In recent years there has been significant research in the field of computational neuroscience and many of these biologically inspired cognitive models are based on the theory of operation of mammalian visual cortex. One such model of neocortex developed by George & Hawkins, known as Hierarchical Temporal Memories (HTM), is considered for the research discussed here. We propose a simple hierarchical model that is derived from HTM. The aim of this work is to evaluate the hardware cost and performance against software based simulations. This work presents a detailed hardware implementation and analysis of the derived hierarchical model. We show that these networks are inherently parallel in their architecture, similar to the biological computing, and that parallelism can be exploited by massively parallel architectures implemented using reconfigurable devices such as the FPGA. Hardware implementation accelerates the learning process which is useful in many real world problems. We have implemented a complex network node that operates in real time using an FPGA. The current architecture is modular and allows us to estimate the hardware resources and computational units required to realize large scale networks in the future.

Analogy is ubiquitous in human cognition. One of the important questions related to understanding the situated nature of analogy-making is how people retrieve source analogues via their interactions with external environments. This dissertation studies interactive analogical retrieval in the context of biologically inspired design (BID). BID involves creative use of analogies to biological systems to develop solutions for complex design problems (e.g., designing a device for acquiring water in desert environments based on the analogous fog-harvesting abilities of the Namibian Beetle). Finding the right biological analogues is one of the critical first steps in BID. Designers routinely search online in order to find their biological sources of inspiration. But this task of online bio-inspiration seeking represents an instance of interactive analogical retrieval that is extremely time consuming and challenging to accomplish. This dissertation focuses on understanding and supporting the task of online bio-inspiration seeking. Through a series of field studies, this dissertation uncovered the salient characteristics and challenges of online bio-inspiration seeking. An information-processing model of interactive analogical retrieval was developed in order to explain those challenges and to identify the underlying causes. A set of measures were put forth to ameliorate those challenges by targeting the identified causes. These measures were then implemented in an online information-seeking technology designed to specifically support the task of online bio-inspiration seeking. Finally, the validity of the proposed measures was investigated through a series of experimental studies and a deployment study. The trends are encouraging and suggest that the proposed measures has the potential to change the dynamics of online bio-inspiration seeking in favor of ameliorating the identified challenges of online bio-inspiration seeking.

The purpose of this work is to investigate a series of biologically inspired modifications to state-of-the-art Mel- frequency cepstral coefficients (MFCCs) that may improve automatic speech recognition results. We have provided recommendations to improve speech recognition results de- pending on signal-to-noise ratio levels of input signals. This work has been motivated by noise-robust auditory features (NRAF). In the feature extraction technique, after a signal is filtered using bandpass filters, a spatial derivative step is used to sharpen the results, followed by an envelope detector (recti- fication and smoothing) and down-sampling for each filter bank before being compressed. DCT is then applied to the results of all filter banks to produce features. The Hidden- Markov Model Toolkit (HTK) is used as the recognition back-end to perform speech recognition given the features we have extracted. In this work, we investigate the role of filter types, window size, spatial derivative, rectification types, smoothing, down- sampling and compression and compared the final results to state-of-the-art Mel-frequency cepstral coefficients (MFCC). A series of conclusions and insights are provided for each step of the process. The goal of this work has not been to outperform MFCCs; however, we have shown that by changing the compression type from log compression to 0.07 root compression we are able to outperform MFCCs for all noisy conditions.

- / Doctorat en Sciences / info:eu-repo/semantics/nonPublished

Biological signal processing can help us gain knowledge about biological complexity, as well as using this knowledge to engineer better systems. Three areas are identified as critical to understanding biology: 1) understanding DNA, 2) examining the overall biological function and 3) evaluating these systems in environmental (ie: turbulent) conditions. DNA is investigated for coding structure and redundancy, and a new tandem repeat region, an indicator of a neurodegenerative disease, is discovered. The linear algebraic framework can be used for further analysis and techniques. The work illustrates how signal processing is a tool to reverse engineer biological systems, and how our better understanding of biology can improve engineering designs. Then, the way a single-cell mobilizes in response to a chemical gradient, known as chemotaxis, is examined. Inspiration from receptor clustering in chemotaxis combined with a Hebbian learning method is shown to improve a gradient-source (chemical/thermal) localization algorithm. The algorithm is implemented, and its performance is evaluated in diffusive and turbulent environments. We then show that sensor cross-correlation can be used in solving chemical localization in difficult turbulent scenarios. This leads into future techniques which can be designed for gradient source tracking. These techniques pave the way for use of biologically-inspired sensor networks in chemical localization.

