During training, neural networks solve an optimization problem, trying to find the optimal set of parameters that will lead to the lowest point of the entire error function. This lowest point is the global minimum. Neuton’s patented method of training solves the local minima problem. Part of the scientific breakthrough is that we are not using compression with our models. We iteratively grow our models, neuron to neuron, adding weight by weight from scratch. Our patented method resolves local minimum problems and efficiently grows the network. Hence, we need fewer neurons to minimize the error. In traditional networks getting out of a local minimum means adding additional layers/neurons; Neuton, however, will find the global minimum without increasing the number of neurons excessively. As a result the model is much more compact and accurate in comparison with those of other neural networks.

The vanishing gradient problem is encountered when training neural networks with gradient-based learning methods and backpropagation. In such methods, each of the neural network's parameters receive an update proportional to the partial derivative of the error function with respect to the current weight in each iteration of training. The problem is that in some cases, the gradient will be vanishingly small, effectively preventing the weight from changing its value. In the worst case scenario, this may completely stop the neural network from further training. Neuton’s proprietary algorithm of training and self-organization addresses the problem of vanishing gradients, thus making the model stable and accurate. Unlike traditional neural network architectures, Neuton develops connections among neurons during training.

Due to an optimized neural network structure having up to 1000 times fewer neurons and coefficients, Neuton models work extremely fast enabling a high volume of real-time predictions.