Topic: constrained optimization

Network models are often designed to capture selective aspects of cortical circuits. On one end, mechanistic models such as balanced spiking networks resemble activity regimes observed in data, but are often limited to simple computations. On the other end, functional models like trained deep networks can show comparable performance and dynamical motifs, but are far removed from experimental physiology. Here, we put forth a new framework for excitatory-inhibitory spiking networks which retains key properties of both mechanistic and functional models. Based on previous studies of the geometry o

Our intuition suggests that when a movie is played in reverse, our perception of motion at each location in the reversed movie will be perfectly inverted compared to the original. This intuition is also reflected in classical theoretical and practical models of motion estimation, in which velocity flow fields invert when inputs are reversed in time. However, this symmetry of motion perception upon time reversal is broken in real visual systems. We designed a set of visual stimuli to investigate time reversal symmetry (TRS) breaking in the fruit fly Drosophila’s optomotor rotation behavior. We

The neural control of movement is a fundamental aspect of understanding how the nervous system coordinate and regulate motor behavior. Procedural safety is a critical concern for movement control, especially for online tasks in high-dimensional control spaces. Current safe exploration algorithms exhibit inefficiency and may even become infeasible with large high-dimensional input spaces. Furthermore, existing high-dimensional constrained optimization methods neglect safety in the search process. In this extended abstract, we introduce High-dimensional Safe Bayesian Optimization with local opti