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APPROVED FOR RELEASE: 2007/02/09: CIA-RDP82-00854R004500010009-6 FUR OF FI('IAL [1SE ONLY JPRS LI10211 � 23 [~ecember 1981 Translation ARTIFICIAL SEN~E O~RGANS ~RCBLEMS O~F M~GDELING SENSORY SYSTEMS _ By = S.V. Fomin, Y~. N. Sokolov and ~.G. V~ytleyavichyus Fg~$ FC~REIGN BROADCAST INFOP~v~ATION SERVICE FOR OFFICIAL USE ONLY APPROVED FOR RELEASE: 2007/02/09: CIA-RDP82-00850R000500010009-6 APPROVED FOR RELEASE: 2007/02/09: CIA-RDP82-00854R004500010009-6 NOTE JPRS publications contain information primarily from foreign _ newspapers, periodicals and books, but from news agency ~ransmissions and broadcasts. M,aterials from foreign~language ~ources are translated; those from English-language sources are transcribed or reprinted, with the ~riginal phrasing and _ other characteristics retained. Headlines, edi~~rial reports, and material enclosed in brackets are supplied by JPRS. Processing indicators such as [Text] or [Excerpt] in the first line of each item, or following the last line of a brief, indicate how the original information was processed. Where no processing indicator is given, the infor- mation ~aas summarized or extracted. Unfamiliar names rendered phonetically or transliterated are enclosed in parentheses. Words or names preceded by a qu;s- � tion mark and enclosed in parentheses were not clear in t;1e original but have been supplied as appropriate in context. Other unattributed parenthetical notes within the body of an item originate with the source. Times within items are as given '~y source . Th~ contents of this publication in no way represent the poli- cies, views or at.titudes of the U.S. Government. COPYRIGHT LAWS AND REGULATIONS GOVERNING OWNERSHIP OF MATERIALS REPRODUCED HEREIN REQUIRE THAT DISSEMINATION OF THIS PUBLICATION BE RESTRICTED FOR QI~FICIAL USE ON'LY. \ . APPROVED FOR RELEASE: 2007/02/09: CIA-RDP82-00850R000500010009-6 APPROVED FOR RELEASE: 2007/02/09: CIA-RDP82-00854R004500010009-6 I 1 FOR OFFICIAI~ ~ JSE ONLY ( ~ JPRS L/10211 I ~ 23 December 1981 i E ~ - I. r ARTIFICIAL SEP~SE ORGANS ~ ; PROBLEMS OF MODELING SENSORY SYSTEM~ ~ ~ , ~ ,i Moscow ISKUSSTVENNYYE ORGANY CHUVSTV. PRCBLEMY MODELIROVANIYA i SENSORNYKH SISTEM in Russian 1979 (signed to press 15 Jun 79) I [Book by Sergey Vasil'ye~rich Fomin, Yevgeniy Nikolayevich Soko~.ov and Genrikh Genrikhovich Vaytkyavichyus, Izdatel'stvo "Nauka", UDC 519.9~] i i . ~ .CONTENTS i i ~ 1 ~ ~ Annotation _i Foreword 2 ~ Chapter 1. Construction of Arti'licial Sense Organs 4 ' Chapter 2. Model of Channel Number Coding 10 ~ 38 ~ Chapter 3. Intensity Analyzer Chapter 4. Color Analyzer 47 ~ Chapter 5. Line Slant Analyzer 59 Chapter 6. Visual Analyzer of Direction and Speed ~f M~tion 75 Chapter 7. Stereo Analyzer g5 Chapter 8. Gravity Analyzer 99 Chapter 9. Construction of Analyzers~to Order 102 , Appenlice5: i i. C~ner~~ Theury 109 ~ ~ 123 L. Intensity A?ialyzer ....................................o.............. ' 3. Culor Analyzer 127 i ~ 4. Lir~ Slant Analyzer 131_ ~ i 141 ~ 5. Analyzer of llirection and Speed of Motion ~i ` 143 i 6. Stereo Analyzer .........................e............................ ~ 7. Po.larized Lig}it An~l.yzer 148 ~ Bibliograpliy 150 i i ~ -i i ; -i f - a- [I - US~R - C FOUO] t ~ I ~ I FOR OFFICIAL USE ONLY APPROVED FOR RELEASE: 2007/02/09: CIA-RDP82-00850R000500010009-6 APPROVED FOR RELEASE: 2007/02/09: CIA-RDP82-00850R000500010009-6 FOR OFFICI,~L U5E I.~NLY PUBLICATION DATA English ti~le : Artificial Sensory Systems. Problems ~ of Modeling..Sensory Systems ~ Russian title . Iskusstvennyye organy chuvstv. Problemy - modelirovaniya sensornykh sistem Authors ~ : S. V. Fomin, Ye. N. a~okolov and G. G. Vaytkyavichyus Ed3_tor : Yu. P. Leonov, candidate of engineering sciences Publishing house : "Nauka" Place of publication : Moscow ~ Signed to press : 15 June 19~y Copies . 1200 - COYYRIGHT : Izdatel'stvo "Nauka", 1979 - b - - FOR OFFICIAL USE ONLY APPROVED FOR RELEASE: 2007/02/09: CIA-RDP82-00850R000500010009-6 APPROVED FOR RELEASE: 2407/42/09: CIA-RDP82-40850R000500410009-6 l . } FOR OFFICIAL U3E ONLY ~ i i I ~ ~ - . i f ~I . ~ i i UDC.~:519.95 ~ i . ~ ANNOTATION i This book deals with construction of artificial human and animal sense organs. The analyzer systems, in which there is a mechanism for enhancing differential sensi- r tivity, codes the stimulus.with the number of the most stimulated cha.~nel. Ana- lyzers of intsnsity, color, orientation, line. of direction and speed of movement of an object, its position in space are described. 1 This book is intended for neurophysiolooists, biophysjcists and specialists in I sensitizing robots. ~ There are 74 figures; bibliography lists 111 items. ~ ~ ~ I ~ � ~ i _i I -i i i ~ ~ . ; ~ ~ . ; ' i I ~ , ~ � 1 i i . ~ 1 FOR OF~'ICIAL USE QNI.Y -I APPROVED FOR RELEASE: 2007/02/09: CIA-RDP82-00850R000500010009-6 APPROVED FOR RELEASE: 2047/02/09: CIA-RDP82-00850R000504010009-6 ~ FOR OFFICiAL U5E ONLY FOREWO1tD This book is the result of 15 years of work using methods of psychophysics, neuro- physiology and cybernetics. The r~search was conducted in a man-neuron-model system. Investigation of a concrete sensory function on the psychophysical level, in experi- ments on man, developed in the direction of demonstrating its neuronal mechanisms in animaY experiments. The final stage of the study was a model, for which rather rigid requirements were imposed; the entire model reproduced the psychophysical charac- , terisCics of the function under study, while each neuron-like element of the model reproduced the characteristics of the corresponding real neuron. Construct~on of the models was based on the neurophysiologically validated principle of cod~ng a signal by the number of the detector channel. This principle provided for combining data transmission and processing in a large number of parallel channels. The study of concrete analyzers of color, intensity, motion, orientation and depth made it possible to formulate general principles 4f construction of artificial sense organs out of neuron-like el enents. Expressly these general principles of construc- tion of artificial sense organs, with the features of natural neuronal analyzers, constitute the main content of this book. Adhering to the principle of coding by ch~nnel number in artif icial sensory systems, it is necessary to settle the question of using information presented in this manner in problems of control. Electrophysiological studies of command neurons, which generate "chords" of motioils by means of their systems of oommunication ;~izn moto- neurons, made it po,sible to conclude that control problems are solved by connecting or disconnecting detectors from command neurons. The results of this analysis were .formulated in the description of the conceptual reflex arc. 