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Journal ArticleDOI

Neural pattern discrimination

01 May 1970-Journal of Theoretical Biology (Academic Press)-Vol. 27, Iss: 2, pp 291-337
TL;DR: Some possible neural mechanisms of pattern discrimination are discussed, leading to neural networks which can discriminate any number of essentially arbitrarily complicated space-time patterns and activate cells which can then learn and perform any number in response to the proper input pattern.
About: This article is published in Journal of Theoretical Biology.The article was published on 1970-05-01. It has received 123 citations till now. The article focuses on the topics: Inhibitory postsynaptic potential.
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Journal ArticleDOI
01 Sep 1983
TL;DR: It remains an open question whether the Lyapunov function approach, which requires a study of equilibrium points, or an alternative global approach, such as the LyAPunov functional approach, will ultimately handle all of the physically important cases.
Abstract: Systems that are competitive and possess symmetric interactions admit a global Lyapunov function. However, a global Lyapunov function whose equilibrium set can be effectively analyzed has not yet been discovered. It remains an open question whether the Lyapunov function approach, which requires a study of equilibrium points, or an alternative global approach, such as the Lyapunov functional approach, which sidesteps a direct study of equilibrium points will ultimately handle all of the physically important cases.

2,440 citations

Journal ArticleDOI
TL;DR: In this paper, a model for the parallel development and adult coding of neural feature detectors was proposed, where experience can retune feature detectors to respond to average features chosen from the set even if the average features have never been experienced.
Abstract: This paper analyses a model for the parallel development and adult coding of neural feature detectors. The model was introduced in Grossberg (1976). We show how experience can retune feature detectors to respond to a prescribed convex set of spatial patterns. In particular, the detectors automatically respond to average features chosen from the set even if the average features have never been experienced. Using this procedure, any set of arbitrary spatial patterns can be recoded, or transformed, into any other spatial patterns (universal recoding), if there are sufficiently many cells in the network's cortex. The network is built from short term memory (STM) and long term memory (LTM) mechanisms, including mechanisms of adaptation, filtering, contrast enhancement, tuning, and nonspecific arousal. These mechanisms capture some experimental properties of plasticity in the kitten visual cortex. The model also suggests a classification of adult feature detector properties in terms of a small number of functional principles. In particular, experiments on retinal dynamics, including amarcrine cell function, are suggested.

1,775 citations

Journal ArticleDOI
TL;DR: An historical discussion is provided of the intellectual trends that caused nineteenth century interdisciplinary studies of physics and psychobiology by leading scientists such as Helmholtz, Maxwell, and Mach to splinter into separate twentieth-century scientific movements.

1,586 citations


Cites background or methods from "Neural pattern discrimination"

  • ...The development of competitive learning models was achieved through an interaction between results of Grossberg (1970b, 1972b, 1973) and of Malsburg (1973), leading in Grossberg (1976a, 1976b) to the model in several forms which have subsequently been further analyzed and applied by a number of…...

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  • ...…neural networks defined by nonlinearly coupled STM and LTM traces, and to the mathematical proof that the computational units of these networks are not individual STM and LTM variables, but are rather distributed spatial patterns of STM and LTM variables (Grossberg, 1968b, 1969a, 1969b, 1970a)....

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Book ChapterDOI
TL;DR: In this article, a thought experiment is offered which analyses how a system as a whole can correct errors of hypothesis testing in a fluctuating environment when none of the system's components, taken in isolation, even knows that an error has occurred.
Abstract: This article provides a self-contained introduction to my work from a recent perspective. A thought experiment is offered which analyses how a system as a whole can correct errors of hypothesis testing in a fluctuating environment when none of the system’s components, taken in isolation, even knows that an error has occurred. This theme is of general philosophical interest: How can intelligence or knowledge be ascribed to a system as a whole but not to its parts? How can an organism’s adaptive mechanisms be stable enough to resist environmental fluctuations which do not alter its behavioral success, but plastic enough to rapidly change in response to environmental demands that do alter its behavioral success? To answer such questions, we must identify the functional level on which a system’s behavioral success is defined.

1,195 citations

Journal ArticleDOI
TL;DR: A real-time neural network model, called the vector-integration-to-endpoint (VITE) model is developed and used to simulate quantitatively behavioral and neural data about planned and passive arm movements to demonstrate invariant properties of arm movements.
Abstract: A real-time neural network model, called the vector-integration-to-endpoint (VITE) model is developed and used to simulate quantitatively behavioral and neural data about planned and passive arm movements. Invariants o farm movements emerge through network interactions rather than through an explicitly precomputed trajectory. Motor planning occurs in the form of a target position command (TPC), which specifies where the arm intends to move, and an independently controlled GO command, which specifies the movement's overall speed. Automatic processes convert this information into an arm trajectory with invariant properties. These automatic processes include computation of a present position command (PPC) and a difference vector (DV). The DV is the difference between the PPC and the TPC at any time. The PPC is gradually updated by integrating the DV through time. The GO signal multiplies the DV before it is integrated by the PPC. The PPC generates an outflow movement command to its target muscle groups. Opponent interactions regulate the PPCs to agonist and antagonist muscle groups. This system generates synchronous movements across synergetic muscles by automatically compensating for the different total contractions that each muscle group must undergo. Quantitative simulations are provided of Woodworth's law, of the speed-accuracy trade-offknown as Fitts's law, of isotonic arm-movement properties before and after deafferentation, of synchronous and compensatory "central-error-correction" properties of isometric contractions, of velocity amplification during target switching, of velocity profile invariance and asymmetry, of the changes in velocity profile asymmetry at higher movement speeds, of the automarie compensation for staggered onset times of synergetic muscles, of vector cell properties in precentral motor cortex, of the inverse relation between movement duration and peak velocity, and of peak acceleration as a function of movement amplitude and duration. It is shown that TPC, PPC, and DV computations are needed to actively modulate, or gate, the learning of associative maps between TPCs of different modalities, such as between the eye-head system and the hand-arm system. By using such an associative map, looking at an object can activate a TPC of the hand-arm system, as Piaget noted. Then a VITE circuit can translate this TPC into an invariant movement trajectory. An auxiliary circuit, called the Passive Update of Position (PUP) model is described for using inflow signals to update the PPC during passive arm movements owing to external forces. Other uses of outflow and inflow signals are also noted, such as for adaptive linearization of a nonlinear muscle plant, and sequential readout of TPCs during a serial plan, as in reaching and grasping. Comparisons are made with other models of motor control, such as the mass-spring and minimumjerk models.

769 citations


Cites background from "Neural pattern discrimination"

  • ...Such covariat ion of growth rate with amplitude is a basic property of neurons which obey membrane, or shunting, equations (Grossberg, 1970, 1973, 1982aj Sperling and Sondhi, 1968)....

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References
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1,948 citations

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01 Jan 1955

793 citations


"Neural pattern discrimination" refers background in this paper

  • ...…alternative is ironic in that it works well formally, is compatible with some data about 1(Jlgarithmic transduction from inputs to frequencies in individual cells (Granit, 1955), but seems hard to build into the interaction between cells unless linear and logarithmic transduction laws are mixed....

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Trending Questions (1)
What are the neural mechanisms underlying conditional discrimination?

The provided paper does not specifically discuss the neural mechanisms underlying conditional discrimination.