ISSN:
1432-1416
Keywords:
Neural modeling
;
McCulloch-Pitts networks
;
Compensation algorithm
;
Cognitive systems
;
Fixed points (approximate)
Source:
Springer Online Journal Archives 1860-2000
Topics:
Biology
,
Mathematics
Notes:
Abstract Plasticity of synaptic connections plays an important role in the temporal development of neural networks which are the basis of memory and behavior. The conditions for successful functional performance of these nerve nets have to be either guaranteed genetically or developed during ontogenesis. In the latter case, a general law of this development may be the successive compensation of disturbances. A compensation type algorithm is analyzed here that changes the connectivity of a given network such that deviations from each neuron's equilibrium state are reduced. The existence of compensated networks is proven, the convergence and stability of simulations are investigated, and implications for cognitive systems are discussed.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1007/BF00275816
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