Computational Approaches to Explainable Artificial Intelligence:Advances in Theory, Applications and Trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9 International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications

Computational approaches to explainable artificial intelligence: Advances in theory, applications and trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9th International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications.MCIU - Nvidia(UMA18-FEDERJA-084

A Survey on Reservoir Computing and its Interdisciplinary Applications Beyond Traditional Machine Learning

Reservoir computing (RC), first applied to temporal signal processing, is a recurrent neural network in which neurons are randomly connected. Once initialized, the connection strengths remain unchanged. Such a simple structure turns RC into a non-linear dynamical system that maps low-dimensional inputs into a high-dimensional space. The model's rich dynamics, linear separability, and memory capacity then enable a simple linear readout to generate adequate responses for various applications. RC spans areas far beyond machine learning, since it has been shown that the complex dynamics can be realized in various physical hardware implementations and biological devices. This yields greater flexibility and shorter computation time. Moreover, the neuronal responses triggered by the model's dynamics shed light on understanding brain mechanisms that also exploit similar dynamical processes. While the literature on RC is vast and fragmented, here we conduct a unified review of RC's recent developments from machine learning to physics, biology, and neuroscience. We first review the early RC models, and then survey the state-of-the-art models and their applications. We further introduce studies on modeling the brain's mechanisms by RC. Finally, we offer new perspectives on RC development, including reservoir design, coding frameworks unification, physical RC implementations, and interaction between RC, cognitive neuroscience and evolution.Comment: 51 pages, 19 figures, IEEE Acces

The new technique for accurate estimation of the spinal cord circuitry:recording reflex responses of large motor unit populations

We propose and validate a non-invasive method that enables accurate detection of the discharge times of a relatively large number of motor units during excitatory and inhibitory reflex stimulations. HDsEMG and intramuscular EMG (iEMG) were recorded from the tibialis anterior muscle during ankle dorsiflexions performed at 5%, 10%, and 20% of the maximum voluntary contraction (MVC) force, in 9 healthy subjects. The tibial nerve (inhibitory reflex) and the peroneal nerve (excitatory reflex) were stimulated with constant current stimuli. In total, 416 motor units were identified from the automatic decomposition of the HDsEMG. The iEMG was decomposed using a state-of-the-art decomposition tool and provided 84 motor units (average of two recording sites). The reflex responses of the detected motor units were analyzed using the peri-stimulus time histogram (PSTH) and the peri-stimulus frequencygram (PSF). The reflex responses of the common motor units identified concurrently from the HDsEMG and the iEMG signals showed an average disagreement (the difference between number of observed spikes in each bin relative to the mean) of 8.2±2.2% (5% MVC), 6.8±1.0% (10% MVC), and 7.5±2.2% (20% MVC), for reflex inhibition, and 6.5±4.1%, 12.0±1.8%, 13.9±2.4%, for reflex excitation. There was no significant difference between the characteristics of the reflex responses, such as latency, amplitude and duration, for the motor units identified by both techniques. Finally, reflex responses could be identified at higher force (four of the nine subjects performed contraction up to 50% MVC) using HDsEMG but not iEMG, because of the difficulty in decomposing the iEMG at high forces. In conclusion, single motor unit reflex responses can be estimated accurately and non-invasively in relatively large populations of motor units using HDsEMG. This non-invasive approach may enable a more thorough investigation of the synaptic input distribution on active motor units at various force levels

Aphasia Compendium

Aphasia is an acquired central disorder of language that impairs a person’s ability to understand and/or produce spoken or writing language. The study of aphasia is important in different clinical and fundamental areas, including neurology, psychology, linguistics, and speech-language pathology. This book presents comprehensive information on the diagnosis and treatment of aphasias. Chapters cover such topics as linguistics and the study of aphasias, different types of aphasias, treatment approaches, imaging, and much more

