非言语学习不良青少年空间关系推理及其影响因素研究

It has reported that individuals with nonverbal learning disabilities (NLD) have deficits in visual-spatial organization and strengths in rote language abilities. At present, there are few studies on higher order cognitive abilities of adolescents with NLD, such as the reasoning about spatial relations. The study sampled three groups: a normal group (a control group, C), a nonverbal learning disabilities group (NLD), and a verbal learning disabilities group (VLD). The aim of this study was to examine spatial and nonspatial relation reasoning abilities in adolescents with NLD under figure and word conditions, and assessed the relative involvement of different working memory components in four types of reasoning tasks: reasoning about figure-spatial, figure-nonspatial, verbal-spatial, and verbal-nonspatial relations. Using the double-tasks methodology, visual, spatial, central-executive, and phonological loads were realized. We tried to find how working memory components impact on adolescents with NLD spatial and nonspatial reasoning. The main results of present research are as follows. (1) The NLD group didn’t differ from normal group on reasoning about figure-nonspatial relations. The NLD group scored lower than the C group in spatial problems. So, adolescents with NLD showed a dissociation between spatial and non-spatial relation reasoning. They scored higher in non-spatial problems than in spatial ones. Adolescents with VLD developed well in reasoning about figure-nonspatial relations, but showed deficits in other three tasks. (2) For each reasoning task, the difficult of four types of reasoning problem had different changing trend. For figure and verbal spatial problems, mental model approach can interpret performance of the four problems well. For verbal nonspatial problems, a logical rule approach can interpret performance of the four problems well. (3) Adolescents with NLD did not differ from adolescents with VLD and normal adolescents in phonological, central-executive, and visual dual tasks. But the NLD group had lower performance than the other two groups in spatial dual task. The results showed a dissociation between visual and spatial working memory in NLD group. The VLD group only experienced deficits in central-executive subsystem. (4) The studies found that spatial reasoning mainly loaded spatial working memory, whist the involvement of spatial resources in nonspatial reasoning was little. Visual working memory mainly involved in reasoning about spatial and figure-nonspatial relations, especially in figure-nonspatial problems, and had few impacts on verbal-nonspatial reasoning. Central executive system was involved in all reasoning tasks. The role of phonological loop in the reasoning tasks required further explored. (5) According to the findings, we concluded that the deficits in spatial working memory resulted in poor spatial reasoning abilities for teenagers with NLD, whist because of the limited central executive capability, teenagers with VLD showed poor reasoning abilities. (6) The three groups can used multiple strategies during the reasoning process. They didn’t differ from each other in reasoning strategies. They all used mental model strategy to solve figure and verbal spatial problems, and used logic rule strategy to solve verbal nonspatial problems.

Type-omega DPLs

Type-omega DPLs (Denotational Proof Languages) are languages for proof presentation and search that offer strong soundness guarantees. LCF-type systems such as HOL offer similar guarantees, but their soundness relies heavily on static type systems. By contrast, DPLs ensure soundness dynamically, through their evaluation semantics; no type system is necessary. This is possible owing to a novel two-tier syntax that separates deductions from computations, and to the abstraction of assumption bases, which is factored into the semantics of the language and allows for sound evaluation. Every type-omega DPL properly contains a type-alpha DPL, which can be used to present proofs in a lucid and detailed form, exclusively in terms of primitive inference rules. Derived inference rules are expressed as user-defined methods, which are "proof recipes" that take arguments and dynamically perform appropriate deductions. Methods arise naturally via parametric abstraction over type-alpha proofs. In that light, the evaluation of a method call can be viewed as a computation that carries out a type-alpha deduction. The type-alpha proof "unwound" by such a method call is called the "certificate" of the call. Certificates can be checked by exceptionally simple type-alpha interpreters, and thus they are useful whenever we wish to minimize our trusted base. Methods are statically closed over lexical environments, but dynamically scoped over assumption bases. They can take other methods as arguments, they can iterate, and they can branch conditionally. These capabilities, in tandem with the bifurcated syntax of type-omega DPLs and their dynamic assumption-base semantics, allow the user to define methods in a style that is disciplined enough to ensure soundness yet fluid enough to permit succinct and perspicuous expression of arbitrarily sophisticated derived inference rules. We demonstrate every major feature of type-omega DPLs by defining and studying NDL-omega, a higher-order, lexically scoped, call-by-value type-omega DPL for classical zero-order natural deduction---a simple choice that allows us to focus on type-omega syntax and semantics rather than on the subtleties of the underlying logic. We start by illustrating how type-alpha DPLs naturally lead to type-omega DPLs by way of abstraction; present the formal syntax and semantics of NDL-omega; prove several results about it, including soundness; give numerous examples of methods; point out connections to the lambda-phi calculus, a very general framework for type-omega DPLs; introduce a notion of computational and deductive cost; define several instrumented interpreters for computing such costs and for generating certificates; explore the use of type-omega DPLs as general programming languages; show that DPLs do not have to be type-less by formulating a static Hindley-Milner polymorphic type system for NDL-omega; discuss some idiosyncrasies of type-omega DPLs such as the potential divergence of proof checking; and compare type-omega DPLs to other approaches to proof presentation and discovery. Finally, a complete implementation of NDL-omega in SML-NJ is given for users who want to run the examples and experiment with the language.

