Modelling of Meromorphic Retina

Modelling of Meromorphic RetinaCHAPTER 1 launch and literature review1. INTRODUCTIONThe world look-alikes on how we aw beness datum it perceive it and how we act is according to our cognizance of this world. But where from this perception comes? loss the psychological furcate, we perceive by what we reek and act by what we perceive. The compreh curiosity experiences in valets and an other(a)(prenominal)(a) fleshlys atomic number 18 the faculties by which popside in diversenessation is derive for evaluation and response. in that locationfrom the meets of human beings depend on what they sentiency. Aristotle divided the senses into louvre, namelyH spikeing,Sight,Smell,Taste andTouch.These birth continued to be regarded as the classical five senses, although scientists draw determined the existence of as some(prenominal) as 15 hititive senses. Sense organs interred deep in the tissues of muscles, tendons, and joints, for example, hold in rise to sensation s of weight, slope of the clay, and amount of flexure of the dissimilar joints these organs be c tout ensembleed proprioceptors. Wi illuminately the semicircular disembowel outal of the pinna is the organ of equilibrium, concerned with the sense of balance. General senses, which produce cultivation concerning tangible needs (hunger, thirst, fatigue, and pain), argon overly jazzd. But the beation of entirely these is alleviate the list of five that was granted by Aristotle.Our world is a optic world. Visual perception is by far the nigh consequential stunning process by which we accumulate and extract in mixed bagation from our milieu. mountain is the king to see the features of objects we look at, such(prenominal) as color, shape, size, en whackings, depth, and contrast. Vision is achieved when the eyes and headspring fit together to grad pictures of the world around us. Vision begins with light rays bouncing off the out of objects. Light reflected f rom objects in our world forms a very(prenominal) rich line of descent of k forthwithledge and data. The light reflected has a poor wavelength and last transmission upper berth that allow us a spatially accurate and unfluctuating localisation of function of reflecting surfaces. The spectral versions in wavelength and intensity in the reflected light resemble the corpo sincere properties of object surfaces, and provide means to recognize them. The pedigrees that light our world argon commonly inhomogeneous. The sun, our natural light cum, for example, is in devout approximation a point germ. Inhomogeneous light sources experience shadows and reflections that ar passing cor tie in with the shape of objects. Thus, knowledge of the spatial position and extent of the light source alters further extr go through of teaching closely our environment.Our world is withal a world of become. We and most other animals ar piteous creatures. We navi inlet success amply wi th a dynamic environment, and we economic consumption predominantly ocular culture to do so. A sense of execution is crucial for the perception of our proclaim motion in relation to other moving and static objects in the environment. We must predict accurately the intercourse dynamics of objects in the environment in wander to plan enamour exertions. Take for example the following(a) situation that illustrates the temperament of such a perceptual task the striker a cricket team is approach a bowler. In delight to get the boundary on the globe, he needs an accurate bode of the real motion trajectory of the dinner gown such that he quite a little precisely plan and orchestrate his personate movements to hit the ball. There is unforesightful to a non bad(p)er extent than comely visual information available to him in order to form the task. And once he is in motion the situation pay backs oftentimes much complicated because visual motion information now repre directs the sexual relation motion between him and the ball objet dart the of the essence(p) coordinate ready re principal(prenominal)s static. Yet, despite its difficulty, with appropriate rearing almost of us become astonishingly full at playacting this task. High exercise is important because we live in a highly competitive world. The survival of the fittest applies to us as to some(prenominal) other financial support organism, although the palm of competition magnate nonplus slightly shifted and diverted during juvenile evolutionary trends. This competitive impel not only promotes a visual motion perception scheme that green goddess determine quickly what is moving where, in which direction, and at what invigorate yet it as wellhead as forces this brass to be efficient. Efficiency is crucial in biologic strategys. It encourages solutions that consume the smallest amount of resources of time, substrate, and free verve. The need for cleverness is proceedsous because it drives the arranging to be quicker, to go further, to last longer, and to turn in much resources left to discharge and practice other tasks at the corresponding time. Thus, universe the complex sensorial-motor organisation as the batter is, he cannot pay all of the resources available to solve a single task.Comp atomic number 18d to human perceptual abilities, nature provides us with correct more astonishing examples of efficient visual motion perception. Consider the various flying insects that navigate by visual perception. They weigh only fr action mechanisms of grams, in so far they ar able to navigate successfully at high speeds by complicated environments in which they must adjourn visual motions up to 2000 deg/s.1.1 stylised SYSTEMSWhat applies to biological carcasss applies in any encase to a large extent to any dyed main(a) body that be creates freely in a real-world environment. When human considerate started to shit coloured autonomous systems, it was commonly real that such systems would become part of our everyday life by the form 2001. Number little recognition-fiction stories and movies stupefy back up mints of how such agents should be earn and interfere with human society. And galore(postnominal) of these scenarios seem contingent and desirable. Briefly, we learn a preferably good sense of what these agents should be heart-to-heart of. But the construction is still eluding. The semi- autonomous rover of NASAs recent mar missions or demonstrations of imitative pets be the hardly a(prenominal) examples.Remarkably the progress in this field is slow than the other fields of electronics. Unlike transistor engine room in which explosion of engrossment is delimit by the Moores law and to a fault in hurt of the computational origins the military operation of autonomous systems is still not to the par. To find out the reason lav it we go to understand the restriction of traditional approaches. The autonomous system is the sensation that perceives, takes decision and plans action at a cognitive direct, in doing so it must show some degree of intelligence. Returning back to the slugger example, he knows exactly what he has to do to dispatch the ball to the boundary, he has to get into a business position and then hit the ball with a precise timing. In this process, the photons hit the retina and then muscle force is utilise. The batsman is not aw atomic number 18(p) that this overmuch is going on into his body. The batsman has a anxious system, and peerless of its many functions is to instantiate a transition layerbetween the environment and his cognitive mind. The sense reduces and preprocesses the huge amount of noisy sensory(a) data, categorizes and extracts the relevant information, and translates it into a form that is