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.

Investigation of Event-Based Surfaces for High-Speed Detection, Unsupervised Feature Extraction, and Object Recognition

In this work, we investigate event-based feature extraction through a rigorous framework of testing. We test a hardware efficient variant of Spike Timing Dependent Plasticity (STDP) on a range of spatio-temporal kernels with different surface decaying methods, decay functions, receptive field sizes, feature numbers, and back end classifiers. This detailed investigation can provide helpful insights and rules of thumb for performance vs. complexity trade-offs in more generalized networks, especially in the context of hardware implementation, where design choices can incur significant resource costs. The investigation is performed using a new dataset consisting of model airplanes being dropped free-hand close to the sensor. The target objects exhibit a wide range of relative orientations and velocities. This range of target velocities, analyzed in multiple configurations, allows a rigorous comparison of time-based decaying surfaces (time surfaces) vs. event index-based decaying surface (index surfaces), which are used to perform unsupervised feature extraction, followed by target detection and recognition. We examine each processing stage by comparison to the use of raw events, as well as a range of alternative layer structures, and the use of random features. By comparing results from a linear classifier and an ELM classifier, we evaluate how each element of the system affects accuracy. To generate time and index surfaces, the most commonly used kernels, namely event binning kernels, linearly, and exponentially decaying kernels, are investigated. Index surfaces were found to outperform time surfaces in recognition when invariance to target velocity was made a requirement. In the investigation of network structure, larger networks of neurons with large receptive field sizes were found to perform best. We find that a small number of event-based feature extractors can project the complex spatio-temporal event patterns of the dataset to an almost linearly separable representation in feature space, with best performing linear classifier achieving 98.75% recognition accuracy, using only 25 feature extracting neurons.

Spatial and Temporal Downsampling in Event-Based Visual Classification

As the interest in event-based vision sensors for mobile and aerial applications grows, there is an increasing need for high-speed and highly robust algorithms for performing visual tasks using event-based data. As event rate and network structure have a direct impact on the power consumed by such systems, it is important to explore the efficiency of the event-based encoding used by these sensors. The work presented in this paper represents the first study solely focused on the effects of both spatial and temporal downsampling on event-based vision data and makes use of a variety of data sets chosen to fully explore and characterize the nature of downsampling operations. The results show that both spatial downsampling and temporal downsampling produce improved classification accuracy and, additionally, a lower overall data rate. A finding is particularly relevant for bandwidth and power constrained systems. For a given network containing 1000 hidden layer neurons, the spatially downsampled systems achieved a best case accuracy of 89.38% on N-MNIST as opposed to 81.03% with no downsampling at the same hidden layer size. On the N-Caltech101 data set, the downsampled system achieved a best case accuracy of 18.25%, compared with 7.43% achieved with no downsampling. The results show that downsampling is an important preprocessing technique in event-based visual processing, especially for applications sensitive to power consumption and transmission bandwidth.

Neuromorphic Hardware Architecture Using the Neural Engineering Framework for Pattern Recognition

We present a hardware architecture that uses the neural engineering framework (NEF) to implement large-scale neural networks on field programmable gate arrays (FPGAs) for performing massively parallel real-time pattern recognition. NEF is a framework that is capable of synthesising large-scale cognitive systems from subnetworks and we have previously presented an FPGA implementation of the NEF that successfully performs nonlinear mathematical computations. That work was developed based on a compact digital neural core, which consists of 64 neurons that are instantiated by a single physical neuron using a time-multiplexing approach. We have now scaled this approach up to build a pattern recognition system by combining identical neural cores together. As a proof of concept, we have developed a handwritten digit recognition system using the MNIST database and achieved a recognition rate of 96.55%. The system is implemented on a state-of-the-art FPGA and can process 5.12 million digits per second. The architecture and hardware optimisations presented offer high-speed and resource-efficient means for performing high-speed, neuromorphic, and massively parallel pattern recognition and classification tasks.

Prolonged Incubation of Acute Neuronal Tissue for Electrophysiology and Calcium-imaging

Acute neuronal tissue preparations, brain slices and retinal wholemount, can usually only be maintained for 6 - 8 h following dissection. This limits the experimental time, and increases the number of animals that are utilized per study. This limitation specifically impacts protocols such as calcium imaging that require prolonged pre-incubation with bath-applied dyes. Exponential bacterial growth within 3 - 4 h after slicing is tightly correlated with a decrease in tissue health. This study describes a method for limiting the proliferation of bacteria in acute preparations to maintain viable neuronal tissue for prolonged periods of time (>24 h) without the need for antibiotics, sterile procedures, or tissue culture media containing growth factors. By cycling the extracellular fluid through UV irradiation and keeping the tissue in a custom holding chamber at 15 - 16 °C, the tissue shows no difference in electrophysiological properties, or calcium signaling through intracellular calcium dyes at >24 h postdissection. These methods will not only extend experimental time for those using acute neuronal tissue, but will reduce the number of animals required to complete experimental goals, and will set a gold standard for acute neuronal tissue incubation.

