Neural networks as models of psychopathology

A Survey of Brain-Inspired Intelligent Robots: Integration of Vision, Decision, Motion Control, and Musculoskeletal Systems

Current robotic studies are focused on the performance of specific tasks. However, such tasks cannot be generalized, and some special tasks, such as compliant and precise manipulation, fast and flexible response, and deep collaboration between humans and robots, cannot be realized. Brain-inspired intelligent robots imitate humans and animals, from inner mechanisms to external structures, through an integration of visual cognition, decision making, motion control, and musculoskeletal systems. This kind of robot is more likely to realize the functions that current robots cannot realize and become human friends. With the focus on the development of brain-inspired intelligent robots, this article reviews cutting-edge research in the areas of brain-inspired visual cognition, decision making, musculoskeletal robots, motion control, and their integration. It aims to provide greater insight into brain-inspired intelligent robots and attracts more attention to this field from the global research community.

A computational lens on menopause-associated psychosis

Psychotic episodes are debilitating disease states that can cause extreme distress and impair functioning. There are sex differences that drive the onset of these episodes. One difference is that, in addition to a risk period in adolescence and early adulthood, women approaching the menopause transition experience a second period of risk for new-onset psychosis. One leading hypothesis explaining this menopause-associated psychosis (MAP) is that estrogen decline in menopause removes a protective factor against processes that contribute to psychotic symptoms. However, the neural mechanisms connecting estrogen decline to these symptoms are still not well understood. Using the tools of computational psychiatry, links have been proposed between symptom presentation and potential algorithmic and biological correlates. These models connect changes in signaling with symptom formation by evaluating changes in information processing that are not easily observable (latent states). In this manuscript, we contextualize the observed effects of estrogen (decline) on neural pathways implicated in psychosis. We then propose how estrogen could drive changes in latent states giving rise to cognitive and psychotic symptoms associated with psychosis. Using computational frameworks to inform research in MAP may provide a systematic method for identifying patient-specific pathways driving symptoms and simultaneously refine models describing the pathogenesis of psychosis across all age groups.

Comprehensive review: Computational modelling of schizophrenia

Computational modelling has been used to address: (1) the variety of symptoms observed in schizophrenia using abstract models of behavior (e.g. Bayesian models - top-down descriptive models of psychopathology); (2) the causes of these symptoms using biologically realistic models involving abnormal neuromodulation and/or receptor imbalance (e.g. connectionist and neural networks - bottom-up realistic models of neural processes). These different levels of analysis have been used to answer different questions (i.e. understanding behavioral vs. neurobiological anomalies) about the nature of the disorder. As such, these computational studies have mostly supported diverging hypotheses of schizophrenia's pathophysiology, resulting in a literature that is not always expanding coherently. Some of these hypotheses are however ripe for revision using novel empirical evidence. Here we present a review that first synthesizes the literature of computational modelling for schizophrenia and psychotic symptoms into categories supporting the dopamine, glutamate, GABA, dysconnection and Bayesian inference hypotheses respectively. Secondly, we compare model predictions against the accumulated empirical evidence and finally we identify specific hypotheses that have been left relatively under-investigated.

Charles Bonnet syndrome: evidence for a generative model in the cortex?

Several theories propose that the cortex implements an internal model to explain, predict, and learn about sensory data, but the nature of this model is unclear. One condition that could be highly informative here is Charles Bonnet syndrome (CBS), where loss of vision leads to complex, vivid visual hallucinations of objects, people, and whole scenes. CBS could be taken as indication that there is a generative model in the brain, specifically one that can synthesise rich, consistent visual representations even in the absence of actual visual input. The processes that lead to CBS are poorly understood. Here, we argue that a model recently introduced in machine learning, the deep Boltzmann machine (DBM), could capture the relevant aspects of (hypothetical) generative processing in the cortex. The DBM carries both the semantics of a probabilistic generative model and of a neural network. The latter allows us to model a concrete neural mechanism that could underlie CBS, namely, homeostatic regulation of neuronal activity. We show that homeostatic plasticity could serve to make the learnt internal model robust against e.g. degradation of sensory input, but overcompensate in the case of CBS, leading to hallucinations. We demonstrate how a wide range of features of CBS can be explained in the model and suggest a potential role for the neuromodulator acetylcholine. This work constitutes the first concrete computational model of CBS and the first application of the DBM as a model in computational neuroscience. Our results lend further credence to the hypothesis of a generative model in the brain.

