Uncertainty-modulated prediction errors in cortical microcircuits
Understanding the variability of the environment is essential to function in everyday life. The brain must hence take uncertainty into account when updating its internal model of the world. The basis for updating the model are prediction errors that arise from a difference between the current model...
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| Format: | Article |
| Language: | English |
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eLife Sciences Publications Ltd
2025-06-01
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| Series: | eLife |
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| Online Access: | https://elifesciences.org/articles/95127 |
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| author | Katharina Anna Wilmes Mihai A Petrovici Shankar Sachidhanandam Walter Senn |
| author_facet | Katharina Anna Wilmes Mihai A Petrovici Shankar Sachidhanandam Walter Senn |
| author_sort | Katharina Anna Wilmes |
| collection | DOAJ |
| description | Understanding the variability of the environment is essential to function in everyday life. The brain must hence take uncertainty into account when updating its internal model of the world. The basis for updating the model are prediction errors that arise from a difference between the current model and new sensory experiences. Although prediction error neurons have been identified in layer 2/3 of diverse brain areas, how uncertainty modulates these errors and hence learning is, however, unclear. Here, we use a normative approach to derive how uncertainty should modulate prediction errors and postulate that layer 2/3 neurons represent uncertainty-modulated prediction errors (UPE). We further hypothesise that the layer 2/3 circuit calculates the UPE through the subtractive and divisive inhibition by different inhibitory cell types. By implementing the calculation of UPEs in a microcircuit model, we show that different cell types can compute the means and variances of the stimulus distribution. With local activity-dependent plasticity rules, these computations can be learned context-dependently, and allow the prediction of upcoming stimuli and their distribution. Finally, the mechanism enables an organism to optimise its learning strategy via adaptive learning rates. |
| format | Article |
| id | doaj-art-94de35a20eb542ce88ce47c9f61a5d8f |
| institution | Kabale University |
| issn | 2050-084X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | eLife Sciences Publications Ltd |
| record_format | Article |
| series | eLife |
| spelling | doaj-art-94de35a20eb542ce88ce47c9f61a5d8f2025-08-20T03:26:39ZengeLife Sciences Publications LtdeLife2050-084X2025-06-011310.7554/eLife.95127Uncertainty-modulated prediction errors in cortical microcircuitsKatharina Anna Wilmes0https://orcid.org/0000-0003-4948-1864Mihai A Petrovici1https://orcid.org/0000-0003-2632-0427Shankar Sachidhanandam2https://orcid.org/0000-0002-9359-6653Walter Senn3https://orcid.org/0000-0003-3622-0497Department of Physiology, University of Bern, Bern, SwitzerlandDepartment of Physiology, University of Bern, Bern, SwitzerlandDepartment of Physiology, University of Bern, Bern, SwitzerlandDepartment of Physiology, University of Bern, Bern, SwitzerlandUnderstanding the variability of the environment is essential to function in everyday life. The brain must hence take uncertainty into account when updating its internal model of the world. The basis for updating the model are prediction errors that arise from a difference between the current model and new sensory experiences. Although prediction error neurons have been identified in layer 2/3 of diverse brain areas, how uncertainty modulates these errors and hence learning is, however, unclear. Here, we use a normative approach to derive how uncertainty should modulate prediction errors and postulate that layer 2/3 neurons represent uncertainty-modulated prediction errors (UPE). We further hypothesise that the layer 2/3 circuit calculates the UPE through the subtractive and divisive inhibition by different inhibitory cell types. By implementing the calculation of UPEs in a microcircuit model, we show that different cell types can compute the means and variances of the stimulus distribution. With local activity-dependent plasticity rules, these computations can be learned context-dependently, and allow the prediction of upcoming stimuli and their distribution. Finally, the mechanism enables an organism to optimise its learning strategy via adaptive learning rates.https://elifesciences.org/articles/95127cortexcircuitscells |
| spellingShingle | Katharina Anna Wilmes Mihai A Petrovici Shankar Sachidhanandam Walter Senn Uncertainty-modulated prediction errors in cortical microcircuits eLife cortex circuits cells |
| title | Uncertainty-modulated prediction errors in cortical microcircuits |
| title_full | Uncertainty-modulated prediction errors in cortical microcircuits |
| title_fullStr | Uncertainty-modulated prediction errors in cortical microcircuits |
| title_full_unstemmed | Uncertainty-modulated prediction errors in cortical microcircuits |
| title_short | Uncertainty-modulated prediction errors in cortical microcircuits |
| title_sort | uncertainty modulated prediction errors in cortical microcircuits |
| topic | cortex circuits cells |
| url | https://elifesciences.org/articles/95127 |
| work_keys_str_mv | AT katharinaannawilmes uncertaintymodulatedpredictionerrorsincorticalmicrocircuits AT mihaiapetrovici uncertaintymodulatedpredictionerrorsincorticalmicrocircuits AT shankarsachidhanandam uncertaintymodulatedpredictionerrorsincorticalmicrocircuits AT waltersenn uncertaintymodulatedpredictionerrorsincorticalmicrocircuits |