Deviations in the emergence of representations: a neuroconstructivist framework for analysing developmental disorders

A common way of studying developmental disorders is to adopt a static neuropsychological deficit approach, in which the brain is characterized in terms of a normal brain with some parts or ‘modules’ impaired. In this paper we outline a neuroconstructivist approach in which developmental disorders are viewed as alternative developmental trajectories in the emergence of representations within neural networks. As a concrete instantiation of the assumptions underlying this general approach, we present a number of simulations in an artificial neural network model. The representations that emerge under different architectural, input and developmental timing conditions are then analysed within a multi‐dimensional state space. We explore alternative developmental trajectories in these simulations, demonstrating how initial differences in the same parameter can lead to very different outcomes, and conversely how different starting states can sometimes result in similar end states (phenotypes). We conclude that the assumptions of the neuroconstructivist approach are likely to be more appropriate for analysing developmental deviations in complex dynamic neural networks, such as the human brain.     

Risk assessment of knowledge fusion in an innovation ecosystem based on a GA-BP neural network

The risk assessment of knowledge fusion in innovation ecosystems is directly related to these ecosystems’ success or failure. A back-propagation (BP) neural network optimized by a genetic algorithm (GA) is thus proposed to evaluate the risk of knowledge fusion in innovation ecosystems. First, an index system is constructed for evaluating the risk of knowledge fusion in innovation ecosystems, and data are collected by questionnaire for use as training data for the neural networks. To realize machine learning, 84 datasets were generated, of which 60 were used to train the network, and 24 were used to test the network in MATLAB (R2014b). Evaluation models were then constructed by the BP neural network and GA-BP neural network, and their accuracy was judged by comparing the evaluation value with the target value. The comparison shows that the GA-BP neural network has faster convergence speed and higher stability, can achieve the goal more often, and reduces the possibility of the BP neural network falling into a local optimum instead of reaching global optimization. The GA-BP neural network model for the knowledge fusion risk assessment of innovation ecosystems provides a new method for practice.     

The processing of verbs and nouns in neural networks: insights from synthetic brain imaging

The paper presents a computational model of language in which linguistic abilities evolve in organisms that interact with an environment. Each individual's behavior is controlled by a neural network and we study the consequences in the network's internal functional organization of learning to process different classes of words. Agents are selected for reproduction according to their ability to manipulate objects and to understand nouns (objects' names) and verbs (manipulation tasks). The weights of the agents' neural networks are evolved using a genetic algorithm. Synthetic brain imaging techniques are then used to examine the functional organization of the neural networks. Results show that nouns produce more integrated neural activity in the sensory-processing hidden layer, while verbs produce more integrated synaptic activity in the layer where sensory information is integrated with proprioceptive input. Such findings are qualitatively compared with human brain imaging data that indicate that nouns activate more the posterior areas of the brain related to sensory and associative processing, while verbs activate more the anterior motor areas.     

Rule extraction using ensemble of neural network ensembles

Neural networks are well-known for their impressive classification performance, and the ensemble learning technique acts as a catalyst to improve this performance even further by integrating multiple networks.However, neural network ensembles, like neural networks, are regarded as a black box because they cannot explain their decision-making process. As a result, despite their high classification performance, neural networks and their ensembles are unsuitable for some applications that require explainable decisions. However, the rule extraction technique can overcome this drawback by representing the knowledge learned by a neural network in the guise of interpretable decision rules. A rule extraction algorithm provides neural networks the ability to justify their classification responses using explainable classification rules. There are several rule extraction algorithms for extracting classification rules from neural networks, but only a few of them use neural network ensembles to generate rules. As a result, this paper proposes a rule extraction algorithm called Rule Extraction Using Ensemble of Neural Network Ensembles (RE-E-NNES) to demonstrate the high performance of neural network ensembles.RE-E-NNES extracts classification rules by ensembling several neural network ensembles. The results demonstrate the efficacy of the proposed RE-E-NNES algorithm in comparison to other existing rule extraction algorithms.     

