Extinction learning is strongly dependent on the contexts, in which acquisition and extinction took place, and we know that the hippocampus is critical for mediating this context dependence. However, we still do not understand the learning and neural mechanisms by which this context-dependence comes about. In this project, computational modeling and robotics will be used to study 1. what distinguishes cues from contextual information, 2. what makes extinction learning strongly context-dependent, while other forms of learning are less so, and 3. why the hippocampus is required for context-dependence.
Guiding questions of A14:
Azizi AH, Schieferstein N, Cheng S (2014) The transformation from grid cells to place cells is robust to noise in the grid pattern. Hippocampus. 24(8): 912–919.
Azizi AH, Wiskott L, Cheng S (2013) A computational model for preplay in the hippocampus. Front Comput Neurosci. 7: 161.
Buhry L, Azizi AH, Cheng S (2011) Reactivation, replay, and preplay: how it might all fit together. Neural Plast. 2011: 203462.
Cheng S, Frank LM (2011) The structure of networks that produce the transformation from grid cells to place cells. Neuroscience. 197: 293–306.
Cheng S (2013) The CRISP theory of hippocampal function in episodic memory. Front Neural Circuits. 7: 88.
Cheng S, Frank LM (2008) New experiences enhance coordinated neural activity in the hippocampus. Neuron. 57(2): 303–313.
Cheng S, Werning M (2016) What is episodic memory if it is a natural kind? Synthese. 193(5): 1345–1385.
Cheng S, Werning M, Suddendorf T (2016) Dissociating memory traces and scenario construction in mental time travel. Neurosci Biobehav Rev. 60: 82–89.
Neher T, Cheng S, Wiskott L (2015) Memory storage fidelity in the hippocampal circuit: the role of subregions and input statistics. PLoS Comput Biol. 11(5): e1004250.
Pyka M, Cheng S (2014) Pattern association and consolidation emerges from connectivity properties between cortex and hippocampus. PLoS One. 9(1): e85016.