Topic: model-based agent

Higher-order brain activity can be quite complex, switching between fast, stimulus- or motor-driven dynamics, and slower, rising and falling persistent activity. Many of these activity motifs can be successfully modelled by recurrent neural networks (RNNs) that are trained on specific tasks. However, the training of RNNs is usually an ill-posed problem so that, at least in principle, multiple solutions exist for any particular task. Accordingly, a specific match of a trained RNN to data can be serendipitous, providing only limited insight into the reasons underlying the similarity. Here we tak