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Modeling the Hallucinating Brain: a Generative Adversarial Framework Masoume Zareh, Mohammad Hossein Manshaei, and Sayed Jalal Zahabi

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Modeling the Hallucinating Brain: a Generative Adversarial Framework Masoume Zareh, Mohammad Hossein Manshaei, and Sayed Jalal Zahabi A PREPRINT, FEBRUARY 2021 1 Modeling the Hallucinating Brain: A Generative Adversarial Framework Masoume Zareh, Mohammad Hossein Manshaei, and Sayed Jalal Zahabi Abstract—This paper looks into the modeling of hallu- Parkinson’s disease, Alzheimer’s disease, migraines, cination in the human’s brain. Hallucinations are known brain tumors, and epilepsy. to be causally associated with some malfunctions within Hallucinations are the unpredictable experience the interaction of different areas of the brain involved of perceptions without corresponding sources in the in perception. Focusing on visual hallucination and its underlying causes, we identify an adversarial mechanism external world. There are five types of hallucinations: between different parts of the brain which are responsible auditory, visual, tactile, olfactory, and taste. Visual in the process of visual perception. We then show how the hallucinations occur in numerous ophthalmologic, characterized adversarial interactions in the brain can be neurologic, medical, and psychiatric disorders [3]. modeled by a generative adversarial network. Visual hallucinations are common in Parkinson’s Index Terms—Generative Adversarial Network (GAN), disease, with a reported prevalence as high as 74% Brain, Hallunication. after 20 years of the disease [4]. Positive symptoms of schizophrenia are hallucinations, delusions, and I. INTRODUCTION racing thoughts. Focusing on hallucination, in this RAIN is an essential organ of the body for paper, we propose and artificial intelligence (AI) B information processing and memory. Therefore, framework for modeling visual hallucinations (VHs). discovering the functionality of the brain has al- Today, probabilistic mathematical and AI tech- ways been a challenge in neuroscience, which has niques have come to assist neuroscientists in ana- drawn special attention in the past two decades. lyzing the brain functionality. This includes deep So far, various aspects of the brain’s functionality learning (DL), reinforcement learning (RL), and and structure have been identified. Moreover, the generative adversarial networks (GANs) [5]. For symptoms of different brain-related neurological instance, in [6] the neural mechanisms have been disorders have been revealed, for many of which studied via probabilistic inference methods. The effective treatment/symptom control drugs have also brain’s structural and functional systems are seen to become available nowadays. possess features of complex networks [7]. It is also The functionality of each particular area in the shown that neurons as agents, can understand their brain, and the connectivity between different areas environment partially, make a decision, and control arXiv:2102.08209v1 [q-bio.NC] 9 Feb 2021 are essential for reacting/responding to different their internal organs [8]. Moreover, Yamins et al. stimulating input signals [1], [2]. Neurotransmitters use deep hierarchical neural networks to delve into serve as a means to connect the different areas of computational sensory systems models, especially the brain, which allows them to interact together for the sensory cortex and visual cortex [9]. information processing [1], [2]. Factors such as aging Recently, utilizing the idea of Generative Adver- or neurological disorders can lead to certain brain sarial Network (GAN), Gershman has proposed an damages. One of the known symptoms of many brain adversarial framework for probabilistic computation diseases is hallucination. Hallucinations can occur in in the brain [10]. There, he explains the psychologi- a wide range of diseases such as in Schizophrenia, cal and neural evidence for this framework and how the generator and discriminator’s breakdown could The authors are with the Department of Electrical and Com- lead to delusions observed in some mental disorders. puter Engineering, Isfahan University of Technology, 841568311, Isfahan, Iran. E-mail: [email protected], [email protected], GANs, which were introduced by Goodfellow in [email protected] 2014 [5], are generative models which allow for the 2 A PREPRINT, FEBRUARY 2021 Figure 1. The overall framework of the generative adversarial network (GAN) architecture. The generator contains a generative network and a discriminative network. The generator generates a new image by random inputs. This generated image is sent to the discriminator alongside real images. The discriminator takes input images and classifies them into two classes: real and fake. generation of new synthetic data instances similar This paper looks into the evidence within the to the training data. It has been mentioned in neurobiology and neuropsychiatry of the human [10] that the idea of the adversarial framework brain aiming at developing a generative adversarial can potentially be applied to other symptoms such framework for approximate inference in the halluci- as hallucinations. Inspired by this remark, in this nating brain. In Section 2, we briefly review the