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sen.cheng@rub.de (+49) 234-32-29486

Prof. Dr. Sen Cheng

Computational Neuroscience

Computational Neuroscience

Ruhr-Universität Bochum
Institut für Neuroinformatik
Universitätsstraße 150
Building NB, Room NB 3/33

D-44801 Bochum, Germany

sen.cheng@rub.de

(+49) 234-32-29486

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 and abroad.

Selected Publications

    2022

  • The cerebellum contributes to context-effects during fear extinction learning: A 7T fMRI study
    Batsikadze, G., Diekmann, N., Ernst, T. M., Klein, M., Maderwald, S., Deuschl, C., et al.
    NeuroImage, 253, 119080

    • 2021

  • Recognition Receiver Operating Characteristic Curves: The Complex Influence of Input Statistics, Memory, and Decision-making
    Hakobyan, O., & Cheng, S.
    Journal of Cognitive Neuroscience, 33(6), 1032–1055
  • Neuronal Sequences during Theta Rely on Behavior-Dependent Spatial Maps
    Parra-Barrero, E., Diba, K., & Cheng, S.
    eLife, 10, e70296

    • 2019

  • Hippocampal Reactivation Extends for Several Hours Following Novel Experience
    Giri, B., Miyawaki, H., Mizuseki, K., Cheng, S., & Diba, K.
    The Journal of Neuroscience, 39(5), 866–875
    link

    • 2016

  • What is episodic memory if it is a natural kind?
    Cheng, S., & Werning, M.
    Synthese, 193(5), 1345–1385
  • Dissociating memory traces and scenario construction in mental time travel
    Cheng, S., Werning, M., & Suddendorf, T.
    Neuroscience & Biobehavioral Reviews, 60, 82–89

    • 2008

  • New Experiences Enhance Coordinated Neural Activity in the Hippocampus
    Cheng, S., & Frank, L. M.
    Neuron , 57(2), 303–313

    in press

  • Gedächtnisverbesserung: Möglichkeiten und kritische Betrachtung
    Cheng, S.
    In F. Hüttemann & Liggieri, K. (Eds.), Die Grenze "Mensch". Diskurse des Transhumanismus. Bielefeld: transcript Verlag

    • 2023

  • A map of spatial navigation for neuroscience
    Parra-Barrero, E., Vijayabaskaran, S., Seabrook, E., Wiskott, L., & Cheng, S.
    Neuroscience & Biobehavioral Reviews, 152, 105200
  • A Multisession SLAM Approach for RatSLAM
    Menezes, M., Muñoz, M., de Freitas, E. P., Cheng, S., de Almeida Neto, A., Ribeiro, P., & Oliveira, A.
    Journal of Intelligent & Robotic Systems, 108(4)
  • Optogenetics reveals paradoxical network stabilizations in hippocampal CA1 and CA3
    de Jong, L. W., Nejad, M. M., Yoon, E., Cheng, S., & Diba, K.
    Current Biology, 33(9), 1689–1703.e5
  • Learning to predict future locations with internally generated theta sequences
    Parra-Barrero, E., & Cheng, S.
    PLOS Computational Biology, 19(5), e1011101
  • Navigation and the efficiency of spatial coding: insights from closed-loop simulations
    Ghazinouri, B., Nejad, M. M., & Cheng, S.
    Brain Structure and Function
  • A model of hippocampal replay driven by experience and environmental structure facilitates spatial learning
    Diekmann, N., & Cheng, S.
    eLife, 12, e82301
  • CoBeL-RL: A neuroscience-oriented simulation framework for complex behavior and learning
    Diekmann, N., Vijayabaskaran, S., Zeng, X., Kappel, D., Menezes, M. C., & Cheng, S.
    Frontiers in Neuroinformatics, 17
  • Solidity Meets Surprise: Cerebral and Behavioral Effects of Learning from Episodic Prediction Errors
    Siestrup, S., Jainta, B., Cheng, S., & Schubotz, R. I.
    Journal of Cognitive Neuroscience, 35(2), 291–313
  • Modeling the function of episodic memory in spatial learning
    Zeng, X., Diekmann, N., Wiskott, L., & Cheng, S.
    Frontiers in Psychology, 14

