An Instruction Language for Self-Construction in the Context of Neural Networks

Biological systems are based on an entirely different concept of construction than human artifacts. They construct themselves by a process of self-organization that is a systematic spatio-temporal generation of, and interaction between, various specialized cell types. We propose a framework for designing gene-like codes for guiding the self-construction of neural networks. The description of neural development is formalized by defining a set of primitive actions taken locally by neural precursors during corticogenesis. These primitives can be combined into networks of instructions similar to biochemical pathways, capable of reproducing complex developmental sequences in a biologically plausible way. Moreover, the conditional activation and deactivation of these instruction networks can also be controlled by these primitives, allowing for the design of a “genetic code” containing both coding and regulating elements. We demonstrate in a simulation of physical cell development how this code can be incorporated into a single progenitor, which then by replication and differentiation, reproduces important aspects of corticogenesis

Precis of neuroconstructivism: how the brain constructs cognition

Neuroconstructivism: How the Brain Constructs Cognition proposes a unifying framework for the study of cognitive development that brings together (1) constructivism (which views development as the progressive elaboration of increasingly complex structures), (2) cognitive neuroscience (which aims to understand the neural mechanisms underlying behavior), and (3) computational modeling (which proposes formal and explicit specifications of information processing). The guiding principle of our approach is context dependence, within and (in contrast to Marr [1982]) between levels of organization. We propose that three mechanisms guide the emergence of representations: competition, cooperation, and chronotopy; which themselves allow for two central processes: proactivity and progressive specialization. We suggest that the main outcome of development is partial representations, distributed across distinct functional circuits. This framework is derived by examining development at the level of single neurons, brain systems, and whole organisms. We use the terms encellment, embrainment, and embodiment to describe the higher-level contextual influences that act at each of these levels of organization. To illustrate these mechanisms in operation we provide case studies in early visual perception, infant habituation, phonological development, and object representations in infancy. Three further case studies are concerned with interactions between levels of explanation: social development, atypical development and within that, developmental dyslexia. We conclude that cognitive development arises from a dynamic, contextual change in embodied neural structures leading to partial representations across multiple brain regions and timescales, in response to proactively specified physical and social environment

Community-Derived Core Concepts for Neuroscience Higher Education

Core concepts provide a framework for organizing facts and understanding in neuroscience higher education curricula. Core concepts are overarching principles that identify patterns in neuroscience processes and phenomena and can be used as a foundational scaffold for neuroscience knowledge. The need for community-derived core concepts is pressing, because both the pace of research and number of neuroscience programs are rapidly expanding. While general biology and many subdisciplines within biology have identified core concepts, neuroscience has yet to establish a community-derived set of core concepts for neuroscience higher education. We used an empirical approach involving more than 100 neuroscience educators to identify a list of core concepts. The process of identifying neuroscience core concepts was modeled after the process used to develop physiology core concepts and involved a nationwide survey and a working session of 103 neuroscience educators. The iterative process identified eight core concepts and accompanying explanatory paragraphs. The eight core concepts are abbreviated as communication modalities, emergence, evolution, gene–environment interactions, information processing, nervous system functions, plasticity, and structure–function. Here, we describe the pedagogical research process used to establish core concepts for the neuroscience field and provide examples on how the core concepts can be embedded in neuroscience education

Large-Scale Simulation of Neural Networks with Biophysically Accurate Models on Graphics Processors

