Neural Networks: How a Multi-Layer Network Learns to Disentangle Exogenous from Self-Generated Signals

Abstract

about MEK-ERK docking, it is entirely plausible to think that there is a mix of processive and distributive phosphorylation, depending on the probability of catalysis versus the probability of undocking. Mutants that change the former but not the latter would be expected to increase both the processivity and the overall rate of ERK activation. It will be interesting to see if other activated MEK mutants follow this pattern.

Development of an Artificial Neural Network for the Detection of Supporting Hindlimb Lameness: A Pilot Study in Working Dogs

“…Instead of utilizing a single neuron, multiple artificial neurons are group in an artificial neural network (ANN), which is organized in layers [16]; for e input layer receives input data and provides the parameters for analysis, the processes input data, and the output layer provides the output results classifie ries (Figure 2). Likewise, in an artificial neuron, data are received as input; then, mathematic algorithms are applied in the artificial neuron "body", producing an output.…”

“…Instead of utilizing a single neuron, multiple artificial neurons are grouped together in an artificial neural network (ANN), which is organized in layers [16]; for example, the input layer receives input data and provides the parameters for analysis, the hidden layer processes input data, and the output layer provides the output results classified in categories (Figure 2). In order to correctly predict different categories with accuracy, "train" the network.…”