Detecting Digital Forgeries Using Bispectral Analysis

With the rapid increase in low-cost and sophisticated digital technology the need for techniques to authenticate digital material will become more urgent. In this paper we address the problem of authenticating digital signals assuming no explicit prior knowledge of the original. The basic approach that we take is to assume that in the frequency domain a "natural" signal has weak higher-order statistical correlations. We then show that "un-natural" correlations are introduced if this signal is passed through a non-linearity (which would almost surely occur in the creation of a forgery). Techniques from polyspectral analysis are then used to detect the presence of these correlations. We review the basics of polyspectral analysis, show how and why these tools can be used in detecting forgeries and show their effectiveness in analyzing human speech.

Advanced Programming Language Features for Executable Design Patterns "Better Patterns Through Reflection

The Design Patterns book [GOF95] presents 24 time-tested patterns that consistently appear in well-designed software systems. Each pattern is presented with a description of the design problem the pattern addresses, as well as sample implementation code and design considerations. This paper explores how the patterns from the "Gang of Four'', or "GOF'' book, as it is often called, appear when similar problems are addressed using a dynamic, higher-order, object-oriented programming language. Some of the patterns disappear -- that is, they are supported directly by language features, some patterns are simpler or have a different focus, and some are essentially unchanged.

Revised Report on the Algorithmic Language Scheme

Data and procedures and the values they amass, Higher-order functions to combine and mix and match, Objects with their local state, the message they pass, A property, a package, the control of point for a catch- In the Lambda Order they are all first-class. One thing to name them all, one things to define them, one thing to place them in environments and bind them, in the Lambda Order they are all first-class. Keywords: Scheme, Lisp, functional programming, computer languages.

Asymptotics of Perturbations to the Wave Equation

M. Hieber, I. Wood: Asymptotics of perturbations to the wave equation. In: Evolution Equations, Lecture Notes in Pure and Appl. Math., 234, Marcel Dekker, (2003), 243-252.

Twierdzenia o punktach stałych pewnych klas operatorów nieliniowych wraz z ich zastosowaniami

Wydział Matematyki i Informatyki

Consciousness CLEARS the Mind

A full understanding of consciouness requires that we identify the brain processes from which conscious experiences emerge. What are these processes, and what is their utility in supporting successful adaptive behaviors? Adaptive Resonance Theory (ART) predicted a functional link between processes of Consciousness, Learning, Expectation, Attention, Resonance, and Synchrony (CLEARS), includes the prediction that "all conscious states are resonant states." This connection clarifies how brain dynamics enable a behaving individual to autonomously adapt in real time to a rapidly changing world. The present article reviews theoretical considerations that predicted these functional links, how they work, and some of the rapidly growing body of behavioral and brain data that have provided support for these predictions. The article also summarizes ART models that predict functional roles for identified cells in laminar thalamocortical circuits, including the six layered neocortical circuits and their interactions with specific primary and higher-order specific thalamic nuclei and nonspecific nuclei. These prediction include explanations of how slow perceptual learning can occur more frequently in superficial cortical layers. ART traces these properties to the existence of intracortical feedback loops, and to reset mechanisms whereby thalamocortical mismatches use circuits such as the one from specific thalamic nuclei to nonspecific thalamic nuclei and then to layer 4 of neocortical areas via layers 1-to-5-to-6-to-4.

ARTSCENE: A Neural System for Natural Scene Classification

How do humans rapidly recognize a scene? How can neural models capture this biological competence to achieve state-of-the-art scene classification? The ARTSCENE neural system classifies natural scene photographs by using multiple spatial scales to efficiently accumulate evidence for gist and texture. ARTSCENE embodies a coarse-to-fine Texture Size Ranking Principle whereby spatial attention processes multiple scales of scenic information, ranging from global gist to local properties of textures. The model can incrementally learn and predict scene identity by gist information alone and can improve performance through selective attention to scenic textures of progressively smaller size. ARTSCENE discriminates 4 landscape scene categories (coast, forest, mountain and countryside) with up to 91.58% correct on a test set, outperforms alternative models in the literature which use biologically implausible computations, and outperforms component systems that use either gist or texture information alone. Model simulations also show that adjacent textures form higher-order features that are also informative for scene recognition.