'Z'he aggregate of receptors, primary detectorg and selective secondary detectors forms the neuronal analyzer. This analyzer is an expreasion of the biological analyzer discovered by I. P. Yavlov. An exogenous f~ignaZ, which elicits a seti ~~f ~excitations in indep~ndent primary detectors, generates an excitation vector. The exc�ltation vector, which acts upon the "fan" of communication [or connection] vectors that cannect primary and secondary detectors, creates single maximum excitation on ~ one of the elements of the population of secondary detectors, coding the signal with the localization site of the excitation maximum. ~ According to the principle of coding a signal by channel numher, a set of stimuli i~ reflected in an rc-dimensional sphere, which is formed by the detector neurons. With a change in sibnal, the excitati~n maxim~am reflecting the signal chang~ shifts over the quasireceptive surface represented by a set [or many] secondary detectors. . : 2 FOR OFFI~CIAL USE ONLY APPROVED FOR RELEASE: 2007/02/09: CIA-RDP82-00850R000500010009-6 APPROVED FOR RELEASE: 2007/02/09: CIA-RDP82-00854R004500010009-6 ; FOR OFFIC[AL USE ONLY , . ~ Reflection of the signal on the sphere.leads to a new apprcach in human psychophysics I ' and metrics of robot perceptive space. The sub~ective difference between stimuli ~ in man and a robot equipped with neuronal analyzers is measure3 by the amall arc of the large cixcle of the n-dimensional sphere. This arc connects points at which I� are localized the secondary detectors reprPsenting the corresponding stimuli. j The precision of function of the human and animal neuronal analyzer is enhaaced as _i a result of operation of adaptation mechanisms of primary detectors and lateral ; inhibition of homologous primary detectors referable to differeat local analyzers�. ; The emphasis of differences between signals is manifested by successive and concu'r- j rent contrasts. Introduction of adaptation and lateral inhibitiion into artificial sense organs providing for enhancement of discritninant sensitivity generates illu- ' sions in them that are analogous to man's p~rceptive illusions. _I ' ~ Thus, artificial sense organs consisting of neuron-like elements reproduce very ; completely the structure and function o� hiunan sense orgar.s. The general principles ~ of construction of artificial sense organs from neuron-like elements map find prac- ~ tical applications in two different fields: development of sensory prostheses. i directly coordinated with the net.~ronal structures of thP human brain and design of ; sense organs for robots with elements of artificial intellisence~ 'j Regrettably, Sergey Vasil'yevich Fomin, whose ideas served as the basis of this ~ book, passed away at the final stage of preparing the manuscript and could not -j see it published. _I ~ ~ i i � I , i i i ~ ; I i i , ' . ~ ; =i ~ i ~ ~ . 3 I FOR OFFICIAL USE ONLY APPROVED FOR RELEASE: 2007/02/09: CIA-RDP82-00850R000500010009-6 APPROVED FOR RELEASE: 2047/02/09: CIA-RDP82-00850R000504010009-6 FOR OFF'[CUL USE ONLY . ' CHAPTER 1. CONS1'RUCTION OF ARTIEICZAL SENSE ORGANS Robot's artificial sense organs. Development oi modern technolog~ is largely related to advances in creating integral robots with alements of artificisl intell:i- gence. xhe ability to functi.on in a complex environment is a c~istinctive feature of such robots. ~or this, not only must they have'a sophisticated system of actuating elements, but a well-developed system of artificial sense organs capabl~ of processing a large volume of sensory data for analysis of scenes and complex ~ - acoustical signals. There must be provisions for man's communication.with robots for effective control - thereof. We refer not only to development of an effective language for communica- tion, but obtaining a similarity of internal conceptions of man and robot. In other words, it is necessary for ob3ects distinguished in t~e environment by robot and :aan to�coincide as fully as possible. .Only then will the intexaction of man and r~bot be effe~tive. Finally, all deviations of interual conceptions of the robot from the internal conceptions of man should have a simple and graphic inter- p~etation in terms of human perception. . All these considerations compel us to search for the means of creating artificial - sense organs, the operating principles of which would�be similar to the operating principles of human and animal analyzers. Since human and animal analyzers are , capable of rapidly processing large arrays of input data, we can expect to find ne:a effective means of information processing on this route. , Computex's perception organs. The area of refinement of systems of man's communication with a comt?,ster is an important area of application of artificial sense organs. Although all of the procedures of recognizing speech sounds and analyzing visual scenes can be imp].emented by computers, this would require a large memory and signi'ficant time for conversion of such complex signals. A better way would be to develop specialized parallel-action processors that would _ permit rapid processing of complex acoustical signals and visual scenes. Thus, - the problem of creating artificial 5ense organs for a robot ties in with the prob- lem of creating perception organs for a computer. It is also desirable to have - the internal representations of acoustical and visual signals in the computer:agree with the internal. conceptions of man, so that the language of communication between man and computer would be based on the similarity of conceptions. Prostheses of human sesne organs. In order to perform the task of creating s~ousti~~~ and visual prasthesis directly~related to neuronal mechanisms of the ~ brain, it is necessary for the principles of aignal coding in the prosthesis to conform with the principles of signal coding in the brain. Work on artificial 4 F'~R OFFiCIAL USE ONLY APPROVED FOR RELEASE: 2007/02/09: CIA-RDP82-00850R000500010009-6 APPROVED FOR RELEASE: 2047/02/09: CIA-RDP82-00850R000504010009-6 FOR O~FICIAL USE ONLY sense organs based on biological principles enables us to come close to such agreement, since the reactions at the output of technol~gical neuron-like elements _ ar~e analosues of reactions of real neurons. Thus, development of artificial sense organs based on the functional principles of human and animal sense organs would also serve~ a~ the basis for developing new types of prosthetics of human sensory system~. Man--neuron--model. Development of general theory of artificial sense organs providing for ,~i:milarity of internal conceptions of man and robot is based on psycho- physiology, whi:.f~ emerged on the borderline of psychology, physiology of higher n~rvous activity, neurophysiology and cybernetics. Psychophysialogy is the discip- line that deals,with neuro:ial mechanisms of psychological processes; it is based on the man--neuron--.