Computational Neuroscience

Our group investigates the neural mechanisms underlying learning and memory using computational methods. The main language of communication is English. We highly welcome bright students who would like to pursue a Bachelor/ Master thesis in our group. Our unit is located at the Institute of Neural Computation and is a member of the Mercator Research Group (MRG) "Structure of Memory".


We investigate the neural mechanisms underlying learning and memory using computational approaches. In particular, we study how a brain region, called the hippocampus, is involved in storing and retrieving episodic memories and in generating representations of space. There is overwhelming experimental evidence that the hippocampus is involved in both these functions, but 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.
  • theoretical models of the nature of episodic memory.
  • robotics simulations of spatial memory in rodents.

Below we describe a selection of projects that are currently ongoing in our group.



Martin Pyka in our group has recently developed a method for modeling the anatomical layout of neurons and their projections. In this video, he uses his software to illustrate the peculiar gross anatomy of the hippocampal formation.



We are developing a computational model of encoding, storage and retrieval of episodic memory which takes into account the interrelation between episodic memory and semantic representations. In the model, episodes are stored in terms of higher order information, i.e., semantic representation, not their underlying sensory inputs. We investigate, for example, what role the semantic representation might play in episodic memory and how episodic memory can be used to infer semantic information.



The aim of the project is to understand how the rodent brain generates place-selective responses based on visual inputs alone. To model as closely as possible the conditions that a rodent faces, we let the small ePuck robot explore a real environment to collect images. These images are then processed with an algorithm called slow-feature analysis (SFA). In the future, we will study how to combine this visually-driven (allothetic) information with idiothetic spatial information to generate more robust location estimates.



/>Temporal sequences of neural activation can be observed in the hippocampus during the theta state and during sharp-wave ripple events. Since these temporal sequence are related to the ordering the cells' place fields, it has been suggested that the sequence are important for spatial navigation, planing or learning. We are trying to understand how neural networks generate these sequences and how they propagate to downstream regions.

Babichev, A., Cheng, S., & Dabaghian, Y. A. (2016). Topological Schemas of Cognitive Maps and Spatial Learning. Frontiers in Computational Neuroscience, 10, 18.
Cheng, S., & Werning, M. (2016). What is episodic memory if it is a natural kind? Synthese, 193(5), 1345–1385.
Cheng, S., Werning, M., & Suddendorf, T. (2016). Dissociating memory traces and scenario construction in mental time travel. Neuroscience & Biobehavioral Reviews, 60, 82–89.
Bayati, M., Valizadeh, A., Abbassian, A., & Cheng, S.. (2015). Self-organization of synchronous activity propagation in neuronal networks driven by local excitation. Frontiers in Computational Neuroscience, 9, 69.
Azizi, A. H., Schieferstein, N., & Cheng, S.. (2014). The transformation from grid cells to place cells is robust to noise in the grid pattern. Hippocampus, 24(8), 912–919.
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.
Werning, M., & Cheng, S.. (2014). Is Episodic Memory a Natural Kind? - A Defense of the Sequence Analysis.
Cheng, S., & Werning, M. (2013). Composition and replay of mnemonic sequences: The contributions of REM and slow-wave sleep to episodic memory. Behavioral and Brain Sciences, 36(06), 610–611.
Azizi, A. H., Wiskott, L., & Cheng, S.. (2013). A computational model for preplay in the hippocampus. Frontiers in Computational Neuroscience, 7, 161.
Cheng, S. (2013). The CRISP theory of hippocampal function in episodic memory. Frontiers in Neural Circuits, 7, 88.
Helduser, S., Cheng, S., & Güntürkün, O. (2013). Identification of two forebrain structures that mediate execution of memorized sequences in the pigeon. Journal of Neurophysiology, 109(4), 958–968.
Crotty, P., Lasker, E., & Cheng, S.. (2012). Constraints on the synchronization of entorhinal cortex stellate cells. Phys. Rev. E, 86(1), 011908.
Buhry, L., Azizi, A. H., & Cheng, S.. (2011). Reactivation, Replay, and Preplay: How It Might All Fit Together. Neural Plasticity, 2011, 1–11.
Cheng, S., & Frank, L. M. (2011). The structure of networks that produce the transformation from grid cells to place cells . Neuroscience , 197, 293–306.
Cheng, S., & Frank, L. M. (2008). New Experiences Enhance Coordinated Neural Activity in the Hippocampus . Neuron , 57(2), 303–313.
Cheng, S., & Sabes, P. N. (2007). Calibration of Visually Guided Reaching Is Driven by Error-Corrective Learning and Internal Dynamics. Journal of Neurophysiology, 97(4), 3057–3069.
Cheng, S., & Sabes, P. N. (2006). Modeling Sensorimotor Learning with Linear Dynamical Systems. Neural Computation, 18(4), 760–793.
Cheng, S., Petriconi, S., Pratt, S., Skoby, M., Gale, C., Jeon, S., et al. (2004). Statistical and dynamic models of charge balance functions. Phys. Rev. C, 69(5), 054906.
Pratt, S., & Cheng, S.. (2003). Removing distortions from charge balance functions. Phys. Rev. C, 68(1), 014907.
Cheng, S., & Pratt, S. (2003). Isospin fluctuations from a thermally equilibrated hadron gas. Phys. Rev. C, 67(4), 044904.
Cheng, S. (2002). Statistical physics in a finite volume with absolute conservation laws.
Cheng, S. (2002). Modeling Relativistic Heavy Ion Collisions.
Cheng, S., Pratt, S., Csizmadia, P., Nara, Y., Molnár, D., Gyulassy, M., et al. (2002). Effect of finite-range interactions in classical transport theory. Phys. Rev. C, 65(2), 024901.
Cheng, S., & Pratt, S. (2001). Quantum corrections for pion correlations involving resonance decays. Phys. Rev. C, 63(5), 054904.