Dopaminergic and Non-Dopaminergic Value Systems in Conditioning and Outcome-Specific Revaluation

Animals are motivated to choose environmental options that can best satisfy current needs. To explain such choices, this paper introduces the MOTIVATOR (Matching Objects To Internal Values Triggers Option Revaluations) neural model. MOTIVATOR describes cognitiveemotional interactions between higher-order sensory cortices and an evaluative neuraxis composed of the hypothalamus, amygdala, and orbitofrontal cortex. Given a conditioned stimulus (CS), the model amygdala and lateral hypothalamus interact to calculate the expected current value of the subjective outcome that the CS predicts, constrained by the current state of deprivation or satiation. The amygdala relays the expected value information to orbitofrontal cells that receive inputs from anterior inferotemporal cells, and medial orbitofrontal cells that receive inputs from rhinal cortex. The activations of these orbitofrontal cells code the subjective values of objects. These values guide behavioral choices. The model basal ganglia detect errors in CS-specific predictions of the value and timing of rewards. Excitatory inputs from the pedunculopontine nucleus interact with timed inhibitory inputs from model striosomes in the ventral striatum to regulate dopamine burst and dip responses from cells in the substantia nigra pars compacta and ventral tegmental area. Learning in cortical and striatal regions is strongly modulated by dopamine. The model is used to address tasks that examine food-specific satiety, Pavlovian conditioning, reinforcer devaluation, and simultaneous visual discrimination. Model simulations successfully reproduce discharge dynamics of known cell types, including signals that predict saccadic reaction times and CS-dependent changes in systolic blood pressure.

Spikes, Synchrony, and Attentive Learning by Laminar Thalamocortical Circuits

This article develops the Synchronous Matching Adaptive Resonance Theory (SMART) neural model to explain how the brain may coordinate multiple levels of thalamocortical and corticocortical processing to rapidly learn, and stably remember, important information about a changing world. The model clarifies how bottom-up and top-down processes work together to realize this goal, notably how processes of learning, expectation, attention, resonance, and synchrony are coordinated. The model hereby clarifies, for the first time, how the following levels of brain organization coexist to realize cognitive processing properties that regulate fast learning and stable memory of brain representations: single cell properties, such as spiking dynamics, spike-timing-dependent plasticity (STDP), and acetylcholine modulation; detailed laminar thalamic and cortical circuit designs and their interactions; aggregate cell recordings, such as current-source densities and local field potentials; and single cell and large-scale inter-areal oscillations in the gamma and beta frequency domains. In particular, the model predicts how laminar circuits of multiple cortical areas interact with primary and higher-order specific thalamic nuclei and nonspecific thalamic nuclei to carry out attentive visual learning and information processing. The model simulates how synchronization of neuronal spiking occurs within and across brain regions, and triggers STDP. Matches between bottom-up adaptively filtered input patterns and learned top-down expectations cause gamma oscillations that support attention, resonance, and learning. Mismatches inhibit learning while causing beta oscillations during reset and hypothesis testing operations that are initiated in the deeper cortical layers. The generality of learned recognition codes is controlled by a vigilance process mediated by acetylcholine.

Towards a Unified Theory of Neocortex: Laminar Cortical Circuits for Vision and Cognition

A key goal of computational neuroscience is to link brain mechanisms to behavioral functions. The present article describes recent progress towards explaining how laminar neocortical circuits give rise to biological intelligence. These circuits embody two new and revolutionary computational paradigms: Complementary Computing and Laminar Computing. Circuit properties include a novel synthesis of feedforward and feedback processing, of digital and analog processing, and of pre-attentive and attentive processing. This synthesis clarifies the appeal of Bayesian approaches but has a far greater predictive range that naturally extends to self-organizing processes. Examples from vision and cognition are summarized. A LAMINART architecture unifies properties of visual development, learning, perceptual grouping, attention, and 3D vision. A key modeling theme is that the mechanisms which enable development and learning to occur in a stable way imply properties of adult behavior. It is noted how higher-order attentional constraints can influence multiple cortical regions, and how spatial and object attention work together to learn view-invariant object categories. In particular, a form-fitting spatial attentional shroud can allow an emerging view-invariant object category to remain active while multiple view categories are associated with it during sequences of saccadic eye movements. Finally, the chapter summarizes recent work on the LIST PARSE model of cognitive information processing by the laminar circuits of prefrontal cortex. LIST PARSE models the short-term storage of event sequences in working memory, their unitization through learning into sequence, or list, chunks, and their read-out in planned sequential performance that is under volitional control. LIST PARSE provides a laminar embodiment of Item and Order working memories, also called Competitive Queuing models, that have been supported by both psychophysical and neurobiological data. These examples show how variations of a common laminar cortical design can embody properties of visual and cognitive intelligence that seem, at least on the surface, to be mechanistically unrelated.

