Abstract
This survey presents the most relevant neural network models of autism spectrum disorder and schizophrenia, from the first connectionist models to recent deep neural 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 model 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 excessively 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 of these techniques in the field of computational psychiatry.
Highlights
In the world, there is a prevalence of schizophrenia (SZ) that ranges between four and seven per 1000 individuals (Saha, Chant, Welham, & McGrath, 2005) and a prevalence of Autism Spectrum Disorder (ASD) that ranges between six and 16 per 1000 children (Baio et al, 2018)
In order to test the dopamine-theory of SZ, three experimental tasks were compared to three neural network models, obtaining similar results to empirical observations
In 2012, Yamashita and Tani presented a model of SZ using a recurrent neural network (RNN) (Yamashita & Tani, 2008) such as they are commonly used for the recognition and generation of time series
Summary
There is a prevalence of schizophrenia (SZ) that ranges between four and seven per 1000 individuals (between thirty and fifty million people) (Saha, Chant, Welham, & McGrath, 2005) and a prevalence of Autism Spectrum Disorder (ASD) that ranges between six and 16 per 1000 children (between 1 of 150 and 1 of 59 children) (Baio et al, 2018). SZ and ASD have in common that they both cause deficits in social interaction and are characterized by perceptual peculiarities. Non-specificity, as a biological abnormality related to a psychiatric disorder, can be found in many other neurological disorders Computational modeling of psychopathologies or Computational Psychiatry is one of the potential key players (Montague, Dolan, Friston, & Dayan, 2012; Redish & Gordon, 2016; Wang & Krystal, 2014) to tackle heterogeneity and non-specificity, and to better understand the cognitive processes underlying these disorders. Neural network models serve, due to their analogy to biological neurons, as a tool to test and generate hypotheses on possible neurological causes (Huys, Moutoussis, & Williams, 2011). Artificial neural networks cannot only be useful from the data-driven point of view (e.g., fitting a model to fMRI2 data), but can be used as a simplified model of the human brain to replicate and predict human behavior and to investigate which modifications in the connectionist models cause a specific alteration in the behavior
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