Topic: orthogonal subspaces

All behaviors, even simple actions, result from the interplay of multiple neural computations, and understanding how a given behavior is generated requires delineating these computations from one another. Traditionally, identifying these critical ‘computational building blocks’ requires a researcher to impose structure on the data: specific task epochs are analyzed separately or specific population-level structure is sought using supervised dimensionality reduction methods. While these approaches can be successful, they have obvious drawbacks – many behaviors cannot be easily parsed into disti

Precise movement execution often relies on adequately planning the upcoming action, a process that is largely mediated by the dorsal premotor (PMd) and primary motor (M1) cortices. Due to their anatomical and functional similarities, PMd and M1 are commonly assumed to contain similar planning- related information. Here we show that when planning movements under uncertainty, the activity of PMd and M1 neural populations show distinct features, suggestive of area-specific computations. We analyzed simultaneous recordings from populations of PMd and M1 neurons from monkeys performing both a stand

Trained artificial neural networks have become essential models in neuroscience. However, the robustness of these models to -- seemingly arbitrary -- design or initialization choices is currently debated. Some studies reported universality, others variability between solutions found by training. One choice, the magnitude of output weights, has recently received a lot of attention in machine learning, as it induces two very different classes of solutions in feed-forward networks. How this parameter affects solutions in recurrent neural networks (RNNs) trained on neuroscience tasks is not well