The natural, artificial, and social domains of intelligence: a triune approach

A “triune approach” to the three main domains of intelligence is advocated. It would be the most cogent way to understand the uses and impact of artificial intelligence in its intrinsic relation with human nature and social structures. The enormous technological success of artificial intelligence and the widespread social applications, impinging both in individual lives and in multiple economic and social structures, are making necessary a reflection on the wider dynamics of intelligence, interconnecting the artificial information pathways with the natural information flows and the social structural substrates. As a telling instance, the traditional poor understanding and management of “social emotions” is dangerously amplified in today’s social networks, contributing to unrest, polarization, and widespread desocialization processes. In contemporary societies, the essential link between intelligence and life has to be plainly revealed as a counterpoint to the link between artificial intelligence and computation

From Molecular Recognition to the “Vehicles” of Evolutionary Complexity: An Informational Approach

Countless informational proposals and models have explored the singular characteristics of biological systems: from the initial choice of information terms in the early days of molecular biology to the current bioinformatic avalanche in this “omic” era. However, this was conducted, most often, within partial, specialized scopes or just metaphorically. In this paper, we attempt a consistent informational discourse, initially based on the molecular recognition paradigm, which addresses the main stages of biological organization in a new way. It considers the interconnection between signaling systems and information flows, between informational architectures and biomolecular codes, between controlled cell cycles and multicellular complexity. It also addresses, in a new way, a central issue: how new evolutionary paths are opened by the cumulated action of multiple variation engines or mutational ‘vehicles’ evolved for the genomic exploration of DNA sequence space. Rather than discussing the possible replacement, extension, or maintenance of traditional neo-Darwinian tenets, a genuine informational approach to evolutionary phenomena is advocated, in which systemic variation in the informational architectures may induce differential survival (self-construction, self-maintenance, and reproduction) of biological agents within their open ended environment

Information and symmetry: Adumbrating the abstract core of complex systems

Information and symmetry are essential theoretical concepts that underlie the scientific explanation of a variety of complex systems. In spite of clear-cut developments around both concepts, their intersection is really problematic, either in fields related to mathematics, physics, and chemistry, or even more in those pertaining to biology, neurosciences, and social sciences. The present Special Issue explores recent developments, both theoretical and applied, in most of these disciplines

The Entropy of Laughter: Discriminative Power of Laughter’s Entropy in the Diagnosis of Depression

Laughter is increasingly present in biomedical literature, both in analytical neurological aspects and in applied therapeutic fields. The present paper, bridging between the analytical and the applied, explores the potential of a relevant variable of laughter’s acoustic signature—entropy—in the detection of a widespread mental disorder, depression, as well as in gauging the severity of its diagnostic. In laughter, the Shannon–Wiener entropy of the distribution of sound frequencies, which is one of the key features distinguishing its acoustic signal from the utterances of spoken language, has not been a specific focus of research yet, although the studies of human language and of animal communication have pointed out that entropy is a very important factor regarding the vocal/acoustic expression of emotions. As the experimental survey of laughter in depression herein undertaken shows, it was possible to discriminate between patients and controls with an 82.1% accuracy just by using laughter’s entropy and by applying the decision tree procedure. These experimental results, discussed in the light of the current research on laughter, point to the relevance of entropy in the spontaneous bona fide extroversion of mental states toward other individuals, as the signal of laughter seems to imply. This is in line with recent theoretical approaches that rely on the optimization of a neuro-informational free energy (and associated entropy) as the main “stuff” of brain processing

Plausibility of a Neural Network Classifier-Based Neuroprosthesis for Depression Detection via Laughter Records

The present work explores the diagnostic performance for depression of neural network classifiers analyzing the sound structures of laughter as registered from clinical patients and healthy controls. The main methodological novelty of this work is that simple sound variables of laughter are used as inputs, instead of electrophysiological signals or local field potentials (LFPs) or spoken language utterances, which are the usual protocols up-to-date. In the present study, involving 934 laughs from 30 patients and 20 controls, four different neural networks models were tested for sensitivity analysis, and were additionally trained for depression detection. Some elementary sound variables were extracted from the records: timing, fundamental frequency mean, first three formants, average power, and the Shannon-Wiener entropy. In the results obtained, two of the neural networks show a diagnostic discrimination capability of 93.02 and 91.15% respectively, while the third and fourth ones have an 87.96 and 82.40% percentage of success. Remarkably, entropy turns out to be a fundamental variable to distinguish between patients and controls, and this is a significant factor which becomes essential to understand the deep neurocognitive relationships between laughter and depression. In biomedical terms, our neural network classifier-based neuroprosthesis opens up the possibility of applying the same methodology to other mental-health and neuropsychiatric pathologies. Indeed, exploring the application of laughter in the early detection and prognosis of Alzheimer and Parkinson would represent an enticing possibility, both from the biomedical and the computational points of view

