Circulating through the Pipeline. Algorithmic Subjectivities and Mobile Struggles

We are living within a transformation that has been variously labelled as industrial revolution 4.0, platform economy, digital capitalism. Nevertheless, this change is still mainly conceptualized through the vocabulary of factory system. In this article we aim to contribute to the development of an emerging framework on the 'new capitalism' without any nostalgia for the past by exploring some of its potential interpretative categories (pipeline, algorithmic subjectivities, mobile struggles), based in particular on its spatial configurations and on the production of living labour' subjectivities. Indeed, we are witnessing not simply a wave of technological innovation but a more general transformation of the forms of capitalist valorisation which rely on the role played by spaces and social cooperation. These changes do not affect only spatialities but also the subjective forms of living labour, including his/her practices of organization and struggle

Multisensory bayesian inference depends on synapse maturation during training: Theoretical analysis and neural modeling implementation

Recent theoretical and experimental studies suggest that in multisensory conditions, the brain performs a near-optimal Bayesian estimate of external events, giving more weight to the more reliable stimuli. However, the neural mechanisms responsible for this behavior, and its progressive maturation in a multisensory environment, are still insufficiently understood. The aim of this letter is to analyze this problem with a neural network model of audiovisual integration, based on probabilistic population coding-the idea that a population of neurons can encode probability functions to perform Bayesian inference. The model consists of two chains of unisensory neurons (auditory and visual) topologically organized. They receive the corresponding input through a plastic receptive field and reciprocally exchange plastic cross-modal synapses, which encode the spatial co-occurrence of visual-auditory inputs. A third chain of multisensory neurons performs a simple sum of auditory and visual excitations. Thework includes a theoretical part and a computer simulation study. We show how a simple rule for synapse learning (consisting of Hebbian reinforcement and a decay term) can be used during training to shrink the receptive fields and encode the unisensory likelihood functions. Hence, after training, each unisensory area realizes a maximum likelihood estimate of stimulus position (auditory or visual). In crossmodal conditions, the same learning rule can encode information on prior probability into the cross-modal synapses. Computer simulations confirm the theoretical results and show that the proposed network can realize a maximum likelihood estimate of auditory (or visual) positions in unimodal conditions and a Bayesian estimate, with moderate deviations from optimality, in cross-modal conditions. Furthermore, the model explains the ventriloquism illusion and, looking at the activity in the multimodal neurons, explains the automatic reweighting of auditory and visual inputs on a trial-by-trial basis, according to the reliability of the individual cues

A Computational Model of the Lexical-Semantic System Based on a Grounded Cognition Approach

This work presents a connectionist model of the semantic-lexical system based on grounded cognition. The model assumes that the lexical and semantic aspects of language are memorized in two distinct stores. The semantic properties of objects are represented as a collection of features, whose number may vary among objects. Features are described as activation of neural oscillators in different sensory-motor areas (one area for each feature) topographically organized to implement a similarity principle. Lexical items are represented as activation of neural groups in a different layer. Lexical and semantic aspects are then linked together on the basis of previous experience, using physiological learning mechanisms. After training, features which frequently occurred together, and the corresponding word-forms, become linked via reciprocal excitatory synapses. The model also includes some inhibitory synapses: features in the semantic network tend to inhibit words not associated with them during the previous learning phase. Simulations show that after learning, presentation of a cue can evoke the overall object and the corresponding word in the lexical area. Moreover, different objects and the corresponding words can be simultaneously retrieved and segmented via a time division in the gamma-band. Word presentation, in turn, activates the corresponding features in the sensory-motor areas, recreating the same conditions occurring during learning. The model simulates the formation of categories, assuming that objects belong to the same category if they share some features. Simple exempla are shown to illustrate how words representing a category can be distinguished from words representing individual members. Finally, the model can be used to simulate patients with focalized lesions, assuming an impairment of synaptic strength in specific feature areas

Possible mechanisms underlying tilt aftereffect in the primary visual cortex: A critical analysis with the aid of simple computational models