Calcium Imaging of AM Dyes Following Prolonged Incubation in Acute Neuronal Tissue

Calcium-imaging is a sensitive method for monitoring calcium dynamics during neuronal activity. As intracellular calcium concentration is correlated to physiological and pathophysiological activity of neurons, calcium imaging with fluorescent indicators is one of the most commonly used techniques in neuroscience today. Current methodologies for loading calcium dyes into the tissue require prolonged incubation time (45-150 min), in addition to dissection and recovery time after the slicing procedure. This prolonged incubation curtails experimental time, as tissue is typically maintained for 6-8 hours after slicing. Using a recently introduced recovery chamber that extends the viability of acute brain slices to more than 24 hours, we tested the effectiveness of calcium AM staining following long incubation periods post cell loading and its impact on the functional properties of calcium signals in acute brain slices and wholemount retinae. We show that calcium dyes remain within cells and are fully functional >24 hours after loading. Moreover, the calcium dynamics recorded >24 hrs were similar to the calcium signals recorded in fresh tissue that was incubated for <4 hrs. These results indicate that long exposure of calcium AM dyes to the intracellular cytoplasm did not alter the intracellular calcium concentration, the functional range of the dye or viability of the neurons. This data extends our previous work showing that a custom recovery chamber can extend the viability of neuronal tissue, and reliable data for both electrophysiology and imaging can be obtained >24hrs after dissection. These methods will not only extend experimental time for those using acute neuronal tissue, but also may reduce the number of animals required to complete experimental goals.

Skimming Digits: Neuromorphic Classification of Spike-Encoded Images

The growing demands placed upon the field of computer vision have renewed the focus on alternative visual scene representations and processing paradigms. Silicon retinea provide an alternative means of imaging the visual environment, and produce frame-free spatio-temporal data. This paper presents an investigation into event-based digit classification using N-MNIST, a neuromorphic dataset created with a silicon retina, and the Synaptic Kernel Inverse Method (SKIM), a learning method based on principles of dendritic computation. As this work represents the first large-scale and multi-class classification task performed using the SKIM network, it explores different training patterns and output determination methods necessary to extend the original SKIM method to support multi-class problems. Making use of SKIM networks applied to real-world datasets, implementing the largest hidden layer sizes and simultaneously training the largest number of output neurons, the classification system achieved a best-case accuracy of 92.87% for a network containing 10,000 hidden layer neurons. These results represent the highest accuracies achieved against the dataset to date and serve to validate the application of the SKIM method to event-based visual classification tasks. Additionally, the study found that using a square pulse as the supervisory training signal produced the highest accuracy for most output determination methods, but the results also demonstrate that an exponential pattern is better suited to hardware implementations as it makes use of the simplest output determination method based on the maximum value.

Bayesian Estimation and Inference Using Stochastic Electronics

In this paper, we present the implementation of two types of Bayesian inference problems to demonstrate the potential of building probabilistic algorithms in hardware using single set of building blocks with the ability to perform these computations in real time. The first implementation, referred to as the BEAST (Bayesian Estimation and Stochastic Tracker), demonstrates a simple problem where an observer uses an underlying Hidden Markov Model (HMM) to track a target in one dimension. In this implementation, sensors make noisy observations of the target position at discrete time steps. The tracker learns the transition model for target movement, and the observation model for the noisy sensors, and uses these to estimate the target position by solving the Bayesian recursive equation online. We show the tracking performance of the system and demonstrate how it can learn the observation model, the transition model, and the external distractor (noise) probability interfering with the observations. In the second implementation, referred to as the Bayesian INference in DAG (BIND), we show how inference can be performed in a Directed Acyclic Graph (DAG) using stochastic circuits. We show how these building blocks can be easily implemented using simple digital logic gates. An advantage of the stochastic electronic implementation is that it is robust to certain types of noise, which may become an issue in integrated circuit (IC) technology with feature sizes in the order of tens of nanometers due to their low noise margin, the effect of high-energy cosmic rays and the low supply voltage. In our framework, the flipping of random individual bits would not affect the system performance because information is encoded in a bit stream.

