Topic: Contrastive Hebbian Learning

Humans have the ability to quickly adapt to new tasks and generalise what they learned to improve the learning process itself. At the core of this capacity for meta-learning lies a difficult credit assignment problem. After a learning episode, the system needs to determine how to change the components of plasticity such that the next time a similar task is encountered, a better learning outcome can be arrived at more quickly. In machine learning, this problem is often approached by storing the entire learning trajectory and revisiting it in reverse-time order, a process that places large deman

Contrastive Hebbian Learning (CHL) has recently emerged as a new biologically plausible paradigm to explain the mechanisms of plasticity in the brain. In classical CHL, developed for artificial networks, neurons learn in a two-step procedure where their free response to a given input is contrasted to their clamped response given the same input and an imposed target output. This two-step procedure, which is unlikely to be implementable in biological systems, has recently been made more plausible by exploiting the fundamental idea that neurons can learn by predicting their own future activity [1