AbstractA mathematical model of orientation selectivity in a single hypercolumn of the primary visual cortex developed in a previous work [Ursino, M., & La Cara, G.-E. (2004). Comparison of different models of orientation selectivity based on distinct intracortical inhibition rules. Vision Research, 44, 1641–1658] was used to analyze the possible mechanisms underlying tilt aftereffect (TAE). Two alternative models are considered, based on a different arrangement of intracortical inhibition (an anti-phase model in which inhibition is in phase opposition with excitation, and an in-phase model in which inhibition has the same phase arrangement as excitation but wider orientation selectivity). Different combinations of parameter changes were tested to explain TAE: a threshold increase in excitatory and inhibitory cortical neurons (fatigue), a decrease in intracortical excitation, an increase or a decrease in intracortical inhibition, a decrease in thalamo-cortical synapses. All synaptic changes were calculated on the basis of Hebbian (or anti-Hebbian) rules. Results demonstrated that the in-phase model accounts for several literature results with different combinations of parameter changes requiring: (i) a depressive mechanism to neurons with preferred orientation close to the adaptation orientation (fatigue of excitatory cortical neurons, and/or depression of thalamo-cortical synapses directed to excitatory neurons, and/or depression of intracortical excitatory synapses); (ii) a facilitatory mechanism to neurons with preferred orientation far from the adaptation orientation (fatigue of inhibitory cortical neurons, and/or depression of thalamo-cortical synapses directed to inhibitory neurons, and/or depression of intracortical inhibitory synapses). By contrast, the anti-phase model appeared less suitable to explain experimental data

Organization, Maturation, and Plasticity of Multisensory Integration: Insights from Computational Modeling Studies

In this paper, we present two neural network models – devoted to two specific and widely investigated aspects of multisensory integration – in order to evidence the potentialities of computational models to gain insight into the neural mechanisms underlying organization, development, and plasticity of multisensory integration in the brain. The first model considers visual–auditory interaction in a midbrain structure named superior colliculus (SC). The model is able to reproduce and explain the main physiological features of multisensory integration in SC neurons and to describe how SC integrative capability – not present at birth – develops gradually during postnatal life depending on sensory experience with cross-modal stimuli. The second model tackles the problem of how tactile stimuli on a body part and visual (or auditory) stimuli close to the same body part are integrated in multimodal parietal neurons to form the perception of peripersonal (i.e., near) space. The model investigates how the extension of peripersonal space – where multimodal integration occurs – may be modified by experience such as use of a tool to interact with the far space. The utility of the modeling approach relies on several aspects: (i) The two models, although devoted to different problems and simulating different brain regions, share some common mechanisms (lateral inhibition and excitation, non-linear neuron characteristics, recurrent connections, competition, Hebbian rules of potentiation and depression) that may govern more generally the fusion of senses in the brain, and the learning and plasticity of multisensory integration. (ii) The models may help interpretation of behavioral and psychophysical responses in terms of neural activity and synaptic connections. (iii) The models can make testable predictions that can help guiding future experiments in order to validate, reject, or modify the main assumptions

The Representation of Objects in the Brain, and Its Link with Semantic Memory and Language: a Conceptual Theory with the Support of a Neurocomputational Model

Recognition of objects, their representation and retrieval in memory and the link of this representation with words is a hard cognitive problem, which can be summarized with the term “lexico-semantic memory”. Several recent cognitive theories suggest that the semantic representation of objects is a distributed process, which engages different brain areas in the sensory and motor regions. A further common hypothesis is that each region is organized by conceptual features, that are highly correlated, and neurally contiguous. These theories may be useful to explain the results of clinical tests on patients with lesions of the brain, who exhibit deficits in recognizing objects from words or in evoking words from objects, or to explain the use of appropriate words in bilingual subjects. The study of the cognitive aspects of lexico-semantic memory representation may benefit from the use of mathematical models and computer simulations. Aim of this chapter is to describe a theoretical model of the lexico-semantic system, which can be used by cognitive neuroscientists to summarize conceptual theories into a rigorous quantitative framework, to test the ability of these theories to reproduce real pieces of behavior in healthy and pathological subjects, and to suggest new hypotheses for subsequent testing. The chapter is structured as follows: first the basic assumptions on cognitive aspects of the lexico-semantic memory model are clearly presented; the same aspects are subsequently illustrated via the results of computer simulations using abstract object representations as input to the model. Equations are then reported in an Appendix for readers interested to mathematical issues. The model is based on the following main assumptions: i) an object is represented as a collection of features, topologically ordered according to a similarity principle in different brain areas; ii) the features belonging to the same object are linked together via a Hebbian process during a phase in which objects are presented individually; iii) features are described via neural oscillators in the gamma band. As a consequence, different object representations can be maintained simultaneously in memory, via synchronization of the corresponding features (binding and segmentation problem); iv) words are represented in a lexical area devoted to recognition of words from phonemes; v) words in the lexical area and the features representing objects are linked together via a Hebbian mechanism during a learning phase in which a word is presented together with the corresponding object; vi) the same object representation can be associated to two alternative words (for instance to represent bilinguism). In this case, the two words are connected via inhibitory synapses, to implement a competition among them. vii) the choice of words is further selected by an external inhibitory control system, which suppresses words which do not correspond to the present objective (for instance to choose between alternative languages). Several exempla of model possibilities are presented, with the use of abstract words. These exempla comprehend: the possibility to retrieve objects and words even in case of incomplete or corrupted information on object features; the possibility to establish a semantic link between words with superimposed features; the process of learning a second language (L2) with the support of a language previously known (L1) to represent neurocognitive aspects of bilinguism

