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The study assesses the impact of selected fruit properties and the souring process parameters on the mechanical properties of four varieties of field cucumbers (Śremianin, Śremski, Polan and Izyd) harvested from 2014 to 2016. The analyses focused on the changes in the values of the puncture strength parameters of the peel and the mesocarp, the deformation from the moment of puncture and the energy needed to puncture the peel and the mesocarp of the selected cucumber varieties relative to the year of harvest, fruit size, type of brine, puncture location and souring time. Neural networks were used to model the relationships studied. Sensitivity analysis of the obtained models showed that the length fraction had the greatest impact on the puncture strength and the energy needed to puncture the peel and the mesocarp. On the other hand, deformation was most affected by the souring time and the brine composition.

Electromagnetic mill installation for dry grinding represents a complex dynamical system that requires specially designed control system. The paper presents model-based predictive control which locates closed loop poles in arbitrary places. The controller performs as gain scheduling prototype where nonlinear model – artificial recurrent neural network, is parameterized with additional measurements and serves as a basis for local linear approximation. Application of such a concept to control electromagnetic mill load allows for stable performance of the installation and assures fulfilment of the product quality as well as the optimization of the energy consumption.

Neural image analysis is commonly used to solve scientific problems of biosystems and mechanical engineering. The method has been applied, for example, to assess the quality of foodstuffs such as fruit and vegetables, cereal grains, and meat. The method can also be used to analyse composting processes. The scientific problem lets us formulate the research hypothesis: it is possible to identify representative traits of the image of composted material that are necessary to create a neural model supporting the process of assessment of the content of dry matter and dry organic matter in composted material. The effect of the research is the identification of selected features of the composted material and the methods of neural image analysis resulted in a new original method enabling effective assessment of the content of dry matter and dry organic matter. The content of dry matter and dry organic matter can be analysed by means of parameters specifying the colour of compost. The best developed neural models for the assessment of the content of dry matter and dry organic matter in compost are: in visible light RBF 19:19-2-1:1 (test error 0.0922) and MLP 14:14-14-11-1:1 (test error 0.1722), in mixed light RBF 30:30-8-1:1 (test error 0.0764) and MLP 7:7-9-7-1:1 (test error 0.1795). The neural models generated for the compost images taken in mixed light had better qualitative characteristics.

In recent years, astrocytes have emerged as such elements involved in inter-neuronal communication that they are increasingly considered in the synaptic coupling connecting neurons and synapses, thus constituting tripartite synapses. Many studies have focused on this astrocytic influence. With the description of the basic mechanisms of synapses, as well as the influence of gliotransmission, we emphasize that gliotransmission to extra-synaptic areas has so far been mainly studied in vivo and in vitro. We come closer to the paradigms used in AI, by proposing an analysis of the astrocyte influence on the variations of the parameters governing synaptic and extra-synaptic plasticity.We sought to apply a method that could allow us to make the link between neuronal functioning and cognition, while satisfying the constraints of computational modeling. We have thus modeled the astrocytic influence on extra-synaptic areas, by applying plasticity rules linked to the evolution of postsynaptic calcium. In this thesis work, we also propose an approach in the analysis of the relationship between synaptic activity and astrocyte activity.First, we identified the area of interest for the parameters that describe the conductance of synaptic and postsynaptic ionotropic receptors. Indeed, gliotransmission seems to target NMDA receptors, of which conductance is lower than that of AMPA receptors. In addition, by modeling the modulation of AMPAR conductance as a function of postsynaptic calcium, we demonstrate that the influence of gliotransmission can lead to increasing or attenuating the propagation of synaptic signals.We have set up a method to study the relationship between neuronal and astrocyte activity over time, which is based on the encoding of a signaling pattern that will direct the activity of neurons. The population of astrocytes that unites the synapses concerned will then come into activity. In the same way that we can analyze the activity of a population of neurons by averaging their activity frequencies, we analyzed the proportion of astrocytes in an active state. More specifically, we binarized the activity according to the astrocytic calcium threshold which conditions gliotransmission. Using this method, we can assess the relationship between astrocyte activity, neuronal activity, and the signaling pattern. By applying this process to the architecture of layers of neurons, we observe the relationship between neuronal activity and astrocyte activity increases with the depth of the population. This analysis highlights a subtle relationship between neurons and astrocytes, which can be compared to the problems encountered in artificial intelligence, more particularly to catastrophic forgetting.We conclude that one of the roles of astrocytes could be related to the methods used to fight against catastrophic forgetfulness and therefore justify the permanence of complex cognitive functions. We also highlight the difficulty of this analysis, based on the dynamics studied and on the computational resource necessary for the demonstration of the astrocytic influence., Ces dernières années, les astrocytes apparaissent comme des éléments si impliqués dans la communication inter-neuronale, qu'ils sont de plus en plus considérés dans le couplage synaptique reliant les neurones et les synapses, constituant ainsi des synapses tripartites. De nombreuses études se sont intéressées à cette influence astrocytaire. En faisant la description des mécanismes de base des synapses et les étapes auxquelles on identifie une influence de la gliotransmission astrocytaire, nous soulignons que la gliotransmission à destination des aires extra-synaptiques a jusqu'alors principalement été étudiée in vivo et in vitro. Nous rapprochons cette problématique des paradigmes utilisés en IA, notamment en proposant une analyse de l'influence astrocytaire sur des variations de paramètres régissant la plasticité synaptique et extra-synaptique.Nous avons cherché à appliquer une méthode qui puisse nous permettre de faire le lien entre fonctionnement neuronal et cognition, tout en satisfaisant les contraintes de la modélisation computationnelle. Nous avons ainsi modélisé l'influence astrocytaire sur les aires extra-synaptiques, en appliquant des règles de plasticité liées à l'évolution du calcium post-synaptique. Dans ce travail de thèse, nous proposons également une approche dans l'analyse de la relation entre activité synaptique et activité astrocytaire.Dans un premier temps, nous avons identifié la zone d'intérêt des paramètres qui décrivent la conductance des récepteurs ionotropiques synaptiques et post-synaptiques. En effet, la gliotransmission semble prendre pour cible des récepteurs NMDA, dont la conductance est plus faible que celle des récepteurs AMPA. De plus, en modélisant la modulation de la conductance des AMPAR en fonction du calcium post-synaptique, nous mettons en évidence que l'influence de la gliotransmission peut conduire à augmenter ou atténuer la propagation des signaux synaptiques.Nous avons mis en place une méthode pour étudier la relation entre activité neuronale et astrocytaire dans le temps, qui repose sur l'encodage d'un pattern signalétique qui va diriger l'activité des neurones. La population d'astrocytes qui fédère les synapses concernées, va alors entrer en activité. De la même manière que l'on peut analyser l'activité d'une population de neurones en moyennant sur leurs fréquences d'activité, nous avons analysé la proportion d'astrocyte en état d'activité. Plus particulièrement, nous avons binarisé l'activité en fonction du seuil de calcium astrocytaire qui conditionne la gliotransmission. Grâce à cette méthode, nous pouvons évaluer la relation entre l'activité astrocytaire, l'activité neuronale, et le pattern signalétique. En appliquant ce procédé sur une architecture de couches de neurones, nous observons la relation entre activité neuronale et activité astrocytaire augmente avec la profondeur de la population. Cette analyse met en évidence une relation subtile entre neurones et astrocytes, que l'on peut rapprocher des problématiques rencontrées en intelligence artificielle, plus particulièrement de l'oubli catastrophique.Nous arrivons à la conclusion que l'un des rôles des astrocytes pourrait s'apparenter aux méthodes utilisées pour lutter contre l'oubli catastrophique et donc justifier de la permanence des fonctions cognitives complexes. Nous mettons également en évidence la difficulté que représente cette analyse, basée sur les dynamiques étudiées et sur la ressource computationnelle nécessaire pour la mise en évidence de l'influence astrocytaire.