accessible to cognitive reasoning. Thus it is spend a penny here that the in that location is gang of process that takes pla ce in a biological cognitive system in a very unretentive time duration. And also that an important part of this whole process is transduction although it is not the star that can only when perform the whole complex task. Thus perception is the interpretationof sensory information with respect to the perceptual goal. The process is shown in the fig-1.1.2 DIFFERENCE BETWEEN biological SYSTEMS AND COMPUTERSThe sense is fundamentally other than nonionized than a calculating utensil and science is still a long substance from reasonableness how the whole involvement whole kit and boodle. A computing machine is really loose to understand by comparison. Features (or memorial tablet teachings) that clearly get along a star from a computing device atomic number 18Massive parallelueism,Distributed storage,Asynchronous bear on, andSelf organization.The visualizer is still a fundamentally serially driven machine with a of importized storage and dishonor limit self organ ization. The table 1.1 enlists these differences. tabularize 1.1 Differences in the organization principles and operation of computer and wagThe digital computation whitethorn become so fast that it may solve the present problems and also it may become possible that the autonomous systems argon do by digital comp angiotensin-converting enzyments that be as force-outful as efficient and as prehensile as we may imagine in our wildest dreams. However there ar doubts in it and so we have to switch to an practiceation mannikin that can realize all these things.1.3 NEURAL COMPUTATIONS WITH THE HELP OF ANALOG incorporate CIRCUITSIt was Carver Mead who, elysian by the course The Physics of Computation he jointly taught with John Hopfield and Richard Feynman at Caltech in 1982, depression proposed the idea of embodying skittish computation in ti linear very large- scale mixd (aVLSI) roundabouts.Biological queasy ne cardinalrks be examples of wonderfully engineered and efficient computational systems. When researchers first began to develop mathematical positions for how aflutter systems actually compute and process information, they very soon realized that virtuoso(a) of the main reasons for the impressive computational provide and efficiency of anxious ne bothrks is the embodied computation that takes place among their highly connected nerve cells. And in researches, it is also intimately established that these computations be not underinterpreted digitally although the digital way is much b be(a)r. Real nerve cells have a carrell membrane with a optical condenser that acts as a low-pass filter to the inpouring symptom through its dendrites they have dendritic trees that non-linearly add signals from other neurons, and so forth. Network structure and additive treat seem to be two key properties of nervous systems providing them with efficiency and computational power, but nonetheless two properties that digital computers typical ly do not sh be or exploit.1.4 writings REVIEW1. Biological information- touch systems wage on completely different principles from those with which most engineers ar familiar. For many problems, oddly those in which the gossip data are ill-conditioned and the computation can be specified in a congenator manner, biological solutions are many orders of magnitude more effective than those we have been able to implement utilise digital methods. This advantage can be attributed principally to the use of main(a) physical phenomena as computational primitives, and to the copy of information by the relational values of analog signals, rather than by the absolute values of digital signals. This approach requires adaptational techniques to mitigate the personal effects of function differences. This kind of alteration leads naturally to systems that bring about their environment. large-scale adaptive analog systems are more robust to component humiliation and failure than are m ore conventional systems, and they use far less power. For this reason, adaptive analog applied science can be expected to utilise the full probable of wafer scale atomic number 14 fabrication2. The architecture and realisation of microelectronic components for a retina-implant system that testament provide visual sensations to patients distraint from photoreceptor degeneration. Special forget me drugry has been developed for a fast single-chip CMOS get word sensor system, which provides high dynamic range of more than s counterbalance decades (without any electronic or mechanical shutter) corresponding to the performance of the human eye. This two-base hit sensor system is directly coupled to a digital filter and a signal processor that compute the alleged(prenominal) receptive-field function for generation of the remark data. These outer components are wireless, linked to an engraft bendable te multielectrode stimulator, which generates galvanizing signals for elec tro stimulation of the intact ganglion cellphoneular phones. All components, including additional ironware for digital signal touch and wireless data and power transmission, have been sham utilize in-house stock(a) CMOS engineering3. The electric circuits inspired by the nervous system that either function verifying neuron physiologic models, or that are useful components in man-made perception/action systems. Research also aims at using them in implants. These circuits are computational de evils and intelligent sensors that are very differently organized than digital processors. Their storage and processing capacity is distributed. They are asynchronous and use no clock signal. They are often purely analog and blend time continuous. They are adaptive or can notwithstanding learn on a basic level instead of being programmed. A fiddling entranceway into the area of mind-set research is also included in the course. The students will learn to exploit mechanisms employed by the nervous system for alliance energy efficient analog combine circuits. They will get taste into a multidisciplinary research area. The students will learn to analyze analog CMOS circuits and acquire basic knowledge in brain research methods.4. Smart imagery systems will be an inevitable component of future intelligent systems. formal vision systems, based on the system level integration (or even chip level integration) of an image (usually a CCD) camera and a digital processor, do not have the potential for exertion in customary purpose consumer electronic products. This is simply imputable to the cost, size, and complexity of these systems. Because of these factors conventional vision systems have mainly been limited to particularized industrial and military applications. Vision chips, which include both the photo sensors and parallel processing elements (analog or digital), have been under research for more than a decade and illustrate bright capabilities.5. Dr. Car ver Mead, professor emeritus of calcium Institute of Technology (Caltech), Pasadena pioneered this field. He reasoned that biological evolutionary trends over millions of years have produced organisms that engineers can hold to develop fall apart near systems. By giving senses and sensory-based fashion to machines, these systems can possibly compete with human senses and brings an intersection between biology, computer science and electric engineering. Analog circuits, electrical circuits operated with continuous varying signals, are used to implement these recursive processes with transistors operated in the sub-verge or weak inversion land (a region of operation in which