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  • 1
    Electronic Resource
    Electronic Resource
    Discovering Order in Chaos: Stable Self-Organization of Neural Recognition Codes (1987)
    Oxford, UK : Blackwell Publishing Ltd
    Annals of the New York Academy of Sciences 504 (1987), S. 0 
    Details
    ISSN: 1749-6632
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Natural Sciences in General
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/j.1749-6632.1987.tb48724.x
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  • 2
    Electronic Resource
    Electronic Resource
    Some Psychophysiological and Pharmacological Correlates of a Developmental, Cognitive and Motivational Theory (1984)
    Oxford, UK : Blackwell Publishing Ltd
    Annals of the New York Academy of Sciences 425 (1984), S. 0 
    Details
    ISSN: 1749-6632
    Source: Blackwell Publishing Journal Backfiles 1879-2005
    Topics: Natural Sciences in General
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1111/j.1749-6632.1984.tb23523.x
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  • 3
    Electronic Resource
    Electronic Resource
    Intracellular mechanisms of adaptation and self-regulation in self-organizing networks: The role of chemical transducers (1980)
    Springer
    Bulletin of mathematical biology 42 (1980), S. 365-396 
    Details
    ISSN: 1522-9602
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology , Mathematics
    Notes: Abstract This paper describes mechanisms of intracellular and intercellular adaptation that are due to spatial or temporal factors. The spatial mechanisms support self-regulating pattern formation that is capable of directing self-organization in a large class of systems, including examples of directed intercellular growth, transmitter production, and intracellular conductance changes. A balance between intracellular flows and counterflows causes adaptation. This balance can be shifted by environmental inputs. The decrease in Ca2+-modulated outward K+ conductance in certain molluscan nerve cells is a likely example. Examples wherein Ca2+ acts as a second messenger that shunts receptor sensitivity can also be discussed from this perspective. The systems differ in basic ways from recent diffusion models. Chemical transducers driven by membrane-bound intracellular signals can establish long-range intercellular interactions that compensate for variable intercellular distances and are invariant under developmental size changes; diffusional signals do not. The intracellular adaptational mechanisms are formally analogous to intercellular mechanisms that include cellular properties which are omitted in recent reaction-diffusion models of pattern formation. The cellular models use these properties to compute size-invariant properties despite wide variations in their intercellular signals. Mechanisms of temporal adaptation can be derived from the simplest laws of chemical transduction by using a correspondence principle. These mechanisms lead to such properties of intercellular signals as transient overshoot, antagonistic rebound, and an inverted U in sensitivity as intracellular signals or adaptation levels shift. Such effects are implicated in studies of behavioral, reinforcement, motor control, and cognitive coding.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF02460792
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  • 4
    Electronic Resource
    Electronic Resource
    On learning and energy-entropy dependence in recurrent and nonrecurrent signed networks (1969)
    Springer
    Journal of statistical physics 1 (1969), S. 319-350 
    Details
    ISSN: 1572-9613
    Keywords: learning ; stimulus sampling ; nonlinear difference-differential equations ; global limits and oscillations ; flows on signed networks ; functional-differential systems ; energy-entropy dependence ; pattern completion ; recurrent and nonrecurrent anatomy ; sensory transduction rules ; ratio limit theorems
    Source: Springer Online Journal Archives 1860-2000
    Topics: Physics
    Notes: Abstract Learning of patterns by neural networks obeying general rules of sensory transduction and of converting membrane potentials to spiking frequencies is considered. Any finite number of cellsA can sample a pattern playing on any finite number of cells ∇ without causing irrevocable sampling bias ifA = ℬ orA ∩ ℬ = . Total energy transfer from inputs ofA to outputs of ℬ depends on the entropy of the input distribution. Pattern completion on recall trials can occur without destroying perfect memory even ifA = ℬ by choosing the signal thresholds sufficiently large. The mathematical results are global limit and oscillation theorems for a class of nonlinear functional-differential systems.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01007484
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  • 5
    Electronic Resource
    Electronic Resource
    STORE working memory networks for storage and recall of arbitrary temporal sequences (1994)
    Springer
    Biological cybernetics 71 (1994), S. 469-480 
    Details
    ISSN: 1432-0770
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology , Computer Science , Physics
