Neural networks (Computer science)

Spatially continuous networks with heterogeneous connections are ubiquitous in
biological systems, in part icular neural systems. To understand the mutual effects
of locally homogeneous and globally heterogeneous connectivity, the st ability of the
steady state activity of a neural field as a fun ction of its connectivity is investigated.
The variation of the connectivity is operationalized through manipulation of a heterogeneous
two-point connection embedded into the otherwise homogeneous connectivity
matrix and by variation of connectivity strength and transmission speed. A detailed
discussion of the example of the real Ginzburg-Land au equation with an embedded
two-point heterogeneous connection in addition to the homogeneous coupling due to
the diffusion term is performed. The system is reduced to a set of delay differential
equations and the stability di agrams as a function of the time delay and the local and
global coupling strengths are computed. The major finding is that the stability of
the heterogeneously connected elements with a well-defined velocity defines a lower
bound for the stabil ity of the entire system . Diffusion and velocity dispersion always
result in increased stability. Various other local architectures represented by exponentially
decaying connectivity fun ctions are also discussed. The analysis shows that
developmental changes such as the myelination of the cortical large-scale fib er system generally result in the stabilization of steady state activity via oscillatory instabilities
independent of the local connectivity. Non-oscillatory (Turing) instabilities are shown
to be independent of any influences of time delay.

How do neuronal connectivity and the dynamics of distributed brain networks process
information during bimanual coordination? Contemporary brain theories of cognitive
function posit spatial, temporal and spatiotemporal network reorganization as mechanisms
for neural information processing. In this dissertation, rhythmic bimanual coordination is
studied as a window into neural information processing and subsequently an investigation of
underlying network reorganization processes is performed. Spatiotemporal reorganization
between effectors (limbs) is parameterized in a theoretical model via a continuously varying
cross-talk parameter that represents neural connectivity. Thereby, effector dynamics during
coordinated behavior is shown to be influenced by the cross-talk parameter and time delays
involved in signal processing. In particular, stability regimes of coordination patterns
as a function of cross-talk, movement frequency and the time delays are derived. On the
methodological front , spatiotemporal reorganization of neural masses are used to simulate
electroencephalographic data. A suitable choice of experimental control conditions is used
to derive a paradigmatic framework called Mode Level Cognitive Subtraction (MLCS) which
is demonstrated to facilitate the disambiguation between spatial and temporal components
of the reorganization processes to a quantifiable degree of certainty. In the experimental
section, MLCS is applied to electroencephalographic recordings during rhythmic bimanual
task conditions and unimanual control conditions. Finally, a classification of reorganization
processes is achieved for differing stability states of coordination: inphase (mirror) primarily
entails temporal reorganization of sensorimotor networks localized during unimanual
movement whereas spatiotemporal reorganization is involved during antiphase (parallel)
coordination.

The design and construction of a tri-cable, planar robotic device for use in neurophysical rehabilitation is presented. The criteria for this system are based primarily on marketability factors, rather than ideal models or mathematical outcomes. The device is designed to be low cost and sufficiently safe for a somewhat disabled individual to use unsupervised at home, as well as in a therapist's office. The key features are the use of a barrier that inhibits the user from coming into contact with the cables as well as a "break-away" joystick that the user utilizes to perform the rehabilitation tasks. In addition, this device is portable, aesthetically acceptable and easy to operate. Other uses of this system include sports therapy, virtual reality and teleoperation of remote devices.

The design and construction of a tri-cable, planar robotic device for use in neurophysical rehabilitation is presented. The criteria for this system are based primarily on marketability factors, rather than ideal models or mathematical outcomes. The device is designed to be low cost and sufficiently safe for a somewhat disabled individual to use unsupervised at home, as well as in a therapist's office. The key features are the use of a barrier that inhibits the user from coming into contact with the cables as well as a "break-away" joystick that the user utilizes to perform the rehabilitation tasks. In addition, this device is portable, aesthetically acceptable and easy to operate. Other uses of this system include sports therapy, virtual reality and teleoperation of remote devices.

A self-adaptive software is developed to predict the stock market. It’s Stock
Prediction Engine functions autonomously when its skill-set suffices to achieve its goal,
and it includes human-in-the-loop when it recognizes conditions benefiting from more
complex, expert human intervention. Key to the system is a module that decides of
human participation. It works by monitoring three mental states unobtrusively and in real
time with Electroencephalography (EEG). The mental states are drawn from the
Opportunity-Willingness-Capability (OWC) model. This research demonstrates that the
three mental states are predictive of whether the Human Computer Interaction System
functions better autonomously (human with low scores on opportunity and/or
willingness, capability) or with the human-in-the-loop, with willingness carrying the
largest predictive power. This transdisciplinary software engineering research
exemplifies the next step of self-adaptive systems in which human and computer benefit from optimized autonomous and cooperative interactions, and in which neural inputs
allow for unobtrusive pre-interactions.