A Semantic Model to Study Neural Organization of Language in Bilingualism

A neural network model of object semantic representation is used to simulate learning of new words from a foreign language. The network consists of feature areas, devoted to description of object properties, and a lexical area, devoted to words representation. Neurons in the feature areas are implemented as Wilson-Cowan oscillators, to allow segmentation of different simultaneous objects via gamma-band synchronization. Excitatory synapses among neurons in the feature and lexical areas are learned, during a training phase, via a Hebbian rule. In this work, we first assume that some words in the first language (L1) and the corresponding object representations are initially learned during a preliminary training phase. Subsequently, second-language (L2) words are learned by simultaneously presenting the new word together with the L1 one. A competitive mechanism between the two words is also implemented by the use of inhibitory interneurons. Simulations show that, after a weak training, the L2 word allows retrieval of the object properties but requires engagement of the first language. Conversely, after a prolonged training, the L2 word becomes able to retrieve object per se. In this case, a conflict between words can occur, requiring a higher-level decision mechanism

Capitalism in the Platform Age. Emerging Assemblages of Labor and Welfare in Urban Spaces,

This two-part work brings together the outcomes of the Horizon 2020 Project PLUS, “Platform Labor in Urban Spaces”. Running from December 2018 to March 2022, which included an extension from December 2021 due to the COVID-19 pandemic, this research project investigated the main features and dimensions of the impact of digital platforms on the economy and society, with a specific focus on labour, urban transformations, and welfare. Sixteen partners, including universities, research centres, and cooperatives, investigated the operations of four digital platforms (AirBnb, Deliveroo, Helpling, and Uber) in seven European cities (Barcelona, Berlin, Bologna, Lisbon, London, Paris, and Tallin). The research involved, in different ways, municipalities, independent researchers, platform managers, and established grassroot unions. The fact that the four abovementioned platforms operate in diverse fields—accommodation, food delivery, domestic labour, and transport—has allowed us to carry out a wide-ranging analysis of the rapid spread of digital platforms across the economy and society

Determination of the top-quark mass using top-antitop cross section measurements at LHC

Lo studio della fisica del quark top rappresenta un campo in continuo sviluppo da partedella comunità teorica ed è di grande interesse all’interno del programma di fisica sperimentale ad ATLAS. In questa tesi, la massa del quark top è estratta mediante il confronto di calcoli di QCD della sezione d’urto totale e differenziale della produzione top-antitop in collisioni protone-protone ad energia del centro di massa di √s= 13 TeV, prodotti con il programma MATRIX al next-to-next-to-leading order, con dati sperimentali da collisioni pp a √s= 13 TeV raccolti nel 2015 e nel 2016 dal detector ATLAS al Large Hadron Collider (LHC) del CERN, corrispondenti a una luminosità integrata di 36.1 fb−1. Utilizzando la sezione d’urto totale top-antitop (t ̄t) misurata in eventi dileptonicieμe conducendo un’analisi basata su un approccio Bayesiano che utilizza Markov Chain Monte Carlo (MCMC), il valore ottenuto per la massa del quark top è m_t=(174.4+1.7−2.7) GeV. Dal confronto delle predizioni teoriche per la sezione d’urto differenziale assoluta in funzione della massa invariante (m_t ̄t) con dati misurati nel canale leptoni + jet a livello partonico, una seconda estrazione della massa del quark top ha portato al valore m_t= (171.9+3.0−2.9) GeV, ottenuto analizzando il χ2 tra i valori della predizione e quelli misurati