transistors are creationed to draw afoot(predicate) though the gate potential difference is slightly lower than the minimum potentiality, called limen potential difference, required for normal conduction to take place) where they exhibit exponential function current voltage characteristics and low currents. This circuit paradigm produces high dumbness and low power implementations of some functions that are computationally intensive when compared with other paradigms (triode and saturation operational regions). A triode region is operating transistor with gate voltage higher(prenominal) up the threshold voltage but with the drain-source voltage lower than the difference between the gate-source voltage and threshold voltage. For saturation region, the gate voltage is still above the threshold voltage but with the drain-source voltage above the difference between the gate-source voltage and threshold voltage. Transistor has four terminals drain, gate, source and bulk. Current flows between the drain and the source when enough voltage is applied through the gate that enables conduction. The bulk is the body of the transistor.. As the systems mature, human separate replacements would become a major application area of the Neuromorphic electronics. The fundamental principle is b y observing how biological systems perform these functions robust artificial systems are purported.6. In This proposed work a circuit level model of Neuromorphic Retina, this is a primitive electronic model of biologically inspired smart visual sensors. These visual sensors have combine image attainment and parallel processing. Having these features neuromorphic retina mimes the unquiet circuitry of bionic eye. The proposed electronic model contains adaptive photoreceptors as light sensors and other circuit components such as averaging circuits, circuits fiddleing ganglion cells, neuronic firing circuits etc that junction to sense brightness, size, orientation and shape to distinguish objects in closer proximity. Although image-processing features are available with new-made robots but most of the issues related to image processing are taken care by software resources. Whereas machine vision with the serving of neuromorphic retina is empowered with image processing at the take care end. With added hardware resources, processing at the front end can reduce a volume of engineering resources for qualification electronic devices with sense of vision.1.5 OBJECTIVES OF THE PRESENT WORKThis experience work describes a circuit level model of Neuromorphic Retina, which is a crude electronic model of biologically inspired smart visual sensors. These visual sensors have integrated image acquisition and parallel processing. Having these features neuromorphic retina mimics the neural circuitry of bionic eye. The proposed electronic model contains adaptive photoreceptors as light sensors and other neural firing circuits etc at junction to sense brightness, size, orientation and shape to distinguish objects in closer proximity. Although, image processing features are available with modern robots but most of the issues related to image processing are taken care by software resources. Whereas, machine vision with the help of neuromorphic retina is empowered with image processing at the front end. In this paper it has been shown that with added hardware resources, processing at the front end it can reduce a megabucks of engineering resources as well as time for making electronic devices with sense of vision. . The objectives of present work areModelling of Neuromorphic RetinaThe photoreceptor barThe horrizontal cell crushThe transistor meshwork apply with cmos technologyThe integerated evadeThe integrated block of prs, horizontal cells and bipolar cellsThe head generation circuit1.6 Concluding RemarksIn this chapter, the function of the artificial system, difference between brain and computer work is described. The present work is focused on invention of neuromorphic retina layer circuits. Many successful studies have been carried out by the researchers to knowledge the behavior and failure of neuromorphic retina. Some investigators have performed the experimental work to study the phenomenon of the neuromorphic retina.Chapter 2 co nations the biological neurons and the electronics of neuromorphic retina in this the descriptions of silicon neurons, electrical nodes as neurons, perceptrons, integrate fire neurons, biological significance of neuromorphic systems, neuromorphic electronics engineering methods, process of developing a neuromorphic chip. Chapter 3 describes the artificial silicon retina, physiology of vision, the retina, photon to electrons, wherefore we require the neuromorphic retina?, the equivalent electronic structure, visual path to brain. In chapter 4 designing and implementation of neuromorphic retina in this the description of the photoreceptor block, the horrizontal cell block, the integerated block, the integrated block of photoreceptors, horizontal cells and bipolar cells, the spike generation circuit. In chapter 5 the design analyses and test results of neuromorphic retina layers. The results are summarized in the form of conclusion in Chapter 6CHAPTER-2BIOLOGICAL neurons AND neuromor phic electronics2.1 INTRODUCTIONNeuromorphic systems are inspired by the structure, function and plasticity of biological nervous systems. They are artificial neural systems that mimic recursive behavior of the biological animal systems through efficient adaptive and intelligent control techniques. They are designed to adapt, learn from their environments, and make decisions like biological systems and not to perform better than them. There are no efforts to rid of deficiencies inherent in biological systems. This field, called Neuromorphic engineering, is evolving a new era in computing with a great promise for future medicine, healthcare oral communication and industry. It relies on plenty of experiences which nature offers to develop functional, reliable and effective artificial systems. Neuromorphic computational circuits, designed to mimic biological neurons, are primitives based on the optical and electronic properties of semiconductor materials2.1 BIOLOGICAL NEURONSBiolog ical neurons have a fairly frank large structure, although their operation and small-scale structure is immensely complex. Neurons have one-third main parts a central cell body, called the course, and two different types of branched, arborary structures that extend from the soma, called dendrites and axons. Information from other neurons, in the form of electrical im urges, enters the dendrites at connection points called synapses. The information flows from the dendrites to the soma, where it is processed. The return signal, a train of impulses, is then sent down the axon to the synapses of other neurons. The dendrites trust impulses to the soma while the axon sends impulses away from the soma. Functionally, there are three different types of neuronsSensory neurons They defy information from sense receptors (nerves that help us see, smell, hear taste and feel) to the central nervous system which includes the brain and the spinal anesthesia cord.Motor neurons They carry i nformation from the CNS to effectors (muscles or glands that release all kind of stuff, from water to hormones to ear wax)Interneuron They connect sensory neurons and motor neurons.It has a cell body (or soma) and root-like extensions called mygdale. Amongst the mygdale, one major outgoing trunk is the axon, and the others are dendrites. The signal processing capabilities of a neuron is its ability to vary its inseparable electrical