Grounding Mental Representations in a Virtual Multi-Level Functional Framework

According to the associative theory of learning, reactive behaviors described by stimulus-response pairs result in the progressive wiring of a plastic brain. In contrast, flexible behaviors are supposedly driven by neurologically grounded mental states that involve computations on informational contents. These theories appear complementary, but are generally opposed to each other. The former is favored by neuro-scientists who explore the low-level biological processes supporting cognition, and the later by cognitive psychologists who look for higher-level structures. This situation can be clarified through an analysis that independently defines abstract neurological and informational functionalities, and then relate them through a virtual interface. This framework is validated through a modeling of the first stage of Piaget’s cognitive development theory, whose reported end experiments demonstrate the emergence of mental representations of object displacements. The neural correlates grounding this emergence are given in the isomorphic format of an associative memory. As a child’s exploration of the world progresses, his mental models will eventually include representations of space, time and causality. Only then epistemological concepts, such as beliefs, will give rise to higher level mental representations in a possibly richer propositional format. This raises the question of which additional neurological functionalities, if any, would be required in order to include these extensions into a comprehensive grounded model. We relay previously expressed views, which in summary hypothesize that the ability to learn has evolved from associative reflexes and memories, to suggest that the functionality of associative memories could well provide the sufficient means for grounding cognitive capacities

A computational framework for implementing Baars' global worslaoce theory of consciousness

We consider Baars’ "Global Workspace" theory of consciousness and discuss its possible representation within a model of intelligent agents. We first review a particular agent implementation that is given by an abstract machine, and then identify the extensions that are required in order to accommodate the main aspects of consciousness. According to Baars’ theory, this amounts to unconscious process coalitions that result in the creation of contexts. These extensions can be formulated within a reified virtual machine encompassing a representation of the original machine as well as an additional introspective component. This computational framework is illustrating throughout using a simple working example

Dereplication of natural products from complex extracts by regression analysis and molecular networking: case study of redox-active compounds from Viola alba subsp. dehnhardtii

Introduction : In natural product research, bioassay-guided fractionation was previously widely employed but is now judged to be inadequate in terms of time and cost, particularly if only known compounds are ultimately isolated. The development of metabolomics, along with improvements in analytical tools, allows comprehensive metabolite profiling. This enables dereplication to target unknown active compounds early in the purification workflow. Objectives :Starting from an ethanolic extract of violet leaves, this study aims to predict redox active compounds within a complex matrix through an untargeted metabolomics approach and correlation analysis. Methods : Rapid fractionation of crude extracts was carried out followed by multivariate data analysis (MVA) of liquid chromatography–high resolution mass spectrometry (LC–HRMS) profiles. In parallel, redox active properties were evaluated by the capacity of the molecules to reduce 2,2-diphenyl-1-picrylhydrazyl (DPPH·) and superoxide (O2 ·−) radicals using UV–Vis and electron spin resonance spectroscopies (ESR), respectively. A spectral similarity network (molecular networking) was used to highlight clusters involved in the observed redox activities. Results : Dereplication on Viola alba subsp. dehnhardtii highlighted a reproducible pool of redox active molecules. Polyphenols, particularly O-glycosylated coumarins and C-glycosylated flavonoids, were identified and de novo dereplicated through molecular networking. Confirmatory analyses were undertaken by thin layer chromatography (TLC)–DPPH–MS assays and nuclear magnetic resonance (NMR) spectra of the most active compounds. Conclusion : Our dereplication strategy allowed the screening of leaf extracts to highlight new biologically active metabolites in few steps with a limited amount of crude material and reduced time-consuming manipulations. This approach could be applied to any kind of natural extract for the study of various biological activities

Towards neuro-inspired symbolic models of cognition: linking neural dynamics to behaviors through asynchronous communications

A computational architecture modeling the relation between perception and action is proposed. Basic brain processes representing synaptic plasticity are first abstracted through asynchronous communication protocols and implemented as virtual microcircuits. These are used in turn to build mesoscale circuits embodying parallel cognitive processes. Encoding these circuits into symbolic expressions gives finally rise to neuro-inspired programs that are compiled into pseudo-code to be interpreted by a virtual machine. Quantitative evaluation measures are given by the modification of synapse weights over time. This approach is illustrated by models of simple forms of behaviors exhibiting cognition up to the third level of animal awareness. As a potential benefit, symbolic models of emergent psychological mechanisms could lead to the discovery of the learning processes involved in the development of cognition. The executable specifications of an experimental platform allowing for the reproduction of simulated experiments are given in “Appendix”

Symbolic Neural Dynamics Allow for Modeling Retrograde Amnesia as Well as False Memories

Symbolic neural dynamics abstracting the functionalities of synaptic plasticity has been proposed as a new approach to modeling brain cognitive capabilities and used to define the basic mechanisms of an associative memory. This formalism is extended here to reproduce optogenetic manipulations, thus defining a computational model of memory engrams. It is illustrated through simulations of reversible retrograde amnesia and false memories of contextual fear conditioning that reproduce the behavioral schedules of actual experiments. These results support the hypothesis that separate processes are involved in long term memory i.e., the retention of specific patterns of connectivity between engram cells required for the storage of information, on one hand, and the synaptic strengthening needed for its consolidation and retrieval, on the other. Defined by a logic program, this simulation platform could be used to design and predict the results of experiments involving inhibitory/excitatory loops formed between various brain region

Behaviorism Revisited: Linking Perception and Action Through Symbolic Models of Cognition

Brain simulations as performed today in computational neuroscience rely on analytical methods i.e., mainly differential equations, to model physical processes. The question then arises: will cognitive abilities of real brains spontaneously emerge from these simulations, or should they be encoded and executed on top of them, similarly to the way computer software runs on hardware? Towards this later goal, a new framework linking neural dynamics to behaviors through a virtual machine has been reported and is used here to model brain functionalities in two domains, namely evolutive cases of analogical reasoning and a simple case of meta-cognition. It is argued that this approach to brain modeling could lead to an actualization of the concept of behaviorism as a model for the development of cognition