Neuromorphic vision sensors, whose pixels output events/spikes asynchronously with a high temporal resolution according to the scene radiance change, are naturally appropriate for capturing high-speed motion in the scenes. However, how to utilize the events/spikes to smoothly track high-speed moving objects is still a challenging problem. Existing approaches either employ time-consuming iterative optimization, or require large amounts of labeled data to train the object detector. To this end, we propose a bio-inspired unsupervised learning framework, which takes advantage of the spatiotemporal information of events/spikes generated by neuromorphic vision sensors to capture the intrinsic motion patterns. Without off-line training, our models can filter the redundant signals with dynamic adaption module based on short-term plasticity, and extract the motion patterns with motion estimation module based on the spike-timing-dependent plasticity. Combined with the spatiotemporal and motion information of the filtered spike stream, the traditional DBSCAN clustering algorithm and Kalman filter can effectively track multiple targets in extreme scenes. We evaluate the proposed unsupervised framework for object detection and tracking tasks on synthetic data, publicly available event-based datasets, and spiking camera datasets. The experiment results show that the proposed model can robustly detect and smoothly track the moving targets on various challenging scenarios and outperforms state-of-the-art approaches.

Traditional models of retinal system identification analyze the neural response to artificial stimuli using models consisting of predefined components. The model design is limited to prior knowledge, and the artificial stimuli are too simple to be compared with stimuli processed by the retina. To fill in this gap with an explainable model that reveals how a population of neurons work together to encode the larger field of natural scenes, here we used a deep-learning model for identifying the computational elements of the retinal circuit that contribute to learning the dynamics of natural scenes. Experimental results verify that the recurrent connection plays a key role in encoding complex dynamic visual scenes while learning biological computational underpinnings of the retinal circuit. In addition, the proposed models reveal both the shapes and the locations of the spatiotemporal receptive fields of ganglion cells.

High-speed imaging can help us understand some phenomena that our eyes cannot capture fast enough. Although ultra-fast frame-based cameras (e.g., Phantom) can record millions of fps at reduced resolution, are too expensive to be widely used. Recently, a retina-inspired vision sensor, spiking camera, has been developed to record external information at 40, 000 Hz. The spiking camera uses the asynchronous binary spike streams to represent visual information. Despite this, how to reconstruct dynamic scenes from asynchronous spikes remains challenging. In this paper, we introduce novel high-speed image reconstruction models based on the short-term plasticity (STP) mechanism of the brain, termed TFSTP and TFMDSTP. We first derive the relationship between states of STP and spike patterns. Then, in TFSTP, by setting up the STP model at each pixel, the scene radiance can be inferred by the states of the models. In TFMDSTP, we use the STP to distinguish the moving and stationary regions, and then use two sets of STP models to reconstruct them respectively. In addition, we present a strategy for correcting error spikes. Experimental results show that the STP-based reconstruction methods can effectively reduce noise with less computing time, and achieve best the performances on both real-world and simulated datasets.

Deep convolutional neural networks (CNNs) have demonstrated impressive performance on many visual tasks. Recently, they became useful models for the visual system in neuroscience. However, it is still not clear what is learned by CNNs in terms of neuronal circuits. When a deep CNN with many layers is used for the visual system, it is not easy to compare the structure components of CNNs with possible neuroscience underpinnings due to highly complex circuits from the retina to the higher visual cortex. Here, we address this issue by focusing on single retinal ganglion cells with biophysical models and recording data from animals. By training CNNs with white noise images to predict neuronal responses, we found that fine structures of the retinal receptive field can be revealed. Specifically, convolutional filters learned are resembling biological components of the retinal circuit. This suggests that a CNN learning from one single retinal cell reveals a minimal neural network carried out in this cell. Furthermore, when CNNs learned from different cells are transferred between cells, there is a diversity of transfer learning performance, which indicates that CNNs are cell specific. Moreover, when CNNs are transferred between different types of input images, here white noise versus natural images, transfer learning shows a good performance, which implies that CNNs indeed capture the full computational ability of a single retinal cell for different inputs. Taken together, these results suggest that CNNs could be used to reveal structure components of neuronal circuits, and provide a powerful model for neural system identification.