Source: http://www.google.com/patents/US4914708?dq=7,441,219
Timestamp: 2017-03-28 21:04:30
Document Index: 461712920

Matched Legal Cases: ['ART1', 'ART2', 'ART 2', 'ART 2', 'Art 2', 'Art 2']

Patent US4914708 - System for self-organization of stable category recognition codes for analog ... - Google PatentsSearch Images Maps Play YouTube News Gmail Drive More »Sign inPatentsA neural network includes a feature representation field which receives input patterns. Signals from the feature representation field select a category from a category representation field through a first adaptive filter. Based on the selected category, a template pattern is applied to the feature representation...http://www.google.com/patents/US4914708?utm_source=gb-gplus-sharePatent US4914708 - System for self-organization of stable category recognition codes for analog input patternsAdvanced Patent SearchTry the new Google Patents, with machine-classified Google Scholar results, and Japanese and South Korean patents.Publication numberUS4914708 APublication typeGrantApplication numberUS 07/064,764Publication dateApr 3, 1990Filing dateJun 19, 1987Priority dateJun 19, 1987Fee statusPaidAlso published asDE3852490D1, EP0367790A1, EP0367790B1, WO1988010476A1Publication number064764, 07064764, US 4914708 A, US 4914708A, US-A-4914708, US4914708 A, US4914708AInventorsGail A. Carpenter, Stephen GrossbergOriginal AssigneeBoston UniversityExport CitationBiBTeX, EndNote, RefManPatent Citations (6), Non-Patent Citations (13), Referenced by (52), Classifications (16), Legal Events (5) External Links: USPTO, USPTO Assignment, EspacenetSystem for self-organization of stable category recognition codes for analog input patterns
US 4914708 AAbstract
A neural network includes a feature representation field which receives input patterns. Signals from the feature representation field select a category from a category representation field through a first adaptive filter. Based on the selected category, a template pattern is applied to the feature representation field, and a match between the template and the input is determined. If the angle between the template vector and a vector within the representation field is too great, the selected category is reset. Otherwise the category selection and template pattern are adapted to the input pattern as well as the previously stored template. A complex representation field includes signals normalized relative to signals across the field and feedback for pattern contrast enhancement.
1. In a pattern recognition device comprising a short term feature representation field for receiving input signals defining an input pattern and template signals defining a long term memory template, means for selecting at least one category node based on a pattern from the short term field, means for generating the long term template based on the selected category and means for adapting category selection and the long term memory template to the input signals, a category selection being reset with an insufficient match between the input pattern and template, the improvement wherein the match is based on the closeness between a vector of the template and a vector of the short term field.
2. A device as claimed in claim 1 wherein the closeness of the two vectors is disregarded where the template is not based on previous input patterns such that the category selection is not reset where a category has not been previously selected.
3. A device as claimed in claim 2 wherein the match is based on a norm of weighted sums, each sum being of a short term signal of the short term field and a template signal.
4. A device as claimed in claim 3 wherein the summed short term signal is normalized relative to other like signals of the short term field.
5. A device as claimed in claim 4 further comprising means for enhancing higher level signals of the input relative to lower level signals.
6. A device as claimed in claim 5 further comprising feedback of enhanced signals within the short term field.
7. A device as claimed in claim 6 further comprising feedback of the weighted sum of the short term signal and template signal within the short term field.
8. A device as claimed in claim 7 wherein the short term field comprises matched signals of a matched pattern, each matched signal being the weighted sum of a short term signal in the field and a template signal, the category selection and long term memory template being adapted as dual functions of the difference between the template signal and the matched signal.
9. A device as claimed in claim 2 wherein the short term field comprises matched signals of a matched pattern, each matched signal being the weighted sum of a short term signal in the field and a template signal, the category selection and the long term memory template being adapted as dual functions of the difference between the template signal and the matched signal.
10. A device as claimed in claim 9 wherein the short term signal summed with the template signal is normalized relative to other like signals of the short term field.
11. A device as claimed in claim 2 further comprising means for enhancing higher level signals of the input relative to lower level signals.
