E/I Balanced Adaptive Sequential Neural Posterior Estimation for Inferring the Connection Weights in Mouse V1 Model

Effectively utilizing biological firing rate data to estimate the numerous connection weights in the mouse primary visual cortex (V1) model from the Allen Institute is a challenging task. The existing iterative grid-search algorithm cannot enable the mouse V1 model to better fit the biological firing rate data. To tackle this issue, we propose an excitation-inhibition balanced adaptive sequential neural posterior estimation (E/I balanced ASNPE) approach to accurately infer the connection weights of the mouse V1 model, allowing the neurons’ firing rates to converge to the given biological data. This method fully leverages the structural information of the mouse V1 model, reducing the dimensionality of the weight parameters to be optimized. Initially, sampling is performed in the prior distribution based on the proposed non-dominated sorting adaptive genetic algorithm (NSAGA). This algorithm optimizes the sorting, crossover and mutation processes based on the fitness scores of the current samples and updates the proposal distribution based on these samples, increasing the likelihood of identifying high posterior probability regions in the prior distribution. To avoid bad simulations, we also explore the E/I balance in each layer of the mouse V1 model, adding biological constraints during weight inference with Automatic Posterior Transformation (APT). Experimental results confirm that the proposed E/I balanced ASNPE method significantly outperforms the baseline in all five firing rate fitness scores in the mouse V1 model. This study is pioneering in applying non-dominated sorting genetic algorithms combined with sequential neural posterior estimation to optimize connection weights in large-scale complex biological models.