Open questions for suprathreshold stochastic resonance in sensory neural models for motion detection using artificial insect vision

Stochastic Resonance (SR) occurs when the presence of noise in a nonlinear system can induce an optimal output from that system, and has been observed in a diverse range of physical and biological systems, including neurons. Despite this widespread observation of SR, to date very few engineering applications inspired by SR have been proposed, and one of the goals of our research is to explore possible new practical applications designed to replicate the benefits of SR. In particular, since about 1991, our group has designed and implemented a number of motion detection VLSI chips based on insect vision. We are currently investigating the possibility of replicating the benefits of SR in artificial insect-vision based motion detection systems, in particular a newly described form of SR called Suprathreshold Stochastic Resonance (SSR). The current paper is intended to review and identify the key open questions and avenues for future research relating to SR and SSR in such systems.Mark D. McDonnell and Derek Abbot

A silicon implementation of the fly's optomotor control system

Flies are capable of stabilizing their body during free flight by using visual motion information to estimate self-rotation. We have built a hardware model of this optomotor control system in a standard CMOS VLSI process. The result is a small, low-power chip that receives input directly from the real world through on-board photoreceptors and generates motor commands in real time. The chip was tested under closed-loop conditions typically used for insect studies. The silicon system exhibited stable control sufficiently analogous to the biological system to allow for quantitative comparisons

A Robust Analog VLSI Reichardt Motion Sensor

Silicon imagers with integrated motion-detection circuitry have been developed and tested for the past 15 years. Many previous circuits estimate motion by identifying and tracking spatial or temporal features. These approaches are prone to failure at low SNR conditions, where feature detection becomes unreliable. An alternate approach to motion detection is an intensity-based spatiotemporal correlation algorithm, such as the one proposed by Hassenstein and Reichardt in 1956 to explain aspects of insect vision. We implemented a Reichardt motion sensor with integrated photodetectors in a standard CMOS process. Our circuit operates at sub-microwatt power levels, the lowest reported for any motion sensor. We measure the effects of device mismatch on these parallel, analog circuits to show they are suitable for constructing 2-D VLSI arrays. Traditional correlation-based sensors suffer from strong contrast dependence. We introduce a circuit architecture that lessens this dependence. We also demonstrate robust performance of our sensor to complex stimuli in the presence of spatial and temporal noise

Fly-inspired VLSI vision sensors

Journal ArticleEngineers have long looked to nature for inspiration. The diversity of life produced by five billion years of evolution provides countless existence proofs of organic machines with abilities that far surpass those of our own relatively crude automata. We have learned how to harness large amounts of energy and thus far exceed the capabilities of biological systems in some ways (e.g., supersonic flight, space travel, and global communications). However, biological information processing systems (i.e., brains) far outperform today's most advanced computers at tasks involving real-time pattern recognition and perception in complex, uncontrolled environments. If we take energy efficiency into account, the performance gap widens. The human brain dissipates 12 W of power, independent of mental activity. A modern microprocessor dissipates around 50 W, and is equivalent to a vanishingly small fraction of our brain's functionality

A Robust Analog VLSI Motion Sensor Based on the Visual System of the Fly

Sensing visual motion gives a creature valuable information about its interactions with the environment. Flies in particular use visual motion information to navigate through turbulent air, avoid obstacles, and land safely. Mobile robots are ideal candidates for using this sensory modality to enhance their performance, but so far have been limited by the computational expense of processing video. Also, the complex structure of natural visual scenes poses an algorithmic challenge for extracting useful information in a robust manner. We address both issues by creating a small, low-power visual sensor with integrated analog parallel processing to extract motion in real-time. Because our architecture is based on biological motion detectors, we gain the advantages of this highly evolved system: A design that robustly and continuously extracts relevant information from its visual environment. We show that this sensor is suitable for use in the real world, and demonstrate its ability to compensate for an imperfect motor system in the control of an autonomous robot. The sensor attenuates open-loop rotation by a factor of 31 with less than 1 mW power dissipation

