Robust Generation of Spatio-temporal Activity Patterns in Neuronal Networks Computational Neuroscience


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 memory are best represented as sequences of neural activity patterns unfolding in time and proposed specific contributions of the hippocampal subregions to the storage and retrieval of neuronal sequences (CRISP, see the literature). One central feature of CRISP 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. Therefore, the neural network mechanism in CA3 generating the sequences has to be robust to noise in the triggering cue. A number of neural networks have been proposed that can generate sequential activity, but their robustness to noise has rarely been studied. The goal of this project is to better understand the sensitivity of the various neural network models to noise. Since sequential neuronal activity is associated with a number of brain functions, e.g., movement, the results of this project are likely to be relevant far beyond the study of episodic memory. Prior programming experience is required.


Cheng, S. (2013), The CRISP theory of hippocampal function in episodic memory. Frontiers in Neural Circuits, 7, 88. doi:10.3389/fncir.2013.00088


Mehdi Bayati 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, in particular machine learning, artificial intelligence, and computer vision.

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