12. A device as claimed in claim 11 further comprising feedback of enhanced signals within the short term field.
13. A device as claimed in claim 12 further comprising feedback of template signals within the short term field.
14. A device as claimed in claim 2 wherein the input pattern to the system is an analog pattern.
15. A device as claimed in claim 2 wherein closeness between the vectors is determined from the angle between the vectors.
16. In a pattern recognition device comprising a short term feature representation field for receiving input signals defining an input pattern and template signals defining a long term memory template, means for selecting at least one category node based on a pattern from the short term field, means for generating the long term template based on the selected category and means for adapting category selection and the long term memory template to the input signals, the improvement wherein the short term field comprises a plurality of subfields of short term signals and feedback of short term signals within the short term field.
17. A device as claimed in claim 16 further comprising means for enhancing higher level signals of the input relative to lower level signals.
18. A device as claimed in claim 17 further comprising feedback of enhanced signals within the short term field.
19. A device as claimed in claim 18 further comprising feedback of template signals within the short term field.
20. A device as claimed in claim 16 wherein a feedback signal is normalized relative to other like signals of the short term field.
21. A pattern recognition device comprising a short term feature representation field for receiving input signals defining an input pattern and template signals defining a long term memory template, means for selecting at least one category node based on a pattern from the short term field, means for generating the long term memory template based on the selected category and means for adapting category selection and a long term memory template to the input signals, the improvement wherein the short term field comprises matched signals of a matched pattern, each matched signal being the weighted sum of a short term signal in the field and a template signal, the category selection and the long term memory template being adapted as dual functions of the difference between a template signal and the matched signal.
22. A device as claimed in claim 21 wherein the summed short term signal is normalized relative to other like signals of the short term field.
23. A device as claimed in claim 21 wherein a category selection is reset with an insufficient match between the input pattern and template, the match being based on the closeness between the vector of the template and a vector of the short term field as indicated by an angle between the two vectors.
24. A device as claimed in claim 23 wherein the summed short term signal is normalized relative to other like signals of the short term field.
25. A device as claimed in claim 24 further comprising means for enhancing higher level signals of the input relative to lower level signals.
26. A device as claimed in claim 23 wherein closeness between the vectors is determined from the angle between the vectors.
27. In a pattern recognition device comprising a short term feature representation field for receiving input signals defining an input pattern and template signals defining a long term memory template, means for selecting at least one category based on a pattern from the short term field, means for generating the long term memory template based on the selected category, and means for adapting category selection and the long term memory template to the input signals, the improvement wherein the short term field comprises a plurality of subfields of short term signals, each of the short term signals of at least one subfield being based on an input signal specific to the short term signal and also on non-specific signals generated from across the short term field.
28. A device as claimed in claim 27 wherein each short term signal of a subfield is normalized relative to other signals of the subfield.
29. A device as claimed in claim 28 wherein higher level signals of the normalized subfield are enhanced relative to lower level signals of the subfield.
30. A device as claimed in claim 29 further comprising feedback of an enhanced normalized input pattern to modify the input pattern to the system.
31. A device as claimed in claim 28 wherein a category selection is reset with an insufficient match between the input pattern and template, the match being based on a norm of weighted sums, each sum being of a normalized short term signal and a template signal.
32. A device as claimed in claim 31 further comprising feedback of the weighted sum to modify the normalized short term signal.
33. A device as claimed in claim 27 wherein a category selection is reset with an insufficient match between the input pattern and template, the match being based on a norm of weighted sums, each sum being of a normalized short term signal and a template signal.
34. A device as claimed in claim 33 wherein each short term signal of a subfield is normalized relative to other signals of the subfield.
35. A device as claimed in claim 27 having the ability to self-organize stable recognition categories at variable levels of vigilance in response to arbitrary temporal lists of analog or digital image patterns to learn invariant pattern recognition codes in response to noisy image data, the device having a preprocessor for generating the input pattern, the preprocessor comprising means for automatic separation of image figure of the image pattern from ground, means for sharpening and completing the resultant image boundaries, and means for processing the resultant image using an invariant filter.
36. A device as claimed in claim 35 wherein the means for sharpening and completing performs a boundary contour technique.
