“, Neuroscience, Bd. 197, S. 293–306, 2011, doi: 10.1016/j.neuroscience.2011.09.002.
[2]
L. Buhry, A. H. Azizi, und S. Cheng, „Reactivation, replay, and preplay: how it might all fit together“, Neural plasticity, Bd. 2011, Art. Nr. 203462, 2011, doi: 10.1155/2011/203462.
[1]
S. Cheng und L. M. Frank, „The structure of networks that produce the transformation from grid cells to place cells“, Neuroscience, Bd. 197, S. 293–306, 2011, doi: 10.1016/j.neuroscience.2011.09.002.
[2]
L. Buhry, A. H. Azizi, und S. Cheng, „Reactivation, replay, and preplay: how it might all fit together“, Neural plasticity, Bd. 2011, Art. Nr. 203462, 2011, doi: 10.1155/2011/203462.
[1]
S. Cheng und P. N. Sabes, „Calibration of visually guided reaching is driven by error-corrective learning and internal dynamics“, Journal of neurophysiology, Bd. 97, Nr. 4, S. 3057–3069, 2007, doi: 10.1152/jn.00897.2006.
[1]
S. Cheng und P. N. Sabes, „Modeling sensorimotor learning with linear dynamical systems“, Neural computation, Bd. 18, Nr. 4, S. 760–793, 2006, doi: 10.1162/089976606775774651.
[1]
S. Cheng u. a., „Statistical and dynamic models of charge balance functions“, Physical review C, Bd. 69, Nr. 5, Art. Nr. 054906, 2004, doi: 10.1103/physrevc.69.054906.
[1]
S. Cheng und S. Pratt, „Isospin fluctuations from a thermally equilibrated hadron gas“, Physical review C, Bd. 67, Nr. 4, S. 449041–4490413, 2003, doi: 10.1103/physrevc.67.044904.
[2]
S. Pratt und S. Cheng, „Removing distortions from charge balance functions“, Physical review C, Bd. 68, Nr. 1, Art. Nr. 014907, 2003, doi: 10.1103/physrevc.68.014907.
[1]
S. Cheng u. a., „Effect of finite-range interactions in classical transport theory“, Physical review C, Bd. 65, Nr. 2, Art. Nr. 024901, 2002, doi: 10.1103/physrevc.65.024901.
[2]
S. Cheng, „Statistical physics in a finite volume with absolute conservation laws“, in Proceedings of the 18th Winter Workshop on Nuclear Dynamics, 2002, S. 1–8.
[3]
S. Cheng, „Modeling relativistic heavy ion collisions: a dissertation submitted to Michigan State University, Department of Physics and Astronomy“, Michigan Publishing, University of Michigan Library, East Lansing, 2002. [Online]. Verfügbar unter: http://www.ruhr-uni-bochum.de/cns/pubs/thesis.pdf
[1]
S. Cheng und S. Pratt, „Quantum corrections for pion correlations involving resonance decays“, Physical review C, Bd. 63, Nr. 5, Art. Nr. 054904, 2001, doi: 10.1103/physrevc.63.054904.

Publications

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Alexander May, Lars Schlieper

Quantum Period Finding is Compression Robust Proceedings Article

In: Transactions on Symmetric Cryptology (TOSC 22), S. 183–211, 2022.

Links | Schlagwörter: Crypto Area, Quantum

Alexander May, Julian Nowakowski, Santanu Sarkar

Partial Key Exposure Attack on Short Secret Exponent CRT-RSA Proceedings Article

In: Advances in Cryptology (ASIACRYPT 21), S. 99–129, Springer, 2021.

Links | Schlagwörter: Crypto Flagship, Rank A*/A

Alexander May

How to Meet Ternary LWE Keys Proceedings Article

In: Advances in Cryptology (CRYPTO 21) , S. 701–731, Springer, 2021.

Links | Schlagwörter: Crypto Flagship, Rank A*/A

Elena Kirshanova, Alexander May

How to Find Ternary LWE Keys Using Locality Sensitive Hashing Proceedings Article

In: Cryptography and Coding (IMACC 21), S. 247–264, Springer, 2021.

Links | Schlagwörter: Crypto Others

Alexander May, Lars Schlieper, Jonathan Schwinger

Noisy Simon Period Finding Proceedings Article

In: Cryptographers' Track at the RSA Conference (CT-RSA 21), S. 75–99, Springer, 2021.

Links | Schlagwörter: Crypto Others, Quantum

Markus Dürmuth, Maximilian Golla, Philipp Markert, Alexander May, Lars Schlieper

Towards Quantum Large-Scale Password Guessing on Real-World Distributions Proceedings Article

In: Cryptology and Network Security (CANS 21), S. 412–431, Springer, 2021.

Links | Schlagwörter: Quantum, Security

(Hrsg.)

Quantum Key Search for Ternary LWE Zeitschrift

Lecture Notes in Computer Science Bd. 12841, 2021, besucht am: 01.01.2021.

Links | Schlagwörter: Crypto Others, Quantum

27 Einträge « ‹ 3 von 3 › »

Memberships

  • BITSI – Bochumer Verein zur Förderung der IT-Sicherheit und Informatik
  • CASA – DFG Excellence Cluster
  • QSI – EU Marie Curie Network
  • HGI – Horst Görtz Institute
  • IACR – Cryptology Research

Lectures (Moodle/Notes)

Crypto Challenges

Former PhDs

  1. Önder Askin, 2024
  2. Floyd Zweydinger, 2023
  3. Lars Schlieper, 2022
  4. Alexander Helm, 2020
  5. Andre Esser, 2020
  6. Matthias Minihold, 2019 
  7. Leif Both, 2018
  8. Robert Kübler, 2018
  9. Elena Kirshanova, 2016
  10. Ilya Ozerov, 2016
  11. Gottfried Herold, 2014
  12. Alexander Meurer, 2014
  13. Mathias Herrmann, 2011
  14. Maike Ritzenhofen, 2010

Calvin & Hobbes

Prof. Dr. Sen Cheng

Computational Neuroscience

Professor / Head of Chair

Address:
Ruhr-University Bochum
Faculty of Computer Science
Computational Neuroscience
Universitätsstr. 150
--D-44801 Bochum

Room: NB 3/33

Telephone: +49 (0)234 32-29486

Office Hours: By arrangement

E-Mail: sen.cheng@rub.de

Courses



Research

My research group investigates the neural mechanisms underlying learning and memory. We are primarily interested in the hippocampus, the brain region that is mainly involved in episodic memory, as well as in the learning and memory of sequences. Our research focuses on the dynamics of these processes, which has received relatively little attention to date.

We employ two complementary approaches. Our first approach is modeling, including mathematic models as well as computer simulation of complex networks. While all models are simplified, we aim to build biologically realistic models that capture the essence of the neural circuit mechanism underlying learning and memory. Our second approach is data-mining. We develop methods for model-based data analysis and apply such methods to experimental data. These data include electrophysiological and EEG recordings as well as behavioral data. We collaborate closely with neuroscientists on the RUB campus and at other universities in Germany.


Publications