Figure 2. Example Neural Network.

A schematic of a neural network, where x is a vector of input data for d input features where x_{i, v} is the vth feature for the ith participant and y_i is the network output for the ith participant. In the case of the continuous neural network, y_i is a continuous variable representing the network’s prediction of the time of DLMO for the ith participant. In the case of the classification neural network, y_i falls between 0 and 1 and if y_i< 0.5, the timepoint is classified as falling before DLMO. Nodes l_m,n represent hidden nodes in the network, where m is the layer of the node, and n is the index of the node within each layer. The value of each node is determined by $l_{m,n}=\tanh \left(\sum _{n=1}^{k_{m-1}}{w}_{m,n}{l}_{m-1,n}+b_n\right)$ for b_n a fit constant. The number of nodes in each layer, k_m for b_n a fit constant. The number of nodes in each layer, k_m, is a hyperparameter which was selected through cross validation.