Recentwork has shown that significant gains can be obtained withmodel parallelism, i.e, partitioning a neural network's computational graph onto multiple devices.

Recently, researchers have leveraged such proven techniques in multimodal tasks and achieve promising results.

Here, we present a computational model of temporal processing in the inner retina, including inhibitory feedback circuits and realistic synaptic release mechanisms.

Data-driven machine learning approaches are being increasingly used to solve partial differential equations (PDEs).