Working memory (WM) is a process that allows for the temporary and limited storage of information for an immediate goal or to be stored into a more permanent system. A large number of studies
have led to the widely accepted view that WM is mediated by the frontoparietal network (FPN), consisting
of areas in the prefrontal cortex (PFC) and posterior parietal cortex (PPC). Current evidence suggests that
task specific patterns of neuronal oscillatory activity within the FPN play a fundamental role in WM, and
yet specific spatio-temporal properties of this activity are not well characterized. This study utilized multisite
local field potential (LFP) data recorded from PFC and PPC sites in two macaque monkeys trained to
perform a rule-based, Oculomotor Delayed Match-to-Sample task. The animals were required to learn
which of two rules determined the correct match (Location matching or Identity matching). Following a
500 ms fixation period, a sample stimulus was presented for 500 ms, followed by a randomized delay
lasting 800-1200 ms in which no stimulus was present. At the end of the delay period, a match stimulus
was presented, consisting of two of three possible objects presented at two of three possible locations.
When the match stimulus appeared, the monkey made a saccadic eye movement to the target. The rule in
effect determined which object served as the target. Time-frequency plots of three spectral measures
(power, coherence, and Wiener Granger Causality (WGC) were computed from MultiVariate
AutoRegressive LFP time-series models estimated in a 100-ms window that was slid across each of three
analysis epochs (fixation, sample, and delay). Low (25- 55 Hz) and high gamma (65- 100 Hz) activity were investigated separately due to evidence that they may be functionally distinct. Within each epoch, recording sites in the PPC and PFC were classified into groups according to the similarity of their power t-f plots derived by a K-means clustering algorithm. From the power-based site groups, the corresponding coherence and WGC were analyzed. This classification procedure uncovered spatial, temporal, and frequency dynamics of FPN
involvement in WM and other co-occurring processes, such as sensory and target related processes. These processes were distinguishable by rule and performance accuracy across all three spectral measures- power,
coherence, and WGC. Location and Identity rule were distinguishable by the low and high-gamma range.

A neural network based model for prediction of bridge condition rating is proposed. The back-propagation algorithm is used to train the network to recognize the pattern of deterioration of bridges and use this knowledge in predicting the future condition rating of a bridge. The various factors which influence the deterioration rate are considered as input to the system. The model then predicts the condition rating of the three major sub-components of a bridge viz. the deck, sub-structure and the super-structure. Fuzzy logic is used to evaluate the overall condition rating of the bridge using the condition rating of the components. To demonstrate the superiority of the neural network model over the traditional models, the history of the deterioration rates for the components were also considered in the prediction of their future condition. The proposed system is versatile and can be easily extended to include other parameters and updated from time to time without much effort.

In this thesis we present an intelligent forecaster based on neural network technology to capture the future path of the market indicator. This thesis is about the development of a new methodology in financial forecasting. An effort is made to develop a neural network forecaster using the financial indicators as the input variables. A complex recurrent neural network is used to capture the behavior of the nonlinear characteristics of the S&P 500. The main outcome of this research is, a systematic way of constructing a forecaster for nonlinear and non-stationary data series of S&P 500 that leads to very good out-of-sample prediction. The results of the training and testing of the network are presented along with conclusion. The tool used for the validation of this research is "Brainmaker". This thesis also contains a brief survey of available tools for financial forecasting.

The purpose of this study was to develop a user-friendly mathematical model for prediction of daily, ground level ozone concentration in Palm Beach County, Florida. The focus of this project was to investigate the correlation between hourly ozone concentrations and pre-existing pollutant levels and meteorological data. An artificial neural network model was applied, involving a backpropagation algorithm and the tangent sigmoid as the transfer function. Surface meteorological data and upper air data such as pressure, temperature, dew point temperature, wind speed and wind direction were included in the model, along with the ozone concentration in the hour previous to the forecast. Based on the model results, the 8-hour average ozone concentration is to be forecasted. This will assist state and local air pollution officials in providing the general public with early notice of an impending air quality problem.

The goal of time series forecasting is to identify the underlying pattern and use these patterns to predict the future path of the series. To capture the future path of a dynamic stock market variable is one of the toughest challenges. This thesis is about the development of a new methodology in financial forecasting. An effort is made to develop a neural network forecaster using time-series phenomena. The main outcome of this new approach for financial forecasting is a systematic way of constructing a Neural Network Forecaster for nonlinear and non-stationary time-series data that leads to very good out-of-sample prediction. The tool used for the validation of this research is "Brainmaker". This thesis also contains a small survey of available tools used for financial forecasting.