Plausibility of a Neural Network Classifier-Based Neuroprosthesis for Depression Detection via Laughter Records

The present work explores the diagnostic performance for depression of neural network classifiers analyzing the sound structures of laughter as registered from clinical patients and healthy controls. The main methodological novelty of this work is that simple sound variables of laughter are used as inputs, instead of electrophysiological signals or local field potentials (LFPs) or spoken language utterances, which are the usual protocols up-to-date. In the present study, involving 934 laughs from 30 patients and 20 controls, four different neural networks models were tested for sensitivity analysis, and were additionally trained for depression detection. Some elementary sound variables were extracted from the records: timing, fundamental frequency mean, first three formants, average power, and the Shannon-Wiener entropy. In the results obtained, two of the neural networks show a diagnostic discrimination capability of 93.02 and 91.15% respectively, while the third and fourth ones have an 87.96 and 82.40% percentage of success. Remarkably, entropy turns out to be a fundamental variable to distinguish between patients and controls, and this is a significant factor which becomes essential to understand the deep neurocognitive relationships between laughter and depression. In biomedical terms, our neural network classifier-based neuroprosthesis opens up the possibility of applying the same methodology to other mental-health and neuropsychiatric pathologies. Indeed, exploring the application of laughter in the early detection and prognosis of Alzheimer and Parkinson would represent an enticing possibility, both from the biomedical and the computational points of view

The Transcriptional Regulatory Network of Mycobacterium tuberculosis

Under the perspectives of network science and systems biology, the characterization of transcriptional regulatory (TR) networks beyond the context of model organisms offers a versatile tool whose potential remains yet mainly unexplored. In this work, we present an updated version of the TR network of Mycobacterium tuberculosis (M.tb), which incorporates newly characterized transcriptional regulations coming from 31 recent, different experimental works available in the literature. As a result of the incorporation of these data, the new network doubles the size of previous data collections, incorporating more than a third of the entire genome of the bacterium. We also present an exhaustive topological analysis of the new assembled network, focusing on the statistical characterization of motifs significances and the comparison with other model organisms. The expanded M.tb transcriptional regulatory network, considering its volume and completeness, constitutes an important resource for diverse tasks such as dynamic modeling of gene expression and signaling processes, computational reliability determination or protein function prediction, being the latter of particular relevance, given that the function of only a small percent of the proteins of M.tb is known

The Advancement of Information Science

The advancement of a new scientific perspective, information science, devoted to the study of the vast field of informational phenomena in nature and society, implies putting together a number of cognizing domains which are presently scattered away in many other disciplines. Comparable to previous scientific revolutions spurred by thermodynamics and quantum mechanics, it would be time to go beyond the classical discussions on the concept of information, and associated formal theories, and advance a “new way of thinking”. Cells, Brains, Societies, and Quantum information would be crucial arenas for this discussion. Rather than hierarchy, reduction, or unification, the catchword is unending recombination... A mature information science should offer a new panoramic view on the sciences themselves and contribute to achieve social adaptability & sustainability

Information and Life: Towards a Biological Understanding of Informational Phenomena

This work explores a new understanding of informational phenomena based on the molecular organization of life. One of the central ideas is that the interrelationship among the recently framed fields of genomics, proteomics, and signaling science (crucial elements of the bioinformatic whole enterprise) may provide fundamental aspects of a new information synthesis. Thus, the new knowledge gained on the functionality and “existential flow” of the phenotypic molecular elements (basically the production and degradation of constituent enzymes and proteins—the transient proteome of the cell), which is intimately coupled with the intrinsic dynamics of the “DNA world” and with the communicational events stemming from the cell environment, could represent a microcosm for the whole in-formation phenomena. The variant in-formation spelling emphasizes that the cellular coupling among constitutive (proteomic), generative (genomic), and communicational (signaling) information genera produces a differentiated mode of existence, the living state, which is always in the making, perpetually in formation. The in-formability of the living supports the emergence of a completely new realm of ‘cognitive’ autonomous causality —and implies, in other regards, the emergence of meaning and of agency, and the foundation upon which far more complex, organismic, neuronal, and social events have been evolutionarily deployed. There follows a fundamental break with respect to the mechanistic chains of causality (and explanation) afforded by the reductionist vision. There is also, in this biological approach to informational phenomena, a compelling need for the development of a new communication theory of non-conservative nature. New logical principles are discussed which could guide biological systems in their inner choices between information ‘factories’ and information ‘garbage camps’. Finally, this bottom-up approach to the nature of information, molecular-biologically grounded as it is, does not militate against the top-down strategies. Conversely, it aims at a complementarity with germane conceptualizations that are currently being addressed in theoretical science, philosophy, neurosciences, and in social and technological disciplines