Plato on the foundations of Modern Theorem Provers

Is it possible to achieve such a proof that is independent of both acts and dispositions of the human mind? Plato is one of the great contributors to the foundations of mathematics. He discussed, 2400 years ago, the importance of clear and precise definitions as fundamental entities in mathematics, independent of the human mind. In the seventh book of his masterpiece, The Republic, Plato states “arithmetic has a very great and elevating effect, compelling the soul to reason about abstract number, and rebelling against the introduction of visible or tangible objects into the argument” (525c). In the light of this thought, I will discuss the status of mathematical entities in the twentieth first century, an era when it is already possible to demonstrate theorems and construct formal axiomatic derivations of remarkable complexity with artificial intelligent agents --- the modern theorem provers

Is the free-energy principle a formal theory of semantics? From variational density dynamics to neural and phenotypic representations

The aim of this paper is twofold: (1) to assess whether the construct of neural representations plays an explanatory role under the variational free-energy principle and its corollary process theory, active inference; and (2) if so, to assess which philosophical stance - in relation to the ontological and epistemological status of representations - is most appropriate. We focus on non-realist (deflationary and fictionalist-instrumentalist) approaches. We consider a deflationary account of mental representation, according to which the explanatorily relevant contents of neural representations are mathematical, rather than cognitive, and a fictionalist or instrumentalist account, according to which representations are scientifically useful fictions that serve explanatory (and other) aims. After reviewing the free-energy principle and active inference, we argue that the model of adaptive phenotypes under the free-energy principle can be used to furnish a formal semantics, enabling us to assign semantic content to specific phenotypic states (the internal states of a Markovian system that exists far from equilibrium). We propose a modified fictionalist account: an organism-centered fictionalism or instrumentalism. We argue that, under the free-energy principle, pursuing even a deflationary account of the content of neural representations licenses the appeal to the kind of semantic content involved in the aboutness or intentionality of cognitive systems; our position is thus coherent with, but rests on distinct assumptions from, the realist position. We argue that the free-energy principle thereby explains the aboutness or intentionality in living systems and hence their capacity to parse their sensory stream using an ontology or set of semantic factors.Comment: 35 pages, 4 figures, 1 tabl

Optimization and mechanisms for biodecoloration of a mixture of dyes by Trichosporon akiyoshidainum HP 2023

Trichosporon akiyoshidainum HP2023 is a basidiomycetous yeast isolated from Las Yungas rainforest (Tucumán, Argentina) and selected based on its outstanding textile-dye-decolorizing ability. In this work, the decolorization process was optimized using Reactive Black 5 as dye model. Lactose and urea were chosen as carbon and nitrogen sources through a one-at-time approach. Afterwards, factorial designs were employed for medium optimization, leading to the formulation of a simpler optimized medium which contains in g L−1: lactose 10, yeast extract 1, urea 0.5, KH2PO4 1 and MgSO4 1. Temperature and agitation conditions were also optimized. The optimized medium and incubation conditions for dye removal were extrapolated to other dyes individually and a mixture of them. Dye removal process happened through both biosorption and biodegradation mechanisms, depending primarily on the dye structure. A positive relation between initial inoculum and dye removal rate and a negative relation between initial dye concentration and final dye removal percentages were found. Under optimized conditions, T. akiyoshidainum HP2023 was able to completely remove a mixture of dyes up to a concentration of 300 mg L−1, a concentration much higher than those expected in real effluents.Fil: Martorell, María Martha. Ministerio de Relaciones Exteriores, Comercio Internacional y Culto. Direccion Nacional del Antártico; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Rosales Soro, Maria del Milagro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucuman. Planta Piloto de Procesos Industriales Microbiologicos; ArgentinaFil: Pajot, Hipolito Fernando. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucuman. Planta Piloto de Procesos Industriales Microbiologicos; ArgentinaFil: Castellanos, Lucia Ines. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucuman. Planta Piloto de Procesos Industriales Microbiologicos; Argentin

Screening of decolourizing yeasts with dye assimilation ability from Las Yungas rainforest

