A Python package I'm working on combines several submodules with mixed licensing — some will be open source and redistributed, others will be proprietary and in-house only. I wanted to ease importing of the package by automatically detecting which submodules are present, and dynamically importing only those.
The dynamics of simple nonlinear systems can be dramatically beautiful. I visualised a large number of randomly-generated systems as an exploration.
Seeking a minimum
Seeking a minimum
Stochastic gradient descent is a powerful tool for optimisation, which relies on estimation of gradients over small, randomly-selected batches of data. This approach is efficient (since gradients only need to be evaluated over few data points at a time) and uses the noise inherent in the stochastic gradient estimates to help get around local minima. This is a Matlab implementation of a recent powerful SGD algorithm.
Our brain does not faithfully interpret visual information, but instead uses a complex mixture of prior experience and context to shape our perception of the world. We described a pathway in the brain for this contextual information, from the mouse thalamus to visual cortex.
Sensory perception depends on context. Many models emphasise cortical feedback as the source of contextual modulation. However, higher-order thalamic nuclei, such as the pulvinar, interconnect with many cortical and subcortical areas, suggesting a role for the thalamus in providing sensory and behavioral context — yet the nature of the signals conveyed to cortex by higher-order thalamus remains poorly understood.