    • 2022

  • xRatSLAM: An Extensible RatSLAM Computational Framework
    de Souza Muñoz, M. E., Menezes, M. C., de Freitas, E. P., Cheng, S., de Almeida Ribeiro, P. R., de Almeida Neto, A., & de Oliveira, A. C. M.
    Sensors, 22(21), 8305
  • Navigation task and action space drive the emergence of egocentric and allocentric spatial representations
    Vijayabaskaran, S., & Cheng, S.
    PLOS Computational Biology, 18(10), e1010320
  • Where was the toaster? A systematic investigation of semantic construction in a new virtual episodic memory paradigm
    Zöllner, C., Klein, N., Cheng, S., Schubotz, R. I., Axmacher, N., & Wolf, O. T.
    Quarterly Journal of Experimental Psychology, 174702182211166
  • A Model of Semantic Completion in Generative Episodic Memory
    Fayyaz, Z., Altamimi, A., Zoellner, C., Klein, N., Wolf, O. T., Cheng, S., & Wiskott, L.
    Neural Computation, 34(9), 1841–1870
  • Learning shifts the preferred theta phase of gamma oscillations in CA1
    Rayan, A., Donoso, J. R., Mendez-Couz, M., Dolón, L., Cheng, S., & Manahan-Vaughan, D.
    Hippocampus, 32(9), 695–704
  • The cerebellum contributes to context-effects during fear extinction learning: A 7T fMRI study
    Batsikadze, G., Diekmann, N., Ernst, T. M., Klein, M., Maderwald, S., Deuschl, C., et al.
    NeuroImage, 253, 119080
  • What Happened When? Cerebral Processing of Modified Structure and Content in Episodic Cueing
    Siestrup, S., Jainta, B., El-Sourani, N., Trempler, I., Wurm, M. F., Wolf, O. T., et al.
    Journal of Cognitive Neuroscience, 34(7), 1287–1305
  • Learning Cognitive Map Representations for Navigation by Sensory–Motor Integration
    Zhao, D., Zhang, Z., Lu, H., Cheng, S., Si, B., & Feng, X.
    IEEE Transactions on Cybernetics, 52(1), 508–521

    • 2021

  • A multistage retrieval account of associative recognition ROC curves
    Hakobyan, O., & Cheng, S.
    Learning and Memory, 28(11), 400–404
  • Emergence of complex dynamics of choice due to repeated exposures to extinction learning
    Donoso, J. R., Packheiser, J., Pusch, R., Lederer, Z., Walther, T., Uengoer, M., et al.
    Animal Cognition, 24(6), 1279–1297
  • Recognition Receiver Operating Characteristic Curves: The Complex Influence of Input Statistics, Memory, and Decision-making
    Hakobyan, O., & Cheng, S.
    Journal of Cognitive Neuroscience, 33(6), 1032–1055
  • Self-referential false associations: A self-enhanced constructive effect for verbal but not pictorial stimuli
    Wang, J., Otgaar, H., Howe, M. L., & Cheng, S.
    Quarterly Journal of Experimental Psychology, 74(9), 1512–1524
  • Trial-by-trial dynamics of reward prediction error-associated signals during extinction learning and renewal
    Packheiser, J., Donoso, J. R., Cheng, S., Güntürkün, O., & Pusch, R.
    Progress in Neurobiology, 197, 101901
  • Context-dependent extinction learning emerging from raw sensory inputs: a reinforcement learning approach
    Walther, T., Diekmann, N., Vijayabaskaran, S., Donoso, J. R., Manahan-Vaughan, D., Wiskott, L., & Cheng, S.
    Scientific Reports, 11(1)
  • Neuronal Sequences during Theta Rely on Behavior-Dependent Spatial Maps
    Parra-Barrero, E., Diba, K., & Cheng, S.
    eLife, 10, e70296

