Topic: neural response prediction

Feedforward convolutional neural networks are currently the state-of-the-art approach for characterizing the transfer function of early visual cortical neurons. It is well known that the visual cortex has massive recurrent connections. Would models using recurrent circuits provide better neural response prediction than feedforward models? Here, we show that recurrent models consistently outperform feedforward ones when their parameters and hyperparameters are carefully matched. These benefits are robust against variation in the type of activation functions, loss function, number of channels, a