Methods and Apparatus for Autonomous Robotic Control

Sensory processing of visual, auditory, and other sensor information (e.g., visual imagery, LIDAR, RADAR) is conventionally based on "stovepiped," or isolated processing, with little interactions between modules. Biological systems, on the other hand, fuse multi-sensory information to identify nearby objects of interest more quickly, more efficiently, and with higher signal-to-noise ratios. Similarly, examples of the OpenSense technology disclosed herein use neurally inspired processing to identify and locate objects in a robot's environment. This enables the robot to navigate its environment more quickly and with lower computational and power requirements

Strategies for neural networks in ballistocardiography with a view towards hardware implementation

A thesis submitted for the degree of Doctor of Philosophy at the University of LutonThe work described in this thesis is based on the results of a clinical trial conducted by the research team at the Medical Informatics Unit of the University of Cambridge, which show that the Ballistocardiogram (BCG) has prognostic value in detecting impaired left ventricular function before it becomes clinically overt as myocardial infarction leading to sudden death. The objective of this study is to develop and demonstrate a framework for realising an on-line BCG signal classification model in a portable device that would have the potential to find pathological signs as early as possible for home health care. Two new on-line automatic BeG classification models for time domain BeG classification are proposed. Both systems are based on a two stage process: input feature extraction followed by a neural classifier. One system uses a principal component analysis neural network, and the other a discrete wavelet transform, to reduce the input dimensionality. Results of the classification, dimensionality reduction, and comparison are presented. It is indicated that the combined wavelet transform and MLP system has a more reliable performance than the combined neural networks system, in situations where the data available to determine the network parameters is limited. Moreover, the wavelet transfonn requires no prior knowledge of the statistical distribution of data samples and the computation complexity and training time are reduced. Overall, a methodology for realising an automatic BeG classification system for a portable instrument is presented. A fully paralJel neural network design for a low cost platform using field programmable gate arrays (Xilinx's XC4000 series) is explored. This addresses the potential speed requirements in the biomedical signal processing field. It also demonstrates a flexible hardware design approach so that an instrument's parameters can be updated as data expands with time. To reduce the hardware design complexity and to increase the system performance, a hybrid learning algorithm using random optimisation and the backpropagation rule is developed to achieve an efficient weight update mechanism in low weight precision learning. The simulation results show that the hybrid learning algorithm is effective in solving the network paralysis problem and the convergence is much faster than by the standard backpropagation rule. The hidden and output layer nodes have been mapped on Xilinx FPGAs with automatic placement and routing tools. The static time analysis results suggests that the proposed network implementation could generate 2.7 billion connections per second performance

GANs schön kompliziert: Applications of Generative Adversarial Networks

Scientific research progresses via model-building. Researchers attempt to build realistic models of real-world phenomena, ranging from bacterial growth to galactic motion, and study these models as a means of understanding these phenomena. However, making these models as realistic as possible often involves fitting them to experimentally measured data. Recent advances in experimental methods have allowed for the collection of large-scale datasets. Simultaneously, advancements in computational capacity have allowed for more complex model-building. The confluence of these two factors accounts for the rise of machine learning methods as powerful tools, both for building models and fitting these models to large scale datasets. In this thesis, we use a particular machine learning technique: generative adversarial networks (GANs). GANs are a flexible and powerful tool, capable of fitting a wide variety of models. We explore the properties of GANs that underpin this flexibility, and show how we can capitalize on them in different scientific applications, beyond the image- and text-generating applications they are well-known for. Here we present three different applications of GANs. First, we show how GANs can be used as generative models of neural spike trains, and how they are capable of capturing more features of these spike trains compared to other approaches. We also show how this could enable insight into how information about stimuli are encoded in the spike trains. Second, we demonstrate how GANs can be used as density estimators for extending simulation-based Bayesian inference to high-dimensional parameter spaces. In this form, we also show how GANs bridge Bayesian inference methods and variational inference with autoencoders and use them to fit complex climate models to data. Finally, we use GANs to infer synaptic plasticity rules for biological rate networks directly from data. We then show how GANs be used to test the robustness of the inferred rules to differences in data and network initialisation. Overall, we repurpose GANs in new ways for a variety of scientific domains, and show that they confer specific advantages over the state-of-the-art methods in each of these domains

A Generalized Neural Network Approach to Mobile Robot Navigation and Obstacle Avoidance

In this thesis, we tackle the problem of extending neural network navigation algorithms for various types of mobile robots and 2-dimensional range sensors. We propose a general method to interpret the data from various types of 2-dimensional range sensors and a neural network algorithm to perform the navigation task. Our approach can yield a global navigation algorithm which can be applied to various types of range sensors and mobile robot platforms. Moreover, this method allows the neural networks to be trained using only one type of 2-dimensional range sensor, which contributes positively to reducing the time required for training the networks. Experimental results carried out in simulation environments demonstrate the effectiveness of our approach in mobile robot navigation for different kinds of robots and sensors. Therefore, the successful implementation of our method provides a solution to apply mobile robot navigation algorithms to various robot platforms

