Computational Neuroscience

cheng lab members 2022


We investigate the cognitive and neural mechanisms underlying learning and memory using computational methods. We highly welcome bright students who would like to pursue a Bachelor/Master thesis in our group. The main language of communication is English.

A particular focus of our research is understanding the function of a brain region, called the hippocampus. It is involved in storing and retrieving episodic memories and in generating representations of space. However, it remains unclear why these two functions go together and how these functions are implemented in the hippocampus. To address these two questions, we employ a number of computational and theoretical approaches, including

  • biologically realistic neural network models, which nonetheless are highly simplified, that capture the essence of the neural circuit mechanism underlying learning and memory.
  • algorithmic models of the storage and retrieval of episodic memories.
  • conceptual models of the nature of episodic memory.
  • robotics simulations of spatial memory in rodents.

The two projects described below exemplify the approach of our group.

Modeling spatial learning and navigation

Rodents are known to perform extremely well when navigating complex environments and to adapt rapidly to novel situations. They maneuver through dark sewers while looking for food or fleeing from predators and seem to know exactly where they are and where to go. We are modeling the spatial behavior of rodents using a robot model. A particular interesting question is how animals learn to navigate novel environments and to perform new tasks. We are studying this question using a machine learning approach called reinforcement learning. Within this framework, we implement the neural circuitry of the hippocampus to study how the neural mechanisms underlying spatial learning and navigation. For more details see the "Projects" tab above.


Neural sequences in the hippocampus

The Nobel Prize in Physiology or Medicine in 2014 was awarded to John O’Keefe for the discovery of place cells and to May-Britt Moser and Edvard Moser for the discovery of grid cells. Place cells are found in the hippocampus and they respond, or “spike”, when an animal is located in a particular place, called a “place field”. The timing of spiking is such that, during one cycle of the theta oscillation (8 – 12Hz), groups of place cells spike in a sequence that corresponds to the order of their place fields in space (theta sequences). Intriguingly, the same place cells also spike in sequential order afterwards, when the animal is resting or sleeping (replay sequences). We are developing neural network models to help us understand how theta and replay sequences are generated and what role they play in learning and memory. 


Speaker & projects P0, P2, and P5

Projects A14 & F01 Head: Neurobiology of memory Executive board

International collaborations:

  • Paulo de Almeida Ribeiro, Federal University of Maranhão, Brazil
  • Yuri Dabaghian, The University of Texas Health Science Center at Houston, USA
  • Kamran Diba, University of Michigan, USA
  • Lynn Nadel, University of Arizona, USA
  • Henry Otgaar, Maastricht University, Netherlands
  • Edison Pignaton de Freitas, Federal University of Rio Grande do Sul, Brazil
  • Bailu Si, School of Systems Science, Beijing Normal University, China
  • Thomas Suddendorf, University of Queensland, Australia
  • Alireza Valizadeh, Institute of Advanced Studies, Zanjan, Iran

Under Construction!

Data Analysis

⁃ Pyka-Parametric-Anatomical-Modeling-2014

Parametric Anatomical Modeling is a method to translate large-scale anatomical data into spiking neural networks. PAM is implemented as a Blender addon.

DOI: 10.5281/zenodo.3298590
PUBLICATIONS: Pyka, M., Klatt, S., Cheng, S. (2014): Parametric Anatomical Modeling: a method for modeling the anatomical layout of neurons and their projections. Frontiers in Neuroanatomy, 8, 91.

⁃ Pyka-Pam-Utils-2014

This is a module with some helpful functions to process the data generated by PAM

License: GNU GPL v2.0
DOI: 10.5281/zenodo.3298825
PUBLICATIONS: Pyka, M., Klatt, S., Cheng, S. (2014): Parametric Anatomical Modeling: a method for modeling the anatomical layout of neurons and their projections. Frontiers in Neuroanatomy, 8, 91.

Neural Networks (empty)

Cognitive Models

⁃ Dynamics of Disease States in Depression 

Major depressive disorder (MDD) is a disabling condition that adversely affects a person general health, work or school life, sleeping and eating habits, and person's family. Despite intense research efforts, the response rate of antidepressant treatments are relatively low and the etiology and progression of MDD remain poorly understood. To advance our understanding of MDD, we use computational modelling as described in our article.

The model to simulate the dynamics of disease states in depression can be found below.

License: GNU GPL v3.0
DOI: 10.5281/zenodo.3299247
PUBLICATIONS: Demic, S. & Cheng, S. (2014): Modeling the Disease States in Depression. PLoS ONE 9(10): e110358.

⁃ Episodic Memory Deficits in Depression 

License: GNU GPL v3.0
DOI: 10.5281/zenodo.3299871
PUBLICATIONS: Fang, J., Demic, S., & Cheng, S. (2018) The reduction of adult neurogenesis in depression impairs the retrieval of new as well as remote episodic memory, PLOS ONE, 13(6), e0198406

Reinforcement Learning (empty)

High-quality figures

Our group aims to provide neuroscientific community with a collection of high-quality SVG-figures for free use in publications, presentations, websites etc. via GitHub.

All SVG-files in the repository are distributed under the terms of the Create Commons Attribution 4.0 International License.

Current available images include:

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.

    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

  • Efficient Recurrent Architectures through Activity Sparsity and Sparse Back-Propagation through Time
    Subramoney, A., Nazeer, K. K., Schöne, M., Mayr, C., & Kappel, D.
    In International Conference on Learning Representations
  • 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
  • A Map of Spatial Navigation for Neuroscience
  • 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
  • 2022

  • 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
  • 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
  • Basal ganglia and cortical control of thalamic rebound spikes
    Nejad, M. M., Rotter, S., & Schmidt, R.
    European Journal of Neuroscience, 54(1), 4295–4313
  • 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
  • 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)
  • 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, 1–14
  • 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
  • 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
  • Unsupervised Acquisition of Human Body Models
    Walther, T., & Würtz, R. P.
    Cognitive Systems Research, 47, 68–84
  • 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

  • Unsupervised Acquisition of Human Body Models using Principles of Organic Computing
    Walther, T., & Würtz, R. P.
    ArXiv e-prints
  • 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) London: 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
  • 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
  • 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

  • Effect of synaptic plasticity on the structure and dynamics of disordered networks of coupled neurons
    Bayati, M., & Valizadeh, A.
    Phys. Rev. E, 86(1), 011925
  • 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
  • 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
  • accepted

  • Optogenetics reveals paradoxical network stabilizations in hippocampal CA1 and CA3
    de Jong, L. W., Nejad, M. M., Yoon, E., Cheng, S., & Diba, K.
  • A Multi-session SLAM Approach for RatSLAM
    Menezes, M. C., Munoz, M. E. S., Freitas, E. P., Cheng, S., Neto, A. A., & Oliveira, P. R. A. R. A. C. M.
    Journal of Intelligent & Robotic Systems
  • xRatSLAM: an extensible RatSLAM computational framework
    Munoz, M. E. S., Menezes, M. C., Freitas, E. P., Cheng, S., Ribeiro, P. R. A., Neto, A. A., & Oliveira, A. C. M.
  • Navigation task and action space drive the emergence of egocentric and allocentric spatial representations


  • 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
  • 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

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.

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

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