Environmental pollution by textile-dye containing wastewaters is a matter of worldwide concern. Yeasts bioremediation, a poorly explored and less exploited methodology, stands out as an interesting approach supported on their metabolic diversity and high growth rates. In this work, a screening protocol based on dye tolerance and dye assimilation ability on solid media was performed with a 2000 mg/L dye mixture. Thirty-nine yeast isolates from “Laurel del Monte” underlying soils from “Las Yungas” (Tucumán, Argentina) were obtained. Based on decolourization haloes and colony dyeing, 15 isolates showed the highest decolourization ability on agar plates containing Vilmafix® Blue RR-BB, Vilmafix® Red 7B-HE, Vilmafix® Black B-V and Vilmafix® Yellow 4R-HE, either alone or as a mixture. Screening on agar plates with synthetic media supplemented with each dye as sole C or N source, led to the selection of 10 isolates. Microsatellite RAPD analysis enabled the discrimination of these isolates into six groups. Following tests consisted on the analysis of growth and decolourization kinetic profiles, along with the production of Manganese peroxidase, laccase and tyrosinase activities in dye-supplemented (200 mg/L) liquid media. Time-dependent supernatant spectra, growth curves and enzymatic activities led to the selection of the two most promising isolates for future experimentsFil: Martorell, María Martha. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Planta Piloto de Procesos Industriales Microbiológicos; ArgentinaFil: Pajot, Hipolito Fernando. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Planta Piloto de Procesos Industriales Microbiológicos; ArgentinaFil: Fariña, Julia Ines. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Planta Piloto de Procesos Industriales Microbiológicos; ArgentinaFil: Castellanos, Lucia Ines. Universidad Nacional de Tucumán; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Planta Piloto de Procesos Industriales Microbiológicos; ArgentinaXLVI Reunión Anual Sociedad Argentina de Investigación en Bioquímica y Biología MolecularPuerto MadrynArgentinaSociedad Argentina de Investigación en Bioquímica y Biología Molecula

Embodied Skillful Performance: Where the Action Is

When someone masters a skill, their performance looks to us like second nature: it looks as if their actions are performed smoothly without explicit, knowledge-driven, online monitoring of their performance. Contemporary computational models in motor control theory, however, are instructionist. That is, they cast skilful performance as a knowledge-driven process, one that is driven by explicit motor representations of the action to be performed skillfully, which harness instructions for performance. Optimal control theory, a popular representative of such approaches, casts skillful performance as the execution of motor commands, the deliverances of a motor control system implemented by separable forward and inverse models that work in tandem with a state estimator to control the motor plant. These models rest on the principle that motor control is realized by the concerted action of separate modular subsystems, which transform an explicit motor representation into a sequence of physical movements. This paper aims to show the limitations of such instructionist approaches to skillful performance. Specifically, we address whether the assumption of modular knowledge-driven motor control in optimal control theory (based on motor commands computed by separable state estimators, forward models, and inverse models) is warranted. The first section of this paper examines the instructionist assumption, according to which skillful performance consists in the execution of instructions invested in motor representations. The second and third sections characterize the implementation of motor representations as motor commands, with a special focus on formulations from optimal control theory. The final sections of this paper examine predictive coding and active inference – behavioral modeling frameworks that descend, but are distinct, from optimal control theory – and argue that the instructionist assumption is ill-motivated in light of new developments in motor control theory, which cast motor control and motor planning as a form of (active) inference

Embodied Skillful Performance: Where the Action Is

When someone masters a skill, their performance looks to us like second nature: it looks as if their actions are performed smoothly without explicit, knowledge-driven, online monitoring of their performance. Contemporary computational models in motor control theory, however, are instructionist. That is, they cast skilful performance as a knowledge-driven process, one that is driven by explicit motor representations of the action to be performed skillfully, which harness instructions for performance. Optimal control theory, a popular representative of such approaches, casts skillful performance as the execution of motor commands, the deliverances of a motor control system implemented by separable forward and inverse models that work in tandem with a state estimator to control the motor plant. These models rest on the principle that motor control is realized by the concerted action of separate modular subsystems, which transform an explicit motor representation into a sequence of physical movements. This paper aims to show the limitations of such instructionist approaches to skillful performance. Specifically, we address whether the assumption of modular knowledge-driven motor control in optimal control theory (based on motor commands computed by separable state estimators, forward models, and inverse models) is warranted. The first section of this paper examines the instructionist assumption, according to which skillful performance consists in the execution of instructions invested in motor representations. The second and third sections characterize the implementation of motor representations as motor commands, with a special focus on formulations from optimal control theory. The final sections of this paper examine predictive coding and active inference – behavioral modeling frameworks that descend, but are distinct, from optimal control theory – and argue that the instructionist assumption is ill-motivated in light of new developments in motor control theory, which cast motor control and motor planning as a form of (active) inference