    Notes: Abstract Neural network models of working memory, called “sustained temporal order recurrent” (STORE) models, are described. They encode the invariant temporal order of sequential events in short-term memory (STM) in a way that mimics cognitive data about working memory, including primacy, recency, and bowed order and error gradients. As new items are presented, the pattern of previously stored items remains invariant in the sense that relative activations remain constant through time. This invariant temporal order code enables all possible groupings of sequential events to be stably learned and remembered in real time, even as new events perturb the system. Such competence is needed to design self-organizing temporal recognition and planning systems in which any subsequence of events may need to be categorized in order to control and predict future behavior or external events. STORE models show how arbitrary event sequences may be invariantly stored, including repeated events. A preprocessor interacts with the working memory to represent event repeats in spatially separate locations. It is shown why at least two processing levels are needed to invariantly store events presented with variable durations and interstimulus intervals. It is also shown how network parameters control the type and shape of primacy, recency, or bowed temporal order gradients that will be stored.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00198465
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  • 6
    Electronic Resource
    Electronic Resource
    STORE working memory networks for storage and recall of arbitrary temporal sequences (1994)
    Springer
    Biological cybernetics 71 (1994), S. 469-480 
    Details
    ISSN: 1432-0770
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology , Computer Science , Physics
    Notes: Abstract.  Neural network models of working memory, called “sustained temporal order recurrent” (STORE) models, are described. They encode the invariant temporal order of sequential events in short-term memory (STM) in a way that mimics cognitive data about working memory, including primacy, recency, and bowed order and error gradients. As new items are presented, the pattern of previously stored items remains invariant in the sense that relative activations remain constant through time. This invariant temporal order code enables all possible groupings of sequential events to be stably learned and remembered in real time, even as new events perturb the system. Such competence is needed to design self-organizing temporal recognition and planning systems in which any subsequence of events may need to be categorized in order to control and predict future behavior or external events. STORE models show how arbitrary event sequences may be invariantly stored, including repeated events. A preprocessor interacts with the working memory to represent event repeats in spatially separate locations. It is shown why at least two processing levels are needed to invariantly store events presented with variable durations and interstimulus intervals. It is also shown how network parameters control the type and shape of primacy, recency, or bowed temporal order gradients that will be stored.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/s004220050106
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  • 7
    Electronic Resource
    Electronic Resource
    Neural control of interlimb oscillations (1997)
    Springer
    Biological cybernetics 77 (1997), S. 131-140 
    Details
    ISSN: 1432-0770
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology , Computer Science , Physics
    Notes: Abstract. How do humans and other animals accomplish coordinated movements? How are novel combinations of limb joints rapidly assembled into new behavioral units that move together in in-phase or anti-phase movement patterns during complex movement tasks? A neural central pattern generator (CPG) model simulates data from human bimanual coordination tasks. As in the data, anti-phase oscillations at low frequencies switch to in-phase oscillations at high frequencies, in-phase oscillations occur at both low and high frequencies, phase fluctuations occur at the anti-phase in-phase transition, a “seagull effect” of larger errors occurs at intermediate phases, and oscillations slip toward in-phase and anti-phase when driven at intermediate phases. These oscillations and bifurcations are emergent properties of the CPG model in response to volitional inputs. The CPG model is a version of the Ellias-Grossberg oscillator. Its neurons obey Hodgkin-Huxley type equations whose excitatory signals operate on a faster time scale than their inhibitory signals in a recurrent on-center off-surround anatomy. When an equal command or GO signal activates both model channels, the model CPG can generate both in-phase and anti-phase oscillations at different GO amplitudes. Phase transitions from either in-phase to anti-phase oscillations, or from anti-phase to in-phase oscillations, can occur in different parameter ranges, as the GO signal increases.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/s004220050374
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  • 8
    Electronic Resource
    Electronic Resource
    Pattern formation, contrast control, and oscillations in the short term memory of shunting on-center off-surround networks (1975)
    Springer
    Biological cybernetics 20 (1975), S. 69-98 
    Details
    ISSN: 1432-0770