Fast, Simple and Accurate Handwritten Digit Classification by Training Shallow Neural Network Classifiers with the 'Extreme Learning Machine' Algorithm

Recent advances in training deep (multi-layer) architectures have inspired a renaissance in neural network use. For example, deep convolutional networks are becoming the default option for difficult tasks on large datasets, such as image and speech recognition. However, here we show that error rates below 1% on the MNIST handwritten digit benchmark can be replicated with shallow non-convolutional neural networks. This is achieved by training such networks using the 'Extreme Learning Machine' (ELM) approach, which also enables a very rapid training time (∼ 10 minutes). Adding distortions, as is common practise for MNIST, reduces error rates even further. Our methods are also shown to be capable of achieving less than 5.5% error rates on the NORB image database. To achieve these results, we introduce several enhancements to the standard ELM algorithm, which individually and in combination can significantly improve performance. The main innovation is to ensure each hidden-unit operates only on a randomly sized and positioned patch of each image. This form of random 'receptive field' sampling of the input ensures the input weight matrix is sparse, with about 90% of weights equal to zero. Furthermore, combining our methods with a small number of iterations of a single-batch backpropagation method can significantly reduce the number of hidden-units required to achieve a particular performance. Our close to state-of-the-art results for MNIST and NORB suggest that the ease of use and accuracy of the ELM algorithm for designing a single-hidden-layer neural network classifier should cause it to be given greater consideration either as a standalone method for simpler problems, or as the final classification stage in deep neural networks applied to more difficult problems.

Turn Down That Noise: Synaptic Encoding of Afferent SNR in a Single Spiking Neuron

We have added a simplified neuromorphic model of Spike Time Dependent Plasticity (STDP) to the previously described Synapto-dendritic Kernel Adapting Neuron (SKAN), a hardware efficient neuron model capable of learning spatio-temporal spike patterns. The resulting neuron model is the first to perform synaptic encoding of afferent signal-to-noise ratio in addition to the unsupervised learning of spatio-temporal spike patterns. The neuron model is particularly suitable for implementation in digital neuromorphic hardware as it does not use any complex mathematical operations and uses a novel shift-based normalization approach to achieve synaptic homeostasis. The neuron's noise compensation properties are characterized and tested on random spatio-temporal spike patterns as well as a noise corrupted subset of the zero images of the MNIST handwritten digit dataset. Results show the simultaneously learning common patterns in its input data while dynamically weighing individual afferents based on their signal to noise ratio. Despite its simplicity the interesting behaviors of the neuron model and the resulting computational power may also offer insights into biological systems.

Racing to learn: statistical inference and learning in a single spiking neuron with adaptive kernels

This paper describes the Synapto-dendritic Kernel Adapting Neuron (SKAN), a simple spiking neuron model that performs statistical inference and unsupervised learning of spatiotemporal spike patterns. SKAN is the first proposed neuron model to investigate the effects of dynamic synapto-dendritic kernels and demonstrate their computational power even at the single neuron scale. The rule-set defining the neuron is simple: there are no complex mathematical operations such as normalization, exponentiation or even multiplication. The functionalities of SKAN emerge from the real-time interaction of simple additive and binary processes. Like a biological neuron, SKAN is robust to signal and parameter noise, and can utilize both in its operations. At the network scale neurons are locked in a race with each other with the fastest neuron to spike effectively "hiding" its learnt pattern from its neighbors. The robustness to noise, high speed, and simple building blocks not only make SKAN an interesting neuron model in computational neuroscience, but also make it ideal for implementation in digital and analog neuromorphic systems which is demonstrated through an implementation in a Field Programmable Gate Array (FPGA). Matlab, Python, and Verilog implementations of SKAN are available at: http://www.uws.edu.au/bioelectronics_neuroscience/bens/reproducible_research.

Approximate, computationally efficient online learning in Bayesian spiking neurons

Bayesian spiking neurons (BSNs) provide a probabilistic interpretation of how neurons perform inference and learning. Online learning in BSNs typically involves parameter estimation based on maximum-likelihood expectation-maximization (ML-EM) which is computationally slow and limits the potential of studying networks of BSNs. An online learning algorithm, fast learning (FL), is presented that is more computationally efficient than the benchmark ML-EM for a fixed number of time steps as the number of inputs to a BSN increases (e.g., 16.5 times faster run times for 20 inputs). Although ML-EM appears to converge 2.0 to 3.6 times faster than FL, the computational cost of ML-EM means that ML-EM takes longer to simulate to convergence than FL. FL also provides reasonable convergence performance that is robust to initialization of parameter estimates that are far from the true parameter values. However, parameter estimation depends on the range of true parameter values. Nevertheless, for a physiologically meaningful range of parameter values, FL gives very good average estimation accuracy, despite its approximate nature. The FL algorithm therefore provides an efficient tool, complementary to ML-EM, for exploring BSN networks in more detail in order to better understand their biological relevance. Moreover, the simplicity of the FL algorithm means it can be easily implemented in neuromorphic VLSI such that one can take advantage of the energy-efficient spike coding of BSNs.