potential (membrane potential) through special electro-physical and chemical processes. The portion of axon immediately adjacent to the cell body is called axon hillock. This is the point at which action potentials are usually generated. The branches that issue the main axon are often called collaterals. Certain types of neurons have axons or dendrites coated with a rich insulating substance called medulla oblongata. The coating is called the bulbe sheath and the fiber is said to be myelinated. In some cases, the myelin sheath is surrounded by som e other insulating layer, sometimes called neurilemma. This layer, thinner than the myelin sheath and continuous over the nodes of Ranvier, is made up o thin cells called Schwann cells.Now, how do these things work? Inside and just extracurricular of the neurons are sodium ions (Na+) and potassium ions (K+). Normally, when the neuron is just seance not sending any messages, K+ accumulate inside the neuron while Na+ is kicked out to the area just outside the neuron. Thus, there is a lot of K+ in the neuron and a lot of Na+ just outside of it. This is called the resting potential. Keeping the K+ in and the Na+ is not easy it requires energy from the body to work. An impulse coming in from the dendrites, reverses this balance, causing K+ to leave the neuron and Na+ to come in. This is cognize as depolarization. As K+ leave Na+ enter the neuron, energy is released, as the neuron no longer is doing any work to handgrip K+ in and Na+ out. This energycreates an electrical impulse or ac tion potential that is transmitted from the soma to axon. As the impulse leaves the axon, the neuron repolarizes, that is it takes K+ back in and kicks Na+ out and restores itself to resting potential, ready to send other impulse. This process occurs extremely quickly. A neuron theoretically can send roughly 266 messages in one second. The electrical impulse may rush along other neurons from its synaptic knobs to circulate the message.Experiments have shown that the membrane voltage variation during the generation of an action potential is generally in a form of a spike (a short pulse project 2.2), and the shape of this pulse in neurons is rather separate and mathematically predictable.2.2 SILICON NEURONSNeuromorphic engineers are more interested in the physiological rather than the anatomical model of a neuron though, which is concerned with the functionality rather than only classifying its parts. And their preference lies with models that can be realized in aVLSI circuits. as luck would have it many of the models of neurons have always been suppose as electronic circuits since many of the varying observables in biological neurons are voltages and currents. So it was relatively consecutive forward to implement them in VLSI electronic circuits.There exist now many aVLSI models of neurons which can be classified ad by their level of detail that is represented in them. A drumhead can be found in table 3.1. The most elaborate ones are cognize as silicon neurons. A bit cruder on the level of detail are integrate and fire neurons and even more simplifying are Perceptrons also known as Mc Culloch Pitts neurons. The simplest way even so of representing a neuron in electronics is to represent neurons as electrical nodes.Table 2.1 VLSI models of neurons2.2.1 Electrical NodesasneuronsThe most simple of all neuronal models is to just represent a neurons action at law by a voltage or a current in an electrical circuit, and input and take are identical, wit h no transfer function in-between. If a voltage node represents a neuron, excitatory biguiding connections can be realized simply by tolerant elements between the neurons. If you want to add the possibility for inhibitory and mono directional connections, followers can be used instead of resistors. Or if a current represents neuronal activity then a simple current mirror can implement a synapse. Many useful processing networks can be utilise in this manner or in similar ways. For example a resistive network can compute local averages of current inputs.2.2.2 PerceptronsA perceptron is a simple mathematical model of a neuron. As real neurons it is an entity that is connected to others of its kind by one output and several inputs. naive signals pass through these connections. In the case of the perceptron these signals are not action potentials but real numbers. To draw the comparison to real neurons these numbers may represent average frequencies of action potentials. The output o f a perceptron is a monotonous function (referred to as activation function) of the leaden sum of its inputs (see figure 3.3). Perceptrons are not so much implemented in analog hardware. They have bufferly been formulated as a mathematical rather than an electronic model and traditional computers are good at those whereas it is not so straight forward to implement simple mathematics into aVLSI. Still there exist aVLSI implementations of perceptrons since they still promise the advantage of a real fully parallel, energy and space conservative implementation.A simple aVLSI implementation of a perceptron is given in the schematics in figure 3.4. This particular implementation works well enough in theory, in practice however it is on one hand not flexible enough (particularly the activation function), on the other already difficult to wrinkle by its bias voltages and devoted to noise on the a chip. Circuits that have really been used are based on this one but were more extensive to hire with the problems.2.2.3 Integrate Fire NeuronsThis model of a neuron sticks closer to the original in terms of its signals. Its output and its inputs are pulse signals. In terms of frequencies it actually can be sculpturesque by a perceptron and vice versa. It is however much better accommodate to be implemented in aVLSI. And the spike communication also has hard-hitting advantages in noise robustness. That is also thought to be a reason, why the nervous system uses that kind of communication. An integrate and fire neuron integrates burthen charge inputs triggered by presynaptic action potentials. If the integrated voltage reaches a threshold, the neuron fires a short output pulse and the integrator is reset. These basic properties are depicted in figure 2.5.2.3 BIOLOGICAL SIGNIFICANCE OF NEUROMORPHIC SYSTEMSThe fundamental doctrine of neuromorphic engineering is to utilize algorithmic inspiration of biological systems to engineer artificial systems. It is a kind of technol ogy transfer from biology to engineering that involves the understanding of the functions and forms of the biological systems and consequent morphinginto silicon chips. The fundamental biological unit mimicked in the design of neuromorphic systems is the neurons. Animal brain is composed of these individual units of computation, called neurons and the neurons are the elemental signaling parts of the nervous systems.By examining the retina for instance, artificial neurons that mimic the retinene neurons and chemistry are fabricated on silicon (most common material), tabun arsenide (GaAs) or possibly potential organic semiconductor materials.2.4 NEUROMORPHIC ELECTRONICS ENGINEERING METHODSNeuromorphic systems design methods involves the mapping of models of perfection and sensory processing in biological systems onto analog VLSI systems which emulate the biological functions at the same time resembling their structural architecture. These systems are mainly designed with compleme ntary color metal oxide semiconductors (CMOS) transistors that enable low power consumption, higher chip density and integration, lower cost. These transistors are biased to