Neural Network Models and Mechanisms of Strategy Development

The focus of this paper is mechanisms that may be responsible for intellectual and developmental differences in the cognitive strategies of typical and atypical children, including those with mental retardation. The discussion of these mechanisms is based on behavioral experiments on external memory strategies and on a set of neural network models designed for these tasks. Following the review of the external memory research, the rationale for using neural network models, how they have been used in other research, and their specific application to intellectual and developmental differences in external memory, including the results of several simulations, are reviewed. This is followed by a discussion of the mechanisms of intellectual differences and developmental change included in the models and some challenges for this type of modeling. Neural network modeling is discussed as an asset to research on cognitive development.     

Bridging the gap between physiology and behavior: evidence from the sSoTS model of human visual attention

We present the case for a role of biologically plausible neural network modeling in bridging the gap between physiology and behavior. We argue that spiking-level networks can allow "vertical" translation between physiological properties of neural systems and emergent "whole-system" performance-enabling psychological results to be simulated from implemented networks and also inferences to be made from simulations concerning processing at a neural level. These models also emphasize particular factors (e.g., the dynamics of performance in relation to real-time neuronal processing) that are not highlighted in other approaches and that can be tested empirically. We illustrate our argument from neural-level models that select stimuli by biased competition. We show that a model with biased competition dynamics can simulate data ranging from physiological studies of single-cell activity (Study 1) to whole-system behavior in human visual search (Study 2), while also capturing effects at an intermediate level, including performance breakdown after neural lesion (Study 3) and data from brain imaging (Study 4). We also show that, at each level of analysis, novel predictions can be derived from the biologically plausible parameters adopted, which we proceed to test (Study 5). We argue that, at least for studying the dynamics of visual attention, the approach productively links single-cell to psychological data.     

Phonology, reading acquisition, and dyslexia: insights from connectionist models.

The development of reading skill and bases of developmental dyslexia were explored using connectionist models. Four issues were examined: the acquisition of phonological knowledge prior to reading, how this knowledge facilitates learning to read, phonological and nonphonological bases of dyslexia, and effects of literacy on phonological representation. Compared with simple feedforward networks, representing phonological knowledge in an attractor network yielded improved learning and generalization. Phonological and surface forms of developmental dyslexia, which are usually attributed to impairments in distinct lexical and nonlexical processing "routes," were derived from different types of damage to the network. The results provide a computationally explicit account of many aspects of reading acquisition using connectionist principles.     

Neural networks supporting autobiographical memory retrieval in posttraumatic stress disorder

Posttraumatic stress disorder (PTSD) affects the functional recruitment and connectivity between neural regions during autobiographical memory (AM) retrieval that overlap with default and control networks. Whether such univariate changes relate to potential differences in the contributions of the large-scale neural networks supporting cognition in PTSD is unknown. In the present functional MRI study, we employed independent-component analysis to examine the influence of the engagement of neural networks during the recall of personal memories in a PTSD group (15 participants) as compared to non-trauma-exposed healthy controls (14 participants). We found that the PTSD group recruited similar neural networks when compared to the controls during AM recall, including default-network subsystems and control networks, but group differences emerged in the spatial and temporal characteristics of these networks. First, we found spatial differences in the contributions of the anterior and posterior midline across the networks, and of the amygdala in particular, for the medial temporal subsystem of the default network. Second, we found temporal differences within the medial prefrontal subsystem of the default network, with less temporal coupling of this network during AM retrieval in PTSD relative to controls. These findings suggest that the spatial and temporal characteristics of the default and control networks potentially differ in a PTSD group versus healthy controls and contribute to altered recall of personal memory.     