The cerebral cortex is central to many aspects of cognition and intelligence in humans and other mammals, but our scientific understanding of the computational principles underlying cortical processing is still limited. We might gain insights by considering visual hallucinations, specifically in a pathology known as Charles Bonnet syndrome, where patients suffering from visual impairment experience hallucinatory images that rival the vividness and complexity of normal seeing. Such generation of rich internal imagery could naturally be accounted for by theories that posit that the cortex implements an internal generative model of sensory input. Perception then could entail the synthesis of internal explanations that are evaluated by testing whether what they predict is consistent with actual sensory input. Here, we take an approach from artificial intelligence that is based on similar ideas, the deep Boltzmann machine, use it as a model of generative processing in the cortex, and examine various aspects of Charles Bonnet syndrome in computer simulations. In particular, we explain why the synthesis of internal explanations, which is so useful for perception, goes astray in the syndrome as neurons overcompensate for the lack of sensory input by increasing spontaneous activity.

The role of the cerebellum in schizophrenia: an update of clinical, cognitive, and functional evidences

The role of the cerebellum in schizophrenia has been highlighted by Andreasen's hypothesis of "cognitive dysmetria," which suggests a general dyscoordination of sensorimotor and mental processes. Studies in schizophrenic patients have brought observations supporting a cerebellar impairment: high prevalence of neurological soft signs, dyscoordination, abnormal posture and propioception, impaired eyeblink conditioning, impaired adaptation of the vestibular-ocular reflex or procedural learning tests, and lastly functional neuroimaging studies correlating poor cognitive performances with abnormal cerebellar activations. Despite those compelling evidences, there has been, to our knowledge, no recent review on the clinical, cognitive, and functional literature supporting the role of the cerebellum in schizophrenia. We conducted a Medline research focusing on cerebellar dysfunctions in schizophrenia. Emphasis was given to recent literature (after 1998). The picture arising from this review is heterogeneous. While in some domains, the role of the cerebellum seems clearly defined (ie, neurological soft signs, posture, or equilibrium), in other domains, the cerebellar contribution to schizophrenia seems limited or indirect (ie, cognition) if present at all (ie, affectivity). Functional models of the cerebellum are proposed as a background for interpreting these results.

Apoptosis, neurogenesis, and information content in Hebbian networks

The functional significance of alternate forms of plasticity in the brain (such as apoptosis and neurogenesis) is not easily observable with biological methods. Employing Hebbian dynamics for synaptic weight development, a three-layer neural network model of the hippocampus is used to simulate nonsupervised (autonomous) learning in the context of apoptosis and neurogenesis. This learning is applied to the characters of a pair of related alphabets, first the Roman and then the Greek, resulting in a set of encodings endogenously developed by the network. The learning performance takes the form of a U-shaped curve, showing that apoptosis and neurogenesis favorably inform memory development. We also discover that networks that converge very quickly on the Roman alphabet take much longer to handle the Greek, while networks which converge over an extended timeframe can then adapt very quickly to the new language. We find that the effect becomes increasingly pronounced as the number of neurons in the dentate gyrus layer decreases, and identify a strong correlation between cases where the Roman alphabet is quickly learned and cases where a few neurons saturate many of their weights almost immediately, minimizing participation of other neurons. Cases where learning the Roman alphabet requires more time lead to larger numbers of neurons participating with a larger diversity in synaptic weights. We present an information-theoretic argument about why this implies a better, more flexible learning system and why it leads to faster subsequent correlated Greek alphabet learning, and propose that the reason that apoptosis and neurogenesis work is that they promote this effect.

Suppression of obsessive-compulsive symptoms after epilepsy surgery

We report two male patients with medically intractable epilepsy and obsessive-compulsive disorder (OCD) symptoms. Both patients experienced remission of obsessive-compulsive symptoms after surgical treatment of epilepsy. Although the surgeries targeted different brain regions, the two patients had in common unilateral anterior cingulate cortex ablation. On the basis of these observations, we discuss the pathophysiology of OCD symptoms, emphasizing the role of corticosubcortical pathways in their genesis. Our data suggest that surgeries that affect neural loops associated with obsessive-compulsive symptoms can lead to an improvement of OCD; however, the structures responsible for this effect cannot be conclusively determined.