Shepard and Chipman's second order isomorphism describes how the brain may represent the relations in the world. However, a common interpretation of the theory can cause difficulties. The problem originates from the static nature of representations. In an alternative interpretation, I propose that we assign an active role to the internal representations and relations. It turns out that a collection of such active units can perform analogical tasks. The new interpretation is supported by the existence of neural circuits that may be implementing such a function. Within this framework, perception, cognition, and motor function can be understood under a unifying principle of analogy.

This study presents the application of regression shrinkage and artificial neural networks in predicting the results of 400-metres hurdles races. The regression models predict the results for suggested training loads in the selected three-month training period. The material of the research was based on training data of 21 Polish hurdlers from the Polish National Athletics Team Association. The athletes were characterized by a high level of performance. To assess the predictive ability of the constructed models a method of leave-one-out cross-validation was used. The analysis showed that the method generating the smallest prediction error was the LASSO regression extended by quadratic terms. The optimal model generated the prediction error of 0.59 s. Otherwise the optimal set of input variables (by reducing 8 of the 27 predictors) was defined. The results obtained justify the use of regression shrinkage in predicting sports outcomes. The resulting model can be used as a tool to assist the coach in planning training loads in a selected training period.

The aim of this thesis is to understand how the brain computes and represents symbolic structures, such like those encountered in language or mathematics. The existence of parts in structures like morphemes, words and phrases has been established through decades of linguistic analysis and psycholinguistic experiments. Nonetheless the neural implementation of the operations that support the extreme combinatorial nature of language remains unsettled. Some basic composition operations that allow the stable internal representation of sensory objects in the sensory cortex, like hierarchical pattern recognition, receptive fields, pooling and normalization, have started to be understood[5]. But models of the binding operations required for construction of complex, possibly hierarchical, symbolic structures on which precise manipulation of its components is a requisite, lack empirical testing and are still unable to predict neuroimaging signals. In this sense, bridging the gap between experimental neuroimaging evidence and the available modelling solutions to the binding problem is a crucial step for the advancement of our understanding of the brain computation and representation of symbolic structures. From the recognition of this problem, the goal of this PhD became the identification and experimental test of the theories, based on neural networks, capable of dealing with symbolic structures, for which we could establish testable predictions against existing fMRI and ECoG neuroimaging measurements derived from language processing tasks. We identified two powerful but very different modelling approaches to the problem. The first is in the context of the tradition of Vectorial Symbolic Architectures (VSA) that bring precise mathematical modelling to the operations required to represent structures in the neural units of artificial neural networks and manipulate them. This is Smolensky’s formalism with tensor product representations (TPR)[10], which he demonstrates can encompass most of the previous work in VSA, like Synchronous Firing[9], Holographic Reduced Representations[8] and Recursive Auto-Associative Memories[1]. The second, is the Neural Blackboard Architecture (NBA) developed by Marc De Kamps and Van der Velde[11], that importantly differentiates itself by proposing an implementation of binding by process in circuits formed by neural assemblies of spiking neural networks. Instead of solving binding by assuming precise and particular algebraic operations on vectors, the NBA proposes the establishment of transient connectivity changes in a circuit structure of neural assemblies, such that the potential _ow of neural activity allowed by working memory mechanisms after a binding process takes place, implicitly represents symbolic structures. The first part of the thesis develops in more detail the theory behind each of these models and their relationship from the common perspective of solving the binding problem. Both models are capable of addressing most of the theoretical challenges posed currently for the neural modelling of symbolic structures, including those presented by Jackendo_[3]. Nonetheless they are very different, Smolenky’s TPR relies mostly on spatial static considerations of artificial neural units with explicit completely distributed and spatially stable representations implemented through vectors, while the NBA relies on temporal dynamic considerations of biologically based spiking neural units with implicit semi-local and spatially unstable representations implemented through neural assemblies. For the second part of the thesis, we identified the superposition principle, which consists on the addition of the neural activations of each of the sub-parts of a symbolic structure, as one of the most crucial assumptions of Smolensky’s TPR. (...), Le travail présenté dans cette thèse fait partie d’un programme de recherche qui vise à comprendre comment le cerveau traite et représente les structures symboliques dans des domaines comme le langage ou les mathématiques. L’existence de structures composées de sous-éléments, tel que les morphèmes, les mots ou les phrases est très fortement suggérée par les analyses linguistiques et les données expérimentales de la psycholinguistique. En revanche, l’implémentation neuronale des opérations et des représentations qui permettent la nature combinatoire du langage reste encore essentiellement inconnue. Certaines opérations de composition élémentaires permettant une représentation interne stable des objets dans le cortex sensoriel, tel que la reconnaissance hiérarchique des formes, sont aujourd’hui mieux comprises [5]. En revanche, les modèles concernant les opérations de liaisons(binding) nécessaires à la construction de structures symboliques complexes et possiblement hiérarchiques, pour lesquelles des manipulations précises des composants doit être possible, sont encore peu testés de façon expérimentale et incapables de prédire les signaux en neuroimagerie. Combler le fossé entre les données de neuroimagerie expérimentale et les modèles proposés pour résoudre le problème de binding est une étape cruciale pour mieux comprendre les processus de traitements et de représentation des structures symboliques. Au regard de ce problème, l’objectif de ce travail était d’identifier et de tester expérimentalement les théories basées sur des réseaux neuronaux, capables de traiter des structures symboliques pour lesquelles nous avons pu établir des prédictions testables, contre des mesures existantes de neuroimagerie fMRI et ECoG dérivées de tâches de traitement du langage. Nous avons identifié deux approches de modélisation pertinentes. La première approche s’inscrit dans le contexte des architectures symboliques vectorielles (VSA), qui propose une modélisation mathématique précise des opérations nécessaires pour représenter les structures dans des réseaux neuronaux artificiels. C’est le formalisme de Paul Smolensky[10], utilisant des produit tensoriel (TPR) qui englobe la plupart des architectures VSA précédemment proposées comme, par exemple, les modèles d’Activation synchrones[9], les représentations réduites holographique[8], et les mémoires auto-associatives récursives[1]. La seconde approche que nous avons identifiée est celle du "Neural Blackboard Architecture" (NBA), développée par Marc De Kamps et Van der Velde[11]. Elle se démarque des autres en proposant une implémentation des mécanismes associatifs à travers des circuits formés par des assemblages de réseaux neuronaux. L’architecture du Blackboard repose sur des changements de connectivité transitoires des circuits d’assemblages neuronaux, de sorte que le potentiel de l’activité neurale permise par les mécanismes de mémoire de travail après un processus de liaison, représente implicitement les structures symboliques. Dans la première partie de cette thèse, nous détaillons la théorie derrière chacun de ces modèles et les comparons, du point de vue du problème de binding. Les deux modèles sont capables de répondre à la plupart des défis théoriques posés par la modélisation neuronale des structures symboliques, notamment ceux présentées par Jackendo[3]. Néanmoins, ces deux classes de modèles sont très différentes. Le TPR de Smolenky s’appuie principalement sur des considérations spatiales statiques d’unités neurales artificielles, avec des représentations explicites complètement distribuées et spatialement stables mises en œuvre par des vecteurs. La NBA en revanche, considère les dynamiques temporelles de décharge de neurones artificiels, avec des représentations spatialement instables implémentées par des assemblages neuronaux. (...)

