\begin{optiontableAuto}
                  NCycles  &  \mc{1}{c}{--}  &              500  &  \mc{1}{l}{--}  &  Number of training cycles \\
             HiddenLayers  &  \mc{1}{c}{--}  &            N,N-1  &  \mc{1}{l}{--}  &  Specification of hidden layer architecture \\
               NeuronType  &  \mc{1}{c}{--}  &          sigmoid  &  \mc{1}{l}{--}  &  Neuron activation function type \\
               RandomSeed  &  \mc{1}{c}{--}  &                1  &  \mc{1}{l}{--}  &  Random seed for initial synapse weights (0 means unique seed for each run; default value '1') \\
            EstimatorType  &  \mc{1}{c}{--}  &              MSE  &  MSE, CE, linear, sigmoid, tanh, radial  &  MSE (Mean Square Estimator) for Gaussian Likelihood or CE(Cross-Entropy) for Bernoulli Likelihood \\
          NeuronInputType  &  \mc{1}{c}{--}  &              sum  &  sum, sqsum, abssum  &  Neuron input function type \\
           TrainingMethod  &  \mc{1}{c}{--}  &               BP  &  BP, GA, BFGS  &  Train with Back-Propagation (BP), BFGS Algorithm (BFGS), or Genetic Algorithm (GA - slower and worse) \\
             LearningRate  &  \mc{1}{c}{--}  &             0.02  &  \mc{1}{l}{--}  &  ANN learning rate parameter \\
                DecayRate  &  \mc{1}{c}{--}  &             0.01  &  \mc{1}{l}{--}  &  Decay rate for learning parameter \\
                 TestRate  &  \mc{1}{c}{--}  &               10  &  \mc{1}{l}{--}  &  Test for overtraining performed at each \#th epochs \\
          EpochMonitoring  &  \mc{1}{c}{--}  &            False  &  \mc{1}{l}{--}  &  Provide epoch-wise monitoring plots according to TestRate (caution: causes big ROOT output file!) \\
                 Sampling  &  \mc{1}{c}{--}  &                1  &  \mc{1}{l}{--}  &  Only 'Sampling' (randomly selected) events are trained each epoch \\
            SamplingEpoch  &  \mc{1}{c}{--}  &                1  &  \mc{1}{l}{--}  &  Sampling is used for the first 'SamplingEpoch' epochs, afterwards, all events are taken for training \\
       SamplingImportance  &  \mc{1}{c}{--}  &                1  &  \mc{1}{l}{--}  &   The sampling weights of events in epochs which successful (worse estimator than before) are multiplied with SamplingImportance, else they are divided. \\
         SamplingTraining  &  \mc{1}{c}{--}  &             True  &  \mc{1}{l}{--}  &  The training sample is sampled \\
          SamplingTesting  &  \mc{1}{c}{--}  &            False  &  \mc{1}{l}{--}  &  The testing sample is sampled \\
                ResetStep  &  \mc{1}{c}{--}  &               50  &  \mc{1}{l}{--}  &  How often BFGS should reset history \\
                      Tau  &  \mc{1}{c}{--}  &                3  &  \mc{1}{l}{--}  &  LineSearch size step \\
                   BPMode  &  \mc{1}{c}{--}  &       sequential  &  sequential, batch  &  Back-propagation learning mode: sequential or batch \\
                BatchSize  &  \mc{1}{c}{--}  &               -1  &  \mc{1}{l}{--}  &  Batch size: number of events/batch, only set if in Batch Mode, -1 for BatchSize=number\_of\_events \\
       ConvergenceImprove  &  \mc{1}{c}{--}  &            1e-30  &  \mc{1}{l}{--}  &  Minimum improvement which counts as improvement ($<$0 means automatic convergence check is turned off) \\
         ConvergenceTests  &  \mc{1}{c}{--}  &               -1  &  \mc{1}{l}{--}  &  Number of steps (without improvement) required for convergence ($<$0 means automatic convergence check is turned off) \\
             UseRegulator  &  \mc{1}{c}{--}  &            False  &  \mc{1}{l}{--}  &  Use regulator to avoid over-training \\
              UpdateLimit  &  \mc{1}{c}{--}  &            10000  &  \mc{1}{l}{--}  &  Maximum times of regulator update \\
          CalculateErrors  &  \mc{1}{c}{--}  &            False  &  \mc{1}{l}{--}  &  Calculates inverse Hessian matrix at the end of the training to be able to calculate the uncertainties of an MVA value \\
              WeightRange  &  \mc{1}{c}{--}  &                1  &  \mc{1}{l}{--}  &  Take the events for the estimator calculations from small deviations from the desired value to large deviations only over the weight range 
\end{optiontableAuto}
