Inhibitory normalization of error signals improves learning in neural circuits
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 ...
| Authors | Roy Henha Eyono, Daniel Levenstein, Arna Ghosh et al. |
|---|---|
| Primary category | q-bio.NC |
| Published | 18 March 2026 |
| Last updated at source | 18 March 2026 |
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