Online monitoring applications requiring advanced pattern recognition capabilities implemented in resource-constrained wireless sensor systems are challenging to construct using standard digital computers. An interesting alternative solution is to use a low-power neuromorphic processor like the ROLLS, with subthreshold mixed analog/digital circuits and online learning capabilities that approximate the behavior of real neurons and synapses. This requires that the monitoring algorithm is implemented with spiking neural networks, which in principle are efficient computational models for tasks such as pattern recognition. In this work, I investigate how spiking neural networks can be used as a pre-processing and feature learning system in a condition monitoring application where the vibration of a machine with healthy and faulty rolling-element bearings is considered. Pattern recognition with spiking neural networks is investigated using simulations with Brian -- a Python-based open source toolbox -- and an implementation is developed for the ROLLS neuromorphic processor. I analyze the learned feature-response properties of individual neurons. When pre-processing the input signals with a neuromorphic cochlea known as the AER-EAR system, the ROLLS chip learns to classify the resulting spike patterns with a training error of less than 1 %, at a combined power consumption of approximately 30 mW. Thus, the neuromorphic hardware system can potentially be realized in a resource-constrained wireless sensor for online monitoring applications.However, further work is needed for testing and cross validation of the feature learning and pattern recognition networks.i

Spiking neural networks (SNN) are dynamic models of biological neurons, that communicates with event-based signals called spikes. SNN that reproduce observed properties of biological senses like vision are developed to better understand how such systems function, and to learn how more efficient sensor systems can be engineered. A branching parameter describes the average probability for spikes to propagate between two different neuron populations. The adaptation of branching parameters towards critical values is known to be important for maximizing the sensitivity and dynamic range of SNN. In this thesis, a recently proposed SNN model for visual feature learning and pattern recognition known as the E-I Net model is studied and extended with a critical branching mechanism. The resulting modified E-I Net model is studied with numerical experiments and two different types of sensory queues. The experiments show that the modified E-I Net model demonstrates critical branching and power-law scaling behavior, as expected from SNN near criticality, but the power-laws are broken and the stimuli reconstruction error is higher compared to the error of the original E-I Net model. Thus, on the basis of these experiments, it is not clear how to properly extend the E-I Net model properly with a critical branching mechanism. The E-I Net model has a particular structure where the inhibitory neurons (I) are tuned to decorrelate the excitatory neurons (E) so that the visual features learned matches the angular and frequency distributions of feature detectors in visual cortex V1 and different stimuli are represented by sparse subsets of the neurons. The broken power-laws correspond to different scaling behavior at low and high spike rates, which may be related to the efficacy of inhibition in the model.