Wykorzystanie popiołu lotnego do konstrukcji ziemnych wymaga jego własciwego zageszczenia. Zageszczanie powoduje wzrost gestosci gruntu, zwieksza jego wytrzymałosc i zdolnosc do przenoszenia obciazen, a takze zmniejsza scisliwosc i przepuszczalnosc. Oznaczenie zageszczalnosci popiołu lotnego musi byc przeprowadzane na próbkach jednokrotnie zageszczanych, poniewaz sferyczne ziarna popiołu sa niszczone w trakcie ubijania, w zwiazku z tym, laboratoryjne ustalenie krzywej zageszczalnosci popiołu jest bardzo czasochłonne. Celem artykułu było wykorzystanie modelowania neuronowego do prognozy kształtu krzywej zageszczalnosci popiołu lotnego. Podjeto próbe zastosowania sztucznych sieci neuronowych SSN typu MLP do opisu punktów krzywej zageszczalnosci. Kazdy punkt krzywej (wi, ρdi) został opisany przez dwie niezalezne SSN. Wykorzystano SSN o róznych wejsciach, którymi były nowo utworzone zmienne - składowe główne, zalezne od uziarnienia (srednic efektywnych d10-d90 oraz wskazników jednorodnosci i krzywizny uziarnienia), metody zageszczenia oraz gestosci własciwej szkieletu gruntowego. Wektorami wyjscia były współrzedne punktów krzywej zageszczalnosci popiołu lotnego. Najlepszymi sieciami neuronowymi były sieci o topologii: 6-3-1, 6-2-1 i 6-4-1 dla prognozy wartosci wilgotnosci wi, oraz 5-3-1 i 6-3-1 dla predykcji wartosci gestosci objetosciowej szkieletu gruntowego ρdi. Uzyskano sieci neuronowe o zadowalajacej precyzji, szczególnie w przypadku wartosci ρdi. Modelowanie krzywej za pomoca SSN umozliwiło ustalenie przyblizonego kształtu krzywej zageszczalnosci popiołu lotnego.

In this paper we introduce some mathematical and numerical tools to analyze and interpret inhomogeneous quadratic forms. The resulting characterization is in some aspects similar to that given by experimental studies of cortical cells, making it particularly suitable for application to second-order approximations and theoretical models of physiological receptive fields. We first discuss two ways of analyzing a quadratic form by visualizing the coefficients of its quadratic and linear term directly and by considering the eigenvectors of its quadratic term. We then present an algorithm to compute the optimal excitatory and inhibitory stimuli, i.e. the stimuli that maximize and minimize the considered quadratic form, respectively, given a fixed energy constraint. The analysis of the optimal stimuli is completed by considering their invariances, which are the transformations to which the quadratic form is most insensitive. We introduce a test to determine which of these are statistically significant. Next we propose a way to measure the relative contribution of the quadratic and linear term to the total output of the quadratic form. Furthermore, we derive simpler versions of the above techniques in the special case of a quadratic form without linear term and discuss the analysis of such functions in previous theoretical and experimental studies. In the final part of the paper we show that for each quadratic form it is possible to build an equivalent two-layer neural network, which is compatible with (but more general than) related networks used in some recent papers and with the energy model of complex cells. We show that the neural network is unique only up to an arbitrary orthogonal transformation of the excitatory and inhibitory subunits in the first layer.

We examine a simple biologically-motivated neural network, the three-layer version of the Chialvo-Bak `minibrain' [Neurosci. 90 (1999) 1137], and present numerical results which indicate that a non-equilibrium phase transition between ordered and disordered phases occurs subject to the tuning of a control parameter. Scale-free behaviour is observed at the critical point. Notably, the transition here is due solely to network geometry and not any noise factor. The phase of the network is thus a design parameter which can be tuned. The phases are determined by differing levels of interference between active paths in the network and the consequent accidental destruction of good paths., Comment: 10 pages, 5 figures. Version accepted for publication in Physica A

Thalamic neurons have been long assumed to fire in tonic mode during perceptive states, and in burst mode during sleep and unconsciousness. However, recent evidence suggests that bursts may also be relevant in the encoding of sensory information. Here, we explore the neural code of such thalamic bursts. In order to assess whether the burst code is generic or whether it depends on the detailed properties of each bursting neuron, we analyzed two neuron models incorporating different levels of biological detail. One of the models contained no information of the biophysical processes entailed in spike generation, and described neuron activity at a phenomenological level. The second model represented the evolution of the individual ionic conductances involved in spiking and bursting, and required a large number of parameters. We analyzed the models' input selectivity using reverse correlation methods and information theory. We found that n-spike bursts from both models transmit information by modulating their spike count in response to changes to instantaneous input features, such as slope, phase, amplitude, etc. The stimulus feature that is most efficiently encoded by bursts, however, need not coincide with one of such classical features. We therefore searched for the optimal feature among all those that could be expressed as a linear transformation of the time-dependent input current. We found that bursting neurons transmitted 6 times more information about such more general features. The relevant events in the stimulus were located in a time window spanning 〜100 ms before and 〜20 ms after burst onset. Most importantly, the neural code employed by the simple and the biologically realistic models was largely the same, implying that the simple thalamic neuron model contains the essential ingredients that account for the computational properties of the thalamic burst code. Thus, our results suggest the n-spike burst code is a general property of thalamic neurons. Fil: Elijah, Daniel H.. University of Manchester; Reino Unido Fil: Samengo, Ines. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Comisión Nacional de Energía Atómica. Gerencia del Area de Investigación y Aplicaciones No Nucleares. Gerencia de Física (Centro Atómico Bariloche); Argentina Fil: Montemurro, Marcelo Alejandro. University of Manchester; Reino Unido