    • 2020

  • Improving sensory representations using episodic memory
    Görler, R., Wiskott, L., & Cheng, S.
    Hippocampus, 30(6), 638–656
    link
  • Automatic Tuning of RatSLAM′s Parameters by Irace and Iterative Closest Point
    Menezes, M. C., Muñoz, M. E. S., Freitas, E. P., Cheng, S., Walther, T., Neto, A. A., et al.
    In IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society (pp. 562–568)

    • 2019

  • Hippocampal Reactivation Extends for Several Hours Following Novel Experience
    Giri, B., Miyawaki, H., Mizuseki, K., Cheng, S., & Diba, K.
    The Journal of Neuroscience, 39(5), 866–875
    link
  • Emerging category representation in the visual forebrain hierarchy of pigeons (Columba livia)
    Azizi, A. H., Pusch, R., Koenen, C., Klatt, S., Bröcker, F., Thiele, S., et al.
    Behavioural Brain Research, 356, 423–434
  • A Parallel RatSlam C++ Library Implementation
    de Souza Muñoz, M. E., Menezes, M. C., de Freitas, E. P., Cheng, S., de Almeida Neto, A., de Oliveira, A. C. M., & de Almeida Ribeiro, P. R.
    In Communications in Computer and Information Science (pp. 173–183) Springer International Publishing
  • Deep reinforcement learning in a spatial navigation task: Multiple contexts and their representation
    Diekmann, N., Walther, T., Vijayabaskaran, S., & Cheng, S.
    In 2019 Conference on Cognitive Computational Neuroscience Berlin, Germany: Cognitive Computational Neuroscience
  • How do memory modules differentially contribute to familiarity and recollection?
    Hakobyan, O., & Cheng, S.
    Behavioral and Brain Sciences, 42, e288

    • 2018

  • A Neuro-Inspired Approach to Solve a Simultaneous Location and Mapping Task Using Shared Information in Multiple Robots Systems
    Menezes, M. C., de Freitas, E. P., Cheng, S., de Oliveira, A. C. M., & de Almeida Ribeiro, P. R.
    In 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV) IEEE
  • Autonomous Exploration Guided by Optimisation Metaheuristic
    Santos, R. G., de Freitas, E. P., Cheng, S., de Almeida Ribeiro, P. R., & de Oliveira, A. C. M.
    In 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV) IEEE
  • Storage fidelity for sequence memory in the hippocampal circuit
    Bayati, M., Neher, T., Melchior, J., Diba, K., Wiskott, L., & Cheng, S.
    PLOS ONE, 13(10), e0204685
  • The reduction of adult neurogenesis in depression impairs the retrieval of new as well as remote episodic memory
    Fang, J., Demic, S., & Cheng, S.
    PLOS ONE, 13(6), e0198406
  • The Interaction between Semantic Representation and Episodic Memory
    Fang, J., Rüther, N., Bellebaum, C., Wiskott, L., & Cheng, S.
    Neural Computation, 30(2), 293–332
  • Doing without metarepresentation: Scenario construction explains the epistemic generativity and privileged status of episodic memory
    Werning, M., & Cheng, S.
    Behavioral and Brain Sciences, 41, e34

    • 2017

  • Generating sequences in recurrent neural networks for storing and retrieving episodic memories
    Bayati, M., Melchior, J., Wiskott, L., & Cheng, S.
    In Proc. 26th Annual Computational Neuroscience Meeting (CNS*2017): Part 2
  • Consolidation of Episodic Memory: An Epiphenomenon of Semantic Learning
    Cheng, S.
    In N. Axmacher & Rasch, B. (Eds.), Cognitive Neuroscience of Memory Consolidation (pp. 57–72) Cham, Switzerland: Springer International Publishing
  • From grid cells to place cells with realistic field sizes
    Neher, T., Azizi, A. H., & Cheng, S.
    PLoS ONE, 12(7), e0181618
  • Taxonomy and Unity of Memory
    Werning, M., & Cheng, S.
    In S. Bernecker & Michaelian, K. (Eds.), The Routledge Handbook of Philosophy of Memory (pp. 7–20) New York: Routledge