37. A device as claimed in claim 35 wherein the invariant filter is a Fourier-Mellon filter.
38. A pattern recognition method comprising:receiving in a short term feature representation field input signals defining an input pattern and template signals defining a long term memory template; selecting at least one category node based on a pattern from the short term field, category selection being reset with an insufficient match between the input pattern and template based on the closeness between a vector of the template and a vector of the short term field; generating the long term template based on the selected category which is not reset; and adapting category selection and the long term memory template to the input signals. 39. A method as claimed in claim 38 wherein the closeness of the two vectors is disregarded where the template is not based on previous input patterns such that the category selection is not reset where a category has not been previously selected.
40. A method as claimed in claim 39 wherein the match is based on a norm of weighted sums, each sum being of a short term signal of the short term field and a template signal.
41. A method as claimed in claim 40 wherein the summed short term signal is normalized relative to other like signals of the short term field.
42. A method as claimed in claim 41 further comprising enhancing higher level signals of the input relative to lower level signals.
43. A method as claimed in claim 42 further comprising feeding enhanced signals back within the short term field.
44. A method as claimed in claim 43 further comprising feeding the weighted sum of the short term signal and template signal back within the short term field.
45. A method as claimed in claim 44 wherein the short term field comprises matched signals of a matched pattern, each matched signal being the weighted sum of a short term signal in the field and a template signal, the category selection and long term memory template being adapted as dual functions of the difference between the template signal and the matched signal.
46. A method as claimed in claim 39 wherein the short term field comprises matched signals of a matched pattern, each matched signal being the weighted sum of a short term signal in the field and a template signal, the category selection and the long term memory template being adapted as dual functions of the difference between the template signal and the matched signal.
47. A method as claimed in claim 46 wherein the short term signal summed with the template signal is normalized relative to other like signals of the short term field.
48. A method as claimed in claim 39 further comprising enhancing higher level signals of the input relative to lower level signals.
49. A method as claimed in claim 48 further comprising feeding enhanced signals back within the short term field.
50. A method as claimed in claim 49 further comprising feeding template signals back within the short term field.
51. A method as claimed in claim 39 wherein the input pattern to the system is an analog pattern.
52. A method as claimed in claim 39 wherein closeness between the vectors is determined from the angle between the vectors.
53. A pattern recognition method comprising:receiving in a short term feature representation field comprising a plurality of subfields of short term signals input signals defining an input pattern and template signals defining a long term memory template and feeding short term signals back within the short term fields; selecting at least one category node based on a pattern from the short term field; generating the long term template based on the selected category; and adapting category selection and the long term memory template to the input signals and feedback of short term signals within the short term field. 54. A method as claimed in claim 53 further comprising enhancing higher level signals of the input relative to lower level signals.
55. A method as claimed in claim 54 further comprising feeding enhanced signals back within the short term field.
56. A method as claimed in claim 55 further comprising feeding template signals back within the short term field.
57. A method as claimed in claim 56 wherein a feedback signal is normalized relative to other like signals of the short term field.
58. A pattern recognition method comprising:receiving in a short term feature representation field input signals defining a long term memory template, the short term field comprising matched signals of a matched pattern, each matched signal being the weighted sum of a short term signal in the filed and a template signal; selecting at least one category node based on a pattern from the short term field; generating the long term memory template based on the selected category; and adapting category selection and a long term memory template to the input signals as dual functions of the difference between a template signal and the matched signal. 59. A method as claimed in claim 58 wherein the summed short term signal is normalized relative to other like signals of the short term field.
60. A method as claimed in claim 58 wherein a category selection is reset with an insufficient match between the input pattern and template, the match being based on the closeness between the vector of the template and a vector of the short term field as indicated by an angle between the two vectors.
61. A method as claimed in claim 60 wherein the summed short term signal is normalized relative to other like signals of the short term field.
62. A method as claimed in claim 61 further comprising means for enhancing higher level signals of the input relative to lower level signals.
63. A method as claimed in claim 60 wherein closeness between the vectors is determined from the angle between the vectors.
64. A pattern recognition method comprising:receiving in a short term feature representation field input signals defining an input pattern and template signals defining a long term memory template, the short term field comprising a plurality of subfields of short term signals, each of the short term signals of at least one subfield being based on an input signal specific to the short term signal and also on non-specific signals generated from across the short term field; selecting at least one category based on a pattern from the short term field; generating the long term memory template based on the selected category; and adapting category selection and the long term memory template to the input signals. 65. A method as claimed in claim 64 wherein each short term signal of a subfield is normalized relative to other signals of the subfield.