Sound localization is a vital aspect of hearing for safe navigation of everyday environments. It is also an important factor in speech intelligibility. This ability is facilitated by the interaural level difference (ILD) cue, which arises from binaural hearing: a sound will be more intense at the nearer ear than the farther. In a hearing-impaired listener, this binaural cue may not be available for use and localization may be diminished. While conventional, bilateral, wide dynamic range compression (WDRC) hearing aids distort the interaural level difference by independently altering sound intensities in each ear, wirelessly-linked devices have been suggested to benefit this task by matching amplification in order to preserve ILD. However, this technology has been shown to have varying degrees of success in aiding speech intelligibility and sound localization. As hearing impairment has wide-ranging adverse impacts to physical and mental health, social activity, and cognition, the task of localization improvement must be urgently addressed. Toward this end, neural modelling techniques are used to determine neural representations of ILD cues for linked and unlinked bilateral WDRC hearing aids. Findings suggest that wirelessly-linked WDRC is preferable over unlinked hearing aids or unaided, hearing-impaired listening, although parameters for optimal benefit are dependent on sound level, frequency content, and preceding sounds. Thesis Master of Applied Science (MASc)

As Francis Crick said, neuroscience is a data rich but theory poor field, and it is missing a broad framework as in physics. We wish to put forward such a unified framework based on existing evidences. Unexpectedly, it is a very simple statistical model. Specifically, we find that neural mechanisms in the spatial and temporal dimensionalities follow similar statistical laws. And they are usually called neural coding and memory respectively. Moreover, memory can be divided into two types: long-term and short-term (or instantaneous). The instantaneous memory is the foundation of consciousness according to Crick. Then we indicate the physical and biological mechanisms behind these statistical laws. In general, they actually reflect random processes of particles such as ions. Detailed model and supporting evidences can be found in our previous work. And this simple model is really powerful in explaining most psychological phenomenon and advanced intelligence such as language.

This paper is based on our previous work on neural coding. It is a self-organized model supported by existing evidences. Firstly, we briefly introduce this model in this paper, and then we explain the neural mechanism of language and reasoning with it. Moreover, we find that the position of an area determines its importance. Specifically, language relevant areas are in the capital position of the cortical kingdom. Therefore they are closely related with autonomous consciousness and working memories. In essence, language is a miniature of the real world. Briefly, this paper would like to bridge the gap between molecule mechanism of neurons and advanced functions such as language and reasoning.

Based on existing data, we wish to put forward a biological model of motor system on the neuron scale. Then we indicate its implications in statistics and learning. Specifically, neuron’s firing frequency and synaptic strength are probability estimates in essence. And the lateral inhibition also has statistical implications. From the standpoint of learning, dendritic competition through retrograde messengers is the foundation of conditional reflex and “grandmother cell” coding. And they are the kernel mechanisms of motor learning and sensory-motor integration respectively. Finally, we compare motor system with sensory system. In short, we would like to bridge the gap between molecule evidences and computational models.

The coding mechanism of sensory memory on the neuron scale is one of the most important questions in neuroscience. We have put forward a quantitative neural network model, which is self-organized, self-similar, and self-adaptive, just like an ecosystem following Darwin's theory. According to this model, neural coding is a “mult-to-one”mapping from objects to neurons. And the whole cerebrum is a real-time statistical Turing Machine, with powerful representing and learning ability. This model can reconcile some important disputations, such as: temporal coding versus rate-based coding, grandmother cell versus population coding, and decay theory versus interference theory. And it has also provided explanations for some key questions such as memory consolidation, episodic memory, consciousness, and sentiment. Philosophical significance is indicated at last.

We have put forwards a unified quantitative framework of vision and audition, based on existing data and theories. According to this model, the retina is a feedforward network self-adaptive to inputs in a specific period. After fully grown, cells become specialized detectors based on statistics of stimulus history. This model has provided explanations for perception mechanisms of colour, shape, depth and motion. Moreover, based on this ground we have put forwards a bold conjecture that single ear can detect sound’s direction. This is complementary to existing theories and has provided better explanations for sound localization.

The prevalence of coherent oscillations in various frequency ranges in the central nervous system raises the question of the mechanisms that synchronize large populations of neurons. We study synchronization in models of large networks of spiking neurons with random sparse connectivity. Synchrony occurs only when the average number of synapses, M that a cell receives is larger than a critical value, $M_c$. Below $M_c$, the system is in an asynchronous state. In the limit of weak coupling, assuming identical neurons, we reduce the model to a system of phase oscillators which are coupled via an effective interaction, $\Gamma$. In this framework, we develop an approximate theory for sparse networks of identical neurons to estimate $M_c$ analytically from the Fourier coefficients of $\Gamma$. Our approach relies on the assumption that the dynamics of a neuron depend mainly on the number of cells that are presynaptic to it. We apply this theory to compute $M_c$ for the integrate-and-fire (\IF) model as a function of the intrinsic neuronal properties (\eg the refractory period $T_r$), the synaptic time constants and the strength of the external stimulus, $\Iapp$. When the neurons are inhibitory, $M_c$ is found to be non-monotonous with the strength of $\Iapp$. For $T_r=0$, we estimate the minimum value of $M_c$ over all the parameters of the model to be $363.8$. Above $M_c$, the neurons tend to fire in: 1) smeared one cluster states at high firing rates and 2) smeared two or more cluster states at low firing rates. For excitatory interactions synchrony can be achieved only if the firing rate is not too high. However, our estimates of $M_c$ are, in general, much smaller than for inhibitory networks for similar level of activity. Above $M_c$ excitatory networks settle into smeared 1-cluster states.Refractoriness decreases $M_c$ at intermediate and high firing rates. These results are compared against numerical simulations. We show numerically that systems with different sizes, $N$, behave in the same way provided the connectivity, $M$, is such a way that $1/ \Meff = 1 / M - 1 / N$ remains constant when $N$ varies. This allows one to extrapolate the large $N$ behavior of a network from numerical simulations of networks of relatively small sizes ($N=800$ in our case). We find that our theory predicts with remarkable accuracy the value of $M_c$ and the patterns of synchrony above $M_c$, provided the synaptic coupling is not too large. We also study the strong coupling regime of inhibitory sparse networks. All of our simulations demonstrate that increasing the coupling strength reduces the level of synchrony of the neuronal activity. Above a critical coupling strength, the network activity is asynchronous. We point out that there is a fundamental limitation for the mechanisms of synchrony relying on inhibition alone, if heterogeneities in the intrinsic properties of the neurons and spatial fluctuations in the external input are also taken into account.