operate in the sub-threshold region to enable the realizations of high dynamic range of currents which are very important for neural systems design.Elements of adaptation and learning (a sort of higher level of adaptation in which past experience is used to efficaciously readjust the response of a system to previously unseen input stimuli) are incorporated into neuromorphic systems since they are expected to emulate the behavior of the biological systems and compensate for imperfections in tModelling of Meromorphic RetinaModelling of Meromorphic RetinaCHAPTER 1INTRODUCTION and literature review1. INTRODUCTIONThe world depends on how we sense it perceive it and how we act is according to our perception of this world. But where from this perception comes? Leaving the psychological part, we perceive by what we se nse and act by what we perceive. The senses in humans and other animals are the faculties by which outside information is received for evaluation and response. Thus the actions of humans depend on what they sense. Aristotle divided the senses into five, namelyHearing,Sight,Smell,Taste andTouch.These have continued to be regarded as the classical five senses, although scientists have determined the existence of as many as 15 additional senses. Sense organs buried deep in the tissues of muscles, tendons, and joints, for example, give rise to sensations of weight, position of the body, and amount of bending of the various joints these organs are called proprioceptors. Within the semicircular canal of the ear is the organ of equilibrium, concerned with the sense of balance. General senses, which produce information concerning bodily needs (hunger, thirst, fatigue, and pain), are also recognized. But the foundation of all these is still the list of five that was given by Aristotle.Our wo rld is a visual world. Visual perception is by far the most important sensory process by which we gather and extract information from our environment. Vision is the ability to see the features of objects we look at, such as color, shape, size, details, depth, and contrast. Vision is achieved when the eyes and brain work together to form pictures of the world around us. Vision begins with light rays bouncing off the surface of objects. Light reflected from objects in our world forms a very rich source of information and data. The light reflected has a short wavelength and high transmission speed that allow us a spatially accurate and fast localization of reflecting surfaces. The spectral variations in wavelength and intensity in the reflected light resemble the physical properties of object surfaces, and provide means to recognize them. The sources that light our world are usually inhomogeneous. The sun, our natural light source, for example, is in good approximation a point source. Inhomogeneous light sources cause shadows and reflections that are highly correlated with the shape of objects. Thus, knowledge of the spatial position and extent of the light source enables further extraction of information about our environment.Our world is also a world of motion. We and most other animals are moving creatures. We navigate successfully through a dynamic environment, and we use predominantly visual information to do so. A sense of motion is crucial for the perception of our own motion in relation to other moving and static objects in the environment. We must predict accurately the relative dynamics of objects in the environment in order to plan appropriate actions. Take for example the following situation that illustrates the nature of such a perceptual task the batsman a cricket team is facing a bowler. In order to get the boundary on the ball, he needs an accurate estimate of the real motion trajectory of the ball such that he can precisely plan and orchestrate h is body movements to hit the ball. There is little more than just visual information available to him in order to solve the task. And once he is in motion the situation becomes much more complicated because visual motion information now represents the relative motion between him and the ball while the important coordinate frame remains static. Yet, despite its difficulty, with appropriate training some of us become astonishingly good at performing this task. High performance is important because we live in a highly competitive world. The survival of the fittest applies to us as to any other living organism, although the fields of competition might have slightly shifted and diverted during recent evolutionary trends. This competitive pressure not only promotes a visual motion perception system that can determine quickly what is moving where, in which direction, and at what speed but it also forces this system to be efficient. Efficiency is crucial in biological systems. It encourages solutions that consume the smallest amount of resources of time, substrate, and energy. The requirement for efficiency is advantageous because it drives the system to be quicker, to go further, to last longer, and to have more resources left to solve and perform other tasks at the same time. Thus, being the complex sensory-motor system as the batsman is, he cannot dedicate all of the resources available to solve a single task.Compared to human perceptual abilities, nature provides us with even more astonishing examples of efficient visual motion perception. Consider the various flying insects that navigate by visual perception. They weigh only fractions of grams, yet they are able to navigate successfully at high speeds through complicated environments in which they must resolve visual motions up to 2000 deg/s.1.1 ARTIFICIAL SYSTEMSWhat applies to biological systems applies also to a large extent to any artificial autonomous system that behaves freely in a real-world environment. W hen humankind started to build artificial autonomous systems, it was commonly accepted that such systems would become part of our everyday life by the year 2001. Numberless science-fiction stories and movies have encouraged visions of how such agents should behave and interfere with human society. And many of these scenarios seem realistic and desirable. Briefly, we have a rather good sense of what these agents should be capable of. But the construction is still eluding. The semi- autonomous rover of NASAs recent Mars missions or demonstrations of artificial pets are the few examples.Remarkably the progress in this field is slow than the other fields of electronics. Unlike transistor technology in which explosion of density is defined by the Moores law and also in terms of the computational powers the performance of autonomous systems is still not to the par. To find out the reason behind it we have to understand the limitation of traditional approaches. The autonomous system is the one that perceives, takes decision and plans action at a cognitive level, in doing so it must show some degree of intelligence. Returning back to the batsman example, he knows exactly what he has to do to dispatch the ball to the boundary, he has to get into a right position and then hit the ball with a precise timing. In this process, the photons hit the retina and then muscle force is applied. The batsman is not aware that this much is going on into his body. The batsman has a nervous system, and one of its many functions is to instantiate a transformation layerbetween the environment and his cognitive mind. The brain reduces and preprocesses the huge amount of noisy sensory data, categorizes and extracts the relevant information, and translates it into a form that is accessible to cognitive reasoning. Thus it is clear here that the there is cluster of