[R] Inferring neural activity before plasticity as a foundation for learning beyond backpropagation

**Paper**: [https://www.nature.com/articles/s41593-023-01514-1](https://www.nature.com/articles/s41593-023-01514-1)

**Preprint version(s)**: [https://www.biorxiv.org/content/10.1101/2022.05.17.492325](https://www.biorxiv.org/content/10.1101/2022.05.17.492325v2)

**Code**: [https://github.com/YuhangSong/Prospective-Configuration](https://github.com/YuhangSong/Prospective-Configuration)

**Abstract**:

>For both humans and machines, the essence of learning is to pinpoint which components in its information processing pipeline are responsible for an error in its output, a challenge that is known as ‘credit assignment’. It has long been assumed that credit assignment is best solved by backpropagation, which is also the foundation of modern machine learning. Here, we set out a fundamentally different principle on credit assignment called ‘**prospective configuration**’. In …

abstract backpropagation challenge components credit error foundation humans information machine machine learning machinelearning machines modern pipeline processing