Probabilistic neural transfer function estimation with Bayesian system identification
Fig 5
Variational models on the second dataset.
(a) Model performance based on test data of the second dataset with different amounts of training data for five models (n = 10 random seeds per model). p = 0.0238 for variational vs. L2+L1 at 20% of training data, p < 0.0001 at 40%, p = 0.0001 at 60%, p = 0.0042 at 80%, p = 0.1096 at 100%. (b) Overall MEI variance for different amounts of training data for variational models (10 seeds per model). (c) Scatter plot for overall response CC and overall MEI variance for different amounts of training data and at 10 seeds. Each dot representing one model. (d) Performance difference between the variational and the L2+L1 models. (e) Scatter plot of model predictions for the variational model and the L2+L1 model at one random seed when using 40% training data. Each dot representing one neuron. (f) Like (e) but using 100% training data. Error bars in (a), (b) and (d) represent standard deviation of n = 10 random seeds for each model.