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

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

Sandhiya Vijayabaskaran

Postdoc A14

Ruhr-Universität Bochum

Behnam Ghazinouri

Doktorand A14

Ruhr-Universität Bochum

Marco Recchioni

Doktorand A14

Ruhr-Universität Bochum

10 project-relevant publications

Diekmann N, Vijayabaskaran S, Zeng X, Kappel D, Menezes MC, Cheng S (2023) CoBeL-RL: A neuroscience-oriented simulation framework for complex behavior and learning. Front Neuroinformatics 17:1134405. https://doi.org/10.3389/fninf.2023.1134405 

Ghazinouri B, Cheng S (2025) The Cost of Behavioral Flexibility: Reversal Learning Driven by a Spiking Neural Network. In: From Animals to Animats 17 (Brock O, Krichmar J, eds), pp 39–50. Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-71533-4_23

Kappel D, Cheng S (2025) Global remapping emerges as the mechanism for renewal of context-dependent behavior in a reinforcement learning model. Front Comput Neurosci 18:1462110. https://doi.org/10.3389/fncom.2024.1462110 

Menezes M, Zeng X, Cheng S (2025) Revealing the mechanisms underlying latent learning with successor representations. bioRxiv:2025.03.01.640768. https://doi.org/10.1101/2025.03.01.640768 

Parra-Barrero E, Vijayabaskaran S, Seabrook E, Wiskott L, Cheng S (2023) A map of spatial navigation for neuroscience. Neurosci Biobehav Rev 152:105200. https://doi.org/10.1016/j.neubiorev.2023.105200

Pusch R, Packheiser J, Azizi AH, Sevincik CS, Rose J, Cheng S, Stüttgen MC, Güntürkün O (2023) Working memory performance is tied to stimulus complexity. Commun Biol 6:1–16. https://doi.org/10.1038/s42003-023-05486-7 

Vijayabaskaran S, Cheng S (2022) Navigation task and action space drive the emergence of egocentric and allocentric spatial representations. PLOS Comput Biol 18:e1010320. https://doi.org/10.1371/journal.pcbi.1010320 

Vijayabaskaran S, Cheng S (2024) Competition and Integration of Visual and Goal Vector Signals for Spatial Navigation bioRxiv:2024.05.14.594206. https://doi.org/10.1101/2024.05.14.594206 

Walther T, Diekmann N, Vijayabaskaran S, Donoso JR, Manahan-Vaughan D, Wiskott L, Cheng S (2021) Contextdependent extinction learning emerging from raw sensory inputs: a reinforcement learning approach. Sci Rep 11:2713. https://doi.org/10.1038/s41598-021-81157-z 

Zhao D, Zhang Z, Lu H, Cheng S, Si B, Feng X (2022) Learning Cognitive Map Representations for Navigation by Sensory–Motor Integration. IEEE Trans Cybern 52:508–521. https://doi.org/10.1109/TCYB.2020.2977999