Modeling Enhanced Replay of Neuronal Sequences in the Hippocampus Computational Neuroscience


A large body of experimental evidence shows that place cells in the hippocampus of rodents generate temporal sequential activity during sleep or immobility awake states. Some of these sequences have been shown to correlate with the previous (replay) or the future (preplay) spatial ordering of the corresponding place fields. Because of the abundant recurrent connections, these sequences are thought to originate in CA3 and trigger neuronal sequences downstream, e.g., in CA1. We previously showed that a continuous attractor model for the CA3 network can reliably generate long neuronal sequences (Azizi, Wiskott, & Cheng, 2013)⁠. These sequences were generated as a result of bump of activity moving through a preconfigured multi-chart network.

Experimental studies have shown that the chance of observing replay sequences is larger than that of the preplay sequences. Therefore our model should imprint the intrinsically generated sequences, such that noisy activity in the offline state have a higher chance of reactivating these imprinted sequences.

In this project, you will work on a previously implemented C-code of the network and implement an appropriate learning mechanism with the right parameters to model the replay phenomenon. Therefore, previous experience and a working knowledge of programming in C is required.


Azizi, A. H., Wiskott, L., & Cheng, S. (2013). A computational model for preplay in the hippocampus. Frontiers in Computational Neuroscience, 7(August), 161, doi:10.3389/fncom.2013.00161


 Dr. Amir Azizi and Prof. Dr. Sen Cheng 

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.

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