title: Coherent ongoing subthreshold state of a cortical neural network regulated by slow- and fast-spiking interneurons.
abstract: Although details of cortical interneurons in anatomy and physiology have been well understood, little is known about how they contribute to ongoing spontaneous neuronal activity that could have a great impact on subsequent neuronal information processing. Simulating a cortical neural network model of an early sensory area, we investigated whether and how two distinct types of inhibitory interneurons, or fast-spiking interneurons with narrow axonal arbors and slow-spiking interneurons with wide axonal arbors, have a spatiotemporal influence on the ongoing activity of principal cells and subsequent cognitive information processing. In the model, dynamic cell assemblies, or population activation of principal cells, expressed information about specific sensory features. Within cell assemblies, fast-spiking interneurons give a feedback inhibitory effect on principal cells. Between cell assemblies, slow-spiking interneurons give a lateral inhibitory effect on principal cells. Here, we show that these interneurons keep the network at a subthreshold level for action potential generation under the ongoing state, by which the reaction time of principal cells to sensory stimulation could be accelerated. We suggest that the best timing of inhibition mediated by fast-spiking interneurons and slow-spiking interneurons allows the network to remain near threshold for rapid responses to input.
authors.affiliation: Department of Intelligent Systems Engineering, Ibaraki University, 4-12-1 Nakanarusawa, Hitachi, Ibaraki, Japan. hoshino@mx.ibaraki.ac.jp
authors.forename: Osamu
authors.initials: O
authors.lastname: Hoshino
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000818:Animals; D002540:Cerebral Cortex / Q000166:cytology*; D003071:Cognition; D003198:Computer Simulation; D007395:Interneurons / Q000502:physiology*; D009415:Nerve Net / Q000502:physiology*; D009433:Neural Inhibition / Q000502:physiology; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D013997:Time Factors