Efficient simulation of large-scale mammalian brain models provides a crucial computational means for understanding complex brain functions and neuronal dynamics. However, such tasks are hindered by significant computational complexities. In this work, we attempt to address the significant computational challenge in simulating large-scale neural networks based on the most biophysically accurate Hodgkin-Huxley (HH) neuron models. Unlike simpler phenomenological spiking models, the use of HH models allows one to directly associate the observed network dynamics with the underlying biological and physiological causes, but at a significantly higher computational cost. We exploit recent commodity massively parallel graphics processors (GPUs) to alleviate the significant computational cost in HH model based neural network simulation. We develop look-up table based HH model evaluation and efficient parallel implementation strategies geared towards higher arithmetic intensity and minimum thread divergence. Furthermore, we adopt and develop advanced multi-level numerical integration techniques well suited for intricate dynamical and stability characteristics of HH models. On a commodity CPU card with 240 streaming processors, for a neural network with one million neurons and 200 million synaptic connections, the presented GPU neural network simulator is about 600X faster than a basic serial CPU based simulator, 28X faster than the CPU implementation of the proposed techniques, and only two to three times slower than the GPU based simulation using simpler spiking models

Adapting Swarm Intelligence For The Self-Assembly And Optimization Of Networks

While self-assembly is a fairly active area of research in swarm intelligence and robotics, relatively little attention has been paid to the issues surrounding the construction of network structures. Here, methods developed previously for modeling and controlling the collective movements of groups of agents are extended to serve as the basis for self-assembly or "growth" of networks, using neural networks as a concrete application to evaluate this novel approach. One of the central innovations incorporated into the model presented here is having network connections arise as persistent "trails" left behind moving agents, trails that are reminiscent of pheromone deposits made by agents in ant colony optimization models. The resulting network connections are thus essentially a record of agent movements. The model's effectiveness is demonstrated by using it to produce two large networks that support subsequent learning of topographic and feature maps. Improvements produced by the incorporation of collective movements are also examined through computational experiments. These results indicate that methods for directing collective movements can be extended to support and facilitate network self-assembly. Additionally, the traditional self-assembly problem is extended to include the generation of network structures based on optimality criteria, rather than on target structures that are specified a priori. It is demonstrated that endowing the network components involved in the self-assembly process with the ability to engage in collective movements can be an effective means of generating computationally optimal network structures. This is confirmed on a number of challenging test problems from the domains of trajectory generation, time-series forecasting, and control. Further, this extension of the model is used to illuminate an important relationship between particle swarm optimization, which usually occurs in high dimensional abstract spaces, and self-assembly, which is normally grounded in real and simulated 2D and 3D physical spaces

Machine learning and its applications in reliability analysis systems

In this thesis, we are interested in exploring some aspects of Machine Learning (ML) and its application in the Reliability Analysis systems (RAs). We begin by investigating some ML paradigms and their- techniques, go on to discuss the possible applications of ML in improving RAs performance, and lastly give guidelines of the architecture of learning RAs. Our survey of ML covers both levels of Neural Network learning and Symbolic learning. In symbolic process learning, five types of learning and their applications are discussed: rote learning, learning from instruction, learning from analogy, learning from examples, and learning from observation and discovery. The Reliability Analysis systems (RAs) presented in this thesis are mainly designed for maintaining plant safety supported by two functions: risk analysis function, i.e., failure mode effect analysis (FMEA) ; and diagnosis function, i.e., real-time fault location (RTFL). Three approaches have been discussed in creating the RAs. According to the result of our survey, we suggest currently the best design of RAs is to embed model-based RAs, i.e., MORA (as software) in a neural network based computer system (as hardware). However, there are still some improvement which can be made through the applications of Machine Learning. By implanting the 'learning element', the MORA will become learning MORA (La MORA) system, a learning Reliability Analysis system with the power of automatic knowledge acquisition and inconsistency checking, and more. To conclude our thesis, we propose an architecture of La MORA

The Effects of Fluency-Based Instruction on Skill Acquisition in Children Diagnosed with Landau Kleffner Syndrome

