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