The Synthesis of Arbitrary Stable Dynamics in Non-linear Neural Networks II: Feedback and Universality

We wish to construct a realization theory of stable neural networks and use this theory to model the variety of stable dynamics apparent in natural data. Such a theory should have numerous applications to constructing specific artificial neural networks with desired dynamical behavior. The networks used in this theory should have well understood dynamics yet be as diverse as possible to capture natural diversity. In this article, I describe a parameterized family of higher order, gradient-like neural networks which have known arbitrary equilibria with unstable manifolds of known specified dimension. Moreover, any system with hyperbolic dynamics is conjugate to one of these systems in a neighborhood of the equilibrium points. Prior work on how to synthesize attractors using dynamical systems theory, optimization, or direct parametric. fits to known stable systems, is either non-constructive, lacks generality, or has unspecified attracting equilibria. More specifically, We construct a parameterized family of gradient-like neural networks with a simple feedback rule which will generate equilibrium points with a set of unstable manifolds of specified dimension. Strict Lyapunov functions and nested periodic orbits are obtained for these systems and used as a method of synthesis to generate a large family of systems with the same local dynamics. This work is applied to show how one can interpolate finite sets of data, on nested periodic orbits.

Reasoning about trust, entrepreneurial services, and the virtual team

This Portfolio of Exploration (PoE) tracks a transformative learning developmental journey that is directed at changing meaning making structures and mental models within an innovation practice. The explicit purpose of the Portfolio is to develop new and different perspectives that enable the handling of new and more complex phenomena through self transformation and increased emotional intelligence development. The Portfolio provides a response to the question: ‘What are the key determinants that enable a Virtual Team (VT) to flourish where flourishing means developing and delivering on the firm’s innovative imperatives?’ Furthermore, the PoE is structured as an investigation into how higher order meaning making promotes ‘entrepreneurial services’ within an intra-firm virtual team, with a secondary aim to identify how reasoning about trust influence KGPs to exchange knowledge. I have developed a framework which specifically focuses on the effectiveness of any firms’ Virtual Team (VT) through transforming the meaning making of the VT participants. I hypothesized it is the way KGPs make meaning (reasoning about trust) which differentiates the firm as a growing firm in the sense of Penrosean resources: ‘inducement to expand and a limit of expansion’ (1959). Reasoning about trust is used as a higher order meaning-making concept in line with Kegan’s (1994) conception of complex meaning making, which is the combining of ideas and data in ways that transform meaning and implicates participants to find new ways of knowledge generation. Simply, it is the VT participants who develop higher order meaning making that hold the capabilities to transform the firm from within, providing a unique competitive advantage that enables the firm to grow.

Development and characterization of novel transgenic techniques for the study of neuronal circuitry

Modern neuroscience relies heavily on sophisticated tools that allow us to visualize and manipulate cells with precise spatial and temporal control. Transgenic mouse models, for example, can be used to manipulate cellular activity in order to draw conclusions about the molecular events responsible for the development, maintenance and refinement of healthy and/or diseased neuronal circuits. Although it is fairly well established that circuits respond to activity-dependent competition between neurons, we have yet to understand either the mechanisms underlying these events or the higher-order plasticity that synchronizes entire circuits. In this thesis we aimed to develop and characterize transgenic mouse models that can be used to directly address these outstanding biological questions in different ways. We present SLICK-H, a Cre-expressing mouse line that can achieve drug-inducible, widespread, neuron-specific manipulations in vivo. This model is a clear improvement over existing models because of its particularly strong, widespread, and even distribution pattern that can be tightly controlled in the absence of drug induction. We also present SLICK-V::Ptox, a mouse line that, through expression of the tetanus toxin light chain, allows long-term inhibition of neurotransmission in a small subset (<1%) of fluorescently labeled pyramidal cells. This model, which can be used to study how a silenced cell performs in a wildtype environment, greatly facilitates the in vivo study of activity-dependent competition in the mammalian brain. As an initial application we used this model to show that tetanus toxin-expressing CA1 neurons experience a 15% - 19% decrease in apical dendritic spine density. Finally, we also describe the attempt to create additional Cre-driven mouse lines that would allow conditional alteration of neuronal activity either by hyperpolarization or inhibition of neurotransmission. Overall, the models characterized in this thesis expand upon the wealth of tools available that aim to dissect neuronal circuitry by genetically manipulating neurons in vivo.