66. A method as claimed in claim 65 wherein higher level signals of the normalized subfield are enhanced relative to lower level signals of the subfield.
67. A method as claimed in claim 66 further comprising feeding an enhanced normalized input pattern back to modify the input pattern to the system.
68. A method as claimed in claim 65 wherein a category selection is reset with an insufficient match between the input pattern and template, the match being based on a norm of weighted sums, each sum being of a normalized short term signal and a template signal.
69. A method as claimed in claim 68 further comprising feeding the weighted sum back to modify the normalized short term signal.
70. A method as claimed in claim 64 wherein a category selection is reset with an insufficient match between the input pattern and template, the match being based on a norm of weighted sums, each sum being of a normalized short term signal and a template signal.
71. A method as claimed in claim 70 wherein each short term signal of a subfield is normalized relative to other signals of the subfield.
72. A method as claimed in claim 64 comprising preprocessing a pattern to generate the input pattern, the preprocessing comprising separating image figure of the image pattern from ground, sharpening and completing the resultant image boundaries, and processing the resultant image using an invariant filter.
73. A method as claimed in claim 72 wherein the sharpening and completing is by a boundary contour technique.
74. A method as claimed in claim 72 wherein the invariant filter is a Fourier-Mellon filter.
Adaptive resonance architectures are neural networks that self-organize stable recognition categories in real time in response to arbitrary sequences of input patterns. The basic principles of adaptive resonance theory (ART) were introduced in Grossberg, "Adaptive pattern classification and universal recoding, II: Feedback, expectation, olfaction, and illusions." Biological Cybernetics, 23 (1976) 187-202. A class of adaptive resonance architectures has since been characterized as a system of ordinary differential equations by Carpenter and Grossberg, "Category learning and adaptive pattern recognition: A neural network model," Proceedings of the Third Army Conference on Applied Mathematics and Computing, ARO Report 86-1 (1985) 37-56, and "A massively parallel architecture for a self-organizing neural pattern recognition machine." Computer Vision, Graphics, and Image Processing, 37 (1987) 54-115. One implementation of an ART system is presented in U.S. application Ser. No. PCT/US86/02553, filed Nov. 26, 1986 by Carpenter and Grossberg for "Pattern Recognition System".
As shown in FIG. 1, ART networks encode new input patterns received at 20, in part, by changing the weights, or long term memory (LTM) traces, of a bottom-up adaptive filter 22. This filter is contained in pathways leading from a feature representation field (F1) to a category representation field (F2) of short term memory. Generally, the short term memory (STM) fields hold new patterns relative to each input pattern. The long term memory (LTM), on the other hand, defines patterns learned from some number of input patterns, that is, over a relatively longer period of time. This bottom-up filtering property is shared by many other models of adaptive pattern recognition and associative learning. In an ART network, however, it is a second, top-down adaptive filter 24 that leads to the crucial property of code self-stabilization. The top-down filtered inputs to F1 form a template pattern and enable the network to carry out attentional priming, pattern matching, and self-adjusting parallel search.
In principle, any new input could create a new category at any time: plasticity, or the potential for change in the LTM, remains intact indefinitely. If at any time, for example, a new input were added to the previously learned set, the system would search the established categories. If an adequate match were found, the LTM category representation would be refined, if necessary, to incorporate the new pattern. If no match were found, a new category would be formed, with previously uncommitted LTM traces encoding the STM pattern established by the input. Nevertheless,the code does tend to stabilize as the category structure becomes increasingly complex, since then each new pattern becomes increasingly likely to fit into an established category.
Preferably, a category selection is reset with an insufficient match between the input pattern and template. The match is based on the closeness between a vector of the template and a vector of the short term field as indicated by an angle between the two vectors. The angle of the two vectors is disregarded and reset is avoided where a category has not been previously selected. That result can be obtained by basing the match on a norm of weighted sums, each sum being of the short term signal and a template signal. Preferably, the summed short term signal is normalized relative to other like signals of the short term field.