~udel grinciple. As they analyze functions on the level of human behavior and verbal reactions, psychophysiologists turn to analysis of neuronal m~chanisms implementing this function. The work ends with construction of a model of the function under study. The model is created with neuron-like elements. Rigid requirements aLe imposed on the model: the model as a~;hole must recreate the function under. study on the behavioral l~vel, while its neuron-like e].ements m ust conform in cliaracteristics to real neurons involved in the mode]-ed function. From the standpoint of development of artificial sensory systems where internal . conceptions of man and robot would coincide, such a model of a sensory funetion is also a technical solution of the problem. � A comparison of concrete models of sensa:y systems makes it possible to construct a single scheme--generalized model of sensory funcCions. It is possible to construct artificial sensory systems that have no direct biological prototypes on the basis of such a generalized model. ~ Information coding in the nervous system. The ner~io~s system performs functions of control, transmission and processing of in.coming information. Inci- dentally, it should be borne in mind that the above separation is quite arbitrary, since cantrol always includes some information processes, whereas the organs that transmit and process information serve as objects of control (for example, perception of visual information depends appreciably on control of eye movements). Moreover, there are many general principles, such as multilevel organization and learning ability, ar.e inl~erent in processes of contr.ol and information processing. The same flow of :;igiials transmitr_ed over a communication line can deliver differ- ent information, dependin~; on expressly what the corresponding receiver reacts to. For example, when we receive a letter ~e arP usually interested only in its text. . However, it is coiiceivable that two individuals who are corresponding a.greed to attribute meaning to, for example, the color of the paper or lettering, rather than the text of the letter. One can selpct such "informative signs" arbitrarily; but the receiver must have the physical properties to perceive them (the color of the paper would mean nothing to a blind person) and to retain thpm in the course of transmitting tlie message (how the letters are written cannot serve as the code if we use telegraphy, rather than the mails). Tt~ese obvious considerations are quite applicable to transmission of infcrmation in ttie nervous system also. Our perception of the world around us is multi- faceted. We perceive bright colors, diverse sounds, odors and peculiar shapes in it. In the nervous system, all relevant information is coded by a specific dis- tribution of excitations in numerous neurons. The question of expressly how 5 FOR OFFICIAL USE ONLY APPROVED FOR RELEASE: 2007/02/09: CIA-RDP82-00850R000500010009-6 APPROVED FOR RELEASE: 2047/02/09: CIA-RDP82-00850R000504010009-6 ~ FOR OFF'ICIAL USE ONLY this coding is done is one of the basic ones in n~urophysiology and biophysic$ of com~plex systems.~ Let us consider two problems, na~mely, coding information by a series of impulses within a single (unbranched) nerve fiber and coding in a complex multichannel system. Coding .information in a nerve fiber. It is a known fact that the inter- spike intervals serve as information carriers when a series of impu'les extends over a nerve fiber, since spikes themselves are rather ~ctandard. But what precisely is significant: the length of different intervals, their mean length over a certain . segment, grouping of impulses in a bundle or something else? This is far from ~lear, and one could hardly offer a single answer that would be suitable for all cases. At first glance, it appears best to code information by the lengths of different Interimpulse intervals, similar to the dots and dashes of the Morse r_ode. This method could provide for a high throughput of the communication channel and high . speed of system operation. Coding information with the average frequency of impu~- sation or �ome other statistical characteristics of a series of impulses cannot pr.ovide such rapid action. However, there are several factors that limit the possibility of coding information by the lengths of different interspike intervals. These factors include, among others, the following.~ Mathematical modeling of processes of dissemination of excitation in a nerve fiber (1] and direct physiologic~l experiments have shown that the impulse sequence, which has a complex structure, does not retain this structure as it ~ravels over a long ner~e fiber if the impulse frequency in a train is high enough. This effect is attributable to the depend~ence of velocity of impulse propagation over the nerve f:Lber on duration of the refractory phase, i.e., time tha~ has elapsed after the preceding impulse. By virtue of this dependence, there is gradual equalization af interspike intervals in a train of spikes, and only information about the mean impulse frequency is retained a.*. ttie output. Of course, this does not happen when impulses follow one another at long enough inte~rvals, since none falls into the phase of relative refraetoriness of the fiber. But then the information is trans- mitted slowly, and the main advantage of coding by individual intervals, high ' throughput, is lost. Thc~se considerations apply mainly to long fibers. Similarly to exponential extinc- tion of potential in a cable, there is exponential leveling down of infc.~rmation about the duration of individual interspike intervals in a long fiber. Tn short fibers, the shape oE the impulse train doee not have time to level down, even wtien f recluency is h igh . It ~t?ould be borne in mind that dissemination of impulses over a fiber in a rela~ively refrar_tory state cannot be construed as a purely laboratory phenomenon observed with unnaturalty high stimulation frequencies. The phase of relative - refractoriness lasts abaut 100 ms and the interpulse interval constitutes only 6-lU ms in the motor axons of the locust. In the internuncial neurons of th~ - spinal cord~of mammals, in the neurons of certain ascending tracts and acoustic nerve fibers freyuencies of up to 100 imp s'1, and in fibers innervating the electrical argans of fish the frequencie.s are even higher, up to 1500 imp s'1. Ttius, the propagation of impulses over a fiber in a relatively refractory state is certainly encountered under natural conditions. 6 F'OR OFFIC[AL ZJSE OIdLY APPROVED FOR RELEASE: 2007/02/09: CIA-RDP82-00850R000500010009-6 APPROVED FOR RELEASE: 2007/02/09: CIA-RDP82-04850R000500010009-6 FOR OF FICIAL USE ONLY ' ; ~ j Smoathing of interpulse intervals is one of the reasons that limit coding by the ~ lengths uf different intervals. Another reason is referable to the distinctions i of the decoding system. Secretion of inediator in synapses in re$ponse to an impulse ! is atochastic in a nwnber of instances. There is a probability of only 1/2 i ; an impulse going to motoneurons over group I fiberf will elicit secretion of inediator. ~ It is obvious that in such a case it becomes unredlistic to consider the possibility of coding infot~nation by individual interimpulse intervals. These circumstances limit the possibilities of cod~ng information in a nerve fiber i with individual impulses. However, such cod:tng is by no means ruled out, particu- ; larly in short fibers. Thus, as far back as the early 1960's, in several well- ~ known studies conducted with neurons of the Aplysia mollusk, it was demonstrated ! that one can obtain difFerent responses from artificial stimulation of these neurons ~ with pulse trains of the same average frequency but different configuration. For I example, the neuronal reaction may change if this pulse train is de~ivered in