An Optoelectronic Stimulator for Retinal Prosthesis

Retinal prostheses require the presence of viable population of cells in the inner retina. Evaluations of retina with Age-Related Macular Degeneration (AMD) and Retinitis Pigmentosa (RP) have shown a large number of cells remain in the inner retina compared with the outer retina. Therefore, vision loss caused by AMD and RP is potentially treatable with retinal prostheses. Photostimulation based retinal prostheses have shown many advantages compared with retinal implants. In contrary to electrode based stimulation, light does not require mechanical contact. Therefore, the system can be completely external and not does have the power and degradation problems of implanted devices. In addition, the stimulating point is flexible and does not require a prior decision on the stimulation location. Furthermore, a beam of light can be projected on tissue with both temporal and spatial precision. This thesis aims at fi nding a feasible solution to such a system. Firstly, a prototype of an optoelectronic stimulator was proposed and implemented by using the Xilinx Virtex-4 FPGA evaluation board. The platform was used to demonstrate the possibility of photostimulation of the photosensitized neurons. Meanwhile, with the aim of developing a portable retinal prosthesis, a system on chip (SoC) architecture was proposed and a wide tuning range sinusoidal voltage-controlled oscillator (VCO) which is the pivotal component of the system was designed. The VCO is based on a new designed Complementary Metal Oxide Semiconductor (CMOS) Operational Transconductance Ampli er (OTA) which achieves a good linearity over a wide tuning range. Both the OTA and the VCO were fabricated in the AMS 0.35 µm CMOS process. Finally a 9X9 CMOS image sensor with spiking pixels was designed. Each pixel acts as an independent oscillator whose frequency is controlled by the incident light intensity. The sensor was fabricated in the AMS 0.35 µm CMOS Opto Process. Experimental validation and measured results are provided

An insect vision-based motion detection chip

The architectural and circuit design aspects of a mixed analog/digital very large scale integration (VLSI) motion detection chip based on models of the insect visual system are described. The chip comprises two one-dimensional 64-cell arrays as well as front-end analog circuitry for early visual processing and digital control circuits. Each analog processing cell comprises a photodetector, circuits for spatial averaging and multiplicative noise cancellation, differentiation, and thresholding. The operation and configuration of the analog cells is controlled by digital circuits, thus implementing a reconfigurable architecture which facilitates the evaluation of several newly designed analog circuits. The chip has been designed and fabricated in a 1.2-μm CMOS process and occupies an area of 2×2 mm²Moini, A. ; Bouzerdoum, A. ; Eshraghian, K. ; Yakovleff, A. ; Xuan Thong Nguyen ; Blanksby, A. ; Beare, R. ; Abbott, D. ; Bogner, R.E

Velocity estimation and comparison of two insect-vision-based motion-detection models

Insects are blessed with a very efficient yet simple visual system which enable them to navigate with great ease and accuracy. Though a lot has been done in the field of insect vision, there is still not a clear understanding of how velocity is determined in biological vision systems. The dominant model for insect motion detection, first proposed by Hassentein and Reichardt in 1956 has gained widespread acceptance in the invertebrate vision community. The template model, proposed later by Horridge in 1990, permits simple tracking techniques and lends itself easily to both hardware and software. Analysis and simulation by Dror suggest that the inclusion of additional system components to perform pre-filtering, response compression, integration and adaptation, to a basic Reichardt correlator can make it less sensitive to contrast and spatial structure thereby providing a more robust estimate of local image velocity. It was found from the data obtained, from the intracellular recordings of the steady state responses of wide field neurons in the hoverfly Volucella, that the shape of the curves obtained, agreed perfectly with the theoretical predictions made by Dror. In order to compare it with the template model, an experiment was done to get the velocity response curves of the template model using the same image statistics. The results leads us to believe that the fly motion detector emulates a modified Reichardt correlator.Sreeja Rajesh, David C. O'Carroll, and Derek Abbot