Indiana University-Purdue University Indianapolis (IUPUI) / Proteins are nano-machines that work inside every living organism. Functional disruption of one or several proteins is the cause for many diseases. However, the functions for most proteins are yet to be annotated because inexpensive sequencing techniques dramatically speed up discovery of new protein sequences (265 million and counting) and experimental examinations of every protein in all its possible functional categories are simply impractical. Thus, it is necessary to develop computational function-prediction tools that complement and guide experimental studies. In this study, we developed a series of predictors for highly accurate prediction of proteins with DNA-binding, RNA-binding and carbohydrate-binding capability. These predictors are a template-based technique that combines sequence and structural information with predicted binding affinity. Both sequence and structure-based approaches were developed. Results indicate the importance of binding affinity prediction for improving sensitivity and precision of function prediction. Application of these methods to the human genome and structure genome targets demonstrated its usefulness in annotating proteins of unknown functions and discovering moon-lighting proteins with DNA,RNA, or carbohydrate binding function. In addition, we also investigated disruption of protein functions by naturally occurring genetic variations due to insertions and deletions (INDELS). We found that protein structures are the most critical features in recognising disease-causing non-frame shifting INDELs. The predictors for function predictions are available at http://sparks-lab.org/spot, and the predictor for classification of non-frame shifting INDELs is available at http://sparks-lab.org/ddig.

Indiana University-Purdue University Indianapolis (IUPUI) / There have been many endeavors to design humanoid robots that have human characteristics such as dexterity, autonomy and intelligence. Humanoid robots are intended to cooperate with humans and perform useful work that humans can perform. The main advantage of humanoid robots over other machines is that they are flexible and multi-purpose. In this thesis, a human-like robotic arm is designed and used in a task which is typically performed by humans, namely, catching a ball. The robotic arm was designed to closely resemble a human arm, based on anthropometric studies. A rigid multibody dynamics software was used to create a virtual model of the robotic arm, perform experiments, and collect data. The inverse kinematics of the robotic arm was solved using a Newton-Raphson numerical method with a numerically calculated Jacobian. The system was validated by testing its ability to find a kinematic solution for the catch position and successfully catch the ball within the robot's workspace. The tests were conducted by throwing the ball such that its path intersects different target points within the robot's workspace. The method used for determining the catch location consists of finding the intersection of the ball's trajectory with a virtual catch plane. The hand orientation was set so that the normal vector to the palm of the hand is parallel to the trajectory of the ball at the intersection point and a vector perpendicular to this normal vector remains in a constant orientation during the catch. It was found that this catch orientation approach was reliable within a 0.35 x 0.4 meter window in the robot's workspace. For all tests within this window, the robotic arm successfully caught and dropped the ball in a bin. Also, for the tests within this window, the maximum position and orientation (Euler angle) tracking errors were 13.6 mm and 4.3 degrees, respectively. The average position and orientation tracking errors were 3.5 mm and 0.3 degrees, respectively. The work presented in this study can be applied to humanoid robots in industrial assembly lines and hazardous environment recovery tasks, amongst other applications.

Indiana University-Purdue University Indianapolis (IUPUI) / Epidemiologic and clinical studies routinely collect longitudinal measures of multiple outcomes. These longitudinal outcomes can be used to establish the temporal order of relevant biological processes and their association with the onset of clinical symptoms. In the first part of this thesis, we proposed to use bivariate change point models for two longitudinal outcomes with a focus on estimating the correlation between the two change points. We adopted a Bayesian approach for parameter estimation and inference. In the second part, we considered the situation when time-to-event outcome is also collected along with multiple longitudinal biomarkers measured until the occurrence of the event or censoring. Joint models for longitudinal and time-to-event data can be used to estimate the association between the characteristics of the longitudinal measures over time and survival time. We developed a maximum-likelihood method to joint model multiple longitudinal biomarkers and a time-to-event outcome. In addition, we focused on predicting conditional survival probabilities and evaluating the predictive accuracy of multiple longitudinal biomarkers in the joint modeling framework. We assessed the performance of the proposed methods in simulation studies and applied the new methods to data sets from two cohort studies. / National Institutes of Health (NIH) Grants R01 AG019181, R24 MH080827, P30 AG10133, R01 AG09956.