A Gradient Rule for the Plasticity of a Neuron's Intrinsic Excitability

While synaptic learning mechanisms have always been a core topic of neural computation research, there has been relatively little work on intrinsic learning processes, which change a neuron’s excitability. Here, we study a single, continuous activation model neuron and derive a gradient rule for the intrinsic plasticity based on information theory that allows the neuron to bring its firing rate distribution into an approximately exponential regime, as observed in visual cortical neurons. In simulations, we show that the rule works efficiently.