DISPLAYING DANTE’S DIVINE COMEDY MINIATED MANUSCRIPTS IN EXHIBITIONS

Ancient manuscripts are part of many collections belonging to historic libraries and museums: due to their fragile nature and to the difficulties to display most of their contents during exhibitions, their study is often complicated for scholars who also need generally special permissions to examine them, mostly for a limited time window. Beginning from these premises, this paper introduces the outcomes of the digital replication and presentation of three manuscripts related to Dante’s Divine Comedy, as proposed on a real exhibition, “Dall’Alma Mater al Mondo. Dante at the University of Bologna”, held in 2021. Some of the principles related to the production of their replicas and the fruition of their contents through dedicated applications targeted to visitors and scholars are presented, with care to the reproduction of details such as the ability to explore 3D replicas of detailed elected pages or to browse many of them on dedicated touch screens

Perception-action circuits for word learning and semantic grounding: a neurocomputational model and neuroimaging study

A neurocomputational architecture of the left-hemispheric areas of the brain is presented which was used to simulate and explain neural correlates of word learning and semantic grounding. The model’s main distinguishing features are that (i) it replicates connectivity and anatomical structure of the relevant brain areas, and (ii) it implements only functional mechanisms reflecting known cellular- and synaptic-level properties of the cerebral cortex. Stimulation of the “sensorimotor” model areas (mimicking early stages of word acquisition) leads to the spontaneous formation of cell assemblies (CAs), network correlates of memory traces for meaningful words. Preliminary results of a recent functional Magnetic Resonance Imaging study confirm the model's predictions, and, for the first time, localise the neural correlates of semantic grounding of novel spoken items in primary visual cortex. Taken together, these results provide strong support for perceptual accounts of word meaning acquisition in the brain, and point to a unifying theory of cognition based on action-perception circuits whose emergence, dynamics and interactions are grounded in known neuroanatomy and neurobiological learning mechanisms

Semantic grounding of novel spoken words in the primary visual cortex

Embodied theories of grounded semantics postulate that, when word meaning is first acquired, a link is established between symbol (word form) and corresponding semantic information present in modality-specific – including primary – sensorimotor cortices of the brain. Direct experimental evidence documenting the emergence of such a link (i.e., showing that presentation of a previously unknown, meaningless word sound induces, after learning, category specific reactivation of relevant primary sensory or motor brain areas), however, is still missing. Here, we present new neuroimaging results that provide such evidence. We taught participants aspects of the referential meaning of previously unknown, senseless novel spoken words (such as “Shruba” or “Flipe”) by associating them with either a familiar action or a familiar object. After training, we used functional magnetic resonance imaging to analyse the participants’ brain responses to the new speech items. We found that hearing the newly learnt object-related word sounds selectively triggered activity in primary visual cortex, as well as secondary and higher visual areas. These results for the first time directly document the formation of a link between novel, previously meaningless spoken items and corresponding semantic information in primary sensory areas in a category specific manner, providing experimental support for perceptual accounts of word meaning acquisition in the brain

A Diagrammatic Inter-Lingua for Planning Domain Descriptions

Sentential and diagrammatic representations are two different formalisms for describing domains and problems. Sentential descriptions are usually more expressive than diagrammatic ones, but tend to present a more complex and less intuitive notation. All modern planning domain description languages are sentential. The complexity of sentential formalisms has been of hindrance to the wider dissemination and take up of planning technology beyond the planning research community. This paper proposes a diagrammatic “meta-language” for planning domain descriptions based on setGraphs as an alternative to sentential languages. SetGraphs represent actions, states and goals in terms of set- and graph theoretic constructs. Through various practical examples, setGraphs are shown to yield simpler and more intuitive domain encodings, and to offer a high degree of elaboration tolerance. A theoretical analysis shows how the representation can be easily encoded using formal languages, and demonstrates that setGraphs are at least as expressive as a standard modern propositional planning domain description language. The model proposed constitutes a “core” representation that can be adopted as a basis for developing different planning domain description languages; it is suitable to be used across different levels of abstraction during the processes of language development and domain knowledge engineering, and it facilitates the elicitation, maintenance and re-use of planning domain descriptions

