  We aimed to explore the capability of deep learning to approximate the
function instantiated by biological neural circuits-the functional connectome.
Using deep neural networks, we performed supervised learning with firing rate
observations drawn from synthetically constructed neural circuits, as well as
from an empirically supported Boundary Vector Cell-Place Cell network. The
performance of trained networks was quantified using a range of criteria and
tasks. Our results show that deep neural networks were able to capture the
computations performed by synthetic biological networks with high accuracy, and
were highly data efficient and robust to biological plasticity. We show that
trained deep neural networks are able to perform zero-shot generalisation in
novel environments, and allows for a wealth of tasks such as decoding the
animal's location in space with high accuracy. Our study reveals a novel and
promising direction in systems neuroscience, and can be expanded upon with a
multitude of downstream applications, for example, goal-directed reinforcement
learning.
