“I did it for profit and science!”
If the honest science of the DSM is all wrong-if the neuroscience is voodoo blackboxes that really don’t mean much at all then the predictive quality of behavioral modeling is lost as well.
Neural networks are nonlinear sophisticated modeling techniques that are able to model complex functions. They can be applied to problems of prediction, classification or control in a wide spectrum of fields such as finance, cognitive psychology/neuroscience, medicine, engineering, and physics.
Neural networks are used when the exact nature of the relationship between inputs and output is not known. A key feature of neural networks is that they learn the relationship between inputs and output through training. There are three types of training in neural networks used by different networks, supervised and unsupervised training, reinforcement learning, with supervised being the most common one.
Some examples of neural network training techniques are backpropagation, quick propagation, conjugate gradient descent, projection operator, Delta-Bar-Delta etc. Some unsupervised network architectures are multilayer perceptrons, Kohonen networks, Hopfield networks, etc.”
If you base neurotech on the wonkey science of big pharma then that makes the neurotech of the blackbox wonkey as well. So for the next fifty or sixty years we will have the “antiblackbox” crowd-who would question the scientific validity of neuroscience.
Why not get that right the first time around?
“Answer: No. There is no chemical which changes color when someone urinates in a swimming pool. There are dyes which could cloud, change color, or produce a color in response to urine, but these chemicals would also be activated by other compounds, producing embarrassing false-positives.”