Joachim Buhmann and Klaus Schulten.
Influence of noise on the behaviour of an autoassociative neural
network.
In J. S. Denker, editor, Neural Networks for Computing, pp.
71-76. American Institute of Physics Publication, Conference Proceedings
151, 1986.
BUHM86B
Recently, we simulated the activity and function of neural networks with neuronal units modelled after their physiological counterparts1,2. Neuronal potentials, single neural spikes and their effects on postsynaptic neurons were taken into account. The neural network studied was endowed with plastic synapses. The synaptic modifications were assumed to follow Hebbian rules, i.e. the synaptic strengths increase if the pre- and postsynaptic cells fire a spike synchronously and decrease if there exists no synchronicity between pre-and postsynaptic spikes. The time scale of the synaptic plasticity was that of mental processes, i.e. a tenth of a second as proposed by v.d. Malsburg3. In this contribution we extend our previous study and include random fluctuations of the neural potentials as observed in electrophysiological recordings4. We will demonstrate that random fluctuations of the membrane potentials raise the sensitivity and performance of the neural network. The fluctuations enable the network to react to weak external stimuli which do not affect networks following deterministic dynamics. We argue that fluctuations and noise in the membrane potential are of functional importance in that they trigger the neural firing if a weak receptor input is presented. The noise regulates the level of arousal. It might be an essential feature of the information processing abilities of neuronal networks and not a mere source of disturbance better to be suppressed. We will demonstrate that the neural network investigated here reproduces the computational abilities of formal associative networks4,5,6.
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