Stochastic inference with spiking neurons in the high-conductance state
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 acro...
Gespeichert in:
| Hauptverfasser: | , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
20 October 2016
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| In: |
Physical review
Year: 2016, Jahrgang: 94, Heft: 4 |
| ISSN: | 2470-0053 |
| DOI: | 10.1103/PhysRevE.94.042312 |
| Online-Zugang: | Verlag, Volltext: http://dx.doi.org/10.1103/PhysRevE.94.042312 Verlag, Volltext: https://link.aps.org/doi/10.1103/PhysRevE.94.042312 
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| Verfasserangaben: | Mihai A. Petrovici, Johannes Bill, Ilja Bytschok, Johannes Schemmel, and Karlheinz Meier |
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| 520 | |a 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. | ||
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