KNOWLEDGE AND DOCUMENTATION OF RENAISSANCE WORKS OF ART: THE REPLICA OF THE “ANNUNCIATION” BY BEATO ANGELICO

The Annunciation by Guido di Pietro from Mugello, known as Beato Angelico, is a wide tempera painting with some fine gold foil placed on a wooden support, today hosted at the Museum of the Basilica of Santa Maria delle Grazie, in San Giovanni Valdarno. On the occasion of the exhibition “Masaccio e Angelico. Dialogo sulla verità nella pittura”, the museum asked to the Department of Architecture at the University of Bologna to develop a digital high-resolution surrogate to favour deep investigations, to plan restoration and to simply tell the stories behind the artwork. Two tasks were accomplished: to let visitors discover the secrets in the painting and to let scholars study the artwork, to better understand the masterpiece. This paper introduces the outcomes of the research developed to digitize the Annunciation, following a dedicated pipeline developed to improve the fruition of its digital replica, originated from different input sources, and surrogating the user experience on the real object. This work presents a method for the 3D reconstruction of the surfaces based on different techniques for elements with different depth resolutions (i.e., the painting and the wooden frame) which combine photogrammetry and photometric stereo exploiting both procedures and pushing forward the boundaries of Gigapixel Imaging and photogrammetric-based 3D model representation

Influence of verbal labels on concept formation and perception in a deep unsupervised neural network model

OBJECTIVES/RESEARCH QUESTION: Whether language influences perception and thought remains a subject of intense debate. Would the presence or absence of a linguistic label facilitate or hinder the acquisition of new concepts? We here address this question in a neurocomputational model. METHODS: We used a computational brain model of fronto-occipital (extrasylvian) and fronto-temporal (perisylvian) cortex including spiking neurons. With Hebbian learning, the network was trained to associate word forms (phonological patterns, or “labels”) in perisylvian areas with semantic grounding information (sensory-motor patterns, or “percepts”) in extrasylvian areas. To study the effects of labels on the network’s ability to spontaneously develop distinct semantic representations from the multiple perceptual instances of a concept, we modelled each to-be-learned concept as a triplet of partly overlapping percepts and trained the model under two conditions: each instance of a perceptual triplet (patterns in extrasylvian areas) was repeatedly paired with patterns in perisylvian areas consisting of either (1) a corresponding word form (label condition), or (2) white noise (no-label condition). To quantify the emergence of neuronal representations for the conceptually-related percepts, we measured the dissimilarity (Euclidean distance) of neuronal activation vectors during perceptual stimulation. Category learning performance was measured as the difference between within- and between-concept dissimilarity values (DissimDiff) of perceptual activation patterns. RESULTS: The presence or absence of a linguistic label had a significant main effect on category learning (F=2476, p<0.0001, DissimDiff with labels m=0.92, SD=0.32; no-labels m=0.36, SD=0.21). DissimDiff values were also significantly larger in areas most important for semantic processing, so-called semantic-hubs, than in sensorimotor areas (main effect of centrality, F=2535, p<0.0001). Finally, a significant interaction between centrality and label type (F=711, p<0.0001) revealed that the label-related learning advantage was most pronounced in semantic hubs. CONCLUSION: These results suggest that providing a referential verbal label during the acquisition of a new concept significantly improves the cortex’ ability to develop distinct semantic-category representations from partly-overlapping (and non-overlapping) perceptual instances. Crucially, this effect is most pronounced in higher-order semantic-hub areas of the network. In sum, our results provide the first neurocomputational evidence for a “Whorfian” effect of language on perception and concept formation

Influence of language on concept formation and perception in a brain-constrained deep neural network model