Each of the large circles of FIG. 3 represents the computation of the L2 -norm of all the signals of a particular subfield. For example, the L2 -norm of a w is defined as: ##EQU1## Each of the smaller circles denotes a computation which is further described below to generate each of the subfield signals wi, xi, vi, ui, pi, qi and ri. The clear arrow heads denote inputs to the several computations which are specific to a particular input signal Ii and a particular matched signal pi. Solid arrowheads denote nonspecific inputs based on signals of a full subfield. Those nonspecific inputs are taken from the computations shown to the right of FIG. 4.
The signal pi communicates through adaptive filters with F2. In FIG. 3, a single pair 32 of filters pathways is shown between a single signal pi and a single node yJ in F2. Such a pair is connected between each signal pi and each category node yj of F2. This is indicated by the broken lines of FIG. 4 where each broken line represents a pair 32 of filter elements. In the bottom-up adaptive filter each signal pi is multiplied by a weight Zij indicated by the size of a hat on the line leading to the F2 node. Also, each signal from a node yj is multiplied by a weight zji and applied to a signal pi.
As in conventional ART systems the weighted inputs from F1 select at least one category of F2. Where only a single category node is chosen, only that node (YJ) applies a signal through the vectors of weights zJ =(zJ1, . . . zJm) to any number of nodes in F1. The received signals represent a previously learned template pattern. F1 must then match the previously learned pattern with the incoming pattern in a manner which will be discussed below. If a sufficient match is found, the previously learned pattern may be modified by modifying the weights of both the bottom-up and top-down filter elements leading to and from the selected category node to incorporate information of the new incoming pattern. If the match is insufficient, the orienting subsystem resets the F2 field to disable the previously selected node, and subsequent nodes are chosen through a search. Finally, when an adequate match is found with a selected category, which may be a previously unlearned category, the filter coefficients ziJ and zJi are adapted to the present input.
The potential, or STM activity, Vi of the ith node at any one of the F1 processing stages obeys a membrane equation (Hodgkin and Huxley, 1952) of the form ##EQU2## (i=1 . . . M). Term Ji 30 is the total excitatory input to the with node and Ji - is the total inhibitory input. In the absence of all inputs, Vi decays to 0. The dimensionless parameter represents the ratio between the short STM relaxation time and the long LTM relaxation time. With the LTM rate 0(1), then
0&lt;&#949;&lt;&lt;1.                                            2
Also, B≡0 in the F1 equations of the present system. Thus the STM equations, in the singular form as ε→0, are: ##EQU3## In this form, the dimensionless equations (4)-(10) characterize the STM activities, pi, qi, ri, ui, vi, wi, and xi, computed within F1 : ##EQU4## where //V// denotes the L2 -norm of the vector V and where yj is the STM activity of the jth F2 node. The nonlinear signal function f in equations (4) and (6) is typically of the form which is continuously differentiable, or ##EQU5## which is piecewise linear. The principal free parameters of the model are a, b, and θ.
The category representation field F2 is the same as in prior ART systems. The key properties of F2 are contrast enhancement of the filtered, patterned F1 →F2 input; and reset, or enduring inhibition, of active F2 noted whenever a pattern mismatch occurs at F1.
Contrast enhancement may be carried out, for example, by competition within F2. Choice is the extreme case of contrast enhancement. F2 makes a choice when the node receiving the largest total input quenches activity in all other nodes. In other words, let Tj be the summed filtered F1 →F2 input to the jth F2 node: ##EQU6## (j=M+1 . . . N). Then F2 makes a choice if, when ##EQU7## the Jth F2 node becomes maximally active while all other nodes are inhibited.
The top-down and bottom-up LTM trace equations (17) and (18) are dual ##EQU10## If F2 makes a choice, (14)-(18) imply that, if the Jth F2 node is active, then ##EQU11## and ##EQU12## with 0<d<1. If j≠J, dzji /dt=dzij /dt=0.
The orienting subsystem resets F2 whenever ##EQU13## where the dimensionless vigilance parameter is set between 0 and 1.
ii. e≡0 (equations (5), (7), (10) and (21));
iv. d ε (0,1) is fixed (equations (15), (16), (19), and (20)); and
v. c ε ##EQU14## is fixed (equation (10)).
