Stochastic inference with spiking neurons in the high-conductance state

• Verfasst von: Petrovici, Mihai A. [VerfasserIn]
• Verfasst von: Bill, Johannes [VerfasserIn]
• Verfasst von: Bytschok, Ilja [VerfasserIn]
• Verfasst von: Schemmel, Johannes [VerfasserIn]
• Verfasst von: Meier, Karlheinz [VerfasserIn]
• Titel: Stochastic inference with spiking neurons in the high-conductance state
• Verf.angabe: Mihai A. Petrovici, Johannes Bill, Ilja Bytschok, Johannes Schemmel, and Karlheinz Meier
• Fussnoten: Gesehen am 15.08.2017
• Titel Quelle: Enthalten in: Physical review
• Jahr Quelle: 2016
• Band/Heft Quelle: 94(2016,4) Artikel-Nummer 042312, 14 Seiten
• ISSN Quelle: 2470-0053
• DOI: doi:10.1103/PhysRevE.94.042312
• Datenträger: Online-Ressource
• Sprache: eng
• K10plus-PPN: 1562447858

Abstract

The highly variable dynamics of neocortical circuits observed in vivo have been hypothesized to represent a signature of ongoing stochastic inference but stand in apparent contrast to the deterministic response of neurons measured in vitro. Based on a propagation of the membrane autocorrelation across spike bursts, we provide an analytical derivation of the neural activation function that holds for a large parameter space, including the high-conductance state. On this basis, we show how an ensemble of leaky integrate-and-fire neurons with conductance-based synapses embedded in a spiking environment can attain the correct firing statistics for sampling from a well-defined target distribution. For recurrent networks, we examine convergence toward stationarity in computer simulations and demonstrate sample-based Bayesian inference in a mixed graphical model. This points to a new computational role of high-conductance states and establishes a rigorous link between deterministic neuron models and functional stochastic dynamics on the network level.