Topic: ensemble learning

The best current models of visual cortical neural responses rely on internal representations of large deep neural networks (DNNs) trained for object recognition. However, the inner workings of these DNN models are mostly uninterpretable due to their massive size—tens of millions of parameters. Do we need such large models in the first place? In this work, we focused on modeling visual cortical neurons in macaque mid-level visual area V4. We used ensemble learning and closed-loop experiments with active learning to train a DNN model to accurately predict V4 responses. However, our model had 90