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In this paper, asynchronous receptive circuit of retinal pathway is proposed for cone cells. Cone cells respond differently to different wavelengths of light and they are responsible for color vision. The cells present in retinal pathway namely cone photoreceptor, bipolar cell, amacrine cell and ganglion cell are mimicked using 180 nm Complementary Metal Oxide Semiconductor (CMOS) technology in strong inversion region. The proposed circuit is event based and hence asynchronous in nature. Additionally, amacrine stage is mimicked such that analog multiplexing occurs which is matching with its biological behavior. Both these features give output near to response of actual biological retinal pathway. The circuit detects wavelengths in visible spectrum of light with temporal contrast. At amacrine stage, selectivity is achieved without any external control signal which shows intelligent behavior at analog circuit level. The concept of color sensitivity of cone is considered to generate input stimulus of the circuit using Commission on Illumination (CIE) standard. The power consumed by the entire pathway circuit is 183 mW. • Asynchronous receptive circuit of retinal pathway is proposed for cone cells responsible for color detection. • Amacrine stage is mimicked such that analog multiplexing occurs, matching with its biological behavior. • Waveforms of photoreceptor current and membrane potential are reconstructed and verified using defined mathematical model. • The input stimulus to the photoreceptor is generated using CIE standards. • Proposed circuit is able to generate and change spiking rate based on input light stimulus with 183mW power consumption. [ABSTRACT FROM AUTHOR]

With the development of the Internet of Things (IoT), intelligent security systems, assisted driving and artificial intelligence, traditional machine vision sensors no longer meet the needs of low power consumption, high sampling rate, high dynamic range and low data redundancy, therefore, the emergence of new vision sensors is urgently needed. Drawing on the information extraction and encoding methods of biological retinas, in-sensor computing photoelectric detection systems have been developed. It has a more efficient sampling model and data transfer mode than frame-based and separated sensory-computing systems. This paper provides an overview of several major silicon-based in-sensor computing neuromorphic vision sensors. Using bio-vision mechanisms as an entry point, the architecture and working principles of these neuromorphic optoelectronic sensors are focused on, their application scenarios, advantages and limitations are compared, and the challenges and possible directions for development are analysed. [ABSTRACT FROM AUTHOR]

Abstract: A biologically inspired approach to learning temporally correlated patterns from a spiking silicon retina is presented. Spikes are generated from the retina in response to relative changes in illumination at the pixel level and transmitted to a feed-forward spiking neural network. Neurons become sensitive to patterns of pixels with correlated activation times, in a fully unsupervised scheme. This is achieved using a special form of Spike-Timing-Dependent Plasticity which depresses synapses that did not recently contribute to the post-synaptic spike activation, regardless of their activation time. Competitive learning is implemented with lateral inhibition. When tested with real-life data, the system is able to extract complex and overlapping temporally correlated features such as car trajectories on a freeway, after only 10 min of traffic learning. Complete trajectories can be learned with a 98% detection rate using a second layer, still with unsupervised learning, and the system may be used as a car counter. The proposed neural network is extremely robust to noise and it can tolerate a high degree of synaptic and neuronal variability with little impact on performance. Such results show that a simple biologically inspired unsupervised learning scheme is capable of generating selectivity to complex meaningful events on the basis of relatively little sensory experience. [Copyright &y& Elsevier]

Abstract: Currently optical motion analysis outside of clinical or laboratory setups is a tedious procedure as controlled light conditions, correct camera setup and diffcult post processing of video data are required to ensure a correct marker tracking. In this project we will for the first time investigate methods for optical motion analysis on the basis of data acquired by a novel type of biologically inspired vision sensor. These Silicon Retina sensors provide on-chip motion detection and an effcient, event based communication scheme, the address-event representation (AER), ideally suited for capturing, tracing and analyzing high speed motion yielding address event data (AE data). We believe that the approach is superior to the state-of-the-art motion capturing with high frame rate video cameras because it supplies continuous, frame-less motion information with low latency (Δt = 10ns) and high temporal resolution at low data rates. A simple movement (free fall) is analysed and data are evaluated against two different state-of-the-art motion analysis systems (infrared cameras (Vicon Bonita (240Hz)), full frame camera (Basler 602fc (300Hz)). Using Pearson''s correlation coeffcient it is shown that the trajectories highly correlate between all three systems (r ≥ 0.999) but object tracking algorithms for AE data need further enhancement. Furthermore more complex movements were filmed and visually compared to frame based data. It is concluded that AE data could prove to be beneficial for certain motion analysis tasks although algorithms have to be developed to fully exploit the nature of the signal. [Copyright &y& Elsevier]

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. [Copyright &y& Elsevier]

We have developed a vision system which emulates the orientation selective receptive fields of simple cells in the primary visual cortex. The system consists of a silicon retina and a Field Programmable Gate Array (FPGA). First, the image is filtered by the Laplacian–Gaussian-like receptive field of the silicon retina, and then it is transferred to the FPGA. The orientation selection chip selectively aggregates multiple pixels of the silicon retina, mimicking the feedforward model proposed by Hubel and Wiesel. The present system exhibits the orientation selectivity with even and odd-type responses. The design of the present chip is applicable to implement higher order cells in the primary visual cortex, such as the complex cell. [Copyright &y& Elsevier]

An abstract of the article "Retinomorphic Image Processing," by Kuntal Ghosh and colleagues is presented.