With these simplifying assumptions, the system can be summarized as follows, letting zJ =(zJ1, . . . ,zJm):
(i) seven F1 subfields ##EQU15##
(ii) choice at F2 (equations (13) and (15));
(iii) LTM equations (19) and (20);
Note that // x //=1 and //u//=1, and that during learning ##EQU16## if the Jth F2 node is active.
When the Jth F2 node is active, ##EQU17##
FIG. 5 illustrates //r// as a function of // cd zJ // for various values of cos(u,zJ). The Jth F2 note will remain active only if ρ>// r //. Since ρ<1, FIG. 5 shows that this will occur either if cos(u, zJ) is close to 1 or if // zJ // is close to 0. That is, no reset occurs either if the STM vector u is nearly parallel to the LTM vector zJ or if the top-down LTM traces zJi are all small. By equation (19), zJ becomes parallel to u during learning, thus inhibiting reset. Reset must also be inhibited, however, while a new category is being established. FIG. 5 showns that this can be accomplished by making all // zj // small before any learning occurs, that is, initial values of top-down LTM traces:
zji (0)&#8771;0,(i=1 . . . N, j=M+1 . . . N). 32
Finally, FIG. 5 shows that, for a given ρ, the system is maximally sensitive to mismatches when // cd zJj //=1 no matter what value cos(u, zJ) might have. Equations (19) and (32) imply that 0≦//zJ //<1/(1-d) and that //zJ //→1/(1-d) during learning. In order to avoid the possibility of reset during learning, therefore, it is useful to require that // cd zJ // 1; i.e., ##EQU18## Moreover, the system is maximally sensitive to mismatches if equality holds in (33). In fact, by (31), at a given vigilance level, the Jth F2 node is reset shortly after activation if cos(u, zJ) is sufficiently far from 1, where u equals the STM vector established in F1 by presentation of the bottom-up input, I, alone.
An intuitive discussion of the operation will now be described, particulary with reference to FIG. 3. When a signal Ii is first applied to the node wi, there is no feedback from ui. The signal xi therefore becomes the input signal Ii normalized to other input signals. By this normalization, differences in height of like shapes due to amplification of one shape over the other are removed. Any incoming pattern would result in a signal xi which has a constant norm; in the present case that norm is l . Thus, if input pattern 5 of category 3 in FIG. 6 had a norm of 1, pattern 10 would be amplified to the same norm of 1, and pattern 5 would remain unchanged. Note that normalization requires a specific signal for each computation of xi which is associated with a single system input as well as a nonspecific signal which is based on all signals received at all nodes of F1.
The normalized signal xi is processed to provide for enhancement of larger signals and to repress lower signals which are treated as noise. Typically, the signal is processed according to either of the functions defined in equations 11 and 12. Note that for each function, any signal xi which is below a value is repressed. Using the function of equation 2, it is repressed to zero. Typically, θ is set near 1/√m. Any signal xi which is below θ is repressed while other signals are passed unchanged to node vi. For example, if θ=1/√m and the "input mean" is defined as norm of ##EQU19## then activity is repressed at those nodes whose input Ii is below the input mean.
Note the feedback through path bri in a computation of vi. With this feedback, a signal from a top down template is applied to the lower loop of F1 and thus enhances the higher signals of the top down template and represses the lower signals of the top down template. Thus, the contrast enhancement is applied to both the incoming signal Ii and the template signal through zJi so that the weight which is then learned in the adaptive filters is a function of both the present input and past inputs. A significant result is that, if the F1 feedback signals af(ui) and bri are strong, the system tends not to relearn signals applied to the input which have been previously discarded from a previously learned template. For example, where
a signal Ii is large but corresponding signal zJi of a previously learned category has been set low, cqi is low but, because the overall template has been previously learned, the norm //ca// is high. Thus, the numerator of equation 10 remains near the same, whereas the denominator becomes much larger, and ri is substantially reduced. Feedback of ri through the function f tends to repress the signal which is then learned at pi.
