To understand complex biological systems requires the integration of experimental and
computational research—in other words a systems biology approach. Computational …

The adoption of powerful software tools and computational methods from the software
industry by the scientific research community has resulted in a renewed interest in …

A central goal of artificial intelligence in high-stakes decision-making applications is to
design a single algorithm that simultaneously expresses generalizability by learning …

We introduce a new class of time-continuous recurrent neural network models. Instead of
declaring a learning system's dynamics by implicit nonlinearities, we construct networks of …

Continuous-time neural networks are a class of machine learning systems that can tackle
representation learning on spatiotemporal decision-making tasks. These models are …

How does neural activity drive muscles to produce behavior? The recent development of
genetic lines in Hydra that allow complete calcium imaging of both neuronal and muscle …

We propose a neural information processing system obtained by re-purposing the function
of a biological neural circuit model to govern simulated and real-world control tasks. Inspired …

In this paper, we design novel liquid time-constant recurrent neural networks for robotic
control, inspired by the brain of the nematode, C. elegans. In the worm's nervous system …

Given the inherent complexity of the human nervous system, insight into the dynamics of
brain activity can be gained from studying smaller and simpler organisms. While some of the …

Although a number of studies have explored deep learning in neuroscience, the application
of these algorithms to neural systems on a microscopic scale, ie parameters relevant to …