re- i verse order. Evidently, in this and other similar cases, the neuron rQacts to the ~ temporal pattern of the pulse train as a certain whole [2, 3]. ~ Coding informa.tion by channel number. Recent experimental and theoretical ' studies indicate that there is widespread so-called coding of information by the j channel number, or locus [site] cnding in a living organism, particularly its i sensory systems. According to i:his conception, the sensory system has a set of i detector neurons, one of which is stimulated mora than its neighbors bp a speci~ic stimulus. The number of the detector neu~on that is maximally excited determines ~ the sensation that is elicited by the coded signal parameter (brightness, color,.. ! direction of movement). If two stimuli elicit maximal excitation of the same ~ detector, they are not distinguished according to a given parameter. I It wss demonstrated experimentally that the reaction of individual sensory neurons ~ presents distinct specificity for the value of .the parameter of the stimulus deli- vered [4-6]. In particular, some of the neurons of tha visual ans~lyzer do not react at all to diffuse illuminatiori of the retina [7~. One must del.iner an appro- priately organized image to a specific part of the retina to elicit a reaction in such a neuron. The mechanism of lateral inhibition [8] provides for the high i sensitivity of detector systems. However, ln the opinion Qf the critics of the detector conception, detectors are temporary combinations of neurons that reflect ~ only the process of "running" a certain program ~for pattern recognition throug~ ~ the neuronal network. After running this program, the "detector functions" of the I cell may disappear and it could take on other functions. ~ i Current experiments on stability of detector properties of nEUrons revpaled that in ~ individual neurons these properties are either genetically determined or formed j in early ontogenesis, ~and then persist for the entire lifetime of an animal. ~ Coding by channe'1 number as a process of parallel information processing by no means ~ rules out successive analysis. If a etimulus is complex enough and the animal ' does not have to deal with it often, perception may proceed step by step, by ~ isolating the impurtant and simple features of the analyzed image. This is asso- ; ciated with successive acCivation of different detector neurons, which reflects E the process of successive assembly of the "internal. image" of an exogenous i sttmulus from simE~le ta~s [9, 10]. Such is the function, for example, of eye ! movements in percE~ption of a complex image [11]. It should also be stressed that ~ coding by channel number provides for concurrent transmission and processing of ~ ~ ~ ' 7 ~ FOR OFFICIAL USE ONLY ~ APPROVED FOR RELEASE: 2007/02/09: CIA-RDP82-00850R000500010009-6 APPROVED FOR RELEASE: 2007/02/09: CIA-RDP82-00854R000540010009-6 H~lt UF'I~lt'lA4 USE ONLY information. Concurrent ["parallel"~ information processing enables living organ- . isms to effectively.solve identification problems. " is also A property of living systems, which we could call "functional stability, related to coding by channel number. We use functional stability to refer to viability, the capacity of a living system to perform its main functions, even if in a somewhat.reduced form (for example, a doincthe resencetoffsomerinjury,teven legs, whereas a car missing a wheel cannot), P rather significant. The concept of functional stability is related, to some extent, to the concept of reliability in the meaning of von Neumann, but does not c:oincide with i+t. In his well-known wqrk, von Neumann [12] discussed the reliabi- lity of a system which, like an electric switch, has two states--on and off. A different situation is inherent in living systems, in that along with the two ex- treme states--complete work capacity and camplete breakdown--there are also various intermediate states of partial work capacity. It is logical to intxoduce the con- cept of functional stability in the presence of injury for such systems. Evidently, the principles of constructing functionally must be other than Neumann's principles of reliability. The combination of coding by channel number and appropriately organized lateral inhibitory connections could serve as the basis for constructing neuronal systems that are quite perfect in functional stability, although some elements of these structures do not have such perfection [13]. In conclusion, it should be indicated that informatiAn processing in neural networks is often described as follows: all processing occurs in neurons, while ~he nerve fibers that connect them play merely a passive, transmitting role. However, there is every reason to be~ieve that this conception, is not entirely,correct. Let us ~ consider the process of propagation of excitation over a branching fiber in the belief that this process is described by the well-known equations of Hodgkin-Huxley. As shown b;� estimates, the branching node of such a fiber could P1aY and pand of � logic element, implementing certain basic logic functions of "or," " "inhibition,"~depending on the conditiona (proportion of diameters of fibers forming the"ramification, difference in times of input signal in the unit via different fibers). ~ . ~ . By combining the main logic functions one can obtain others. Here it is suggested that there are natural analogies with so called homogeneous environments--technical equipment similar in properties to branching neural networks, which have recently gained rath~�r wide popularity as the basis for construction of various computer systems. Thus, even a single branching unit of a nerve fibdetector.peThe possibility ofex _ physiological functions, for example, serve as a complex logical information processing in branching structures siiggests that, per- haps, the role of dendrite arborizat~.ons in information processing is much greater than usually believed. With reference to neuronal nets, we are apparently over- simplifyin~; the individual cell. Stating that "the brain is a computer," we relcgate the modest role of a single elemsnrloser tonthe truth. Perhaps the formula that "the neuron is a computer i - f detector cotild be performed by parts - According to the�furegoing,:the function c, of a neuron, rather than the neuron as a whole. The material submitted below is 8 FOR OFF[CIAL USE ONLY APPROVED FOR RELEASE: 2007/02/09: CIA-RDP82-00850R000500010009-6 APPROVED FOR RELEASE: 2007142/09: CIA-RDP82-40854R040500010009-6 FOR ()FFICIAL USE ONLY , not in contradictic,n with this conclusion, although the term "detector" implies a single element of the corresponding neuronal net. The cambination of numerous ; independent detectors in a single element does not introduce any appreciahle difficulty in considering the function of the entire analyzer. _I ~ i i 9 FOR OFFICIAL USE ONLY i , APPROVED FOR RELEASE: 2007/02/09: CIA-RDP82-00850R000500010009-6 APPROVED FOR RELEASE: 2007/02/09: CIA-RDP82-00854R004500010009-6 FOR OFFICIAL USE ONLY CHAPTER 2. MODEL OF CHANNEL NUMBER CODING . ~ Spherical model. The function of signal discrimination in a model of coding by channel number is provided