  This survey presents the most relevant neural network models of autism
spectrum disorder and schizophrenia, from the first connectionist models to
recent deep network architectures. We analyzed and compared the most
representative symptoms with its neural model counterpart, detailing the
alteration introduced in the network that generates each of the symptoms, and
identifying their strengths and weaknesses. We additionally cross-compared
Bayesian and free-energy approaches, as they are widely applied to modeling
psychiatric disorders and share basic mechanisms with neural networks. Models
of schizophrenia mainly focused on hallucinations and delusional thoughts using
neural dysconnections or inhibitory imbalance as the predominating alteration.
Models of autism rather focused on perceptual difficulties, mainly excessive
attention to environment details, implemented as excessive inhibitory
connections or increased sensory precision. We found an excessive tight view of
the psychopathologies around one specific and simplified effect, usually
constrained to the technical idiosyncrasy of the used network architecture.
Recent theories and evidence on sensorimotor integration and body perception
combined with modern neural network architectures could offer a broader and
novel spectrum to approach these psychopathologies. This review emphasizes the
power of artificial neural networks for modeling some symptoms of neurological
disorders but also calls for further developing these techniques in the field
of computational psychiatry.