Landau Kleffner Syndrome, or acquired epileptic aphasia, is an epileptic syndrome involving a neurological impairment related to the appearance of paroxysmal (i.e., sudden intense) electroencephalograph (EEG) activity (Pearl, Carrazana & Holmes, 2001). Landau Kleffner syndrome results from an epileptogenic lesion arising in the speech cortex during a critical period of development, which may interfere with the establishment of satisfactory and functional circuits for normal language function (Morrell et al., 1995). LKS is a complex and severe syndrome that affects all aspects of a child\u27s life, including communication, socialization, and the everyday ability to function within the environment. An option for treatment of LKS is Multiple Subpial Transection Surgery (MST). MST surgery is a surgical procedure designed to eradicate the capacity of cortical tissue to generate seizures or subclinical epileptiform activity, while maintaining the cortical functions of the remaining tissues (Grote, Van Slyke, & Hoeppner, 1999). Once surgery is complete, it is necessary to provide direct, intensive instruction to rebuild language skills starting from very basic (preverbal) components (Vance, 1991). The Morningside Model of Generative Instruction is a model of selected basic psychomotor component skills (e.g., point, pinch, reach, turn, squeeze, & shake) that are explicitly taught in a hierarchical sequence. These skills are built to a fluent level, and then sequenced into complex behavioral repertoires (Johnson & Street, 2004). The examination of the relationship between fluency-based instruction and skill acquisition for children diagnosed with LKS will contribute to the literature by extending and clarifying the role of fluency-based instruction (and specifically Morningside Model of Generative Instruction) for use with children with LKS. The current study used a changing criterion design to measure rates of responding in identified basic and combined psychomotor skills. A pre-existing data set was utilized to examine the effects of fluency-based instruction in basic psychomotor skill acquisition, maintenance, and generalization to an identified set of combined skills. Results indicated overall increases in basic psychomotor skill acquisition, and confirmation of fluency-based instruction as an efficacious, research based treatment for children

Does Speech-To-Text Assistive Technology Improve the Written Expression of Students with Traumatic Brain Injury?

Traumatic Brain Injury outcomes vary by individual due to age at the onset of injury, the location of the injury, and the degree to which the deficits appear to be pronounced, among other factors. As an acquired injury to the brain, the neurophysiological consequences are not homogenous; they are as varied as the individuals who experience them. Persistent impairment in executive functions of attention, initiation, planning, organizing, and memory are likely to be present in children with moderate to severe TBIs. Issues with sensory and motor skills, language, auditory or visual sensation changes, and variations in emotional behavior may also be present. Germane to this study, motor dysfunction is a common long-term sequelae of TBI that manifests in academic difficulties. Borrowing from the learning disability literature, children with motor dysfunction are likely to have transcription deficits, or deficits related to the fine-motor production of written language. This study aimed to compare the effects of handwriting with an assistive technology accommodation on the writing performance of three middle school students with TBIs and writing difficulties. The study utilized an alternating treatments design (ATD), comparing the effects of handwriting responses to story prompts to the use of speech-to-text AT to record participant responses. Speech-to-text technology, like Dragon Naturally Speaking converts spoken language into a print format on a computer screen with a high degree of accuracy. In theory, because less effort is spent on transcription, there is a reduction in cognitive load, enabling more time to be spent on generation skills, such as idea development, selecting more complex words that might be otherwise difficult to spell, and grammar. Overall, all three participants showed marked improvement with the application of speech-to-text AT. The results indicate a positive pattern for the AT as an accommodation with these children that have had mild-to-moderate TBIs as compared to their written output without the AT accommodation. The findings of this study are robust. Through visual analysis of the results, it is evident that the speech-to-text dictation condition was far superior to the handwriting condition (HW) with an effect size that ranged + 3.4 to + 8.8 across participants indicating a large treatment effect size. Perhaps more impressive, was 100 percent non-overlap of data between the two conditions across participants and dependent variables. The application of speech-to-text AT resulted in significantly improved performance across writing indicators in these students with a history of TBIs. Speech-to-Text AT may prove to be an excellent accommodation for children with TBI and fine motor skill deficits. The conclusions drawn from the results of this study indicate the Speech-to-Text AT was more effective than a handwriting condition for all three participants. By providing this AT, these students each improved in the quality, construction, and duration of their written expression as evidenced in the significant gains in TWW, WSC, and CWS