by the fact that a specific value of parameter of an exogenous signal generates a solitary maximum of excitation on one of the spe- cialized detector neurons.. Detector refers to a neuron that is selectively . adjusted for a specific value of the signal parameter. This selective adjustment of the detector is obtained by a specific system of communications, by which the detector is connected to neurons of the 'underlying level or receptors. Each de- tector forms one of the parallel channels for information processing. With a change in th.e exogenous signal, the maximum excitation shif ts from one detector to another. If two signals elicit maximum excitation of the same detector, they are not distinguished. Schematically, a detector can be described as a formal - neuron with several inputs through which signals come from underlying neurons-- _ primary detectors or receptors. Each c~f the inputs should provide independent information to ttie detector (Figure 1). The independence of the inputs means that one cannot predict the activity of one input on the basis of knowledge about the activity of any other input. The diagram illustrates a secondary detector. The arrow shows the direction of the signal at the detector output. The lines converging on the detector illustrate arrival of signals that converge on the detector (f2--excitation coming over the ith channel). The small circles at the point of contact between the input and detector represent the coefficients of synaptic transmission; e~2--coeff icient of synaptic transmission of the ith channel on the ~jth detector. The dotted line refers to part of the inputs not shown on the diagram. The detector adds the paired products of eacti input signal multiplied by the corresponding coefficient of synaptic - transmission: ~ n ~ ~l~ = c~ i1~~ + . + c~ifi + . + e~nfn � ~ ~~jifi~ _ i=1 where d~ is magnitude of excitation of the ~jth detector. The set of delivered stimuli forms excitation vector F={fl, fi~ fn}� The set of communica- tion coefficients forms the communication vector C~j ={e~l, e~j2, e~jn}. Ttie reaction at the detector output equals the scalar product of excitation vector multiplied by communication vector d~j =(F, C~). When the input signal changes, so does the i. ronal react3on, and it reaches a maximum when the excitation vector is collinear with the communication vector. The modulus of the communication vector - is constant (C~~ = con~t. . If ~F~ = l,~d~jmax = ~os ~F~ I~~IIFI - - 1' m). 10 FOR OFFICIAL USE ONLY APPROVED FOR RELEASE: 2007/02/09: CIA-RDP82-00850R000500010009-6 APPROVED FOR RELEASE: 2047/02/09: CIA-RDP82-00850R000504010009-6 , ~ 1 + FnR (1M1~1('IA1. II~M: nN1.Y ~ i . . ~ i ~ j - ~ .r f ~i. ~ Jk ~ ~ ~ ' ~ d~ j' . � ; ~ X ~ f' f 0). A maximum increase in sensitivity is obtained when the adaptive stimulus corres�- , ponds to the following values for the argument of the cosine curve that describe the sensitfvity of primary detectora: , f (~)=0~ 90~ l80, 270� . . (1.23) . and differential sensitivity does not change under the influence of adaptation if: , f (~)-45, i35~ 225. 3i5�. (1.23a) , - Such an increase in analyzer sensitivity is useful from the functional point of view: during prolonged viewing of the eame stimulus there is an increase in the system's capacity to detect minor deviations from the adapting stimulus. It should be noted that if the sensitivity of primary detectors increases during adaptation, the differential senaitivity of the analyzer to stimuli clase to the adapti~~e one, on the contrary, diminishes ~~ ~If the density of the detectors is not constanC, ~ will be a certain function of I. ' If this function is known one can determine Weber's ratio, with consideration of the changing threshold. - Adaptation in the intensity analyzer. Adaptation at the inp'ut of primary detectors: Let adaptive stimulus SI be delivered to the input of the system. A signal is delivered to the input o~ horizontal cells which equals.: 124 FOR OFFICiAL USE ONLY APPROVED FOR RELEASE: 2007/02/09: CIA-RDP82-00850R000500010009-6 APPROVED FOR RELEASE: 2007/02/09: CIA-RDP82-00850R040500010009-6 ~ FOR OFFICIAL USE OI~iLY where Pp(I~ is the receptor's reaction to the adaptive stimulus, RB is the seC of receptors of the main field. If the adaptive stimu3us is followed by delivery of stimulus SI whose intensity is I units, the signal at the input of the primary de- tectors can be calculated as follows: - sinau(1), if u~j)=~p~~j~-~~1'r~l.)~a~ , . (2.11) . f~ _ if u(1) ~ 0; ' - ~ . cos au (l), if u (1) ~ 0, ~ . . _ . ' ' 0, if u(~ G0. ~ . ~ Adaptation of primary detectors: Let adaptive stimulua SIa, which excites primary detectors to level (2.11) be delivered to the input of the system. Quantities (2.11) are components of ex citation vector ~(I~ generated by stimulus SIa. Under the influence of prolonged excitation, the sensitivity of primary detectors dimini- shes proportionally to the level of their excitation by the adaptive stimulus. As - a result, stimulus SI delivered right after the adaptive one generates the follow- ing~.excitaZion vector: ' _ _ ^ F(Ill.)=~~1.~ . ~ (2.12) where.A(Ia, t) is the adaption operator (Appendix I) and t is the time of delivery , of the adaptive stimulus. ~ Knowing the corresponding excitation vectors (2.12) and zero vector of "gray proper," one can calculate the angle between them: ~ , ; (I ) = I~' (l - 0)~ F ~111.)~� (2.13) - ~ ~ Overall level of activity of intensity analyzer. We shall call the sum , of a~tivity of all secondary detectors the overall level of analyzer activity. The excitation profile on the set of secondary detectors (aee Appendix 1) is des- . cribed by the function cos [f(I) - f(I~)], where I~ is the intensity of the optimum stimulus for the ~jth detector. In this case, overall activity of all detectors will be: ~ ~ �rs ~ ~ ' � : - S (1) - j cos [f ~1~ - i di ~J~), . o � i.e., . � s~n~~~ f cn+d~n ~~n. cz. ~4> Expression (2.14) assumes a maximum value when cos f(I) = sin f(I), i.e., when the components of the excitation vector equal one another. Thus, total illumination of 125 ~ , FOR OFFiC[AL USE ONLY APPROVED FOR RELEASE: 2007/02/09: CIA-RDP82-00850R000500010009-6 APPROVED FOR RELEASE: 2007/02/09: CIA-RDP82-00850R000500010009-6 FOR OFFICIAL USE ONL'Y . the entire retina from zero to a certain intensity I first leads to an increase in _ overall analyzer activity, which reaches a maximum with intensities that generate ~ an excitation vector with = 45�, after which further increase in intensity leads ~ to decrease in overall analyzer activity. ` 1 Overall activity of primary detectors behaves analogously: SP(I) = cos f(I) + sin f(I). ' With increase in stimulus intensity Sp(I) increases, reaching a maximum at th.e point ` where cos f(I) = sin f(I), and further increase in intensity leads to decrease in Sp (I) . An analogous function of intensity was described in a study of the magnitude of evoked potential or mean level of activity derived from the optic nerve [18]. In the foregoing, photosensitive elements with characteristics of the (2.3) and (2.4) types were used as photoreceptors. However, the dynamic range of such charac- teristics does not exceed two logarithmic units. We obtain even closer coincidence of the model's characteristics with the analogous ones of man if we use as photo-~ receptors elements with characteristics of the following appearance: . " 2.15) r(1)=Na.: ~ where R~X is the maximum response of the receptor, c7 is intensity of light at which the response of photoreceptors equals half its maximum value and n is a cons tant that determines the steepness of responses. For example, with n= 0.5, the range over which Weber's ratio is constant, ~I/I = const, equals about four logarithmic units, while the indicator of xhe law of Steveneon is about~0.4. Expression (2.15) des- cribes receptor responses better than a hyperbolic tangent. 