Learning optimal spike-based representations Group for Neural Theory Group for Neural Theory École Normale Supérieure École Normale Supérieure Paris, France

How can neural networks learn to represent information optimally? We answer this question by deriving spiking dynamics and learning dynamics directly from a measure of network performance. We find that a network of integrate-and-fire neurons undergoing Hebbian plasticity can learn an optimal spike-based representation for a linear decoder. The learning rule acts to minimise the membrane potential magnitude, which can be interpreted as a representation error after learning. In this way, learning reduces the representation error and drives the network into a robust, balanced regime.