Neural mechanisms underlying spatial navigation
Collaborator: Yuri Dabaghian

Navigating from one point to another may seem like a straightforward task, but it relies on the coordinated deployment of various complex cognitive capacities. These include operations such as pattern recognition, usage of knowledge about temporal relationships, or behavioral planning and control. Testing spatial behavior is relatively uncomplicated, and the associated neural responses, although distant from the sensorimotor periphery, are often surprisingly easy to correlate with meaningful variables. Consequently, this simplicity has facilitated the identification of numerous cell types that encode different aspects of spatial behavior. This makes spatial navigation an ideal model for exploring the neural mechanisms responsible for higher-level cognitive functions.Among the extensively studied cell types that modulate spatial information are place cells and grid cells. These cells indicate an animal's location by activating only in specific regions of the environment. Notably, these cells exhibit a type of phase coding: their firing during different phases of the theta oscillation appears to represent the positions recently reached or soon to be reached by the animal. 


Our research delves into understanding how networks of such phase coding cells can facilitate spatial navigation, encompassing functions like path integration, predicting future positions, and planning movements. To achieve this, we employ a combination of computational modeling, simulation work, and the analysis of experimental data.

CoBeL-spike is one of the software developed in our lab to simulate sophisticated interactions between an environment and a biologically plausible neural network.



  • A map of spatial navigation for neuroscience
    Parra-Barrero, E., Vijayabaskaran, S., Seabrook, E., Wiskott, L., & Cheng, S.
    Neuroscience & Biobehavioral Reviews, 152, 105200
  • 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
  • 2021

  • Neuronal Sequences during Theta Rely on Behavior-Dependent Spatial Maps
    Parra-Barrero, E., Diba, K., & Cheng, S.
    eLife, 10, e70296
  • 2017

  • From grid cells to place cells with realistic field sizes
    Neher, T., Azizi, A. H., & Cheng, S.
    PLoS ONE, 12(7), e0181618
  • 2016

  • Topological Schemas of Cognitive Maps and Spatial Learning
    Babichev, A., Cheng, S., & Dabaghian, Y. A.
    Frontiers in Computational Neuroscience, 10, 18
  • 2014

  • 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
  • 2011

  • 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

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