Since grid cells were discovered in the medial entorhinal cortex, several models have been proposed for the transformation from periodic grids to the punctuate place fields of hippocampal place cells. For instance, Cheng and Frank identified the structure of the networks that is common to all robust solutions of the grids-to-places transformation . Furthermore, we recently proposed that episodic memories, memories of personally experienced events, are best represented by temporal sequences of neural activation patterns (CRISP theory ). We have implemented CRISP in neural networks to store and retrieve episodic memories in a cortico-hippocampal network. One important feature of CRISP is that input sequences from grid cells are stored through synaptic plasticity in the feedforward projections of the hippocampus. In this project, we study whether the responses of neurons in the hippocampal sublayers (DG, CA3, and CA1) resemble the responses of recorded place cells. To this end, we let a virtual animal randomly explore an enclosure and record grid cells activity in medial entorhinal cortex based on the animal’s position as a sequence of activity patterns . These sequences are then stored in the cortico-hippocampal network developed previously. Knowledge of the programming language Python is required.
 Cheng S and Frank LM. (2011), The structure of networks that produce the transformation from grid cells to place cells, Neuroscience, 197:293-306, doi: 10.1016/j.neuroscience.2011.09.002
 Cheng S (2013), The CRISP theory of hippocampal function in episodic memory, Front. Neural Circuits, 7:88, doi: 10.3389/fncir.2013.00088
 Neher T, Cheng S and Wiskott L (2015), Memory storage fidelity in the hippocampal circuit: The role of subregions and input statistics. PLoS Comput Biol 11(5): e1004250., doi:10.1371/journal.pcbi.1004250