    • 2016

  • Topological Schemas of Cognitive Maps and Spatial Learning
    Babichev, A., Cheng, S., & Dabaghian, Y. A.
    Frontiers in Computational Neuroscience, 10, 18
  • What is episodic memory if it is a natural kind?
    Cheng, S., & Werning, M.
    Synthese, 193(5), 1345–1385
  • Dissociating memory traces and scenario construction in mental time travel
    Cheng, S., Werning, M., & Suddendorf, T.
    Neuroscience & Biobehavioral Reviews, 60, 82–89

    • 2015

  • Self-organization of synchronous activity propagation in neuronal networks driven by local excitation
    Bayati, M., Valizadeh, A., Abbassian, A., & Cheng, S.
    Frontiers in Computational Neuroscience, 9, 69
  • Memory Storage Fidelity in the Hippocampal Circuit: The Role of Subregions and Input Statistics
    Neher, T., Cheng, S., & Wiskott, L.
    PLoS Computational Biology, 11(5), e1004250
    Journal's article page

    • 2014

  • Modeling the Dynamics of Disease States in Depression
    Demic, S., & Cheng, S.
    PLOS ONE, 9(10), 1–14
  • The transformation from grid cells to place cells is robust to noise in the grid pattern
    Azizi, A. H., Schieferstein, N., & Cheng, S.
    Hippocampus, 24(8), 912–919
  • Parametric Anatomical Modeling: a method for modeling the anatomical layout of neurons and their projections
    Pyka, M., Klatt, S., & Cheng, S.
    Frontiers in Neuroanatomy, 8, 91
  • Is Episodic Memory a Natural Kind?-A Defense of the Sequence Analysis
    Werning, M., & Cheng, S.
    In P. Bello, Guarini, M., McShane, M., & Scassellati, B. (Eds.), Proceedings of the 36th Annual Conference of the Cognitive Science Society (Vol. 2, pp. 964–69) Austin, TX: Cognitive Science Society
    Is Episodic Memory a Natural Kind? - A Defense of the Sequence Analysis

    • 2013

  • Composition and replay of mnemonic sequences: The contributions of REM and slow-wave sleep to episodic memory
    Cheng, S., & Werning, M.
    Behavioral and Brain Sciences, 36(06), 610–611
  • A computational model for preplay in the hippocampus
    Azizi, A. H., Wiskott, L., & Cheng, S.
    Frontiers in Computational Neuroscience, 7, 161
  • The CRISP theory of hippocampal function in episodic memory
    Cheng, S.
    Frontiers in Neural Circuits, 7, 88
  • Identification of two forebrain structures that mediate execution of memorized sequences in the pigeon
    Helduser, S., Cheng, S., & Güntürkün, O.
    Journal of Neurophysiology, 109(4), 958–968

    • 2012

  • Constraints on the synchronization of entorhinal cortex stellate cells
    Crotty, P., Lasker, E., & Cheng, S.
    Phys. Rev. E, 86(1), 011908

    • 2011

  • Reactivation, Replay, and Preplay: How It Might All Fit Together
    Buhry, L., Azizi, A. H., & Cheng, S.
    Neural Plasticity, 2011, 1–11
  • The structure of networks that produce the transformation from grid cells to place cells
    Cheng, S., & Frank, L. M.
    Neuroscience , 197, 293–306

    • 2008

  • New Experiences Enhance Coordinated Neural Activity in the Hippocampus
    Cheng, S., & Frank, L. M.
    Neuron , 57(2), 303–313

    • 2007

  • Calibration of Visually Guided Reaching Is Driven by Error-Corrective Learning and Internal Dynamics
    Cheng, S., & Sabes, P. N.
    Journal of Neurophysiology, 97(4), 3057–3069

    • 2006

  • Modeling Sensorimotor Learning with Linear Dynamical Systems
    Cheng, S., & Sabes, P. N.
    Neural Computation, 18(4), 760–793

    • 2004

  • Statistical and dynamic models of charge balance functions
    Cheng, S., Petriconi, S., Pratt, S., Skoby, M., Gale, C., Jeon, S., et al.
    Phys. Rev. C, 69(5), 054906