The structural complexity of culture cannot be characterized by simply modeling cultural beliefs or inherited ideas. Formal computational and algorithmic models of culture have focused on the inheritance of discrete cultural units, which can be hard to define and map to practical contexts. In cultural anthropology, research involving structuralist and post-structuralist perspectives have helped us better understand culturally-dependent classification systems and oppositional phenomena (e.g. light-dark, hot-cold, good-evil). Contemporary research in cognitive neuroscience suggests that complementary sets may be represented dynamically in the brain, but no model for the evolution of these sets has of yet been proposed. To fill this void, a method for simulating cultural or other highly symbolic behaviors called contextual geometric structures will be introduced. The contextual geometric structures approach is based on a hybrid model that approximates both individual/group cultural practice and a fluctuating environment. The hybrid model consists of two components. The first is a set of discrete automata with a soft classificatory structure. These automata are then embedded in a Lagrangian-inspired particle simulation that defines phase space relations and environmental inputs. The concept of conditional features and equations related to diversity, learning, and forgetting are used to approximate the goal-directed and open-ended features of cultural-related emergent behavior. This allows cultural patterns to be approximated in the context of both stochastic and deterministic evolutionary dynamics. This model can yield important information about multiple structures and social relationships, in addition to phenomena related to sensory function and higher-order cognition observed in neural systems.

Metabolic-repair models, or (M,R)-systems were introduced in Relational Biology by Robert Rosen. Subsequently, Rosen represented such (M,R)-systems (or simply MRs)in terms of categories of sets, deliberately selected without any structure other than the discrete topology of sets. Theoreticians of life's origins postulated that Life on Earth has begun with the simplest possible organism, called the primordial. Mathematicians interested in biology attempted to answer this important question of the minimal living organism by defining the functional relations that would have made life possible in such a minimal system- a grandad and grandma of all living organisms on Earth.

The simulation data presented here, and the conceptual framework developed for their interpretation are, both, in need of substantial refinement and extension. However, granting that they are initial pointers of some merit, and elementary indicators of general principles, several implications follow: the activity patterns of neurons and their assemblies are interdependent with the extracellular milieu in which they are embedded, and to whose time varying composition they contribute. The complexity of this interdependence in the temporal dimension forecloses any time and context invariant relation between what the experimenter may consider stimulus input and its representation in neural activity. Hence, ideas of coding by (quasi)-digital neurons are called in question by the mutual interdependence of neurons and their humoral milieu. Instead, concepts of 'mass action' in the Nervous system gain a new perspective: this time augmented by including the chemical medium surrounding neurons as part of the dynamics of the system as a whole. Accordingly, a meaningful way to describe activity in a neuron assembly would be in terms of a state space in which it can move along an infinite number of trajectories.

S'ha investigat molt tant els mecanismes neuronals bàsics de l'audició com l'organització psicològica de la percepció de la parla. Tanmateix, en ambdós temes n'hi ha una relativa escassetat en quant a modelització. Aquí describim dos treballs de modelització. Un d'ells proposa un nou mecanisme de millora de selectivitat de freqüències que explica resultats de experiments neurofisiològics investigant manifestacions de forward masking y sobretot auditory streaming en l'escorça auditiva principal (A1). El mecanisme funciona en una xarxa feed-forward amb depressió sináptica entre el tàlem y l'escorça, però mostrem que és robust a l'introducció d'una organització realista del circuit de A1, que per la seva banda explica cantitat de dades neurofisiològics. L'altre treball descriu un mecanisme candidat d'explicar la trobada en estudis psicofísics de diferències en la percepció de paraules entre bilinguës primerencs y simultànis. Simulant tasques de decisió lèxica y discriminació de fonemes, fortifiquem l'hipòtesi de que persones sovint exposades a variacions dialectals de paraules poden guardar aquestes en el seu lèxic, sense alterar representacions fonemàtiques ., Though much experimental research exists on both basic neural mechanisms of hearing and the psychological organization of language perception, there is a relative paucity of modelling work on these subjects. Here we describe two modelling efforts. One proposes a novel mechanism of frequency selectivity improvement that accounts for results of neurophysiological experiments investigating manifestations of forward masking and above all auditory streaming in the primary auditory cortex (A1). The mechanism works in a feed-forward network with depressing thalamocortical synapses, but is further showed to be robust to a realistic organization of the neural circuitry in A1, which accounts for a wealth of neurophysiological data. The other effort describes a candidate mechanism for explaining differences in word/non-word perception between early and simultaneous bilinguals found in psychophysical studies. By simulating lexical decision and phoneme discrimination tasks in an attractor neural network model, we strengthen the hypothesis that people often exposed to dialectal word variations can store these in their lexicons, without altering their phoneme representations., Se ha investigado mucho tanto los mecanismos neuronales básicos de la audición como la organización psicológica de la percepción del habla. Sin embargo, en ambos temas hay una relativa escasez en cuanto a modelización. Aquí describimos dos trabajos de modelización. Uno propone un nuevo mecanismo de mejora de selectividad de frecuencias que explica resultados de experimentos neurofisiológicos investigando manifestaciones de forward masking y sobre todo auditory streaming en la corteza auditiva principal (A1). El mecanismo funciona en una red feed-forward con depresión sináptica entre el tálamo y la corteza, pero mostramos que es robusto a la introducción de una organización realista del circuito de A1, que a su vez explica cantidad de datos neurofisiológicos. El otro trabajo describe un mecanismo candidato de explicar el hallazgo en estudios psicofísicos de diferencias en la percepción de palabras entre bilinguës tempranos y simultáneos. Simulando tareas de decisión léxica y discriminación de fonemas, fortalecemos la hipótesis de que personas expuestas a menudo a variaciones dialectales de palabras pueden guardar éstas en su léxico, sin alterar representaciones fonémicas.

A categorical and Łukasiewicz-Topos framework for Algebraic Logic models of nonlinear dynamics in complex functional systems such as Neural Networks, Cell Genome and Interactome Networks is introduced. Łukasiewicz Algebraic Logic models of both neural and genetic networks and signaling pathways in cells are formulated in terms of nonlinear dynamic systems with n-state components that allow for the generalization of previous logical models of both genetic activities and neural networks. An algebraic formulation of variable next-state/transfer functions is extended to a Łukasiewicz Topos with an N-valued Łukasiewicz Algebraic Logic subobject classifier description that represents non-random and nonlinear network activities as well as their transformations in developmental processes and carcinogenesis.

