Reconstruction of Natural Visual Scenes from Neural Spikes with Deep Neural Networks

Neural coding is one of the central questions in systems neuroscience for understanding how the brain processes stimulus from the environment, moreover, it is also a cornerstone for designing algorithms of brain-machine interface, where decoding incoming stimulus is needed for better performance of physical devices. Traditionally, the neural signal of interest for decoding visual scenes has been focused on fMRI data. However, our visual perception operates in a fast time scale of millisecond in terms of an event termed neural spike. So far there are few studies of decoding by using spikes. Here we fulfill this aim by developing a novel decoding framework based on deep neural networks, named spike-image decoder (SID), for reconstructing natural visual scenes, including static images and dynamic videos, from experimentally recorded spikes of a population of retinal ganglion cells. The SID is an end-to-end decoder with one end as neural spikes and the other end as images, which can be trained directly such that visual scenes are reconstructed from spikes in a highly accurate fashion. In addition, we show that SID can be generalized to arbitrary images by using image datasets of MNIST, CIFAR10, and CIFAR100. Furthermore, with a pre-trained SID, one can decode any dynamic videos, with the aid of an encoder, to achieve real-time encoding and decoding visual scenes by spikes. Altogether, our results shed new light on neuromorphic computing for artificial visual systems, such as event-based visual cameras and visual neuroprostheses.