Frontiers in psychodynamic neuroscience

he term psychodynamics was introduced in 1874 by Ernst von Brücke, the renowned German physiologist and Freud’s research supervisor at the University of Vienna. Together with Helmholtz and others, Brücke proposed that all living organisms are energy systems, regulated by the same thermodynamic laws. Since Freud was a student of Brücke and a deep admirer of Helmholtz, he adopted this view, thus laying the foundations for his metapsychology. The discovery of the Default Network and the birth of Neuropsychoanalysis, twenty years ago, facilitated a deep return to this classical conception of the brain as an energy system, and therefore a return to Freud's early ambition to establish psychology as natural science. Our current investigations of neural networks and applications of the Free Energy Principle are equally ‘psychodynamic’ in Brücke’s original sense of the term. Some branches of contemporary neuroscience still eschew subjective data and therefore exclude the brain’s most remarkable property – its selfhood – from the field, and many neuroscientists remain skeptical about psychoanalytic methods, theories, and concepts. Likewise, some psychoanalysts continue to reject any consideration of the structure and functions of the brain from their conceptualization of the mind in health and disease. Both cases seem to perpetuate a Cartesian attitude in which the mind is linked to the brain in some equivocal relationship and an attitude that detaches the brain from the body -- rather than considering it an integral part of the complex and dynamic living organism as a whole. Evidence from psychodynamic neuroscience suggests that Freudian constructs can now be realized neurobiologically. For example, Freud’s notion of primary and secondary processes is consistent with the hierarchical organization of self-organized cortical and subcortical systems, and his description of the ego is consistent with the functions of the Default Network and its reciprocal exchanges with subordinate brain systems. Moreover, thanks to new methods of measuring brain entropy, we can now operationalize the primary and secondary processes and therefore test predictions arising from these Freudian constructs. All of this makes it possible to deepen the dialogue between neuroscience and psychoanalysis, in ways and to a degree that was unimaginable in Freud's time, and even compared to twenty years ago. Many psychoanalytical hypotheses are now well integrated with contemporary neuroscience. Other Freudian and post-Freudian hypotheses about the structure and function of the mind seem ripe for the detailed and sophisticated development that modern psychodynamic neuroscience can offer. This Research Topic aims to provide comprehensive coverage of the latest advances in psychodynamic neuroscience and neuropsychoanalysis. Potential authors are invited to submit papers (original research, case reports, review articles, commentaries) that deploy, review, compare or develop the methods and theories of psychodynamic neuroscience and neuropsychoanalysis. Potential authors include researchers, psychoanalysts, and neuroscientists

Alexithymia and facial emotion recognition ability: An embodied hot and cold simulation perspective

