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A conventional weight in an artificial neural network has a single trainable real value and produces a linear relationship between the weight input and the weight output. A real synaptic cleft is also trainable but provides a more complex relationship. It is obvious to wonder if adding extra complexity to the conventional weight response would lead to more capable networks. This work describes a weight...
Echo State Networks ESNs are specific kind of recurrent networks providing a black box modeling of dynamic non-linear problems. Their architecture is distinguished by a randomly recurrent hidden infra-structure called dynamic reservoir. Coming up with an efficient reservoir structure depends mainly on selecting the right parameters including the number of neurons and connectivity rate within it. Despite...
The attractor-based complexity of a Boolean neural network is a measure which refers to the ability of the network to perform more or less complicated classification tasks of its inputs via the manifestation of meaningful or spurious attractor dynamics. Here, we study the attractor-based complexity of a Boolean model of the basal ganglia-thalamocortical network. We show that the regulation of the...
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