Visual perception initiated with a low-level derivation of Spatio-temporal edges and advances to a higher-level perception of filled surfaces. According to the isomorphic theory, this perceptual filling-in is governed by an activation spread across the retinotopic map, driven from edges to interiors. Here we propose two biologically plausible spiking neural networks, which demonstrate perceptual filling-in by resolving the Poisson equation. Each network exhibits a distinct dynamic and architecture and could be realized and further integrated in the brain.

There is an apparent discrepancy between visual perception, which is colorful, complete, and in high resolution, and the saccadic, and spatially heterogeneous retinal input data. In this work, we computationally emulated foveated color maps and intensity channels as well as intra-saccadic motion data using a neuromorphic event camera. We used a convolutional neural network, U-Net, and adversarial optimization to demonstrate how retinal inputs can be used for the reconstruction of colorful images in high resolution. Our model may set the groundwork for the development of biologically plausible neural networks for computational vision perception.

While subjective visual experiences are remarkably stable and coherent, their underlying data is incomplete and heavily influenced by the eyes' saccadic rhythm. In this work, we show that a deep and recurrent neural network can effectively reconstruct vibrant images from restricted retinal inputs during active vision. Our method includes the creation of a dataset for synthetic retinal inputs, containing intensity, color, and event-camera-generated motion data. We demonstrate the importance of both long-short-term memory and corollary discharge signals to image stabilization and the system's sensitivity to noise, corresponding to recent experimental findings. Our study contributes to the advancement of realistic and dynamic models for image reconstruction, providing insights into the complexities of active visual perception.