Collaborator: Prof. Dr Kamran Diba, Prof. Dr Laurenz Wiskott, Prof. Dr Alireza Valizadeh

Despite several decades of research the precise neuronal mechanisms underlying episodic memory, our memory of experienced events in our lives, remain unclear. We recently suggested that episodic memories are best represented as sequences of neural activity patterns and proposed specific contributions of the hippocampal subregions to the storage and retrieval of neuronal sequences. One central feature of the CRISP theory is that hippocampal area CA3 intrinsically produces sequences. During memory encoding, intrinsic CA3 sequences are associated with sequences that are driven by sensory inputs. During memory retrieval, intrinsic CA3 sequences have to be reactivated based on partial, noisy cues. Each element in the retrieved sequence, a CA3 pattern, in turn leads to the retrieval of the associated CA1 pattern. This CA3-CA1 association for pattern completion and therefore more accurate memory recall. In CRISP, the role of the dentate gyrus is to help initiate different intrinsic sequences in CA3, even for similar input sequences, i.e. sequence separation. Using neural network models, we investigate under which conditions the hippocampal circuit can perform the hypothesized functions robustly, so that neuronal sequences are stored and robustly retrieved.



  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 2011

  • Reactivation, Replay, and Preplay: How It Might All Fit Together
    Buhry, L., Azizi, A. H., & Cheng, S.
    Neural Plasticity, 2011, 1–11

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