A01 A02 A03 A04 A05 A06 A07 A09 A10 A11 A12 A13 A14 A18 A19 A21 F01 F02 INF

A14 – Modeling context-dependent acquisition and extinction learning

Sen Cheng

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:

  • What distinguishes cues from contextual information in (extinction) learning?
  • What makes extinction learning strongly context-dependent, while other forms of learning are less so?
  • Why is the hippocampus required for context-dependence?
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Sen Cheng

Projektleiter A14, F01

Ruhr-Universität Bochum

David Kappel

Postdoc A14

Ruhr-Universität Bochum

Nicolas Diekmann

Doktorand A14

Ruhr-Universität Bochum

Sandhiya Vijayabaskaran

Doktorandin A14

Ruhr-Universität Bochum

10 project-relevant publications

Azizi AH, Wiskott L, Cheng S (2013) A computational model for preplay in the hippocampus. Front Comput Neurosci. 7:161.

Muñoz MES, Enrique M, Menezes MC, Freitas EP, Cheng S, Neto AA, Oliveira ACM, Ribeiro PRA, Rogério P (2019) A Parallel RatSlam C++ Library Implementation. In Cota VR, Barone DAC, & Dias DRC (Eds.): Communications in Computer and Information Science, Computational Neuroscience. Second Latin American Workshop, LAWCN 2019, São João Del-Rei, Brazil, September 18–20, 2019, Proceedings (1st ed., pp. 173–183). Cham: Springer International Publishing.

Fang J, Demic S, Cheng S (2018) The reduction of adult neurogenesis in depression impairs the retrieval of new as well as remote episodic memory. PLoS One. 13(6): e0198406.

Giri B, Miyawaki H, Mizuseki K, Cheng S, Diba K (2019) Hippocampal Reactivation Extends for Several Hours Following Novel Experience. J Neurosci. 39(5): 866–875.

Görler R, Wiskott L, Cheng S (2020) Improving sensory representations using episodic memory. Hippocampus. 30(6): 638–656.

Menezes MC, Freitas EP, Cheng S, Oliveira ACM, Ribeiro PRA (2018) A Neuro-Inspired Approach to Solve a Simultaneous Location and Mapping Task Using Shared Information in Multiple Robots Systems. In: 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV). IEEE, Singapore: 1753–1758.

Menezes MC, Muñoz MES, Freitas EP, Cheng S, Walther T, Neto AA, Ribeiro PRA, Oliveira ACM (2020) Automatic Tuning of RatSLAM’s Parameters by Irace and Iterative Closest Point. In IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society: 562–568.

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.

Santos RG, Freitas EP, Cheng S, Ribeiro PRA, Oliveira ACM (2018) Autonomous Exploration Guided by Optimisation Metaheuristic. In: 2018 15th International Conference on Control, Automation, Robotics and Vision (ICARCV). IEEE, Singapore: 1759–1764.

Zhao D, Zhang Z, Lu H, Cheng S, Si B, Feng X (2020) Learning Cognitive Map Representations for Navigation by Sensory-Motor Integration. IEEE Trans Cybern.