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology , Computer Science , Physics
    Notes: Abstract The transformation of spatial patterns and their storage in short term memory by shunting neural networks are studied herein. Various mechanisms are described for real-time regulation of the amount of contrast with which a pattern will be stored. Parametric studies are described for the amount of contrast in the network responses to patterns presented at variable background or overall activity levels. Mechanisms for removing spurious peak splits and other disinhibitory responses are described. Furman's (1965) results on processing of patterns by shunting networks are generalized and reanalysed. Periodic responses (stable and unstable) corresponding to the time scale of slow cortical waves can be generated if a tonic input is set between two threshold activity levels. Their frequency as a function of tonic input size is unimodal. Order-preserving limit cycles are never found in STM; hence sustained slow oscillations as a mechanism for storing a pattern in STM are ruled out in favor of steady states (i.e., fast oscillations) with spatially graded activity levels. Such slow oscillations can, nonetheless, continuously retune the network's responsiveness to the patterns that perturb it.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00327046
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  • 9
    Electronic Resource
    Electronic Resource
    Neural expectation: cerebellar and retinal analogs of cells fired by learnable or unlearned pattern classes (1972)
    Springer
    Biological cybernetics 10 (1972), S. 49-57 
    Details
    ISSN: 1432-0770
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology , Computer Science , Physics
    Notes: Abstract Neural networks are introduced which can be taught by classical or instrumental conditioning to fire in response to arbitrary learned classes of patterns. The filters of output cells are biased by presetting cells whose activation prepares the output cell to “expect” prescribed patterns. For example, an animal that learns to expect food in response to a lever press becomes frustrated if food does not follow the lever press. It's expectations are thereby modified, since frustration is negatively reinforcing. A neural analog with aspects of cerebellar circuitry is noted, including diffuse mossy fiber inputs feeding parallel fibers that end in Purkinje cell dendrites, climbing fiber inputs ending in Purkinje cell dendrites and giving off collaterals to nuclear cells, and inhibitory Purkinje cell outputs to nuclear cells. The networks are motivated by studying mechanisms of pattern discrimination that require no learning. The latter often use two successive layers of inhibition, analogous to horizontal and amacrine cell layers in vertebrate retinas. Cells exhibiting hue (in)constancy, brightness (in)constancy, or movement detection properties are included. These results are relevant to Land's retinex theory and to the existence of opponent- and nonopponent-type cell responses in retinal cells. Some adaptation mechanisms, and arousal mechanisms for crispening the pattern weights that can fire a given cell, are noted.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00288784
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  • 10
    Electronic Resource
    Electronic Resource
    A neural network model for cursive script production (1993)
    Springer
    Biological cybernetics 70 (1993), S. 15-28 
    Details
    ISSN: 1432-0770
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology , Computer Science , Physics
    Notes: Abstract This article describes a neural network model, called the VITEWRITE model, for generating handwriting movements. The model consists of a sequential controller, or motor program, that interacts with a trajectory generator to move a hand with redundant degrees of freedom. The neural trajectory generator is the vector integration to endpoint (VITE) model for synchronous variable-speed control of multi-joint movements. VITE properties enable a simple control strategy to generate complex handwritten script if the hand model contains redundant degrees of freedom. The proposed controller launches transient directional commands to independent hand synergies at times when the hand begins to move, or when a velocity peak in a given synergy is achieved. The VITE model translates these temporally disjoint synergy commands into smooth curvilinear trajectories among temporally overlapping synergetic movements. The separate “score” of onset times used in most prior models is hereby replaced by a self-scaling activity-released “motor program” that uses few memory resources, enables each synergy to exhibit a unimodal velocity profile during any stroke, generates letters that are invariant under speed and size rescaling, and enables effortless connection of letter shapes into words. Speed and size rescaling are achieved by scalar GO and GRO signals that express computationally simple volitional commands. Psychophysical data concerning hand movements, such as the isochrony principle, asymmetric velocity profiles, and the two-thirds power law relating movement curvature and velocity arise as emergent properties of model interactions.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00202562
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