    • 2003

  • Removing distortions from charge balance functions
    Pratt, S., & Cheng, S.
    Phys. Rev. C, 68(1), 014907
  • Isospin fluctuations from a thermally equilibrated hadron gas
    Cheng, S., & Pratt, S.
    Phys. Rev. C, 67(4), 044904

    • 2002

  • Statistical physics in a finite volume with absolute conservation laws
    Statistical Physics in a Finite Volume with Absolute Conservation Laws
  • Modeling Relativistic Heavy Ion Collisions
    MODELING RELATIVISTIC HEAVY ION COLLISIONS
  • Effect of finite-range interactions in classical transport theory
    Cheng, S., Pratt, S., Csizmadia, P., Nara, Y., Molnár, D., Gyulassy, M., et al.
    Phys. Rev. C, 65(2), 024901

    • 2001

  • Quantum corrections for pion correlations involving resonance decays
    Cheng, S., & Pratt, S.
    Phys. Rev. C, 63(5), 054904

Winter Term 2023/2024

LecturesArtificial Neural Networks
SeminarsJournal Club: Learning and Memory
SeminarsResearch Colloquium: Interdisciplinary Perspectives on Episodic Memory

Summer Term 2023

LecturesIntroduction to Computational Neuroscience
SeminarsJournal Club "Learning and Memory"
SeminarsSeminar Computational Neuroscience
SeminarsSeminar From Biological to Artificial Neural Networks

Winter Term 2022/2023

LecturesArtificial Neural Networks
SeminarsJournal Club: Learning and Memory
SeminarsResearch Colloquium: Interdisciplinary Perspectives on Episodic Memory

Summer Term 2022

LecturesIntroduction to Computational Neuroscience
SeminarsJournal Club "Learning and Memory"
SeminarsResearch Colloquium: Interdisciplinary Perspectives on Episodic Memory

Winter Term 2021/2022

LecturesArtificial Neural Networks
SeminarsJournal Club: Learning and Memory
SeminarsResearch Colloquium: Interdisciplinary Perspectives on Episodic Memory

Summer Term 2021

LecturesIntroduction to Computational Neuroscience
SeminarsJournal Club "Learning and Memory"
SeminarsResearch Colloquium: Interdisciplinary Perspectives on Episodic Memory

Winter Term 2020/2021

LecturesArtificial Neural Networks
SeminarsJournal Club: Learning and Memory

Summer Term 2020

SeminarsJournal Club "Learning and Memory"

Winter Term 2019/2020

LecturesArtificial Neural Networks
SeminarsJournal Club: Learning and Memory

Summer Term 2019

SeminarsJournal Club "Learning and Memory"
LecturesMathematical Psychology

Winter Term 2018/2019

SeminarsComputational Cognitive Modeling
LecturesIntroduction to Perception
SeminarsJournal Club: Learning and Memory
SeminarsThe Neural Basis of Vision

Summer Term 2018

SeminarsDiscourse in Episodic Memory
SeminarsJournal Club "Learning and Memory"
LecturesMathematical Psychology

Winter Term 2017/2018

SeminarsColloquium: Learning and Memory
SeminarsComputational Cognitive Modeling
LecturesIntroduction to Perception

Summer Term 2017

SeminarsDiscourse in Episodic Memory
SeminarsJournal Club "Learning and Memory"
LecturesMathematical Psychology

Winter Term 2016/2017

SeminarsColloquium: Learning and Memory
SeminarsComputational Cognitive Modeling
LecturesIntroduction to Perception
SeminarsThe Neural Basis of Vision

Summer Term 2016

SeminarsS Discourse in Episodic Memory
SeminarsS Journal Club "Learning and Memory"
SeminarsS Mathematical Psychology

Summer Term 2023

LecturesIntroduction to Artificial Intelligence

Summer Term 2022

LecturesIntroduction to Artificial Intelligence

Summer Term 2021

LecturesIntroduction to Artificial Intelligence

    2020

  • Learning pose-invariant object representations using Power-SFA
    van Wickern, J.
    Bachelor's thesis, Applied Informatics, Univ. of Bochum, Germany