The asymptotic limit theorems of information theory permit a concise formulation of Bernard Baars' global workspace/global broadcast picture of consciousness, focusing on how networks of unconscious cognitive modules are driven by the classic 'no free lunch' argument into shifting, tunable, alliances having variable thresholds for signal detection. The model directly accounts for the punctuated characteristics of many conscious phenomena, and derives the inherent necessity of inattentional blindness and related effects.

We extend a cognitive paradigm for gene expression based on the asymptotic limit theorems of information theory to the epigenetic epidemiology of mental disorders. In particular, we recognize the fundamental role culture plays in human biology, another heritage mechanism parallel to, and interacting with, the more familiar genetic and epigenetic systems. We do this via a model through which culture acts as another tunable epigenetic catalyst that both directs developmental trajectories, and becomes convoluted with individual ontology, via a mutually-interacting crosstalk mediated by a social interaction that is itself culturally driven. We call for the incorporation of embedding culture as an essential component of the epigenetic regulation of human mental development and its dysfunctions, bringing what is perhaps the central reality of human biology into the center of biological psychiatry. Current US work on gene-environment interactions in psychiatry must be extended to a model of gene-environment-culture interaction to avoid becoming victim of an extreme American individualism that threatens to create paradigms particular to that culture and that are, indeed, peculiar in the context of the world's cultures. The cultural and epigenetic systems of heritage may well provide the 'missing' heritability of complex diseases now under so much intense discussion.

Serial endosymbiosis theory provides a unifying paradigm for examining the interaction of cognitive modules at vastly different scales of biological, social, and cultural organization. A trivial but not unimportant model associates a dual information source with a broad class of cognitive processes, and punctuated phenomena akin to phase transitions in physical systems, and associated coevolutionary processes, emerge as consequences of the homology between information source uncertainty and free energy density. The dynamics, including patterns of punctuation similar to ecosystem resilience transitions, are largely dominated by the availability of 'Roman roads' constituting channels for the transmission of information between modules.

Informatics studies all aspects of the structure of natural and artificial information systems. Theoretical and abstract approaches to information have made great advances, but human information processing is still unmatched in many areas, including information management, representation and understanding. Neurocognitive informatics is a new, emerging field that should help to improve the matching of artificial and natural systems, and inspire better computational algorithms to solve problems that are still beyond the reach of machines. In this position paper examples of neurocognitive inspirations and promising directions in this area are given.

A quantitative interrelation between the physical parameters of electromagnetic and acoustic waves perceived by the sight and hearing organs of a human has been discovered. It is shown that visible light wavelengths can be correlated with the range most used by humans for acoustic communication, including, in particular, verbal dialogue. It is shown, that maxima of spectroscopic sensitivity of the eye receptors are placed on the scale of wavelengths like musical consonance intervals (the small third, major third, quart).

In this paper the problem of regressors selection in Virtual Instruments (VI) design is addressed The VI is designed to replace the on line analyzer of a Sulfur Recovery Unit (SRU) of a large refinery located in Sicily during maintenance operations. It is designed by using nonlinear MA models implemented by a MLP neural network. The use of a set of cross-correlation functions, proposed by Billings and Voon to evaluate the performance of nonlinear models is used to select the regressors of the discrete-time NMA model by implementing an automatic regressor selection algorithm. The designed Soft Sensor has been implemented at the refinery to be tested on line.

Experimental evidence for self-organised criticality (SOC) in non-conservative systems has recently been found in studies of rat cortical slices. The size distribution of observed neuronal avalanches has been attested to obey $3/2$ power-law scaling. A mean-field sandpile model of a noisy neuronal system is proposed to refute the irreconcilability between non-conservation and criticality put forward by longstanding SOC hypotheses. The model predicts that neuronal networks achieve and maintain criticality despite non-conservation due to the presence of background activity originating from membrane potential fluctuations within individual neurons. Furthermore, small networks are demonstrated to tip towards epileptiform activity when background activity is strong. This finding ties in redundancy, an intriguing feature of brain networks, to robustness of SOC behaviour.

A self-organising model is proposed to explain the criticality in cortical networks deduced from recent observations of neuronal avalanches. Prevailing understanding of self-organised criticality (SOC) dictates that conservation of energy is essential to its emergence. Neuronal networks however are inherently non-conservative as demonstrated by microelectrode recordings. The model presented here shows that SOC can arise in non-conservative systems as well, if driven internally. Evidence suggests that synaptic background activity provides the internal drive for non-conservative cortical networks to achieve and maintain a critical state. SOC is robust to any degree $\eta \in (0,1]$ of background activity when the network size $N$ is large enough such that $\eta N\sim 10^3$. For small networks, a strong background leads to epileptiform activity, consistent with neurophysiological knowledge about epilepsy.

Our nervous system can efficiently recognize objects in spite of changes in contextual variables such as perspective or lighting conditions. Several lines of research have proposed that this ability for invariant recognition is learned by exploiting the fact that object identities typically vary more slowly in time than contextual variables or noise. Here, we study the question of how this “temporal stability” or “slowness” approach can be implemented within the limits of biologically realistic spike-based learning rules. We first show that slow feature analysis, an algorithm that is based on slowness, can be implemented in linear continuous model neurons by means of a modified Hebbian learning rule. This approach provides a link to the trace rule, which is another implementation of slowness learning. Then, we show analytically that for linear Poisson neurons, slowness learning can be implemented by spike-timing–dependent plasticity (STDP) with a specific learning window. By studying the learning dynamics of STDP, we show that for functional interpretations of STDP, it is not the learning window alone that is relevant but rather the convolution of the learning window with the postsynaptic potential. We then derive STDP learning windows that implement slow feature analysis and the “trace rule.” The resulting learning windows are compatible with physiological data both in shape and timescale. Moreover, our analysis shows that the learning window can be split into two functionally different components that are sensitive to reversible and irreversible aspects of the input statistics, respectively. The theory indicates that irreversible input statistics are not in favor of stable weight distributions but may generate oscillatory weight dynamics. Our analysis offers a novel interpretation for the functional role of STDP in physiological neurons., Author Summary Neurons interact by exchanging information via small connection sites, so-called synapses. Interestingly, the efficiency of synapses in transmitting neuronal signals is not static, but changes dynamically depending on the signals that the associated neurons emit. As neurons receive thousands of synaptic input signals, they can thus “choose” the input signals they are interested in by adjusting their synapses accordingly. This adaptation mechanism, known as synaptic plasticity, has long been hypothesized to form the neuronal correlate of learning. It raises a difficult question: what aspects of the input signals are the neurons interested in, given that the adaptation of the synapses follows a certain mechanistic rule? We address this question for spike-timing–dependent plasticity, a type of synaptic plasticity that has raised a lot of interest in the last decade. We show that under certain assumptions regarding neuronal information transmission, spike-timing–dependent plasticity focuses on aspects of the input signals that vary slowly in time. This relates spike-timing–dependent plasticity to a class of abstract learning rules that were previously proposed as a means of learning to recognize objects in spite of contextual changes such as size or position. Based on this link, we propose a novel functional interpretation of spike-timing–dependent plasticity.

