Influence of noise on the behaviour of an autoassociative neural network

Buhmann, Joachim M.; Schulten, Klaus

doi:10.1063/1.36222

Cited by 2 publications

(3 citation statements)

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“…This provides yet another example of the possible beneficial aspects of noise in the processing of information by the central nervous system. The positive role of noise was recognized earlier by Buhmann andSchulten (1986, 1987), who found that noise, deliberately added to the deterministic equations governing individual neurons in a network, significantly enhanced the network's performance. They concluded that "...the noise...is an essential feature of the information processing capabilities of the neural network, and not a mere source of disturbance, better suppressed..."…”

Section: Discussionmentioning

confidence: 91%

Single effective neuron: dendritic coupling effects and stochastic resonance

1993

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Abstract. We consider a model of a neuron coupled with a surrounding dendritic network subject to Langevin noise and a weak periodic modulation. Through an adiabatic elimination procedure, the single-neuron dynamics are extracted from the coupled stochastic differential equations describing the network of dendrodendritic interactions. Our approach yields a "reduced neuron" model whose dynamics may correspond to neurophysiologically realistic behavior for certain ranges of soma and bath parameters. Cooperative effects (e.g., stochastic resonance) arising from the interplay between the noise and modulation are discussed in detail.

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Section: Discussionmentioning

confidence: 91%

Single effective neuron: dendritic coupling effects and stochastic resonance

1993

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“…Therefore, A(t) can be thought to depend on relative membrane potentials of all neurons in the nerve system. The second term in the right-hand side of equation (1.2) represents the fluctuating contribution to the membrane potential due to the spontaneous fluctuation (Abeles (1982), Buhmann and Schulten (1986)). It is given by a stochastic differential of a Gaussian stochastic process w={w(t)lO<_t<~} called a Wiener process such that…”

Section: (12) Dx(t) = A(t)dt+ Dw(t)mentioning

confidence: 99%

“…means to take the mathematical expectation and the diffusion constant v is of the order of 10 (milli-volt)2/(milli-second) (Buhmann and Schulten (1986)). It is a standard result of the probability theory that the stochastic differential equation (1.2) determines a stochastic process x={x(t)lO<_t<~} called semi-martingale if the drift A(t) and the initial condition x(0) are given (Nelson (1967)).…”

Section: ) E[ldw(t)l 2] = Vdtmentioning

confidence: 99%