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.

In this letter, we propose a complete probabilistic tactile-based framework to enable robots to autonomously explore unknown workspaces and recognize objects based on their physical properties. Our framework consists of three components: 1) an active pretouch strategy to efficiently explore unknown workspaces; 2) an active touch learning method to learn about unknown objects based on their physical properties (surface texture, stiffness, and thermal conductivity) with the least number of training samples; and 3) an active touch algorithm for object discrimination, which selects the most informative exploratory action to apply to the object, so that the robot can efficiently distinguish between objects with a few number of actions. Our proposed framework was experimentally evaluated using a robotic arm equipped with multimodal artificial skin. The robot with the active pretouch method reduced the uncertainty of the workspace up to 30% and 70% compared to uniform and random strategies, respectively. By means of the active touch learning algorithm, the robot used 50% fewer samples to achieve the same learning accuracy than the baseline methods. By taking advantage of the prior knowledge obtained during the learning process, the robot actively discriminated objects with an improvement of 10% recognition accuracy compare to the random action selection approach.

One of the biggest challenges in robotics is interacting under uncertainty. Unlike robots, humans learn, adapt, and perceive their body as a unity when interacting with the world. Here, we investigate the suitability of active inference , a computational model proposed for the brain and governed by the free-energy principle, for robotic body perception and action in a nonsimulated environment. We designed and deployed the algorithm on the humanoid iCub showing how our proposed model enabled the robot to have adaptive body perception and to perform robust upper body reaching and head object tracking behaviors even under high levels of sensor noise and discrepancies between the model and the real robot. Estimation and control are formalized as an inference problem where the body posterior state distribution is approximated by means of the variational free-energy bound, yielding to a minimization of the prediction error. Besides, our study forecasts reactive actions in the presence of sensorimotor conflicts, a mechanism that may be relevant in human body adaptation to uncertain situations.

Recent advances in neuroscience have characterised brain function using mathematical formalisms and first principles that may be usefully applied elsewhere. In this paper, we explain how active inference—a well-known description of sentient behaviour from neuroscience—can be exploited in robotics. In short, active inference leverages the processes thought to underwrite human behaviour to build effective autonomous systems. These systems show state-of-the-art performance in several robotics settings; we highlight these and explain how this framework may be used to advance robotics.

The predictive functions that permit humans to infer their body state by sensorimotor integration are critical to perform safe interaction in complex environments. These functions are adaptive and robust to non-linear actuators and noisy sensory information. This paper introduces a computational perceptual model based on predictive processing that enables any multisensory robot to learn, infer and update its body configuration when using arbitrary sensors with Gaussian additive noise. The proposed method integrates different sources of information (tactile, visual and proprioceptive) to drive the robot belief to its current body configuration. The motivation is to provide robots with the embodied perception needed for self-calibration and safe physical human-robot interaction. We formulate body learning as obtaining the forward model that encodes the sensor values depending on the body variables, and we solve it by Gaussian process regression. We model body estimation as minimizing the discrepancy between the robot body configuration belief and the observed posterior. We minimize the variational free energy using the sensory prediction errors (sensed vs expected). In order to evaluate the model we test it on a real multi-sensory robotic arm. We show how different sensor modalities contributions, included as additive errors, improve the refinement of the body estimation and how the system adapts itself to provide the most plausible solution even when injecting strong sensory visuo-tactile perturbations. We further analyse the reliability of the model when different sensor modalities are disabled. This provides grounded evidence about the correctness of the perceptual model and shows how the robot estimates and adjusts its body configuration just by means of sensory information.