Biologically inspired feature extraction for rotation and scale tolerant pattern analysis

Biologically motivated information processing has been an important area of scientific research for decades. The central topic addressed in this dissertation is utilization of lateral inhibition and more generally, linear networks with recurrent connectivity along with complex-log conformal mapping in machine based implementations of information encoding, feature extraction and pattern recognition. The reasoning behind and method for spatially uniform implementation of inhibitory/excitatory network model in the framework of non-uniform log-polar transform is presented. For the space invariant connectivity model characterized by Topelitz-Block-Toeplitz matrix, the overall network response is obtained without matrix inverse operations providing the connection matrix generating function is bound by unity. It was shown that for the network with the inter-neuron connection function expandable in a Fourier series in polar angle, the overall network response is steerable. The decorrelating/whitening characteristics of networks with lateral inhibition are used in order to develop space invariant pre-whitening kernels specialized for specific category of input signals. These filters have extremely small memory footprint and are successfully utilized in order to improve performance of adaptive neural whitening algorithms. Finally, the method for feature extraction based on localized Independent Component Analysis (ICA) transform in log-polar domain and aided by previously developed pre-whitening filters is implemented. Since output codes produced by ICA are very sparse, a small number of non-zero coefficients was sufficient to encode input data and obtain reliable pattern recognition performance

Toward a further understanding of object feature binding: a cognitive neuroscience perspective.

The aim of this thesis is to lead to a further understanding of the neural mechanisms underlying object feature binding in the human brain. The focus is on information processing and integration in the visual system and visual shortterm memory. From a review of the literature it is clear that there are three major competing binding theories, however, none of these individually solves the binding problem satisfactorily. Thus the aim of this research is to conduct behavioural experimentation into object feature binding, paying particular attention to visual short-term memory. The behavioural experiment was designed and conducted using a within-subjects delayed responset ask comprising a battery of sixty-four composite objects each with three features and four dimensions in each of three conditions (spatial, temporal and spatio-temporal).Findings from the experiment,which focus on spatial and temporal aspects of object feature binding and feature proximity on binding errors, support the spatial theories on object feature binding, in addition we propose that temporal theories and convergence, through hierarchical feature analysis, are also involved. Because spatial properties have a dedicated processing neural stream, and temporal properties rely on limited capacity memory systems, memories for sequential information would likely be more difficult to accuratelyr ecall. Our study supports other studies which suggest that both spatial and temporal coherence to differing degrees,may be involved in object feature binding. Traditionally, these theories have purported to provide individual solutions, but this thesis proposes a novel unified theory of object feature binding in which hierarchical feature analysis, spatial attention and temporal synchrony each plays a role. It is further proposed that binding takes place in visual short-term memory through concerted and integrated information processing in distributed cortical areas. A cognitive model detailing this integrated proposal is given. Next, the cognitive model is used to inform the design and suggested implementation of a computational model which would be able to test the theory put forward in this thesis. In order to verify the model, future work is needed to implement the computational model.Thus it is argued that this doctoral thesis provides valuable experimental evidence concerning spatio-temporal aspects of the binding problem and as such is an additional building block in the quest for a solution to the object feature binding problem

Mathematical Modeling of Lateralization and Asymmetries in Cortical Maps

Recent experimental work in neurobiology has defined asymmetries and lateralization in the topographic maps found in mirror-image regions of the sensorimotor cerebral cortex. However, the mechanisms underlying these asymmetries are currently not established, and in some cases are quite controversial. In order to explore some possible causes of map asymmetry and lateralization, several neural network models of cortical map lateralization and asymmetries based on self-organizing maps are created and studied both computationally and theoretically. Activation levels of the elements in the models are governed by large systems of highly nonlinear ordinary differential equations (ODEs), where coefficients change with time and their changes depend on the activation levels. Special metrics for objective evaluation of simulation results (represented as paired receptive field maps) are introduced and analysed. The behavior of the models is studied when their parameters are varied systematically and also when simulated lesions are introduced into one of the hemispheric regions. Some very sharp transitions and other interesting phenomena have been found computationally. Many of these computationally observed phenomena are explained by theoretical analysis of total hemispheric activation in a simplified model. The connection between a bifurcation point of the system of ODEs and the sharp transition in the model's computational behavior is established. More general understanding of topographic map formation and changes under various conditions is achieved by analysis of activation patterns (i.e., $\omega$-limit sets of the above system of ODEs). This is the first mathematical model to demonstrate spontaneous map lateralization and asymmetries, and it suggests that such models may be generally useful in better understanding the mechanisms of cerebral lateralization. The mathematical analysis of the models leads to a better understanding of the mechanisms of self-organization in the topographic maps based on competitive distribution of activation and competitive learning. Also cross-referenced as UMIACS-TR-99-4