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Deep Belief Networks (DBNs) have recently shown impressive performance on a broad range of classification problems. Their generative properties allow better understanding of the performance, and provide a simpler solution for sensor fusion tasks. However, because of their inherent need for feedback and parallel update of large numbers of units, DBNs are(More)
We introduce an algorithm to do backpropagation on a spiking network. Our network is "spiking" in the sense that our neurons accumulate their activation into a potential over time, and only send out a signal (a " spike ") when this potential crosses a threshold and the neuron is reset. Neurons only update their states when receiving signals from other(More)
Although fields such as e-commerce, information systems, and computer-mediated communication (CMC) acknowledge the importance of validity, validating research tools or measures in these domains seems the exception rather than the rule. This article extends the concept of validation to one of an emerging genre of web-based tools that provide new measures,(More)
We propose a neural mass model for anatomically-constrained effective connectivity among neuronal populations residing in four layers (L2/3, L4, L5 and L6) within a cortical column. Eight neuronal populations in a given column--an excitatory population and an inhibitory population per layer--are assumed to be coupled via effective connections of unknown(More)