Computational Neuroscience


We are excited to announce that the DFG (Deutsche Forschungsgemeinschaft) has approved funding for the research unit FOR 2812 "Constructing scenarios of the past: A new framework in episodic memory". Work on this project will commence in July 2019. To read the official RUB press release (German only) visit


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.

Cheng lab members hiking


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. 

International collaborations:

  • Kamran Diba, University of Michigan, USA

  • Chengyu Li, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, China

  • Edison Pignaton de Freitas, Federal University of Rio Grande do Sul, Brazil

  • Bailu Si, Shenyang Institute of Automation, Chinese Academy of Sciences, China

  • Patrick Crotty, Colgate University, USA

  • Yuri Dabaghian, Baylor College of Medicine, USA

  • Lynn Nadel, University of Arizona, USA

  • Thomas Suddendorf, University of Queensland, Australia

  • Alireza Valizadeh, Institute of Advanced Studies, Zanjan, Iran


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.

For further information consult the  project page (see above).

The model to simulate the dynamics of disease states in depression can be downloaded as a zip File and can be used with Matlab.

Demic, S. & Cheng, S. (2014): Modeling the Disease States in Depression. PLoS ONE 9(10): e110358.



With Parametric Anatomical Modeling (PAM), we propose a technique and a Python implementation to create artificial neural networks that meet connectivity patterns and connection lengths of large scale neural networks.

The basic idea of PAM is to trace neural, synaptic and intermediate layers from anatomical data and relate those layers to each other. With a set of mapping techniques, complex relationships between those layers can be defined to determine how axonal and dendritic projections traverse through space and where synapses are formed.

For further information consult the project page (see above).

PAM is available as an Addon for Blender and can be downloaded from a repository on Github. An importer for the neural network simulator NEST is available in a separate repository.

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.



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 underlie the Create Commons Attribution 4.0 International License.

A ZIP-file containing all of the currently available figures as well as additional information concerning their creation can be downloaded here.

Modeling the Dynamics of Disease States in Depression

We study under what conditions the model can account for the occurrence and recurrence of depressive episodes and how we can model the effects of antidepressant treatments and cognitive behavioral therapy within the same dynamical systems model through changing a small subset of parameters.

Parametric Anatomical Modeling (PAM)

With Parametric Anatomical Modeling (PAM), we propose a technique and a Python implementation to create artificial neural networks that meet connectivity patterns and connection lengths of large scale neural networks.


Rodents perform extremely well in navigating complex environments like dark sewers while looking for food or fleeing from predators. The role of the hippocampus in such spatial navigation efforts has interested researchers from the neuroscience domain for decades: in 1971 so-called ‘place cells’ had been discovered in the rat’s hippocampus. While these cell-types allow the animals to swiftly self-localize in a given environment (cognitive mapping), rodents also seem to rely on grid cells (discovered 2005 in the rat’s hippocampus) for path planning and spatial navigation. In addition, recent publications postulate a relationship between theta waves generated in the hippocampus and decision making in spatial navigation tasks.


  • 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
  • 2018

  • 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
  • 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
  • Gedächtnisverbesserung: Möglichkeiten und kritische Betrachtung
    Cheng, S.
    In F. Hüttemann & Liggieri, K. (Eds.), Die Grenze . Diskurse des Transhumanismus. (p. invited contribution) Bielefeld: transcript Verlag
  • 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 (p. forthcoming) London: Routledge
  • No need for meta-representation: How scenario construction explains the epistemic generativity and privileged epistemic status of episodic memory
    Werning, M., & Cheng, S.
    Behavioral and Brain Sciences, in press
  • 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

  • Hippocampal reactivation extends for several hours following novel experience
    Giri, B., Miyawaki, H., Mizuseki, K., Cheng, S., & Diba, K.
    Journal of Neuroscience


  • Generating Neural Network Connectivity from a Visual Representation
    Herbers, P.
    Master’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.

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

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