  Neural population responses in sensory systems are driven by external
physical stimuli. This stimulus-response relationship is typically
characterized by receptive fields, which have been estimated by neural system
identification approaches. Such models usually requires a large amount of
training data, yet, the recording time for animal experiments is limited,
giving rise to epistemic uncertainty for the learned neural transfer functions.
While deep neural network models have demonstrated excellent power on neural
prediction, they usually do not provide the uncertainty of the resulting neural
representations and derived statistics, such as the stimuli driving neurons
optimally, from in silico experiments. Here, we present a Bayesian system
identification approach to predict neural responses to visual stimuli, and
explore whether explicitly modeling network weight variability can be
beneficial for identifying neural response properties. To this end, we use
variational inference to estimate the posterior distribution of each model
weight given the training data. Tests with different neural datasets
demonstrate that this method can achieve higher or comparable performance on
neural prediction, with a much higher data efficiency compared to Monte Carlo
dropout methods and traditional models using point estimates of the model
parameters. Furthermore, our approach enables to identify response properties
with credible intervals and perform statistical test for the learned neural
features, which avoid the idiosyncrasy of a single model. Finally, in silico
experiments show that our model generates stimuli driving neuronal activity
significantly better than traditional models, particularly in the limited-data
regime.
