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Concentrations of Cu, Ag and Zn were measured in the soft tissues of the estuarine bivalve Macoma balthica in South San Francisco Bay at near-monthly intervals for periods of two to three years at four stations, and eight years at a metal-enriched station. The amplitude and frequency of fluctuations differed among stations and among metals. Fluctuations(More)
We propose, implement, and discuss an abstract model of the mammalian neocortex. This model is instantiated with a sparse recurrently connected neural network that has spiking leaky integrator units and continuous Hebbian learning. First we study the structure, modularization, and size of neocortex, and then we describe a generic computational model of the(More)
In this letter, we study an abstract model of neocortex based on its modularization into mini- and hypercolumns. We discuss a full-scale instance of this model and connect its network properties to the underlying biological properties of neurons in cortex. In particular, we discuss how the biological constraints put on the network determine the network's(More)
Based on the results from a 6-week monitoring campaign in an area close to a major highway north of Stockholm, Sweden, NOx emission factors representative for vehicle speeds of 100-120 km per h were determined to 0.61 g/veh,km for light duty and to 7.1 g/veh,km for heavy duty vehicles. The corresponding factors for particle number were 1.4 x 10(14) and 52 x(More)
We investigate the effects of patchy (clustered) connectivity in sparsely connected attractor neural networks (NNs). This study is motivated by the fact that the connectivity of pyramidal neurons in layer II/III of the mammalian visual cortex is patchy and sparse. The storage capacity of hypercolumnar attractor NNs that use the Hopfield and Willshaw(More)
Biologically detailed computational models of large-scale neuronal networks have now become feasible due to the development of increasingly powerful massively parallel supercomputers. We report here about the methodology involved in simulation of very large neuronal networks. Using conductance-based multicompartmental model neurons based on Hodgkin-Huxley(More)
We review the structure of cerebral cortex to find out the number of neurons and synapses and its modular structure. The organization of these neurons is then studied and mapped onto the framework of an artificial neural network (ANN). The computational requirements to run this ANN model are then estimated. The conclusion is that it is possible to simulate(More)