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  • 1
    Electronic Resource
    Electronic Resource
    Competitive and Temporal Inhibition Structures with Spiking Neurons (2000)
    Springer
    Neural processing letters 11 (2000), S. 197-208 
    Details
    ISSN: 1573-773X
    Keywords: spiking neurons ; competitive processing ; temporal inhibition ; attentional control mechanisms ; bio-inspired neural systems
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract The paper describes the implementation of competitive neural structures based on a spiking neural model that includes multiplicative or shunting synapses enabling non-saturated stable states in response to different stationary inputs as well as controllable transient responses. A VLSI-viable implementation of this model has been previously proposed and tested [1]. It has the possibility of modulating the output spike frequency by an additional input without affecting other neuron variables such as the membrane potential. This feature is exploited in the simulation of a Selective Temporal Inhibition network that is suitable for implementing attentional control systems.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1009611609606
    Library Location Call Number Volume/Issue/Year Availability
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    Paper (German National Licenses)
    Fulltext
  • 2
    Electronic Resource
    Electronic Resource
    Obtaining Fault Tolerant Multilayer Perceptrons Using an Explicit Regularization (2000)
    Springer
    Neural processing letters 12 (2000), S. 107-113 
    Details
    ISSN: 1573-773X
    Keywords: backpropagation ; regularization ; multilayer perceptron ; fault tolerance ; mean square sensitivity
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract When the learning algorithm is applied to a MLP structure, different solutions for the weight values can be obtained if the parameters of the applied rule or the initial conditions are changed. Those solutions can present similar performance with respect to learning, but they differ in other aspects, in particular, fault tolerance against weight perturbations. In this paper, a backpropagation algorithm that maximizes fault tolerance is proposed. The algorithm presented explicitly adds a new term to the backpropagation learning rule related to the mean square error degradation in the presence of weight deviations in order to minimize this degradation. The results obtained demonstrate the efficiency of the learning rule proposed here in comparison with other algorithm.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1009698206772
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Paper (German National Licenses)
    Fulltext
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