The functional role of sequentially neuromodulated synaptic plasticity in behavioural learning
To survive, animals have to quickly modify their behaviour when the reward changes. The internal representations responsible for this are updated through synaptic weight changes, mediated by certain neuromodulators conveying feedback from the environment. In previous experiments, we discovered a for...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2021-06-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8192019/?tool=EBI |
| _version_ | 1818930006099755008 |
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| author | Grace Wan Yu Ang Clara S. Tang Y. Audrey Hay Sara Zannone Ole Paulsen Claudia Clopath |
| author_facet | Grace Wan Yu Ang Clara S. Tang Y. Audrey Hay Sara Zannone Ole Paulsen Claudia Clopath |
| author_sort | Grace Wan Yu Ang |
| collection | DOAJ |
| description | To survive, animals have to quickly modify their behaviour when the reward changes. The internal representations responsible for this are updated through synaptic weight changes, mediated by certain neuromodulators conveying feedback from the environment. In previous experiments, we discovered a form of hippocampal Spike-Timing-Dependent-Plasticity (STDP) that is sequentially modulated by acetylcholine and dopamine. Acetylcholine facilitates synaptic depression, while dopamine retroactively converts the depression into potentiation. When these experimental findings were implemented as a learning rule in a computational model, our simulations showed that cholinergic-facilitated depression is important for reversal learning. In the present study, we tested the model’s prediction by optogenetically inactivating cholinergic neurons in mice during a hippocampus-dependent spatial learning task with changing rewards. We found that reversal learning, but not initial place learning, was impaired, verifying our computational prediction that acetylcholine-modulated plasticity promotes the unlearning of old reward locations. Further, differences in neuromodulator concentrations in the model captured mouse-by-mouse performance variability in the optogenetic experiments. Our line of work sheds light on how neuromodulators enable the learning of new contingencies. Author summary Reversal learning likely involves changes in synaptic connections, a neural mechanism known as synaptic plasticity, so old information can be updated. We previously discovered that acetylcholine, an important neuromodulator in the brain, changes synaptic connections in a way that favours reversal learning. Specifically, acetylcholine weakens active synapses in brain slices, but these synapses can later be strengthened by a reward signal. Based on this result in slices, we used a computational model to propose a behavioural function for the action of acetylcholine on synaptic connections. In the model, acetylcholine would weaken synaptic connections associated with an old reward, allowing an agent to quickly learn a new reward location. We tested this hypothesis here by silencing acetylcholine neurons in mice while they navigated a maze for food rewards. These animals were able to learn the location of the first food reward, but were impaired when the reward was shifted to a new location. The behavioural results of this study suggest that acetylcholine indeed facilitates reversal learning, which the computational model attributes to a weakening of synaptic connections that do not lead to reward. Taken together, our experimental and computational work show how synaptic strength changes, gated by neuromodulators, affect learning behaviour. |
| first_indexed | 2024-12-20T03:53:50Z |
| format | Article |
| id | doaj.art-ca14b24a657a430da9587a5667584597 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-20T03:53:50Z |
| publishDate | 2021-06-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-ca14b24a657a430da9587a56675845972022-12-21T19:54:24ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582021-06-01176The functional role of sequentially neuromodulated synaptic plasticity in behavioural learningGrace Wan Yu AngClara S. TangY. Audrey HaySara ZannoneOle PaulsenClaudia ClopathTo survive, animals have to quickly modify their behaviour when the reward changes. The internal representations responsible for this are updated through synaptic weight changes, mediated by certain neuromodulators conveying feedback from the environment. In previous experiments, we discovered a form of hippocampal Spike-Timing-Dependent-Plasticity (STDP) that is sequentially modulated by acetylcholine and dopamine. Acetylcholine facilitates synaptic depression, while dopamine retroactively converts the depression into potentiation. When these experimental findings were implemented as a learning rule in a computational model, our simulations showed that cholinergic-facilitated depression is important for reversal learning. In the present study, we tested the model’s prediction by optogenetically inactivating cholinergic neurons in mice during a hippocampus-dependent spatial learning task with changing rewards. We found that reversal learning, but not initial place learning, was impaired, verifying our computational prediction that acetylcholine-modulated plasticity promotes the unlearning of old reward locations. Further, differences in neuromodulator concentrations in the model captured mouse-by-mouse performance variability in the optogenetic experiments. Our line of work sheds light on how neuromodulators enable the learning of new contingencies. Author summary Reversal learning likely involves changes in synaptic connections, a neural mechanism known as synaptic plasticity, so old information can be updated. We previously discovered that acetylcholine, an important neuromodulator in the brain, changes synaptic connections in a way that favours reversal learning. Specifically, acetylcholine weakens active synapses in brain slices, but these synapses can later be strengthened by a reward signal. Based on this result in slices, we used a computational model to propose a behavioural function for the action of acetylcholine on synaptic connections. In the model, acetylcholine would weaken synaptic connections associated with an old reward, allowing an agent to quickly learn a new reward location. We tested this hypothesis here by silencing acetylcholine neurons in mice while they navigated a maze for food rewards. These animals were able to learn the location of the first food reward, but were impaired when the reward was shifted to a new location. The behavioural results of this study suggest that acetylcholine indeed facilitates reversal learning, which the computational model attributes to a weakening of synaptic connections that do not lead to reward. Taken together, our experimental and computational work show how synaptic strength changes, gated by neuromodulators, affect learning behaviour.https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8192019/?tool=EBI |
| spellingShingle | Grace Wan Yu Ang Clara S. Tang Y. Audrey Hay Sara Zannone Ole Paulsen Claudia Clopath The functional role of sequentially neuromodulated synaptic plasticity in behavioural learning PLoS Computational Biology |
| title | The functional role of sequentially neuromodulated synaptic plasticity in behavioural learning |
| title_full | The functional role of sequentially neuromodulated synaptic plasticity in behavioural learning |
| title_fullStr | The functional role of sequentially neuromodulated synaptic plasticity in behavioural learning |
| title_full_unstemmed | The functional role of sequentially neuromodulated synaptic plasticity in behavioural learning |
| title_short | The functional role of sequentially neuromodulated synaptic plasticity in behavioural learning |
| title_sort | functional role of sequentially neuromodulated synaptic plasticity in behavioural learning |
| url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8192019/?tool=EBI |
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