idea paper, we seek evidence and provide methodology of GAN as a preliminary. In Section 3, we point out on how the idea of the GANs mechanism can be the relevant evidence within the mechanism of visual employed as an adversarial framework for modeling hallucinations. Then, we develop our framework for the hallucination observed in some mental disorders visual hallucinations in Section 4. Finally, we discuss (such as Parkinson’s disease and Schizophrenia). the challenges of this framework in Section 5. The inference is ascertaining the probability of each potential cause given an observation [11]. II. GAN IN BRIEF Approximate inference algorithms fall into two Generative adversarial network (GAN) is a genera- families: Monte Carlo algorithms and variational tive model in which a generator network (GN) and a algorithms [6]. Note that, while computational neu- discriminator network (DN) contest with each other, roscientists often prefer to follow approximate in- in an adversarial setting (in Fig 1). In this setting, the ference, by exploring the biological implementation DN and the GN play the two-player minimax game. of Monte Carlo and variational methods, inspired GANs can be used for both semi-supervised and by [10], our approach here is based to model VHs unsupervised learning [13]. The common analogy through an adversarial inference setup. Adversar- for GAN is to think of GN as an art forger and ial inference has some important advantages over DN as an art expert. The forger creates forgeries to standard Monte Carlo and variational approaches. make realistic images, and the expert receives both First, it can be applied to more complex models. forgeries and real (authentic) images and aims to tell Second, the inference is more efficient than the them apart. Both of them are trained simultaneously standard Monte Carlo algorithms and it can use and in competition with each other, as shown in more flexible approximate posteriors compared to Fig. 1. standard variational algorithms [10]. Moreover, GAN In words of statistical learning, on the discrim- based adversarial learning techniques directly learn a inator side, DN has a training set consisting of generative model to construct high-quality data [12], samples drawn from the distribution pdata and learns and therefore usually more realistic than variational to represent an estimate of the distribution. As a approaches. result DN is to classify the given input as real ZAREH et al.: MODELING THE HALLUCINATING BRAIN 3 Figure 2. Functional anatomy of a healthy human brain with regards to vision. or fake. On the generator side, GN is learned to result of such interactions between different areas of map noise variables z onto samples as genuine the brain that the human perceives the object. For as possible, according to the prior distribution of example, Fig. 2 shows the functional anatomy of the noise variables Pz(z). This way, GN and a a healthy human brain with regards to vision. As DN contest in a two-player minimax game. In this it is shown on the figure, the information passes game, DN intends to maximize the probability of from the retina via the optic nerve and optic tract to distinguisihing between the real samples and those the lateral geniculate nucleus (LGN) in the thalamus. generated by GN. As for GN, it aims to minimize The signals project from there, via the optic radiation the probability of detecting the fake samples by DN. to the primary visual cortex-cells which process The relevant objective function can be written as: simple local attributes such as the orientation of lines and edges. From the primary visual cortex, information is organized as two parallel hierarchical min max E [log D(x)]+ E [log(1−D(G(z)))] G D x∼Pdata(x) z∼Pz(z) (1) processing streams [4]: 1) The ventral stream which identifies the features Indeed, by such ability in generating synthesized of the objects and passes them from the primary data, GANs will come to our aid in many ap- visual cortex to the inferior temporal cortex. plications such as super-resolution, image caption 2) The dorsal stream which processes spatial rela- generation, data imputation, etc., in which lack of tions between objects and projects through the sufficient real data has been a challenge. In this paper, primary visual cortex to the superior temporal however, we benefit from GAN from a modeling and parietal cortices. perspective. In particular, we take advantage of Finally, the prefrontal cortex areas (such as the GAN adversarial framework as a basis for modeling Inferior frontal gyrus and Medial Prefrontal Cortex) visual hallucinations. In the next section, we briefly analyze the received data from other areas by real review what hallucination refers to in view of brain’s and fake point of views. neurology. If the connectivity between any of the above explained brain areas is disrupted, humans cannot III. HALLUCINATION understand the object or may perceive it falsely. In a healthy brain, when the human sees an object, A relatively common form of memory distortion some human brain areas interact together. It is as a arises when individuals must discriminate items they 4 A PREPRINT, FEBRUARY 2021 have seen from those they have imagined (reality ical GAN-based generative model for hallucinations, monitoring) [14].
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