Simulated apoptosis/neurogenesis regulates learning and memory capabilities of adaptive neural networks

Characterization of neuronal death and neurogenesis in the adult brain of birds, humans, and other mammals raises the possibility that neuronal turnover represents a special form of neuroplasticity associated with stress responses, cognition, and the pathophysiology and treatment of psychiatric disorders. Multilayer neural network models capable of learning alphabetic character representations via incremental synaptic connection strength changes were used to assess additional learning and memory effects incurred by simulation of coordinated apoptotic and neurogenic events in the middle layer. Using a consistent incremental learning capability across all neurons and experimental conditions, increasing the number of middle layer neurons undergoing turnover increased network learning capacity for new information, and increased forgetting of old information. Simulations also showed that specific patterns of neural turnover based on individual neuronal connection characteristics, or the temporal-spatial pattern of neurons chosen for turnover during new learning impacts new learning performance. These simulations predict that apoptotic and neurogenic events could act together to produce specific learning and memory effects beyond those provided by ongoing mechanisms of connection plasticity in neuronal populations. Regulation of rates as well as patterns of neuronal turnover may serve an important function in tuning the informatic properties of plastic networks according to novel informational demands. Analogous regulation in the hippocampus may provide for adaptive cognitive and emotional responses to novel and stressful contexts, or operate suboptimally as a basis for psychiatric disorders. The implications of these elementary simulations for future biological and neural modeling research on apoptosis and neurogenesis are discussed.

Basal ganglia hemorrhagic ablation associated with temporary suppression of obsessive-compulsive symptoms

Currently, basal ganglia (BG) are considered regulators of motor and emotional activity. It's operationality encompass Obsessive Compulsive Disorder (OCD). The case of a patient suffering with severe OCD is described of note, his symptoms disappeared following a hemorrhage of the left BG. However, once the hemorrhage was reabsorbed his symptoms returned. It is possible that lesions affecting cerebral OCD association circuits may influence the evolution of obsessive-compulsive symptoms.

Biologic models of traumatic memories and post-traumatic stress disorder. The role of neural networks

Neural networks and their behavior provide an information-processing model for initiation and maintenance of the biologic aspects of post-traumatic stress disorder (PTSD). The repeated replaying of the intrusive and distressing recollections that follow a trauma modifies the structure of the neural networks involved in the processing of traumatic memories. The hypothesis is proposed that this repetition instigates the mechanisms of iterative learning, top-down activation and pruning. The development of the symptoms of PTSD can be explained by current knowledge about modeling disturbances of parallel distributing processing. The noradrenergic neurons play a central role in coordinating the interaction of multiple cortical regions, which is an essential aspect of parallel distributed processing. Disturbances of this system in PTSD are likely to be manifest as a dysfunctional modulation of working memory and involuntary traumatic recollection. Modifications of neural networks have a secondary effect of kindling in the hippocampus that further moderates the individual's sensitivity to a range of stressors. Therefore, a neural network model of PTSD provides a method for conceptualizing the onset of PTSD symptoms and their subsequent modification with the passage of time.

The perception of rorschach inkblots in schizophrenia: a neural network model

Schizophrenia is a psychiatric disorder characterized by a variety of cognitive deficits, including perceptual distortions and hallucinations. In recent years several studies have proposed that schizophrenia may involve a disturbance of "context". We have used a three layer neural network model constructed from an input layer followed by two computational layers to simulate responses of schizophrenic patients to the Rorschach test. In this test subjects respond to a set of ambiguous patterns created by ink blots on paper. Our model proposes that a disturbance of context caused by altered noise-to-signal ratio at the level of the single units, is responsible for schizophrenic responses to the Rorschach test. The assumption that catecholaminergic neurotransmitter systems regulate noise-to-signal ratio in cortical neurons constitutes a link between findings of altered neurotransmitter activity and deficits of cognitive functions requiring contextual integration in schizophrenia. The development of models for specific task deficits in schizophrenia could advance our insights regarding the neurological mechanisms underlying serious mental disorders such as schizophrenia.