126 FOR OFFICIAL USE aNLY APPROVED FOR RELEASE: 2007/02/09: CIA-RDP82-00850R000500010009-6 APPROVED FOR RELEASE: 2007/02/09: CIA-RDP82-00854R004500010009-6 i ' FOR OFFICIAL USE ONLY ~ I ' ' . ; ~ ` ` . � ~ Y- . ~ I APPENDIX 3. COLOR ANALYZER I Sensitivity characteristics of cones and primary color detectors. ~ Color perception is possible only when there is sufficient illumination, when the outside world is perceived by the conea. In twilight condifions, when only the ; rods are functional, man cannot perceive colors, and the world around him is ~ perceived as being black and white. ~ There are three types of cones, R, G and B. If the sensitivity characteristics of cones were to be described as functions of frequency of monochromatic radiation, these characteristics would have the same appearance for all three types of cones, ~ . although their characteristics are shifted in relation to one another. The general characteristic thus obtained coincides, with accuracy to a constant factor, with the so-called Dartnall nomogram [34]. Hereafter, it is assumed that the cone charseteristics are Dartnall functions. The question arises: Are Dartnall functions optimal from the standpoint of integral sensitivity of the entire analyzer? For this, let us rewrite functional - rf _ _ . ~ ~ (f~) = ~ ~a IF Ur (f~ -1- T)~I~ (3. 4~ (~o determine the cosin, see Appendix 1, expression 1.2), using smallness T, i.e.~, ~ f2(~I-T~ = f2(~~ + TfZ(~), in a somewhat different form: ~ f p, . . , ~1 ~ir ~~P)) = J cos {F (fr I~ ~fr ~FT ~fi ~ (3.2) v. . where F' is a vector with components of the {dfy(~)ld~} type. Functional (3.2) does not overtly contain variable d. Consequently, for Dartnall ' functions {fi(~)}, i= 1, 2, 3, to describe the extremal, the corresponding Hamiltonian [104] along these extremal must be constant: II - -4~ ~b~~/, conat, ' � (3.3) . , � where @ is a.subintegral function of expres~sion (3.2) and ~f2 is its partial deri- vative ~/a; fi. ~ 127 i FOR OFFICIAL USE ONLY APPROVED FOR RELEASE: 2007/02/09: CIA-RDP82-00850R000500010009-6 APPROVED FOR RELEASE: 2007/02/09: CIA-RDP82-00854R004500010009-6 M'uR uF'F'IC'IAI. uS~ oNI.Y In determining functional (3.2), it must be borne in mind that Dartnall functions are not orthogonal. Condition (3.3) with symmetrical position of Dartnall func- tions on the frequency axis was dirsctly checked in [103, 106]. It was found that condition (3.3) is satisfied over the entire range of the visible spectrum. Thus, there are grounds to m~aintain that Dartnall functions are close,to optimal. However, it is not desirable to make direct use of cones with Dartnall functions of sens~tivity as primary detectiors determining the componeats of the color excitation vector. The fact�of the matter is that, in this case, the sub~ective distance be- tween colors would be small and the excitation vector would not exceed the range of one octant. ~ For this reason, detectors whose responses were obtained as a linear combination of responses of individual cones are used as primary detectors. Also, the direction of the excitation vector should change as much as possible with change in spectral composition of illumination. In the ideal case, the sensitivity characteristics of primary detectors should be described by the corresponding directing cosine curves. Let us now consider the construction of secondary detectors, without determining - the characteristics of primary detectors. As we know, the reaction of the ~th se- condary detector c~;n be found from the expression d ~1~ R) _ F (3.4) Let us now stipulate that, with frequency of light radiation the ~th detec- tor is excited more than any other detector. This condition is met if, at a given - value of the reaction of the ~th detector reaches a maximum, and the maximum reaction of any detector equals a certain constant value B. In other words, dd tl, ~l ( aF (avllf ~ ~ . (3.5) ' ~ (r-r~ `C~' ~ / Ir-~?f ~ � . provided that ' (3.6) d (f ~ ~p f)=B~ where ~j = 1, . , ?n. In this case, let us first consider condition (3.5), and use (3.6) to�fiMd:unknown constants that determine the modulus of communication vector. From (3.5) we get ad a~, r1(~~ = I I( a~~ I co8 ~C~, a~ ~I _ p~ . ( 3. 7 i ~ r-p~ . i.e., ~ . 1 ap l~) I . r-.f. Communication vector C~j must be orthogonal to vector dF(~)~d~l~ a~~j, tangential to spatial ~:urve {fk(~)}, k= 1, 2, 3, and ~CL is the interval of frequencies of visibl~ monochramatic radiation. The sought vector C~ is in a plane that is ortho- gonal vector dF(~)Ic~I~ There may be many such vectors. Let us choose one of them so that the angle between it and excitation vector F(~~) would be as ~ 128 _ FOR OFFICIAL USE ONLY APPROVED FOR RELEASE: 2007/02/09: CIA-RDP82-00850R000500010009-6 APPROVED FOR RELEASE: 2007/02/09: CIA-RDP82-00850R000500010009-6 - FOR OFF[CIAL USE ONLY , small as possible. To find such a vector, let us pro3ect on the plane of communica- tion vectors {C~j} excitation vector F(~~) and we shall get vector F~(~~):~ ~eught vector C~ coincides in direction with vector F~(~~), i.e., . _ _T-.._ . ~ ~~=YF~c~,~: c~:s~. where Y is a certain constant. In view of the fact that vector F~(~) is orthogonal to vector F~(~~)--pro3ection of vector F(~ ) on the plane of communication vectors {C~}, we can submit vector F(~~) as the ~ollowing sum: r = F~ (~f) + ~F; (~)1~=a, . or . rr f) = F ~~P~) - ~FY ~v=ri, ~ 3 . 9 ) , ' where S is a certain constant. ~ Since vector F~(~~) is orthogonal to vector F'~(~)I~a~~, with consideration of ~ (3.9) we get: ~F~ f)� F~~~)) ~r=~ f = (F F, I4=4~ . . . Y ~ p~~~ IT-4 f- O . or ' p t~)~ F* (~11 I (3.10) 1' 9 Fp t'=4~ ~ � Moreover, considering condition (3.6), we can obtain the value of the other unknown constant: ~ D 7 ~F ~9'~~ - ~F9 F Ii=4J ~ . ' ~3.11~ Knowing curve {fk(~)}, (~~L, k= 1, 2, 3), we can calculate comm4unication vector C� (3.8), where coefficients Y and ~ can be found from conditions (3.10) and (~.11). The communication vector enables us to calculate the characteristics of output detectors (3.4). Figure 40 illustrates the respones of different output det ectors calculated by the method described in [105]. If we were to choose any thr ee independent orthogonal functions out of the existing set of responses, these functions could be used as the characteristica of primary detectors. Tw o-dimensional invariant spaces of color adaptation operator. Let ' a certain adaptive stisnulus S o be given, to which corresponds excitation vector F(~o) with components {fi(~o)~. Let the vector be transformed under the influence of prolonged viewing of stimulus S~o as follows: ~~Po~~o) = e9~ ~~o~ F ~~o)~ ~ ~3.12) where.