process that takes place in a biological cognitive system in a very short time duration. And also that an important part of this whole process is transduction although it is not the one that can solely perform the whole complex task. Thus perception is the interpretationof sensory information with respect to the perceptual goal. The process is shown in the fig-1.1.2 DIFFERENCE BETWEEN BIOLOGICAL SYSTEMS AND COMPUTERSThe brain is fundamentally differently organized than a computer and science is still a long way from understanding how the whole thing works. A computer is really easy to understand by comparison. Features (or organization principles) that clearly distinguish a brain from a computer areMassive parallelism,Distributed storage,Asynchronous processing, andSelf organization.The computer is still a basically serially driven machine with a centralized storage and minimal self organization. The table 1.1 enlists these differences.Table 1.1 Differences in the organization principles and operation of computer and brainThe digital computation may become so fast that it may solve the present problems and al so it may become possible that the autonomous systems are made by digital components that are as powerful as efficient and as intelligent as we may imagine in our wildest dreams. However there are doubts in it and so we have to switch to an implementation framework that can realize all these things.1.3 NEURAL COMPUTATIONS WITH THE HELP OF ANALOG INTEGRATED CIRCUITSIt was Carver Mead who, inspired by the course The Physics of Computation he jointly taught with John Hopfield and Richard Feynman at Caltech in 1982, first proposed the idea of embodying neural computation in silicon analog very large-scale integrated (aVLSI) circuits.Biological neural networks are examples of wonderfully engineered and efficient computational systems. When researchers first began to develop mathematical models for how nervous systems actually compute and process information, they very soon realized that one of the main reasons for the impressive computational power and efficiency of neural networks is th e collective computation that takes place among their highly connected neurons. And in researches, it is also well established that these computations are not undertaken digitally although the digital way is much simpler. Real neurons have a cell membrane with a capacitance that acts as a low-pass filter to the incoming signal through its dendrites they have dendritic trees that non-linearly add signals from other neurons, and so forth. Network structure and analog processing seem to be two key properties of nervous systems providing them with efficiency and computational power, but nonetheless two properties that digital computers typically do not share or exploit.1.4 LITERATURE REVIEW1. Biological information-processing systems operate on completely different principles from those with which most engineers are familiar. For many problems, particularly those in which the input data are ill-conditioned and the computation can be specified in a relative manner, biological solutions a re many orders of magnitude more effective than those we have been able to implement using digital methods. This advantage can be attributed principally to the use of elementary physical phenomena as computational primitives, and to the representation of information by the relative values of analog signals, rather than by the absolute values of digital signals. This approach requires adaptive techniques to mitigate the effects of component differences. This kind of adaptation leads naturally to systems that learn about their environment. Large-scale adaptive analog systems are more robust to component degradation and failure than are more conventional systems, and they use far less power. For this reason, adaptive analog technology can be expected to utilize the full potential of wafer scale silicon fabrication2. The architecture and realization of microelectronic components for a retina-implant system that will provide visual sensations to patients suffering from photoreceptor dege neration. Special circuitry has been developed for a fast single-chip CMOS image sensor system, which provides high dynamic range of more than seven decades (without any electronic or mechanical shutter) corresponding to the performance of the human eye. This image sensor system is directly coupled to a digital filter and a signal processor that compute the so-called receptive-field function for generation of the stimulation data. These external components are wireless, linked to an implanted flexible silicon multielectrode stimulator, which generates electrical signals for electro stimulation of the intact ganglion cells. All components, including additional hardware for digital signal processing and wireless data and power transmission, have been fabricated using in-house standard CMOS technology3. The circuits inspired by the nervous system that either help verifying neuron physiological models, or that are useful components in artificial perception/action systems. Research also aims at using them in implants. These circuits are computational devices and intelligent sensors that are very differently organized than digital processors. Their storage and processing capacity is distributed. They are asynchronous and use no clock signal. They are often purely analog and operate time continuous. They are adaptive or can even learn on a basic level instead of being programmed. A short introduction into the area of brain research is also included in the course. The students will learn to exploit mechanisms employed by the nervous system for compact energy efficient analog integrated circuits. They will get insight into a multidisciplinary research area. The students will learn to analyze analog CMOS circuits and acquire basic knowledge in brain research methods.4. Smart vision systems will be an inevitable component of future intelligent systems. Conventional vision systems, based on the system level integration (or even chip level integration) of an image (usually a CCD) camera and a digital processor, do not have the potential for application in general purpose consumer electronic products. This is simply due to the cost, size, and complexity of these systems. Because of these factors conventional vision systems have mainly been limited to specific industrial and military applications. Vision chips, which include both the photo sensors and parallel processing elements (analog or digital), have been under research for more than a decade and illustrate promising capabilities.5. Dr. Carver Mead, professor emeritus of California Institute of Technology (Caltech), Pasadena pioneered this field. He reasoned that biological evolutionary trends over millions of years have produced organisms that engineers can study to develop better artificial systems. By giving senses and sensory-based behavior to machines, these systems can possibly compete with human senses and brings an intersection between biology, computer science and electrical engineering. Analog circuits, electrical circuits operated with continuous varying signals, are used to implement these algorithmic processes with transistors operated in the sub-threshold or weak inversion region (a region of operation in which transistors are designed to conduct current though the gate voltage is slightly lower than the minimum voltage, called threshold voltage, required for normal conduction to take place) where they exhibit exponential current voltage characteristics and low currents. This circuit paradigm produces high density and low power