Neuromorphic Vision System Research Articles

Neuromorphic VLSI vision system for real-time texture segregation

The visual system of the brain can perceive an external scene in real-time with extremely low power dissipation, although the response speed of an individual neuron is considerably lower than that of semiconductor devices. The neurons in the visual pathway generate their receptive fields using a parallel and hierarchical architecture. This architecture of the visual cortex is interesting and important for designing a novel perception system from an engineering perspective. The aim of this study is to develop a vision system hardware, which is designed inspired by a hierarchical visual processing in V1, for real time texture segregation. The system consists of a silicon retina, orientation chip, and field programmable gate array (FPGA) circuit. The silicon retina emulates the neural circuits of the vertebrate retina and exhibits a Laplacian-Gaussian-like receptive field. The orientation chip selectively aggregates multiple pixels of the silicon retina in order to produce Gabor-like receptive fields that are tuned to various orientations by mimicking the feed-forward model proposed by Hubel and Wiesel. The FPGA circuit receives the output of the orientation chip and computes the responses of the complex cells. Using this system, the neural images of simple cells were computed in real-time for various orientations and spatial frequencies. Using the orientation-selective outputs obtained from the multi-chip system, a real-time texture segregation was conducted based on a computational model inspired by psychophysics and neurophysiology. The texture image was filtered by the two orthogonally oriented receptive fields of the multi-chip system and the filtered images were combined to segregate the area of different texture orientation with the aid of FPGA. The present system is also useful for the investigation of the functions of the higher-order cells that can be obtained by combining the simple and complex cells.

Adaptive Gain Control for Spike-Based Map Communication in a Neuromorphic Vision System

To support large numbers of model neurons, neuromorphic vision systems are increasingly adopting a distributed architecture, where different arrays of neurons are located on different chips or processors. Spike-based protocols are used to communicate activity between processors. The spike activity in the arrays depends on the input statistics as well as internal parameters such as time constants and gains. In this paper, we investigate strategies for automatically adapting these parameters to maintain a constant firing rate in response to changes in the input statistics. We find that under the constraint of maintaining a fixed firing rate, a strategy based upon updating the gain alone performs as well as an optimal strategy where both the gain and the time constant are allowed to vary. We discuss how to choose the time constant and propose an adaptive gain control mechanism whose operation is robust to changes in the input statistics. Our experimental results on a mobile robotic platform validate the analysis and efficacy of the proposed strategy.

Real-time motion detection with a mixed analogue–digital neuromorphic vision system

Abstract. We fabricated a novel vision system consisting of a neuromorphic silicon retina and FPGA circuits. The silicon retina executes two classes of fundamental image pre-processing: a Laplacian–Gaussian-like spatial filtering and a subtraction of consecutive image frames. The output images of the silicon retina are fed to FPGA circuits, with which image cues, i.e., contours, motion, direction of motion and centre of moving objects, are extracted within a single frame sampling time of the silicon retina. The system has compact hardware and low power consumption and therefore is suitable for controlling autonomous robots.

The making of a neuromorphic visual system

The making of a neuromorphic visual system

Modeling selective attention using a neuromorphic analog VLSI device.

Attentional mechanisms are required to overcome the problem of flooding a limited processing capacity system with information. They are present in biological sensory systems and can be a useful engineering tool for artificial visual systems. In this article we present a hardware model of a selective attention mechanism implemented on a very large-scale integration (VLSI) chip, using analog neuromorphic circuits. The chip exploits a spike-based representation to receive, process, and transmit signals. It can be used as a transceiver module for building multichip neuromorphic vision systems. We describe the circuits that carry out the main processing stages of the selective attention mechanism and provide experimental data for each circuit. We demonstrate the expected behavior of the model at the system level by stimulating the chip with both artificially generated control signals and signals obtained from a saliency map, computed from an image containing several salient features.