Mental Health Supervision by Neural Network Applications

Any educator would say that, there are moments when getting a person to learn something is a struggle. Scientists suggest that a disadvantaged brain with manipulated learning threshold defines the learning inability. They also suggest that improvement in the learning threshold potentially encourages senses such as self-discipline and improves the learning quality. The poor learning quality potentially affects the mental health when one is losing the thinking contrast.

In artificial neuroscience for example; in wake-sleep algorithm or Boltzmann machine, a leading layer projects a contemptible approximation to the model and calculates the learning likelihood, by applying the learning rules. What drives the learning weight is in the lagging layer that is purely based on approximation posterior ranking and recalls a stochastic decision for a hidden layer to remain OFF or switch ON. The model then uses predictive weights to regenerate true data, which according to deep believe theory is a function of sigmoid-believe. The entire process eventually balances the learning threshold. Loss function, however, must match the framing of the specific predictive model to update the weights and reduce the loss on the next evaluation. For example, if the output layer configuration functions inappropriately, the loss function increases the error rate, according to the contrastive divergent thinking theory.

These experiments identified a family of synchronization problems between projecting and predicting layers, named as memory-cheating-state that shows very abstractive results in some test cases when training a neural network.

Very similarly, the human brain is challenged by cognitive stress, which happens when the loss function is unable to balance between projection-prediction, increasing the error rate by generating extra resolution in the posterior distribution.

What is suggested by theories such as “adaptive thresholding”, is to improve the learning quality of a neural network by instead of controlling the increase-rate of the hidden layers, control the loss function independent from memory leakage that potentially minimizes the effects of cognitive resonance, according to new experiments.

This is potentially a new field of artificial neural net application for mental health care.