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Part 1: Document Description
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Citation |
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Title: |
Dopamine and serotonin interplay for valence-based spatial learning |
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Identification Number: |
doi:10.60507/FK2/JGONFT |
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Distributor: |
bonndata |
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Date of Distribution: |
2025-09-02 |
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Version: |
1 |
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Bibliographic Citation: |
Tchumatchenko, Tatjana; Wert Carvajal, Carlos; Reneaux, Melissa; Clopath, Claudia, 2025, "Dopamine and serotonin interplay for valence-based spatial learning", https://doi.org/10.60507/FK2/JGONFT, bonndata, V1 |
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Citation |
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Title: |
Dopamine and serotonin interplay for valence-based spatial learning |
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Identification Number: |
doi:10.60507/FK2/JGONFT |
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Authoring Entity: |
Tchumatchenko, Tatjana (University of Bonn) |
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Wert Carvajal, Carlos (Imperial College London) |
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Reneaux, Melissa (Imperial College London) |
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Clopath, Claudia (Imperial College London) |
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Distributor: |
bonndata |
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Access Authority: |
Tchumatchenko, Tatjana |
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Depositor: |
Walch, Sabrina |
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Date of Deposit: |
2025-09-02 |
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Holdings Information: |
https://doi.org/10.60507/FK2/JGONFT |
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Study Scope |
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Keywords: |
Medicine, Health and Life Sciences |
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Abstract: |
Dopamine (DA) and serotonin (5-HT) are important neuromodulators of synaptic plasticity that have been linked to learning from positive or negative outcomes or valence-based learning. In the hippocampus, both affect long-term plasticity but play different roles in encoding uncertainty or predicted reward. DA has been related to positive valence, from reward consumption or avoidance behavior, and 5-HT to aversive encoding. We propose DA produces overall LTP while 5-HT elicits LTD. Here, we compare two reward-modulated spike timing-dependent plasticity (R-STDP) rules to describe the action of these neuromodulators. We examined their role in cognitive performance and flexibility for computational models of the Morris water maze task and reversal learning. Our results show that the interplay of DA and 5-HT improves learning performance and can explain experimental evidence. This study reinforces the importance of neuromodulation in determining the direction of plasticity. |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Notes: |
<a href="http://creativecommons.org/licenses/by/4.0">CC BY 4.0</a> |
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Other Study Description Materials |
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-1.DS_Store |
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application/octet-stream |
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-2.DS_Store |
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application/octet-stream |
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application/octet-stream |
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application/octet-stream |
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0_job_learning.pickle |
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application/octet-stream |
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0_MWM_seq_normal_0-1.pickle |
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application/octet-stream |
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0_MWM_seq_normal_0.pickle |
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application/octet-stream |
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1_job_learning.pickle |
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application/octet-stream |
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1_MWM_comp_learn_0.pickle |
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application/octet-stream |
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1_rev_MWM_seq_activ_2_hist_0.pickle |
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application/octet-stream |
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2_job_learning.pickle |
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application/octet-stream |
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2_MWM_comp_learn_0.pickle |
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application/octet-stream |
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2_rev_MWM_bio_activ_15_0.pickle |
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application/octet-stream |
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3_job_learning.pickle |
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application/octet-stream |
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3_MWM_seq_dopa_0.pickle |
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application/octet-stream |
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3_rev_MWM_bio_inhib_0.pickle |
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application/octet-stream |
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3_rev_MWM_seq_inhib_2_hist_0_3.pickle |
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application/octet-stream |
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4_job_learning.pickle |
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application/octet-stream |
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4_MWM_bio_dopa_0.pickle |
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application/octet-stream |
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comp_neuromod_MWM_online.py |
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text/x-python |
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comp_neuromod_MWM_rate_online-1.py |
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text/x-python |
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comp_neuromod_MWM_rate_online.py |
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text/x-python |
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Data_analysis.ipynb |
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application/x-ipynb+json |
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multijob.py |
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text/x-python |
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NeuFun_Cuda.py |
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text/x-python |
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pool_multiq.sh |
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application/x-sh |
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README.md |
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text/markdown |
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requirements.txt |
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text/plain |
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rev_MWM_bio_activ_15_hist_0_3.pickle |
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application/octet-stream |
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rev_MWM_bio_inhib_15_hist_0.pickle |
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application/octet-stream |
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RL_plasticity_reversal.py |
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text/x-python |
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seq_neuromod_MWM.py |
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text/x-python |
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seq_neuromod_MWM_constant.py |
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text/x-python |
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seq_neuromod_MWM_rate.py |
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text/x-python |
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seq_neuromod_MWM_rate_constant.py |
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text/x-python |