This work deals with the design and implementation of soft sensors designed to perform the what-if analysis on a Sulfur Recovery Unit (SRU) in a refinery. Soft sensors are mathematical models able to emulate the behaviour of existing sensors on the basis of available measurements. This paper deals with the design of a soft sensor by using a nonlinear one step ahead model, eventually usable on-line. The realized models represented the building blocks for the design of the Virtual Instrument based on a multi steps ahead predictor to be used in the what-if analysis. The instrument allows the estimation the consequences that variations of the input trends produces on system outputs, to eventually improve control policy effectiveness.

Keywords: Learning by Imitation ; Neural Modelling Reference LASA-BOOK-2007-001 Record created on 2007-11-07, modified on 2017-05-10

The Line flow Contingency Selection and Ranking (CS & R) is performed to rank the critical contingencies in order of their severity. An Artificial Neural Network based method for MW security assessment corresponding to line outage events have been reported by various authors in the literature. One way to provide an understanding of the behaviour of Neural Networks is to extract rules that can be provided to the user. The domain knowledge (fuzzy rules extracted from Multi-layer Perceptron model trained by Back Propagation algorithm) is integrated into a Neural Network for fast and accurate CS & R in an IEEE 14-bus system, for unknown load patterns and are found to be suitable for on-line applications at Energy Management Centers. The system user is provided with the capability to determine the set of conditions under which a line-outage is critical, and if critical, then how severe it is, thereby providing some degree of transparency of the ANN solution.

Network theory provides an alternative to the renormalization and phase transition methods used in Wallace's (2005a) treatment of Baars' Global Workspace model. Like the earlier study, the new analysis produces the workplace itself, the tunable threshold of consciousness, and the essential role for embedding contexts, in an explicitly analytic 'necessary conditions' manner which suffers neither the mereological fallacy inherent to brain-only theories nor the sufficiency indeterminacy of neural network or agent-based simulations. This suggests that the new approach, and the earlier, represent different analytically solvable limits in a broad continuum of possible models, analogous to the differences between bond and site percolation or between the two and many-body limits of classical mechanics. The development significantly extends the theoretical foundations for an empirical general cognitive model (GCM) based on the Shannon-McMillan Theorem. Patterned after the general linear model which reflects the Central Limit Theorem, the proposed technique should be both useful for the reduction of expermiental data on consciousness and in the design of devices with capacities which may transcend those of conventional machines and provide new perspectives on the varieties of biological consciousness.

Many activities in Cognitive Science involve complex computer models and simulations of both theoretical and real entities. Artificial Intelligence and the study of artificial neural nets in particular, are seen as major contributors in the quest for understanding the human mind. Computational models serve as objects of experimentation, and results from these virtual experiments are tacitly included in the framework of empirical science. Cognitive functions, like learning to speak, or discovering syntactical structures in language, have been modeled and these models are the basis for many claims about human cognitive capacities. Artificial neural nets (ANNs) have had some successes in the field of Artificial Intelligence, but the results from experiments with simple ANNs may have little value in explaining cognitive functions. The problem seems to be in relating cognitive concepts that belong in the `top-down' approach to models grounded in the `bottom-up' connectionist methodology. Merging the two fundamentally different paradigms within a single model can obfuscate what is really modeled. When the tools (simple artificial neural networks) to solve the problems (explaining aspects of higher cognitive functions) are mismatched, models with little value in terms of explaining functions of the human mind are produced. The ability to learn functions from data-points makes ANNs very attractive analytical tools. These tools can be developed into valuable models, if the data is adequate and a meaningful interpretation of the data is possible. The problem is, that with appropriate data and labels that fit the desired level of description, almost any function can be modeled. It is my argument that small networks offer a universal framework for modeling any conceivable cognitive theory, so that neurological possibility can be demonstrated easily with relatively simple models. However, a model demonstrating the possibility of implementation of a cognitive function using a distributed methodology, does not necessarily add support to any claims or assumptions that the cognitive function in question, is neurologically plausible.

The task of understanding how the brain works has met with only limited success since important design concepts are not as yet incorporated in the analysis. Relevant concepts can be uncovered by studying the powerful methodologies that have evolved in the context of computer programming, raising the question of how the concepts involved there can be realised in neural hardware. Insights can be gained in regard to such issues through the study of the role played by models and representation. These insights lead on to an appreciation of the mechanisms underlying subtle capacities such as those concerned with the use of language. A precise, essentially mathematical account of such capacities is in prospect for the future.

The Line flow Contingency Selection and Ranking (CS & R) is performed to rank the critical contingencies in order of their severity. An Artificial Neural Network based method for MW security assessment corresponding to line outage events have been reported by various authors in the literature. One way to provide an understanding of the behaviour of Neural Networks is to extract rules that can be provided to the user. The domain knowledge (fuzzy rules extracted from Multi-layer Perceptron model trained by Back Propagation algorithm) is integrated into a Neural Network for fast and accurate CS & R in an IEEE 14-bus system, for unknown load patterns and are found to be suitable for on-line applications at Energy Management Centers. The system user is provided with the capability to determine the set of conditions under which a line-outage is critical, and if critical, then how severe it is, thereby providing some degree of transparency of the ANN solution.

A forecast activity in the lowest layer of the atmosphere, well known for its strongly non-linear physics, is presented in this paper. The forecast method is mainly based on a neural network model, whose structure is briefly described. We stress that preprocessing allows us to extract the main periodicities and to train the network on a residual series of radon data: here the network itself is able to catch the hidden non-linear dynamics. Final results show the ability of the model to predict values of radon concentration and stable layer depth, which represent important physical information for air pollution forecasts near the surface.

The theory of computational complexity is used to underpin a recent model of neocortical sensory processing. We argue that encoding into reconstruction networks is appealing for communicating agents using Hebbian learning and working on hard combinatorial problems, which are easy to verify. Computational definition of the concept of intelligence is provided. Simulations illustrate the idea., 6 pages, 2 figures

We adapt an information theory analysis of interacting cognitive biological and social modules to the problem of the global neuronal workspace, the new standard neuroscience paradigm for consciousness. Tunable punctuation emerges in a natural way, suggesting the possibility of fitting appropriate phase transition power law, and away from transition, generalized Onsager relation expressions, to observational data on conscious reaction. The development can be extended in a straightforward manner to include psychosocial stress, culture, or other cognitive modules which constitute a structured, embedding hierarchy of contextual constraints acting at a slower rate than neuronal function itself. This produces a 'biopsychosociocultural' model of individual consciousness that, while otherwise quite close to the standard treatment, meets compelling philosophical and other objections to brain-only descriptions.