Whether language influences perception and thought remains a subject of intense debate (1, 2). We address this question in a brain-constrained neurocomputational model (3) of fronto-occipital (extrasylvian) and fronto-temporal (perisylvian) cortex including spiking neurons. The unsupervised neural network was simultaneously presented with word forms (phonological patterns, “labels”) in perisylvian areas and semantic grounding information (sensory-motor patterns, “percepts”) in extrasylvian areas representing either concrete or abstract concepts. Following the approach used in a previous simulation (4), each to-be-learned concept was modeled as a triplet of partly overlapping percepts; the models were trained under two conditions: each instance of a perceptual triplet (patterns in extrasylvian areas) was repeatedly paired with patterns in perisylvian areas consisting of either (a) a corresponding word form (label condition), or (b) noise (no-label condition). We quantified the emergence of neuronal representations for the conceptually-related percepts using dissimilarity (Euclidean distance) of neuronal activation vectors during perceptual stimulation. Category learning was measured as the difference between within- and between concept dissimilarity values (DissimDiff) of perceptual activation patterns. A repeated-measures ANOVA with factors SemanticType (concrete/abstract) and Labelling showed main effects of both SemanticType and Label, and a significant interaction. We also quantified the “label effect” in percentage change from NoLabel to Label conditions, separately for between- and within-category dissimilarities. This showed that the label effect was mainly driven by changes in between-category dissimilarity, was significantly larger for abstract than concrete concepts, and became even larger in the “deeper” layers of the model. Providing a referential verbal label during the acquisition of a new concept significantly improves the cortex’ ability to develop distinct semantic-category representations from partly-overlapping (and non-overlapping) perceptual instances. Crucially, this effect is most pronounced in higher order semantic-hub areas of the network. These results provide the first neurocomputational evidence for a “Whorfian” effect of language on perception and concept formation

A brain-constrained deep neural-network model that can account for the readiness potential in self-initiated volitional action

The readiness potential (RP) is a gradual buildup of negative electrical potential over the motor cortices prior to onset of a self-initiated movement. It is typically interpreted as having a goal-directed nature, whereby it signals movement planning and preparation. However, a similar buildup can also be observed by averaging continuous random neural fluctuations aligned to crests in their time series [1]. Therefore, an alternative account of the RP is that it reflects ongoing background neuronal noise that has at least a small influence on the precise time of movement onset [2]. While computational modelling studies were used in the past to adjudicate between these accounts, previous attempts did not employ a fully neuroanatomically and neurobiologically realistic architecture, hence falling short of providing a cortical-level mechanistic validation of either theory. Here, we investigated the stochastic origin of the RP by applying a fully brain-constrained deep neural-network model reproducing real cortical neurons dynamics and the structure and connectivity of relevant primary sensorimotor, secondary and association areas of the frontal and temporal lobes. This model has been previously used to account for the neuromechanistic origins and cortical topography of volitional decisions to speak and act [3]. We used the emergent feature of this neural architecture – its ability to exhibit noise-driven periodic spontaneous ignitions of previously learnt internal representations (cell assemblies, CAs, circuits of strongly and reciprocally connected cells distributed across the entire network) – to mimic spontaneous decisions to act as observed in the classical Libet experiment. Specifically, we recorded the network’s activity for 2,000 trials, each trial beginning with a network reset and lasting until the spontaneous ignition of one of the CAs occurred, and used the time interval between trial start and spontaneous CA ignition as a model correlate of waiting times. We found that the model data accounted well for the experimental waiting-time distribution. Furthermore, in line with the stochastic interpretation of the RP, appropriate calibration of the model parameters resulted in subthreshold reverberation of activity within CA circuits, and averaging across cell assemblies’ ignition episodes produced a curve that closely matched the gradual buildup of activity observed in the experimental RP and its onset time. There are various neurophysiological sources of ongoing noise that result from neural activity. Some of this noise might accumulate and reverberate within previously acquired perception-action circuits, and, hence, produce spontaneous action. The present simulation results, obtained with a fully brain-constrained neural architecture, provide further support for this alternative view, placing the classical explanation of the RP further under scrutiny. References 1. Schurger A, Sitt J, Dehaene S. An accumulator model for spontaneous neural activity prior to self-initiated movement. Proc Natl Acad Sci USA. 2012, 109(42), E2904-E2913 2. Schurger A, Mylopoulos M, Rosenthal D. Neural antecedents of spontaneous voluntary movement: a new perspective. TiCS. 2016, 20(2), 77-79 3. Garagnani M, Pulvermüller F. Neuronal correlates of decisions to speak and act: Spontaneous emergence and dynamic topographies in a computational model of frontal and temporal areas. Brain and Language. 2013, 127, 75-85. Eu J Neurosci. 2008, 27(2), 492-51