implementations of some functions that are computationally intensive when compared with other paradigms (triode and saturation operational regions). A triode region is operating transistor with gate voltage above the threshold voltage but with the drain-source voltage lower than the difference between the gate-source voltage and threshold voltage. For saturation region, the gate voltage is still above the threshold voltage but with the d rain-source voltage above the difference between the gate-source voltage and threshold voltage. Transistor has four terminals drain, gate, source and bulk. Current flows between the drain and the source when enough voltage is applied through the gate that enables conduction. The bulk is the body of the transistor.. As the systems mature, human parts replacements would become a major application area of the Neuromorphic electronics. The fundamental principle is by observing how biological systems perform these functions robust artificial systems are designed.6. In This proposed work a circuit level model of Neuromorphic Retina, this is a crude electronic model of biologically inspired smart visual sensors. These visual sensors have integrated image acquisition and parallel processing. Having these features neuromorphic retina mimics the neural circuitry of bionic eye. The proposed electronic model contains adaptive photoreceptors as light sensors and other circuit components such as averaging circuits, circuits representing ganglion cells, neuronal firing circuits etc that junction to sense brightness, size, orientation and shape to distinguish objects in closer proximity. Although image-processing features are available with modern robots but most of the issues related to image processing are taken care by software resources. Whereas machine vision with the help of neuromorphic retina is empowered with image processing at the front end. With added hardware resources, processing at the front end can reduce a lot of engineering resources for making electronic devices with sense of vision.1.5 OBJECTIVES OF THE PRESENT WORKThis project work describes a circuit level model of Neuromorphic Retina, which is a crude electronic model of biologically inspired smart visual sensors. These visual sensors have integrated image acquisition and parallel processing. Having these features neuromorphic retina mimics the neural circuitry of bionic eye. The proposed electronic mod el contains adaptive photoreceptors as light sensors and other neural firing circuits etc at junction to sense brightness, size, orientation and shape to distinguish objects in closer proximity. Although, image processing features are available with modern robots but most of the issues related to image processing are taken care by software resources. Whereas, machine vision with the help of neuromorphic retina is empowered with image processing at the front end. In this paper it has been shown that with added hardware resources, processing at the front end it can reduce a lot of engineering resources as well as time for making electronic devices with sense of vision. . The objectives of present work areModelling of Neuromorphic RetinaThe photoreceptor blockThe horrizontal cell blockThe transistor mesh implemented with cmos technologyThe integerated blockThe integrated block of prs, horizontal cells and bipolar cellsThe spike generation circuit1.6 Concluding RemarksIn this chapter, t he function of the artificial system, difference between brain and computer work is described. The present work is focused on designing of neuromorphic retina layer circuits. Many successful studies have been carried out by the researchers to study the behavior and failure of neuromorphic retina. Some investigators have performed the experimental work to study the phenomenon of the neuromorphic retina.Chapter 2 conations the biological neurons and the electronics of neuromorphic retina in this the descriptions of silicon neurons, electrical nodes as neurons, perceptrons, integrate fire neurons, biological significance of neuromorphic systems, neuromorphic electronics engineering methods, process of developing a neuromorphic chip. Chapter 3 describes the artificial silicon retina, physiology of vision, the retina, photon to electrons, why we require the neuromorphic retina?, the equivalent electronic structure, visual path to brain. In chapter 4 designing and implementation of neuro morphic retina in this the description of the photoreceptor block, the horrizontal cell block, the integerated block, the integrated block of photoreceptors, horizontal cells and bipolar cells, the spike generation circuit. In chapter 5 the design analyses and test results of neuromorphic retina layers. The results are summarized in the form of conclusion in Chapter 6CHAPTER-2BIOLOGICAL neurons AND neuromorphic electronics2.1 INTRODUCTIONNeuromorphic systems are inspired by the structure, function and plasticity of biological nervous systems. They are artificial neural systems that mimic algorithmic behavior of the biological animal systems through efficient adaptive and intelligent control techniques. They are designed to adapt, learn from their environments, and make decisions like biological systems and not to perform better than them. There are no efforts to eliminate deficiencies inherent in biological systems. This field, called Neuromorphic engineering, is evolving a new era in computing with a great promise for future medicine, healthcare delivery and industry. It relies on plenty of experiences which nature offers to develop functional, reliable and effective artificial systems. Neuromorphic computational circuits, designed to mimic biological neurons, are primitives based on the optical and electronic properties of semiconductor materials2.1 BIOLOGICAL NEURONSBiological neurons have a fairly simple large-scale structure, although their operation and small-scale structure is immensely complex. Neurons have three main parts a central cell body, called the soma, and two different types of branched, treelike structures that extend from the soma, called dendrites and axons. Information from other neurons, in the form of electrical impulses, enters the dendrites at connection points called synapses. The information flows from the dendrites to the soma, where it is processed. The output signal, a train of impulses, is then sent down the axon to the synapses of other neurons. The dendrites send impulses to the soma while the axon sends impulses away from the soma. Functionally, there are three different types of neuronsSensory neurons They carry information from sense receptors (nerves that help us see, smell, hear taste and feel) to the central nervous system which includes the brain and the spinal cord.Motor neurons They carry information from the CNS to effectors (muscles or glands that release all kind of stuff, from water to hormones to ear wax)Interneuron They connect sensory neurons and motor neurons.It has a cell body (or soma) and root-like extensions called mygdale. Amongst the mygdale, one major outgoing trunk is the axon, and the others are dendrites. The signal processing capabilities of a neuron is its ability to vary its intrinsic electrical potential (membrane potential) through special electro-physical and chemical processes. The portion of axon immediately adjacent to the cell body is called axon hillock. This is t he point at which action potentials are usually generated. The branches that leave the main axon are often called collaterals. Certain types of neurons have axons or dendrites coated with a fatty insulating substance called myelin. The coating is called the