Difficulty with knowing or identifying one’s internal feeling states is considered the hallmark feature of the personality construct of alexithymia. It is currently unclear, however, whether alexithymia also involves difficulty recognising external emotion cues, such as facial expressions of emotion. Facial expressions provide salient cues about one’s feelings, intentions and motivations that allow us to navigate our social contexts. Better facial emotion recognition is associated with positive outcomes across psychological, social and physical health domains. Typically, the ability to recognise facial emotions has been assessed using cognitive tests underpinned by the traditional cognitive science perspective which postulates that our conceptual knowledge of emotion is stored as symbolic representations within the semantic memory network. Yet, the neuroscientific discovery of sensory-motor mirror neurons has led to embodied accounts of cognition that assume motor and somatosensory systems within the brain are also involved in recognising others’ emotion, with mirror neuron activity producing a re-experiencing or simulation of the observed emotion of the other in one’s self. The primary aim of this dissertation was to examine facial emotion recognition and alexithymia from a broad theoretical perspective. As the small number of previous studies on facial emotion recognition and alexithymia are mostly derived from the cognitive science perspective, this thesis included the embodied cognition theoretical viewpoint. In a series of six experiments, university students were assigned to three groups based on their scores on the Toronto Alexithymia Scale (TAS-20). Participants with TAS-20 scores above the clinical cut-off were divided into two groups, a ‘High Alexithymia’ group (HA) and a ‘Moderate Alexithymia’ group (MA), using median split for a comparison of mean scores. A control group comprised students with TAS-20 scores in the normal range. To reduce the confounding of results due to variables known to impair facial emotion recognition performance, all participants with clinical range scores on the Depression and Anxiety Scales (DASS) were excluded from participation. Significant differences were found between the HA and MA groups on various measures of performance in most of these experiments, using a different sample of participants for each experiment. Experiment 1(a) examined the ability to accurately and rapidly identify dynamic facial expressions. Performance on this task is assumed to depend upon explicit access to schematic representations of emotion, which are proposed to be impoverished or unavailable to consciousness in individuals with alexithymia. While previous studies have typically utilised static stimuli, dynamic stimuli were used to provide ecological validity and to increase the level of task difficulty. Experiment 1(a) found that the HA and MA groups combined were significantly less accurate and slower to recognise facial expressions than controls. An unexpected finding was that the HA group were faster (but as accurate) on the task than the MA group. It was thought that addressing the question of how groups perform under dynamic and static conditions might help to clarify this unusual pattern of results. This assumption was based on prior research findings suggesting that static faces are processed using motor simulation and dynamic faces using emotion simulation. Experiment 1(b) presented the same task as Experiment 1(a) under Static and Dynamic conditions. The results of Experiment 1(a) were replicated in the Dynamic condition of Experiment 1(b), with a different group of participants. Importantly, while the HA group were significantly faster to recognise facial expressions than the MA group under Dynamic conditions, these groups performed with equivalent speed under Static conditions. It was argued that this result is consistent with the idea that the HA group relied on the recruitment of motor-simulation processes to improve their performance in Experiments 1(a) and 1(b). A difficulty in interpreting the above studies is that the task itself relies upon linguistic processes. To reduce the possibility of a verbal deficit accounting for the results, Experiment 2 (a and b) required participants to make ‘same’ or ‘different’ discriminations of two static facial expression stimuli of high or low intensity of expression. No differences were found between groups in Experiment 2(a). Experiment 2(b) then increased the level of task difficulty by employing low intensity of expression stimuli. This was done to address the potential issue of using shallow information-processing strategies to perform the discrimination task. Results showed that while the HA group was less accurate than the MA group and controls, the MA group was slower than the HA group and controls. It was argued that the difficult task conditions of Experiment 2(b) involving degraded stimuli may have prompted the use of a compensatory motor-based strategy among the HA group, resulting in the facilitation of their speed, but not accuracy, to discriminate facial expressions, relative to the MA group. Experiment 3 (a and b) examined facial emotion recognition from an embodied cognition perspective that allowed the examination of facial mimicry, which refers to congruent ‘micro-expressions’ of facial muscle activity that putatively support the recognition of others’ emotions. In Experiment 3(a), controls produced a greater amount of facial mimicry than the groups with clinical range TAS-20 scores and the HA group produced more mimicry the MA group. In addition, while the HA group and controls produced greater amounts of mimicry in the Happy condition than the Anger condition, the MA group produced equivalently low amounts of smile and frown facial reactions in both Expression conditions. It has been suggested that facial mimicry may not be an exclusively automatic reaction but rather depends on a number of goal-directed or top-down factors. Experiment 3(b) thus sought to further our understanding of these factors by exploring differences in performance following manipulation of the instruction given to participants. Within group comparisons showed that while controls produced a significant increase in frown facial mimicry when asked to adopt the observed emotion (Emotion Simulation condition) than when they passively viewed dynamic faces, the HA group produced similar levels of facial mimicry across the instruction conditions, while the MA group produced less frown facial mimicry in the Emotion Simulation condition than the Passive Viewing condition. It was argued this pattern of results is also consistent with the idea that the HA group employ a compensatory strategy involving the enhanced recruitment of action relevant simulation. It was also argued that the decrease in frown facial mimicry by the MA group only in response to the instruction to simulate anger may indicate that they rely instead on context dependent implicit learning of facial mimicry responses. A potential limitation of these experiments is the allocation of participants to categorical groups based on extreme TAS-20 total scores. On-going investigation of potential subtypes in alexithymia is clearly an important issue for future research. This series of experiments confirm that facial emotion recognition, discrimination and facial mimicry processes are detrimentally affected in alexithymia. This thesis also demonstrates the importance and utility of integrating notions of embodied forms of emotional processing into the conceptual framework of alexithymia and acts as a basis to prompt new treatment strategies for alexithymia based on simulation principles

Hierarchy, sequence, function: a contribution to the architecture of the human neurocognitive system

Koester D. Hierarchy, sequence, function: a contribution to the architecture of the human neurocognitive system. Bielefeld: Universität Bielefeld; 2016.The present work aims to contribute evidence to further our understanding of the human neurocognitive system by testing for interactions or sharing of resources among cognitive domains. To this end, several methods were employed and various variables were analysed. Following an action-centred and modular approach to the cognitive system in accordance with the ideomotor framework, I pursued the hypotheses that there are functional interactions among (some) cognitive domains and that hierarchical processing might be characteristic of multiple (if not all) domains