Using information theory to measure the emergence of artificial free will in a spiking brain-constrained model of the human cortex

Cell Assembly (CA) circuits are known to emerge in neurocomputational models as a result of Hebbian-like learning. Intriguingly, when a brain-like architecture is used, CAs spontaneously "ignite" in absence of any stimulus, and the patterns of network activation occurring during such ignitions closely match those observed in the human brain during non-stimulus driven, endogenous decisions to act [1]. This suggests that sub-threshold reverberation of noise within CA circuits (which drives their ignition) may be a possible mechanism underlying seemingly "free" and volitional (yet possibly pre-consciously determined) action decisions [2]. It is unclear, however, whether such spontaneous CA ignitions are truly an emergent property of the brain-like model, or whether they are somehow "pre-encoded" in the system's features. Can we provide objective evidence supporting (or falsifying) the hypothesis that these spontaneous events are de facto non pre-determined and can be thus be considered as the network's own endogenous "action decisions"? To investigate this issue, we used a spiking brain-constrained model of six cortical areas and, after replicating the previously documented CA emergence and spontaneous ignitions in it, we analysed its emergent properties using information theoretic measures. Recent techniques in information theory allow quantifying emergence in complex systems (including the brain) [3]. Here, we applied these measures to test for the presence of emergence during spontaneous, unprovoked CA circuit ignition. Specifically, we analysed the different modalities of emergence associated with cell assembly ignition and lifecycle (downward causation and causal decoupling). Preliminary results show the highest levels of emergent behaviour (specifically, causal decoupling) during cell assembly ignition, which gradually fade as CA activation dissipates. Such increased levels of causal decoupling observed during (and prior to) CA ignition episodes confirm the presence of an emergent feature in the neural model. In summary, we present here the application of formal criteria used for determining the presence of emergence in complex systems to a spiking, brain-constrained neurocomputational model of the cortex that can mechanistically explain the neural origins of so-called "free", volitional action decisions. Initial results of the information-theoretical analysis indicate that spontaneous CA circuit ignitions, driven by reverberation of noise within them, truly constitute an emergent feature of the brain-like architecture, suggesting that this phenomenon should be considered as an endogenous (i.e., internally generated, and not pre-determined) feature of the artificial neural network. References: [1] Garagnani, M. & Pulvermüller, F. (2013) Neuronal correlates of decisions to speak and act: spontaneous emergence and dynamic topographies in a computational model of frontal and temporal areas. Brain and Language 127(1):75–85. [2] Schurger A, Mylopoulos M, Rosenthal D. (2016) Neural antecedents of spontaneous voluntary movement: a new perspective. Trends in Cognitive Sciences. 20(2), 77-79. [3] Rosas F.E., Mediano P.A.M., Jensen H.J., Seth A.K., Barrett A.B., Carhart-Harris R.L., et al. (2020) Reconciling emergences: An informationtheoretic approach to identify causal emergence in multivariate data. PLoS Comput Biol 16(12): e1008289

Down syndrome, accelerated aging and immunosenescence

Down syndrome is the most common chromosomal disorder, associated with moderate to severe intellectual disability. While life expectancy of Down syndrome population has greatly increased over the last decades, mortality rates are still high and subjects are facing prematurely a phenomenon of atypical and accelerated aging. The presence of an immune impairment in Down syndrome subjects is suggested for a long time by the existence of an increased incidence of infections, the incomplete efficacy of vaccinations, and a high prevalence of autoimmunity. Immunologic abnormalities have been described since many years in this population, both from a numerical and a functional points of view, and these abnormalities can mirror the ones observed during normal aging. In this review, we summarize our knowledge on immunologic disturbances commonly observed in subjects with Down syndrome, and in innate and adaptive immunity, as well as regarding chronic inflammation. We then discuss the role of accelerated aging in these observed abnormalities and finally review the potential age-associated molecular and cellular mechanisms involved