  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.