空间导航的脑网络基础和调控机制

空间导航在生活中时刻发生,空间能力衰退是阿尔兹海默症的重要早期表现。早期关于空间导航神经机制的研究主要关注单个脑区的特异性功能,但这些脑区如何交互以整合不同模态的信息支持复杂导航行为尚不清楚。脑成像技术、脑网络建模方法和神经调控手段的发展,为在脑网络水平理解人类空间导航的认知神经机制提供了重要研究手段。本研究试图融合空间导航认知神经机制研究的最新进展,借助脑网络建模、大数据分析、微电流刺激等前沿研究手段,研究空间导航脑网络的关键拓扑属性特征(如模块化、核心节点等),探寻该功能特异性神经网络的重要影响因素和调控机制,并构建空间导航的脑网络理论模型。研究成果将有利于理解人类复杂导航行为的脑网络基础,为阿尔兹海默症等相关认知障碍脑疾病的筛查和诊断提供重要参考。     

The developmental cognitive neuroscience of functional connectivity

Developmental cognitive neuroscience is a rapidly growing field that examines the relationships between biological development and cognitive ability. In the past decade, there has been ongoing refinement of concepts and methodology related to the study of ‘functional connectivity’ among distributed brain regions believed to underlie cognition and behavioral control. Due to the recent availability of relatively easy-to-use tools for functional connectivity analysis, there has been a sharp upsurge of studies that seek to characterize normal and psychopathologically abnormal brain functional integration. However, relatively few studies have applied functional and effective connectivity analysis techniques to developmental cognitive neuroscience. Functional and effective connectivity analysis methods are ideally suited to advance our understanding of the neural substrates of cognitive development, particularly in understanding how and why changes in the functional ‘wiring’ of neural networks promotes optimal cognitive control throughout development. The purpose of this review is to summarize the central concepts, methods, and findings of functional integration neuroimaging research to discuss key questions in the field of developmental cognitive neuroscience. These ideas will be presented within a context that merges relevant concepts and proposals from different developmental theorists. The review will outline a few general predictions about likely relationships between typical ‘executive’ cognitive maturation and changes in brain network functional integration during adolescence. Although not exhaustive, this conceptual review also will showcase some of recent findings that have emerged to support these predictions.     

Productivity and Coauthorship in JPSSM: A Social Network Analysis

This paper examines the key individual contributors and institutional contributors to JPSSM, covering 628 articles written by 761 authors since the journal’s inception in fall/winter 1980 until its fall 2009 issue. The nature and the dynamics of the coauthor networks of the journal’s leading individual contributors are further investigated. Results indicate that leading contributors to JPSSM are also major contributors to other academic outlets that have published sales research. These authors possess at least one common trait: they effectively network and collaborate with other sales scholars. In addition, their coauthor networks change over time, both in membership and in structure. For the most, coauthor networks evolve by reducing some members and bringing in new ones. In many cases, however, membership change is accompanied by a structural change, usually from a fragmented network to a dense network. Research findings have important implications for understanding the development of sales knowledge and the contribution of sales scholars and their institutions. University administrators can also use the findings of this paper as a benchmark to define “reasonable” publication expectations for faculty with an interest in sales management research.     

Law,discipline and governance based on cognitive process simulation – analysis of legal forms in the modern rights relationship

People are becoming more and more humanized in the process of understanding the law. According to the right to discipline, the law has its own core setting factors, while some limits can't reach people's desire. Therefore, the legal and illegal mode of transcending rights is very important. In order to analyze the legal form of modern rights, in this paper, the cognitive learning and memory process of human brain were simulated through the artificial neural network and the understanding of human brain structure, and the role of law, discipline and governance was reflected. In the study, the structure and algorithm of the model neural network were optimized, the memory forgetting curve mechanism that can simulate the human brain was introduced, and thus the network recognition rate was improved. And in the algorithm, the calculation of matching degree was avoided, and the computational complexity was reduced to the sample. Then the sample was compared with the SOM, ART1, and PNN algorithms. The experimental simulation results show that the recognition speed of this sample is 1.9 times faster than that of ART1, 58 times than that of SOM, and 1.5 times than that of the PNN network.     

