Stochastic inference with spiking neurons in the high-conductance state
This provides a computational role for high-conductance states in neural circuits, linking deterministic models to stochastic inference, which is incremental but addresses a key theoretical gap in neuroscience.
The paper tackled the discrepancy between deterministic neuron models and stochastic network dynamics by deriving an analytical neural activation function for high-conductance states, showing that spiking neurons can achieve correct firing statistics for sampling from target distributions, with simulations demonstrating Bayesian inference in mixed graphical models.
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.