Exploring the role of intrinsic plasticity for the learning of sensory representations

Intrinsic plasticity (IP) refers to a neuron’s ability to regulate its firing activity by adapting its intrinsic excitability. Previously, we showed that model neurons combining IP with Hebbian synaptic plasticity can adapt their weight vector to discover heavy-tailed directions in the input space. In this paper we consider networks of coupled model neurons and show how a population of such units can solve a standard non-linear ICA problem. We also present a simple model for the formation of maps of oriented receptive fields in primary visual cortex. Together, our results indicate that intrinsic plasticity may play an important role for learning efficient representations in populations of cortical neurons.