title: Using potassium currents to solve signal-to-noise problems in inhibitory feedforward networks of the striatum.
abstract: Fast-spiking (FS) interneurons provide the main route of feedforward inhibition from cortex to spiny projection neurons in the striatum. A steep current-firing frequency curve and a dense local axonal arbor suggest that even small excitatory inputs could translate into powerful feedforward inhibition, although such an arrangement is also sensitive to amplification of spurious synaptic inputs. We show that a transient potassium (KA) current allows the FS interneuron to strike a balance between sensitivity to correlated input and robustness to noise, thereby increasing its signal-to-noise ratio (SNR). First, a compartmental FS neuron model was created to match experimental data from striatal FS interneurons in cortex-striatum-substantia nigra organotypic cultures. Densities of sodium, delayed rectifier, and KA channels were optimized to replicate responses to somatic current injection. Spontaneous alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) and gamma-aminobutyric acid (GABA) synaptic currents were adjusted to the experimentally measured amplitude, rise time, and interevent interval histograms. Second, two additional adjustments were required to emulate the remaining experimental observations. GABA channels were localized closer to the soma than AMPA channels to match the synaptic population reversal potential. Correlation among inputs was required to produce the observed firing rate during up-states. In this final model, KA channels were essential for suppressing down-state spikes while allowing reliable spike generation during up-states. This mechanism was particularly important under conditions of high dopamine. Our results suggest that KA channels allow FS interneurons to operate without a decrease in SNR during conditions of increased dopamine, as occurs in response to reward or anticipated reward.
authors.affiliation: School of Computer Science and Communication, Royal Institute of Technology, Stockholm, Sweden.
authors.forename: J Hellgren
authors.initials: JH
authors.lastname: Kotaleski
authors.forename: D
authors.initials: D
authors.lastname: Plenz
authors.forename: K T
authors.initials: KT
authors.lastname: Blackwell
mesh_terms: D000818:Animals; D003198:Computer Simulation; D003342:Corpus Striatum / Q000502:physiology*; D005246:Feedback / Q000502:physiology; D006801:Humans; D008959:Models, Neurological; D015233:Models, Statistical; D009415:Nerve Net / Q000502:physiology*; D011188:Potassium / Q000378:metabolism*; D024642:Potassium Channels, Voltage-Gated / Q000502:physiology*; D009435:Synaptic Transmission / Q000502:physiology*
chemical_list: D024642:Potassium Channels, Voltage-Gated; D011188:Potassium