Embodied Skillful Performance: Where the Action Is

When someone masters a skill, their performance looks to us like second nature: it looks as if their actions are performed smoothly without explicit, knowledge-driven, online monitoring of their performance. Contemporary computational models in motor control theory, however, are instructionist. That is, they cast skilful performance as a knowledge-driven process, one that is driven by explicit motor representations of the action to be performed skillfully, which harness instructions for performance. Optimal control theory, a popular representative of such approaches, casts skillful performance as the execution of motor commands, the deliverances of a motor control system implemented by separable forward and inverse models that work in tandem with a state estimator to control the motor plant. These models rest on the principle that motor control is realized by the concerted action of separate modular subsystems, which transform an explicit motor representation into a sequence of physical movements. This paper aims to show the limitations of such instructionist approaches to skillful performance. Specifically, we address whether the assumption of modular knowledge-driven motor control in optimal control theory (based on motor commands computed by separable state estimators, forward models, and inverse models) is warranted. The first section of this paper examines the instructionist assumption, according to which skillful performance consists in the execution of instructions invested in motor representations. The second and third sections characterize the implementation of motor representations as motor commands, with a special focus on formulations from optimal control theory. The final sections of this paper examine predictive coding and active inference – behavioral modeling frameworks that descend, but are distinct, from optimal control theory – and argue that the instructionist assumption is ill-motivated in light of new developments in motor control theory, which cast motor control and motor planning as a form of (active) inference

Markov Blankets in the Brain

Recent characterisations of self-organising systems depend upon the presence of a Markov blanket: a statistical boundary that mediates the interactions between what is inside of and outside of a system. We leverage this idea to provide an analysis of partitions in neuronal systems. This is applicable to brain architectures at multiple scales, enabling partitions into single neurons, brain regions, and brain-wide networks. This treatment is based upon the canonical micro-circuitry used in empirical studies of effective connectivity, so as to speak directly to practical applications. This depends upon the dynamic coupling between functional units, whose form recapitulates that of a Markov blanket at each level. The nuance afforded by partitioning neural systems in this way highlights certain limitations of modular perspectives of brain function that only consider a single level of description.Comment: 25 pages, 5 figures, 1 table, Glossar

Embodied Skillful Performance: Where the Action Is

When someone masters a skill, their performance looks to us like second nature: it looks as if their actions are performed smoothly without explicit, knowledge-driven, online monitoring of their performance. Contemporary computational models in motor control theory, however, are instructionist. That is, they cast skilful performance as a knowledge-driven process, one that is driven by explicit motor representations of the action to be performed skillfully, which harness instructions for performance. Optimal control theory, a popular representative of such approaches, casts skillful performance as the execution of motor commands, the deliverances of a motor control system implemented by separable forward and inverse models that work in tandem with a state estimator to control the motor plant. These models rest on the principle that motor control is realized by the concerted action of separate modular subsystems, which transform an explicit motor representation into a sequence of physical movements. This paper aims to show the limitations of such instructionist approaches to skillful performance. Specifically, we address whether the assumption of modular knowledge-driven motor control in optimal control theory (based on motor commands computed by separable state estimators, forward models, and inverse models) is warranted. The first section of this paper examines the instructionist assumption, according to which skillful performance consists in the execution of instructions invested in motor representations. The second and third sections characterize the implementation of motor representations as motor commands, with a special focus on formulations from optimal control theory. The final sections of this paper examine predictive coding and active inference – behavioral modeling frameworks that descend, but are distinct, from optimal control theory – and argue that the instructionist assumption is ill-motivated in light of new developments in motor control theory, which cast motor control and motor planning as a form of (active) inference