Current methodologies in the neurosciences have difficulty in accounting for complex phenomena such as language, which can however be quite well characterised in phenomenological terms. This paper addresses the issue of unifying the two approaches. We typically understand complicated systems in terms of a collection of models, each characterisable in principle within a formal system, it being possible to explain higher-level properties in terms of lower level ones by means of a series of inferences based on these models. We consider the nervous system to be a mechanism for implementing the demands of an appropriate collection of models, each concerned with some aspect of brain and behaviour, the observer mechanism of Baas playing an important role in matching model and behaviour in this context. The discussion expounds these ideas in detail, showing their potential utility in connection with real problems of brain and behaviour, important areas where the ideas can be applied including the development of higher levels of abstraction, and linguistic behaviour, as described in the works of Karmiloff-Smith and Jackendoff respectively.

Adapting Dretske's approach on the necessary conditions for mental process, we apply a communication theory analysis of interacting cognitive biological and social modules to the global neuronal workspace. Using an obvious canonical homology with statistical physics, the method, when iterated, generates a fluctuating dynamic threshold recognizably similar to a phase transition in a physical system, but constrained to a manifold/atlas structure analogous to a tunable retina. The resulting 'General Cognitive Model' can be extended in a straightforward manner to include the effects of psychosocial stress, culture, or other cognitive modules which constitute a structured, embedding, hierarchy of contextual constraints acting at a slower rate than neuronal function itself. This produces an empirically-testable 'bipsychosociocultural' treatment of individual consciousness that, while otherwise remarkably similar to the standard development, meets compelling philosophical and other objections to brain-only descriptions.

Many activities in Cognitive Science involve complex computer models and simulations of both theoretical and real entities. Artificial Intelligence and the study of artificial neural nets in particular, are seen as major contributors in the quest for understanding the human mind. Computational models serve as objects of experimentation, and results from these virtual experiments are tacitly included in the framework of empirical science. Simulations of cognitive functions, like learning to speak, or discovering syntactical structures in language, are the basis for many claims about human capacities in language acquisition. This raises the question whether results obtained from experiments that are essentially performed on data structures are equivalent to results from "real" experiments. This paper examines some design methodologies for models of cognitive functions using artificial neural nets. The process of conducting the cognitive simulations is largely a projection of theories, or even unsubstantiated conjectures, onto simulated neural structures and an interpretation of the experimental results in terms of the human brain. The problem with this process is that results from virtual experiments are taken to refer unambiguously to the human brain; and the more the language of human cognitive function is deployed in both theory construction and (virtual) experimental interpretation, the more convincing the impression. Additionally, the complexity of the methodologies, principles, and visualization techniques, in the implementation of the computational model, masks the lack of actual similarities between model and real world phenomena. Some computational models involving artificial neural nets have had some success, even commercially, but there are indications that the results from virtual experiments have little value in explaining cognitive functions. The problem seems to be in relating computational, or mathematical, entities to real world objects, like neurons and brains. I argue that the role of Artificial Intelligence as a contributor to the knowledge base of Cognitive Science is diminished as a consequence.

What type of artificial systems will claim to be conscious and will claim to experience qualia? The ability to comment upon physical states of a brain-like dynamical system coupled with its environment seems to be sufficient to make claims. The flow of internal states in such system, guided and limited by associative memory, is similar to the stream of consciousness. Minimal requirements for an artificial system that will claim to be conscious were given in form of specific architecture named articon. Nonverbal discrimination of the working memory states of the articon gives it the ability to experience different qualities of internal states. Analysis of the inner state flows of such a system during typical behavioral process shows that qualia are inseparable from perception and action. The role of consciousness in learning of skills, when conscious information processing is replaced by subconscious, is elucidated. Arguments confirming that phenomenal experience is a result of cognitive processes are presented. Possible philosophical objections based on the Chinese room and other arguments are discussed, but they are insufficient to refute claims articon’s claims. Conditions for genuine understanding that go beyond the Turing test are presented. Articons may fulfill such conditions and in principle the structure of their experiences may be arbitrarily close to human.

If we want to better understand how the microtubules can translate and input the information carried by the electrophysiologic impulses that enter the brain cortex, a detailed investigation of the local electromagnetic field structure is needed. In this paper are assessed the electric and the magnetic field strengths in different neuronal compartments. The calculated results are verified via experimental data comparison. It is shown that the magnetic field is too weak to input information to microtubules and no Hall effect, respectively QHE is realistic. Local magnetic flux density is less than 1/300 of the Earth’s magnetic field that’s why any magnetic signal will be suffocated by the surrounding noise. In contrast the electric field carries biologically important information and acts upon voltage-gated transmembrane ion channels that control the neuronal action potential. If mind is linked to subneuronal processing of information in the brain microtubules then microtubule interaction with the local electric field, as input source of information is crucial. The intensity of the electric field is estimated to be 10V/m inside the neuronal cytoplasm however the details of the tubulin-electric field interaction are still unknown. A novel hypothesis stressing on the tubulin C-termini intraneuronal function is presented replacing the current flawed models (Tuszynski 2003, Mershin 2003, Hameroff 2003, Porter 2003) presented at the Quantum Mind II Conference held at Tucson, Arizona, 15-19 March 2003, that are shown in this presentation to be biologically and physically inconsistent.

We study a modification of the Hoppensteadt-Izhikevich canonical model for networks of class I neurons, in which the 'pulse' emitted by a neuron is smooth rather than a delta-function. We prove two types of results about synchronization and desynchronization of such networks, the first type pertaining to 'pulse' functions which are symmetric, and the other type in the regime in which each neuron is connected to many other neurons.

We review recent developments in the measurement of the dynamics of the response properties of auditory cortical neurons to broadband sounds, which is closely related to the perception of timbre. The emphasis is on a method that characterizes the spectro-temporal properties of single neurons to dynamic, broadband sounds, akin to the drifting gratings used in vision. The method treats the spectral and temporal aspects of the response on an equal footing., Comment: 27 pages, 17 figures, straight LaTeX +psfig. Originally submitted to the neuro-sys archive which was never publicly announced (was 9804002)

The corticothalamic feedback and the thalamic reticular nucleus have gained much attention lately because of their integrative and modulatory functions. A previous study by the author suggested that this circuitry can process analogies (i.e., the {\em analogy hypothesis}). In this paper, the proposed model was implemented as a network of leaky integrate-and-fire neurons to test the {\em analogy hypothesis}. The previous proposal required specific delay and temporal dynamics, and the implemented network tuned accordingly functioned as predicted. Furthermore, these specific conditions turn out to be consistent with experimental data, suggesting that a further investigation of the thalamocortical circuit within the {\em analogical framework} may be worthwhile.