myelin sheath and the fiber is said to be myelinated. In some cases, the myelin sheath is surrounded by another insulating layer, sometimes called neurilemma. This layer, thinner than the myelin sheath and continuous over the nodes of Ranvier, is made up o thin cells called Schwann cells.Now, how do these things work? Inside and just outside of the neurons are sodium ions (Na+) and potassium ions (K+). Normally, when the neuron is just sitting not sending any messages, K+ accumulate inside the neuron while Na+ is kicked out to the area just outside the neuron. Thus, there is a lot of K+ in the neuron and a lot of Na+ just outside of it. This is called the resting potential. Keeping the K+ in and the Na+ is not easy it requires en ergy from the body to work. An impulse coming in from the dendrites, reverses this balance, causing K+ to leave the neuron and Na+ to come in. This is known as depolarization. As K+ leave Na+ enter the neuron, energy is released, as the neuron no longer is doing any work to keep K+ in and Na+ out. This energycreates an electrical impulse or action potential that is transmitted from the soma to axon. As the impulse leaves the axon, the neuron repolarizes, that is it takes K+ back in and kicks Na+ out and restores itself to resting potential, ready to send another impulse. This process occurs extremely quickly. A neuron theoretically can send roughly 266 messages in one second. The electrical impulse may stimulate other neurons from its synaptic knobs to propagate the message.Experiments have shown that the membrane voltage variation during the generation of an action potential is generally in a form of a spike (a short pulse figure 2.2), and the shape of this pulse in neurons is rat her stereotype and mathematically predictable.2.2 SILICON NEURONSNeuromorphic engineers are more interested in the physiological rather than the anatomical model of a neuron though, which is concerned with the functionality rather than only classifying its parts. And their preference lies with models that can be realized in aVLSI circuits. Luckily many of the models of neurons have always been formulated as electronic circuits since many of the varying observables in biological neurons are voltages and currents. So it was relatively straight forward to implement them in VLSI electronic circuits.There exist now many aVLSI models of neurons which can be classified by their level of detail that is represented in them. A summary can be found in table 3.1. The most detailed ones are known as silicon neurons. A bit cruder on the level of detail are integrate and fire neurons and even more simplifying are Perceptrons also known as Mc Culloch Pitts neurons. The simplest way however of repre senting a neuron in electronics is to represent neurons as electrical nodes.Table 2.1 VLSI models of neurons2.2.1 Electrical NodesasneuronsThe most simple of all neuronal models is to just represent a neurons activity by a voltage or a current in an electrical circuit, and input and output are identical, with no transfer function in-between. If a voltage node represents a neuron, excitatory bidirectional connections can be realized simply by resistive elements between the neurons. If you want to add the possibility for inhibitory and mono directional connections, followers can be used instead of resistors. Or if a current represents neuronal activity then a simple current mirror can implement a synapse. Many useful processing networks can be implemented in this manner or in similar ways. For example a resistive network can compute local averages of current inputs.2.2.2 PerceptronsA perceptron is a simple mathematical model of a neuron. As real neurons it is an entity that is connect ed to others of its kind by one output and several inputs. Simple signals pass through these connections. In the case of the perceptron these signals are not action potentials but real numbers. To draw the analogy to real neurons these numbers may represent average frequencies of action potentials. The output of a perceptron is a monotonic function (referred to as activation function) of the weighted sum of its inputs (see figure 3.3). Perceptrons are not so much implemented in analog hardware. They have originally been formulated as a mathematical rather than an electronic model and traditional computers are good at those whereas it is not so straight forward to implement simple mathematics into aVLSI. Still there exist aVLSI implementations of perceptrons since they still promise the advantage of a real fully parallel, energy and space conservative implementation.A simple aVLSI implementation of a perceptron is given in the schematics in figure 3.4. This particular implementation works well enough in theory, in practice however it is on one hand not flexible enough (particularly the activation function), on the other already difficult to tune by its bias voltages and prone to noise on the a chip. Circuits that have really been used are based on this one but were more extensive to deal with the problems.2.2.3 Integrate Fire NeuronsThis model of a neuron sticks closer to the original in terms of its signals. Its output and its inputs are pulse signals. In terms of frequencies it actually can be modeled by a perceptron and vice versa. It is however much better suited to be implemented in aVLSI. And the spike communication also has distinct advantages in noise robustness. That is also thought to be a reason, why the nervous system uses that kind of communication. An integrate and fire neuron integrates weighted charge inputs triggered by presynaptic action potentials. If the integrated voltage reaches a threshold, the neuron fires a short output pulse and the in tegrator is reset. These basic properties are depicted in figure 2.5.2.3 BIOLOGICAL SIGNIFICANCE OF NEUROMORPHIC SYSTEMSThe fundamental philosophy of neuromorphic engineering is to utilize algorithmic inspiration of biological systems to engineer artificial systems. It is a kind of technology transfer from biology to engineering that involves the understanding of the functions and forms of the biological systems and consequent morphinginto silicon chips. The fundamental biological unit mimicked in the design of neuromorphic systems is the neurons. Animal brain is composed of these individual units of computation, called neurons and the neurons are the elementary signaling parts of the nervous systems.By examining the retina for instance, artificial neurons that mimic the retinal neurons and chemistry are fabricated on silicon (most common material), gallium arsenide (GaAs) or possibly prospective organic semiconductor materials.2.4 NEUROMORPHIC ELECTRONICS ENGINEERING METHODSNeuromo rphic systems design methods involves the mapping of models of perfection and sensory processing in biological systems onto analog VLSI systems which emulate the biological functions at the same time resembling their structural architecture. These systems are mainly designed with complementary metal oxide semiconductors (CMOS) transistors that enable low power consumption, higher chip density and integration, lower cost. These transistors are biased to operate in the sub-threshold region to enable the realizations of high dynamic range of currents which are very important for neural systems design.Elements of adaptation and learning (a sort of higher level of adaptation in which past experience is used to effectively readjust the response of a system to previously unseen input stimuli) are incorporated into neuromorphic systems since they are expected to emulate the behavior of the biological systems and compensate for imperfections in t