title: Learning intrinsic excitability in medium spiny neurons.
abstract: We present an unsupervised, local activation-dependent learning rule for intrinsic plasticity (IP) which affects the composition of ion channel conductances for single neurons in a use-dependent way. We use a single-compartment conductance-based model for medium spiny striatal neurons in order to show the effects of parameterization of individual ion channels on the neuronal membrane potential-curent relationship (activation function). We show that parameter changes within the physiological ranges are sufficient to create an ensemble of neurons with significantly different activation functions. We emphasize that the effects of intrinsic neuronal modulation on spiking behavior require a distributed mode of synaptic input and can be eliminated by strongly correlated input. We show how modulation and adaptivity in ion channel conductances can be utilized to store patterns without an additional contribution by synaptic plasticity (SP). The adaptation of the spike response may result in either "positive" or "negative" pattern learning. However, read-out of stored information depends on a distributed pattern of synaptic activity to let intrinsic modulation determine spike response. We briefly discuss the implications of this conditional memory on learning and addiction.
authors.affiliation: Carl Correns Foundation for Mathematical Biology, Mountain View, CA, 94040, USA.
authors.forename: Gabriele
authors.initials: G
authors.lastname: Scheler