Event-based feature extraction using adaptive selection thresholds

Unsupervised feature extraction algorithms form one of the most important building blocks in machine learning systems. These algorithms are often adapted to the event-based domain to perform online learning in neuromorphic hardware. However, not designed for the purpose, such algorithms typically require significant simplification during implementation to meet hardware constraints, creating trade offs with performance. Furthermore, conventional feature extraction algorithms are not designed to generate useful intermediary signals which are valuable only in the context of neuromorphic hardware limitations. In this work a novel event-based feature extraction method is proposed that focuses on these issues. The algorithm operates via simple adaptive selection thresholds which allow a simpler implementation of network homeostasis than previous works by trading off a small amount of information loss in the form of missed events that fall outside the selection thresholds. The behavior of the selection thresholds and the output of the network as a whole are shown to provide uniquely useful signals indicating network weight convergence without the need to access network weights. A novel heuristic method for network size selection is proposed which makes use of noise events and their feature representations. The use of selection thresholds is shown to produce network activation patterns that predict classification accuracy allowing rapid evaluation and optimization of system parameters without the need to run back-end classifiers. The feature extraction method is tested on both the N-MNIST (Neuromorphic-MNIST) benchmarking dataset and a dataset of airplanes passing through the field of view. Multiple configurations with different classifiers are tested with the results quantifying the resultant performance gains at each processing stage

A simple sequent calculus for nominal logic

The front end of the human auditory system, the cochlea, converts sound signals from the outside world into neural impulses transmitted along the auditory pathway for further processing. The cochlea senses and separates sound in a nonlinear active fashion, exhibiting remarkable sensitivity and frequency discrimination. Although several electronic models of the cochlea have been proposed and implemented, none of these are able to reproduce all the characteristics of the cochlea, including large dynamic range, large gain and sharp tuning at low sound levels, and low gain and broad tuning at intense sound levels. Here, we implement the 'Cascade of Asymmetric Resonators' (CAR) model of the cochlea on an FPGA. CAR represents the basilar membrane filter in the 'Cascade of Asymmetric Resonators with Fast-Acting Compression' (CAR-FAC) cochlear model. CAR-FAC is a neuromorphic model of hearing based on a pole-zero filter cascade model of auditory filtering. It uses simple nonlinear extensions of conventional digital filter stages that are well suited to FPGA implementations, so that we are able to implement up to 1224 cochlear sections on Virtex-6 FPGA to process sound data in real time. The FPGA implementation of the electronic cochlea described here may be used as a front-end sound analyser for various machine-hearing applications

Mixed signal phase sensitive detection

Phase sensitive detection is a fundamental technique in signal processing. We present a mixed signal approach which takes advantage of both analog and digital features to produce a simple and optimal phase sensitive detector with several advantages over the standard analog or digital approaches

Emergence of cross-correlation functions in neural spike interval distributions

In this paper, we show that the paradox is removed by differentiating the significance of input spokes according to the state of the downstream (receiving) neuron. Ig the downstream neuron is very close to the threshold of spiking, then the timing of an input spike is likely to be significant. If the downstream neuron is not close to threshold, then the timing of individual input spikes is of little importance, but their rate of occurrence is significant

A neuromorphic cross-correlation chip

The cross-correlation and auto-correlation operations are important in many signal processing tasks. Often these operations are resource intensive or limited by noise in low-power systems. In this paper we present the design and measured results of an analogue integrated circuit (IC) that performs cross-correlations using a neuromorphic algorithm

A robust implementation of the spatial pooler within the theory of Hierarchical Temporal Memory (HTM)

In this study learning reinforcement and noise rejection of a spatial pooler was examined, the first learning stage in a Hierarchical Temporal Memory (HTM) network. Hierarchical Temporal Memory (HTM) is a proposed model within the field of neuromorphic engineering. It describes a top down approach to understanding how the human brain performs higher reasoning and has application as a machine-learning algorithm. Final results displayed an increase in permanence values associated with the learning of the input pseudo-sensory signal and the system was able to accurately recognize the input signal with up to twenty percent of the binary data randomly modified. These results demonstrated conclusive evidence that HTM is a possible choice when machine intelligence is a system requirement

A model of the humanoid body for self collision detection based on elliptical capsules

This paper presents a self collision detection scheme for humanoid robots using elliptical and circular capsules as bounding volumes. A capsule is defined as an elliptical or circular cylinder capped with ellipsoids or spheres respectively. The humanoid body is modeled using elliptical capsules, while the moving segments, i.e. arms and legs, of the humanoid are modeled using circular capsules. This collision detection model provides a good fit to the humanoid body shape while being simple to implement. A case study of the self collision free workspace of the humanoid arm is then presented to illustrate the effectiveness of the collision detection scheme

An asynchronous parallel neuromorphic ADC architecture

A new parallel ADC architecture is presented which makes use of neuromorphic principles to be fast, accurate, and robust to noise and circuit mismatch. The architecture uses spiking integrate-and-fire neurons as base elements, with lateral inhibition to decohere the parallel pathways, and alternate on-and off-triggered paths to maintain a constant spike rate. Results from a proof-of-concept circuit reinforce the analytical conclusion that this circuit can make a practical ADC

Online and adaptive pseudoinverse solutions for ELM weights

The ELM method has become widely used for classification and regressions problems as a result of its accuracy, simplicity and ease of use. The solution of the hidden layer weights by means of a matrix pseudoinverse operation is a significant contributor to the utility of the method; however, the conventional calculation of the pseudoinverse by means of a singular value decomposition (SVD) is not always practical for large data sets or for online updates to the solution. In this paper we discuss incremental methods for solving the pseudoinverse which are suitable for ELM. We show that careful choice of methods allows us to optimize for accuracy, ease of computation, or adaptability of the solution

The effect of backing material on the transmitting response level and bandwidth of a wideband underwater transmitting transducer using 1-3 piezocomposite material

Increasing operating depths of autonomous underwater vehicles have necessitated the development of underwater transducers that can operate at a greater depth. This paper investigates the possibility of incorporating rigid backing material into the transducer design to increase its stiffness and depth capability without adversely affecting its wide bandwidth and high transmitting levels. The transducer design under consideration uses 1-3 piezocomposite material, matching layer, coupling layer, stiff backing material (backing plates) and operates at 300 kHz with 200 kHz 6dB bandwidth