Ability to learn from instructions or demonstrations is one of the fundamental properties of the brain that is necessary to acquire new knowledge or to develop novel skills and behavioural patterns. Although the concept of instruction-based learning has been studied for several decades now, the biological basis of this process remains unrevealed.

Some questions that need to be addressed are: where and how to search for the instruction-based learning in the brain? What is the neural representation of instructive signals? How do the biological neurons learn to generate desired outputs given these instructions?

In the talk I will discuss a biologically plausible model of supervised learning that addresses the above questions. I will demonstrate properties of the model in the context of such tasks as prediction, classification or internal representations. I will argue that supervised learning can contribute to reliable and precise spike-based information processing in the nervous system even in the presence of noise of different origin.