    • 2019

  • Differential Visual Discrimination Performance Dependent on Stimulus Category in a Deep Neural Network Model of the Visual Cortex
    Boschuk, V.
    Bachelor's thesis, Applied Informatics, Univ. of Bochum, Germany
  • Simulating the effects of the balance between excitatory and inhibitory neurons on the dynamics of spiking neural networks
    Droßel, A.
    Master’s thesis, Physics, TU Dortmund, Germany
  • Reinforcement learning in multiple contexts: How does the network represent the learned information?
    Diekmann, N.
    Master’s thesis, Applied Informatics, Univ. of Bochum, Germany
  • Transfer of Learned Associations in a Reinforcement Learning Context
    Vijayabaskaran, S.
    Master’s thesis, Computational Engineering, Univ. of Bochum, Germany
  • Driving network oscillations in ING and PING models with realistic spiking inputs
    Sowade, J.
    Master’s thesis, Applied Informatics, Univ. of Bochum, Germany

    • 2018

  • Storing and retrieving neural sequences in a network model of the hippocampus with spiking neurons and STDP
    Dyck, S.
    Master’s thesis, Cognitive Science, Univ. of Bochum, Germany
  • Spontaneous sequence generation in a neural network model of the hippocampus
    Matzke, S.
    Bachelor's thesis, Applied Informatics, Univ. of Bochum, Germany

    • 2017

  • Generating Neural Network Connectivity from a Visual Representation
    Herbers, P.
    Master’s thesis, Applied Informatics, Univ. of Bochum, Germany
    pdf

    • 2016

  • Propagation of sequential activity in feedforward neural networks
    Hakobyan, O.
    Master’s thesis, Cognitive Science, Univ. of Bochum, Germany
  • Uncovering the representation of visual categories in neural ensembles
    Klatt, S.
    Bachelor's thesis, Applied Informatics, Univ. of Bochum, Germany
  • Modeling the effect of depression on mortality.
    Ogiermann, D.
    Bachelor's thesis, Applied Informatics, Univ. of Bochum, Germany
  • Using Kalman filtering to combine path integration and external spatial information in a mobile robot
    Diekmann, N.
    Bachelor's thesis, Applied Informatics, Univ. of Bochum, Germany

Dynamics of extinction learning in behavior and neural activity

How does learning unfold over time? This question can be studied in experimental data and with computational modeling. We analyze behavioral and neural activity data that was collected by collaborating labs. In our theoretical work, we employ simple associative models as well as deep reinforcement learning, which allows us to study the emerging representations and to correlate them to experimental data.

Emergent behavior and neural representations in spatial learning

Spatial navigation might appear to be a simple behavior, but closer inspection reveals that it is the complex result of many interacting sub-processes. We use deep reinforcement learning to understand how goal-directed behavior emerges in an artificial agent, how the deep neural network represents spatial information, and how the model's representations are related to neural codes for space in the hippocampus.

Nature and function of episodic memory

There is still great uncertainty as to what episodic memory is and what function it might serve. Within the research unit FOR 2812, we are working on an interdisciplinary framework for episodic memory.

Neural mechanisms underlying spatial navigation

A large number of cell types in the mammalian brain code for various types of spatial information, e.g., head direction cells, place cells, and grid cells. We study how networks of these cell types could support spatial navigation by combining computational modeling and data analysis.

Sequence memory in the hippocampus

The CRISP theory suggests that episodic memories are best represented by neuronal sequences and specific mechanisms by which sequences are stored and retrieved from the hippocampal circuit. Using neural network models, we investigate under which conditions the hippocampal circuit can perform the hypothesized functions reliably and robustly.

About the INI

The Institut für Neuroinformatik (INI) is a central research unit of the Ruhr-Universität Bochum. We aim to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory systems and while acting in those environments through effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental approaches from psychology and neurophysiology as well as theoretical approaches from physics, mathematics, electrical engineering and applied computer science, in particular machine learning, artificial intelligence, and computer vision.

Contact

Universitätsstr. 150, Building NB, Room 3/32
D-44801 Bochum, Germany

Tel: (+49) 234 32-28967
Fax: (+49) 234 32-14210

sekretariat@ini.rub.de
www.ini.rub.de