~ (~o, t) is the adaptation operator. ~(~p, t) is a diagonal operator, with the following coefficients on the main diagonal: aii =1- T ~~)I ~3.13) 129 ' FOR OFFICIAL USE ONLY APPROVED FOR RELEASE: 2007/02/09: CIA-RDP82-00850R000500010009-6 APPROVED FOR RELEASE: 2007/02/09: CIA-RDP82-00850R000500010009-6 ~ FOR OFF'[CIAL USE ONLY In the general case, vector F(~o/~o) is not collinear to vector F(~o). The question ~ arises: In what cases is the trajectory of the vector flat in the course of a~ap- ! tation and, in addition, does it contain the vector of white color2 In other words, ~ in what cases does adaptation have no effect on the perceived color of the stj~nulus? Let there be the three following vectors: purely monochromatic vector in the absence of adaptation F(~), white vector F(8) with components fZ(d) and vector dF(~o/~o) tangential to the tra3ectory of adaptation. The components of the last vector equal Y~~t~~ {f~(~Q)}, 1, 2, 3. If all these vectors are in the same plane at any point in time t, the tra~ectory of vector F(~o/~o) W~11 be flat. In this case, all three vectors should be linked with a linear function. Then the determinant plotted on these vectors should equal 0: ~ -i i i ~~~1i� f~) _ -7~ ~t) f ~a) ~fi ~~o) f~ ~~o) ~'Po) =0. (3.14) ~ ~~o) ~ ~~o~ ~ ~T~) Since we are dealing with an alternant [Vandermond determinant], condition (3.14) can be replaced with condition: ! v~-i,~u~-i,iv,-~a=o� c3. ~5~ Thus, for ust exciteutonan equalaextentgathleastitwoyofuthegthreetprimarytdetectors. ; stimulus m i . i 130 FOR OFF[CIAL USE ONLY APPROVED FOR RELEASE: 2007/02/09: CIA-RDP82-00850R000500010009-6 APPROVED FOR RELEASE: 2007/02/09: CIA-RDP82-00850R000500010009-6 FOR OFF[CIAL USE ONLY l . : AppENDIX 4. LINE SLANT ANALYZER T~rimary detectors of line slanof illiumination byfineanssofion orloffnreceptive gradient: Distinction of the range fields has a substantial flaw: in order to distinguiah a black line on a white background and white line on a black background, different systems of receptive fields must be used: off-on in the first case and on-off in the second. St~ch a sys- ~ tem is cumbersome and unreliable. Let us consider another method that is suitable for distinguishing both a white out- Figure 74 illustrates a line on a black background and a black outline on white. system that will function only if its receptor layer makes small random ~umps all of the time. Let the image of a black-white border be projected on the receptive field. Two groups of receptors can be distinguished. On some receptors, the level of illuaiination changes constantly since the range of change in illuminThe tions shifts from one receptor to another in the presence of random~trem~esult, illumination level remains constant on the rest of the receptors. ~ a variable signal arises a_t the output of the first group of receptors and a con- stant one at the output of the second group of receptore. The signal passes from ' the receptor to the ionuof~the signalrviantheeinhibitory channelalags in relatiouion channel. Transmiss to the excitatory channel. When there is an unchanging image on tthe retina, signals from the constantly illu- minated group of receptors passing via the inhibitory and excitatory channels com- pensate one another. As a result, the output signal of these neurons is found to - � equal zero soon after turning the stimulus on. If the signal at the output of ~ ~ the receptors changes in time, excitation and inhibition passing to the input of the second neuron are unable to compensate one another. As a result, th~s signal passes only from the receptors, on which the limit of illumination is projected in the presence of tremor. This method makes it poasible to single out a white - outline on a~black background and a black outline on a white background. This method can also be well-used in the visual analyzer, since the eye has the - required random tremor. Thus, frequency of tremor in man constitutes 150 Hz and amplitude is about 18 s of the visual angaewithin therrangenof 1-3sconesn[11].~ an image in the centra~ part of the retin Interaction betweenvariable~component,ageneratingnphasic reactiona8innganglionr distinction of the cells [22]. ' , 131 ~ FOR OFF'ICU1L USE ONLY APPROVED FOR RELEASE: 2007/02/09: CIA-RDP82-00850R000500010009-6 APPROVED FOR RELEASE: 2047/02/09: CIA-RDP82-00850R000504010009-6 FOR OFFICIAL USE ONLY In the following, it is assumed that - ~n to construct primary detectors of ~ ~ff orientation use is made of signals taken � from neurons, on which there is preli- ~ minary distinction of image outline. Organization of receptive field of primary detectors: Let a segment of + image outline be pro3ected in the re- ~ ~ ~ e� ceptive f ield of a 1oca1 analyzer. t Under the influence of light at the out- - put of receptors or neurons distinguish- - ing the outline, there is appearance of ~ signal r2(I), i= 1, s, where i is receptor number,.s is the number of ` f- photoelements in the receptive field � ~ on and I is intensity of receptor illumina- ~ tion. The set of signals {rZ(I)}(i = 1, : -~-d - ~ f . . . , s) is a discrete approximation ~ ~ . of the image delivered to the receptive ~ II ~ 1 R field o.f the local image analyzer. We . shall consider set {r2(I) } (i = 1, , 8) as components of s-dimensional~ _ f-~--~ A n �jf vector The orientation of a local Fibure 74. line segment is given by the number ~ Distinguishing outline of an image by (angle of tilt in a given system of menas of tremor coordinates), which takes on.any yalue in the interval [0, ~r]. It is assumed that the value of the parameter is coded unambiguously by the direction of exeita- . tion vector F(~) or number i~[0, 2~r]. ' Vector F(~) ~must be at least two-dimensional, and hereafter we assume that dimen- - sionality equals two. Consequently, ~ae must have two independent primary detectors whose responses are described by the functions: _ f' (~p) ` sin 2~ x f~(~) = cas 2~. (4.1) Thus, the image on the retina generates s-dimensional vector o~. Then, by means of a certain degenerate operator oBl, vector-~ is transformed into the second excitation vector The appearance of operator ~1(~ is determined ~ by the contacts between receptors aad primary detectors. Apparently, there are many [or a set] such transformations, with which the following equation applies: - We shall discuss below two methods of determining operator ~1: Let the receptive _ field of primary detectors be divided into two interaecting connected zones. We shall call the zone, illumination of which elicits inhibition of a primary detector, the inhibitory zone, and the one illumination of which excites the primary detector;, the excitatory zone. Let the influence of each point of the illuminated receptive~ field,on detector activity be conatant and independent of the position of the point in the receptive field. The pro~ection of the line on the receptive field elicits a reaction by the primary detector, formed by the sum of signals from 132 FOR OFFICIAL USE ONLY APPROVED FOR RELEASE: 2007/02/09: CIA-RDP82-00850R000500010009-6 APPROVED FOR RELEASE: 2007/02/09: CIA-RDP82-00850R000500010009-6 FOR OFFICIAL U5E ONZY the inhibitory RT and excitatory Rg areas. These equal the cosine and sine of the dual angle of inclination of line In or.der to determine the form of re- ceptive field of the corresponding pximary detector, let us transform (4.1) into the following expressions: fa = si~~ 2~ = cos~ -I- 45~ - sin' 45~~ (4 . 