Inhibitory normalization of error signals improves learning in neural circuits
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866915872023511040 |
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| author | Eyono, Roy Henha Levenstein, Daniel Ghosh, Arna Cornford, Jonathan Richards, Blake |
| author_facet | Eyono, Roy Henha Levenstein, Daniel Ghosh, Arna Cornford, Jonathan Richards, Blake |
| contents | Normalization is a critical operation in neural circuits. In the brain, there is evidence that normalization is implemented via inhibitory interneurons and allows neural populations to adjust to changes in the distribution of their inputs. In artificial neural networks (ANNs), normalization is used to improve learning in tasks that involve complex input distributions. However, it is unclear whether inhibition-mediated normalization in biological neural circuits also improves learning. Here, we explore this possibility using ANNs with separate excitatory and inhibitory populations trained on an image recognition task with variable luminosity. We find that inhibition-mediated normalization does not improve learning if normalization is applied only during inference. However, when this normalization is extended to include back-propagated errors, performance improves significantly. These results suggest that if inhibition-mediated normalization improves learning in the brain, it additionally requires the normalization of learning signals. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_17676 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Inhibitory normalization of error signals improves learning in neural circuits Eyono, Roy Henha Levenstein, Daniel Ghosh, Arna Cornford, Jonathan Richards, Blake Neurons and Cognition Artificial Intelligence Machine Learning Normalization is a critical operation in neural circuits. In the brain, there is evidence that normalization is implemented via inhibitory interneurons and allows neural populations to adjust to changes in the distribution of their inputs. In artificial neural networks (ANNs), normalization is used to improve learning in tasks that involve complex input distributions. However, it is unclear whether inhibition-mediated normalization in biological neural circuits also improves learning. Here, we explore this possibility using ANNs with separate excitatory and inhibitory populations trained on an image recognition task with variable luminosity. We find that inhibition-mediated normalization does not improve learning if normalization is applied only during inference. However, when this normalization is extended to include back-propagated errors, performance improves significantly. These results suggest that if inhibition-mediated normalization improves learning in the brain, it additionally requires the normalization of learning signals. |
| title | Inhibitory normalization of error signals improves learning in neural circuits |
| topic | Neurons and Cognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2603.17676 |