发展性阅读障碍视听时间整合缺陷可能的机制：视听时间再校准能力受损

发展性阅读障碍的本质一直是研究者争论的焦点。大量研究发现,阅读障碍者具有视听时间整合缺陷。然而,这些研究仅考察了阅读障碍者视听时间整合加工的整体表现,也就是平均水平的表现,却对整合加工的变化过程缺乏探讨。视听时间再校准反映了视听时间整合的动态加工过程,对内部时间表征与感觉输入之间差异的再校准困难则会导致多感觉整合受损,而阅读障碍者的再校准相关能力存在缺陷。因此,视听时间再校准能力受损可能是发展性阅读障碍视听时间整合缺陷的根本原因。未来的研究需要进一步考察发展性阅读障碍者视听时间再校准能力的具体表现,以及这些表现背后的认知神经机制。     

Attention genes

A major problem for developmental science is understanding how the cognitive and emotional networks important in carrying out mental processes can be related to individual differences. The last five years have seen major advances in establishing links between alleles of specific genes and the neural networks underlying aspects of attention. These findings have the potential of illuminating important aspects of normal development and its pathologies. We need to learn how genes and experience combine to influence the structure of neural networks and the efficiency with which they are exercised. Methods for addressing these issues are central to progress in the decade ahead.     

Temporal dynamics of masked word reading

The repercussions of unconscious priming on the neural correlates subsequent cognition have been explored previously. However, the neural dynamics during the unconscious processing remains largely uncharted. To assess both the complexity and temporal dynamics of unconscious cognition the present study contrasts the evoked response from classes of masked stimuli with three different levels of complexity; words, consonant strings, and blanks. The evoked response to masked word stimuli differed from both consonant strings and blanks, which did not differ from each other. This response was qualitatively different to any evoked potential observed when stimuli were consciously visible and peaked at 140ms, earlier than is usually associated with differences between words and strings and 100ms earlier than word-consonant string differences in the visible condition. The evoked response demonstrates a qualitatively distinct signature of unconscious cognition and directly demonstrates the extraction of abstract information under subliminal conditions.     

Explainability of neural networks for child language: Agent-First strategy in comprehension of Korean active transitive construction

This study investigates how neural networks address the properties of children's linguistic knowledge, with a focus on the Agent-First strategy in comprehension of an active transitive construction in Korean. We develop various neural-network models and measure their classification performance on the test stimuli used in a behavioural experiment involving scrambling and omission of sentential components at varying degrees. Results show that, despite some compatibility of these models’ performance with the children's response patterns, their performance does not fully approximate the children's utilisation of this strategy, demonstrating by-model and by-condition asymmetries. This study's findings suggest that neural networks can utilise information about formal co-occurrences to access the intended message to a certain degree, but the outcome of this process may be substantially different from how a child (as a developing processor) engages in comprehension. This implies some limits of neural networks on revealing the developmental trajectories of child language.

Research Highlights

• This study investigates how neural networks address properties of child language.
• We focus on the Agent-First strategy in comprehension of Korean active transitive.
• Results show by-model/condition asymmetries against children's response patterns.
• This implies some limits of neural networks on revealing properties of child language.

     

Hierarchical growing grid networks for skeleton based action recognition

In this paper, a novel cognitive architecture for action recognition is developed by applying layers of growing grid neural networks. Using these layers makes the system capable of automatically arranging its representational structure. In addition to the expansion of the neural map during the growth phase, the system is provided with a prior knowledge of the input space, which increases the processing speed of the learning phase. Apart from two layers of growing grid networks the architecture is composed of a preprocessing layer, an ordered vector representation layer and a one-layer supervised neural network. These layers are designed to solve the action recognition problem. The first-layer growing grid receives the input data of human actions and the neural map generates an action pattern vector representing each action sequence by connecting the elicited activation of the trained map. The pattern vectors are then sent to the ordered vector representation layer to build the time-invariant input vectors of key activations for the second-layer growing grid. The second-layer growing grid categorizes the input vectors to the corresponding action clusters/sub-clusters and finally the one-layer supervised neural network labels the shaped clusters with action labels. Three experiments using different datasets of actions show that the system is capable of learning to categorize the actions quickly and efficiently. The performance of the growing grid architecture is compared with the results from a system based on Self-Organizing Maps, showing that the growing grid architecture performs significantly superior on the action recognition tasks.     

Kohonen neural networks and language.

Kohonen neural networks are a type of self-organizing network that recognizes the statistical characteristics of input datasets. The application of this type of neural network to language theory is demonstrated in the present note by showing three brief applications: recognizing word borders, learning the limited phonemes of one's native tongue, and category-specific naming impairments.