2) , f, = cos 2~ = cos' ~ - sin' (4 . 3) Thus, according to (4.2) and (4.3), the overall signal from excitatory regions should equal cos2 ~ and cos2 (~+45�) and the itthibitory one should equal sin2~ and.sin2 (~+45�), respectively. Consequently, the length of the presented line lying in the excited part of the receptive field is pB = cos2~ or cos2 (~+45�), and for the line segment in the inhibitory zone, sin2 ~ or sin2 (~+45�). The overall length of the vector-radius for the entire receptive field is sin2 cos2 1. Thus, the vector-radius of the receptive field is constant--thQ receptive field is in the I form of a circle. The f law of such organization of the receptive field is that, in this case, the re- action of primary detectors depends on the presence of noise contained in the image. - Indeed, let the receptive field of the analyzer be illuminated by random spats. The spots are small, but their density is rather high. If the density of noi~e is high enough, the overall output signal of the primary detec~or from both zones of the receptive field separately is pr~portional to the area of these regions. The ~rea of the excitatory region in the previously proposed case is: . ZR tR .Se = i f p'~ _ ~ ~ cos~ ~ - . o u _ lo;-}- 8 sin 2~p Io` ~h sIn 4~ lo r:. ~ (4 . 4) The area of the inhibitory region is found as the difference between areas of the circle and excitatory region, i.e., sr=,~~~,~= g,~� (4.5) From these results we see that with noisy illumination of the receptive field the signal at the output of primary detectors does not equal zero: fi Se Sr = -a14~ = SB - Sr = --n/4, ( 4 . 6 ) which is equivalent to excitation vector ~ 112�30') with~equal negati�ve com- ponent3. Thus, in the presence at the input of only noisy illumination, there is the il?usion of perception of a line tilted at 112�30'. In order to eliminate this flaw, let us consider a dif�erent structure of organiza- tion of the primary detector receptive field. - Each p~int on the receptive field has the samP effect on activity of a primary detector. When an outline is projected in the detector's receptive field, the absolute value of its reaction equals the length of the line in the receptive field. 133 FOR OFFICIAL USE ONLY APPROVED FOR RELEASE: 2007/02/09: CIA-RDP82-00850R000500010009-6 APPROVED FOR RELEASE: 2047/02/09: CIA-RDP82-00850R000504010009-6 FOR OFFICIAL USE ONLY _ The overall reaction of the primary detector should equal fl(~) = ain 2~ and f2(~) = cos 2~, respectively. Cansequently, the receutive field in polar coordinates has the following appearance: p~ _ ~ sin ~ ( N P~ _ ~ cos 2~ . _ (4 . 7 ) Figure 49 illustrates the form of recepti~~e field thus obtained. In constructing ~ a primary detector, it must be borne in mind that a"plus" refers to regions that have an excitatory effect on the activity of the primary detector and "minus" t~ those that have an inhibitory effect. With such organization of the receptive field, the reaction of primary detectors does not depend on presence of random uniform noise in illumination. Indeed, in this case the areas of~the inhibitory and excitatory zones are equal, so that the signals from both zones balanre one another. Finally, there can also be a third form of receptive field. Let there be a center given in the receptive field, through which a line is drawn. The tilt of this line equals zero. The orientation of the arbitrary line is determined by angle which it forms with the line that ha~ zero tilt. The magnitude o~ contact between the receptor and primarq detector is proportional to cos 2~ (or sin 2~). Mixture effects in orientation analyzer: Let there be two centered lines, L~1 and L~2, pro,jected on the receptive field, which correspond to excitation vectors: c~ (~p~) _(cos 2~p1, aiM 2cp1) H~' (~s) _{cos 2cps, sjn 2~p,). (4.8) As a result of joint excitation of primary detectors, their excitation level is: - , _ ~ - - _ f, (~pi, = sln 2~1-}- sIn 2~, - 2 aln (~1-{- ~os (~pl - . ~ (4.9) f~ 'PS) = 2 cos ~~i -3- ~g ~~i - ' ' The vector with components (4.9) corresponds in direction with the excitation vector generated by line tilt (~1-I~2)/2. Thus, with simultaneous delivery to the receptive field of two lines, there is generation of a vector that corresponds to a line, which is the bissectrix of the small angle between these lines. Adaptation effects in orientation analyzer. Normalization effect: During prolonged viewing of a line, its slan*_ does not change when this line corresponds to one of the eigen vectors of the adaptation operator, i.~., the vector in which either all not~-zero components equal one another, or else only one c.omponent does not equal zero (Appendix 1). The former case corresponds to lines with tilt: ~ =1E+~18, (k= 9, 3~ 5, 7), . ~ (4.10) and the latter, with tilt: ~k=ku/8, (k=0~ 2, /f, 6). ~ . , (4.11) Let us consider the adaptation properties under the influence of lines with tilts of the (4.10) and (4.11) type. For this, let us determine how the angle between the arbitrary line and closest line with tilt (4.11) changes with adaptation, i.e., ~ ~ ~ 134 FOR OFF[C[AL USE ONLY . APPROVED FOR RELEASE: 2007/02/09: CIA-RDP82-00850R000500010009-6 APPROVED FOR RELEASE: 2007/02/09: CIA-RDP82-00850R000500014449-6 FOR OFF(CIAL USE ONLY a coe F d E ti -'r I 1~ (~?p l~ (~~)1~ _ a~ [ (~PI c~ ('P~)l = . a~ , ~ (E - Y (t) I 1r q' 1~ (~)-1 ~ ~ . Putting ai=!-Y (t)~/i (~p)~, we have ~ ; ~S - a eos ~ ~~k)~ - d Ear1t ( sk)1t~(Yl ~ (4.12) - dt ~ dt [En3~r ~T~1 Let cpk =0 or ~pk= a/2, � f 1\~k~ = Cn9 ~k 1 {1 9~~k~ - 9~R 2~~ ~ i ~ 4� 13 ~ Considering (4.13), expression (4.12) assumes the following appear- ance: d R~1~ (~1 fl Tk = ~ ~ - dt ~ ~a~~ ~ ~ . - _ Ri/i ~~l ~ail~- a~l~Ea~ail~ , (4.14) _ IBa~~~'/. f~~~~t)~ Since the denominator of (4.14) is positive, the sign of the entire expression is determined by the sign of the numerator, After reducing in the numerator dl(t) - we have S~ (t) = r~~ [aia, - ala=1 fi fi ~~k)� _ ~ (4 .15 ) Since a2>0, a2f2>0 also, and the sign of (4.15) is determined by the sign of the expression: . s9 _ ~ai~i - aia:) ft ~~P) ~i ~~k)� ~ (4.16) ; After substituting in (4.16) the values a2 = 1- Y(t) ~ f2(~) I and ai ='Y~ ~t~ ~,fi�~ ~ , we get - - , Sa (t) =t~ f~ (~k) f~ (~)I - I t~ (~)I) ~r' ~4. i~> - Since �Y' (t) ~ 0, the sign is determined by the sign of - a~ (t) = t~ l, (~k) (I t~ (~)I - I f~ (~)I . ~ ` (4. is> . Let us first consider the case ~k = 0, i.e., fl(~k) = 1 in the interval of 0f2~~)� Thus, with ~C[0, 45�], expression (4.18) is negative. In other words, during adaptation the angle of F(~/~), o~(~k), (c~J~ = 0; 0-45� , then ~ f l($) f 2(~) t, f i�)>~ ~ f l~~k) = l, consequently - da(t)~f 2(~) ~ and, consequently, d4 (t) 0, ~f2(~)~>Ifl(~)~ and, consequently, expre~sion (4.19) is negative. Let then = 135� (f2(~k) _-1), ~Cf112�30', 157�30') and 135�, i.e., f2(~)