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A highly simplified network model of cortical associative memory, based on Hebb's theory of cell assemblies, has been developed and simulated. The network comprises realistically modelled pyramidal-type cells and inhibitory fast-spiking interneurons and its connectivity is adopted from a trained...
We show how the kohonen self-organizing feature map model can be extended so that partial training data can be utilized. Given input stimuli in which values for some elements or features are absent, the match computation and the weight updates are performed in the input subspace defined by the...
The Hopfield network provides a simple model of an associative memory in a neuronal structure. It is, however, based on highly artificial assumptions, especially the use of formal two-state neurons or graded-response neurons. The authors address the question of what happens if formal neurons are...
The authors present a new learning algorithm for neural networks with discrete synaptic couplings. The main difference with respect to other previous algorithms is that it is defined in the continuous space. Its performance and features are analysed in detail.
The sensory pathways of animals are well adapted to processing a special class of signals, namely stimuli from the animal's environment. An important fact about natural stimuli is that they are typically very redundant and hence the sampled representation of these signals formed by the array of...
Single-neuron spike dynamics is reconsidered in a situation in which the neural afferent spike input, originating from non-specific spontaneous activity, is very large compared with the input produced by specific (task related) operation of a cortical module. This the authors argue is the...
The need for efficient implementation of neural networks in silicon is used to motivate the investigation of alternatives to the McCulloch-Pitts neuron; in particular, one which computes the norm of a difference rather than an inner product. Earlier work is reviewed briefly and formal...
A new access to the asymptotic analysis of autoassociation properties in recurrent McCulloch-Pitts networks in the range of low activity is proposed. Using information theory, this method examines the static structure of stable states imprinted by a Hebbian storing process. In addition to the...
The McCulloch-Pitts neuron model with semi-linear transducer function suffers from saturation when the fan-in is large. This leads to network paralysis. This paper suggests a scaling factor to the McCulloch-Pitts neuron model. Simulation results confirm the effectiveness of the proposed...
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