(1) stably self-organize an invariant pattern recognition code in response to a sequence of analog or digital input patterns;
(2) be attentionally primed to ignore all but a designated category of input patterns;
(3) automatically shift its prime as it satisfies internal criteria in response to the occurrence of a previously primed category of input patterns;
(4) learn to generate an arbitrary spatiotemporal output pattern in response to any input pattern exemplar of an activated recognition category.
This architecture exploits properties of the ART1 adaptive resonance theory architectures which have been developed in Carpenter and Grossberg, "Category learning and adaptive pattern recognition: A neural network model," Proceedings of the Third Army Conference on Applied Mathematics and Computing, 1985, ARO Report 86-1, 37-56, and "A massively parallel architecture for a self-organizing neural pattern recognition machine," Computer Vision, Graphics, and Image Processing, 1987, 37, 54-115; the ART2 architecture described here; the Boundary Contour System for boundary segmentation and the Feature Contour System for figural filling-in which have been developed in Cohen and Grossberg, "Neural dynamics of brightness perception: Features, boundaries, diffusion, and resonance," Perception and Psychophysics, 1984, 36, 428-456, Grossberg, 1987, and Grossberg and Mingolla, "Neural dynamics of form perception: Boundary completion, illusory figures, and neon color spreading," Psychological Review, 1985, 92, 173-211, and "Neural dynamics of perceptual groupings: Textures, boundaries, and emergent segmentations," Perception and Psychophysics, 1985, 38, 141-171: Theorems on associative pattern learning and associative map learning in Grossberg, "On learning and energy-entropy dependent in recurrent and nonrecurrent signed networks," Journal of Statistical Physics, 1969, 1, 319-350, "Some networks that can learn, remember, and reproduce any number of complicated space-time patterns II," Studies in Applied Mathematics, 1970, 49, 135-166, and "Studies of mind and brain: neural principles of learning, preception, development, cognition, and motor control," Boston: Reidel Press 1982; and circuit designs to focus attention on desired goal objects by using learned feedback interactions between external sensory events and internal homeostatic events in Grossberg, "A neural theory of punishment and avoidance, II: Quantitative theory," Mathematical Biosciences, 1972, 15, 253-285, "Studies of mind and brain: Neural principles of learning, perceptionn, development, cognition, and motor control," Boston: Reidel Press, 1982, and The adaptive brain, I: Cognition, learning, reinforcement, and rhythm, Amsterdam: Elsevier/North-Holland, 1987. The overall circuit design embodies an intentional learning machine in which distinct cognitive, homeostatic, and motor representations are self-organized in a coordinated fashion.
The ART 2 architecture of this invention stably self-organizes disjoint recognition categories in response to temporal sequences of analog or digital input patterns. A vigilance parameter can be set to determine the coarseness of this classification, thereby compensating for source of variability including noise, in the input exemplars of the emergent recognition categories. These input patterns may be the output patterns of a preprocessor stage; in particular, the preprocessor outputs may represent invariant spectra computed by one or more prior stages of input filtering (FIG. 9). The capacity of ART 2 to be activated by any number of arbitrarily chosen analog or digital input patterns without destabilizing its emergent classification provides great freedom in designing such preprocessors for specialized application.
While this invention has been particularly shown and described with references to preferred embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. For example, although the orienting subsystem may be removed, more trials may be required for the learning sequences to self-stabilize, and some erroneous groupings may be made initially. Further, one would lose the flexibility of controlling vigilance. Also, although for some systems, the F1 internal feedback may be removed, certain inputs might never stabilize to a specific category. Also, the functions included in the feedback loops can be modified to provide for any desired signal enhancement and repression.
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OF MASSACHUSETTS,MASSACFree format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:CARPENTER, GAIL A.;GROSSBERG, STEPHEN;REEL/FRAME:004781/0820Effective date: 19871001Apr 16, 1993FPAYFee paymentYear of fee payment: 4Jan 7, 1997CCCertificate of correctionSep 30, 1997FPAYFee paymentYear of fee payment: 8Sep 27, 2001FPAYFee paymentYear of fee payment: 12RotateOriginal ImageGoogle Home - Sitemap - USPTO Bulk Downloads - Privacy Policy - Terms of Service - About Google Patents - Send FeedbackData provided by IFI CLAIMS Patent Services