The ripple pond: enabling spiking networks to see

We present the biologically inspired Ripple Pond Network (RPN), a simply connected spiking neural network which performs a transformation converting two dimensional images to one dimensional temporal patterns (TP) suitable for recognition by temporal coding learning and memory networks. The RPN has been developed as a hardware solution linking previously implemented neuromorphic vision and memory structures such as frameless vision sensors and neuromorphic temporal coding spiking neural networks. Working together such systems are potentially capable of delivering end-to-end high-speed, low-power and low-resolution recognition for mobile and autonomous applications where slow, highly sophisticated and power hungry signal processing solutions are ineffective. Key aspects in the proposed approach include utilizing the spatial properties of physically embedded neural networks and propagating waves of activity therein for information processing, using dimensional collapse of imagery information into amenable TP and the use of asynchronous frames for information binding.

The adaptation of spike backpropagation delays in cortical neurons

We measured the action potential backpropagation delays in apical dendrites of layer V pyramidal neurons of the somatosensory cortex under different stimulation regimes that exclude synaptic involvement. These delays showed robust features and did not correlate to either transient change in the stimulus strength or low frequency stimulation of suprathreshold membrane oscillations. However, our results indicate that backpropagation delays correlate with high frequency (>10 Hz) stimulation of membrane oscillations, and that persistent suprathreshold sinusoidal stimulation injected directly into the soma results in an increase of the backpropagation delay, suggesting an intrinsic adaptation of the backpropagating action potential (bAP), which does not involve any synaptic modifications. Moreover, the calcium chelator BAPTA eliminated the alterations in the backpropagation delays, strengthening the hypothesis that increased calcium concentration in the dendrites modulates dendritic excitability and can impact the backpropagation velocity. These results emphasize the impact of dendritic excitability on bAP velocity along the dendritic tree, which affects the precision of the bAP arrival at the synapse during specific stimulus regimes, and is capable of shifting the extent and polarity of synaptic strength during suprathreshold synaptic processes such as spike time-dependent plasticity.

Towards true unipolar ECG recording without the Wilson central terminal (preliminary results)

We present an innovative bio-potential front-end capable of recording true unipolar ECG leads for the first time without making use of the Wilson central terminal. In addition to the convenience in applications such as continuous monitoring and rapid diagnosis, the information in unipolar recordings may yield unique diagnostic information as it avoids the need to essentially subtract data or make use of the averaging effect imposed from the Wilson central terminal. The system also allows direct, real-time software calculation of signals corresponding to standard ECG leads which achieve correlations in excess of 92% with a gold standard ECG during a parallel in vivo recording. In addition, the implemented circuit is wideband (0.05-1000 Hz), compatible with standard (Ag/AgCl) bio-potential electrodes, and dry (paste-less) textile electrodes. The circuit is also low power, requiring less than 50 mW (when powered at 12 V) per standard ECG lead (two channels required). It is therefore well suited for wearable, long-term applications.

Learning the pseudoinverse solution to network weights

The last decade has seen the parallel emergence in computational neuroscience and machine learning of neural network structures which spread the input signal randomly to a higher dimensional space; perform a nonlinear activation; and then solve for a regression or classification output by means of a mathematical pseudoinverse operation. In the field of neuromorphic engineering, these methods are increasingly popular for synthesizing biologically plausible neural networks, but the "learning method"-computation of the pseudoinverse by singular value decomposition-is problematic both for biological plausibility and because it is not an online or an adaptive method. We present an online or incremental method of computing the pseudoinverse precisely, which we argue is biologically plausible as a learning method, and which can be made adaptable for non-stationary data streams. The method is significantly more memory-efficient than the conventional computation of pseudoinverses by singular value decomposition.

An FPGA Implementation of a Polychronous Spiking Neural Network with Delay Adaptation

We present an FPGA implementation of a re-configurable, polychronous spiking neural network with a large capacity for spatial-temporal patterns. The proposed neural network generates delay paths de novo, so that only connections that actually appear in the training patterns will be created. This allows the proposed network to use all the axons (variables) to store information. Spike Timing Dependent Delay Plasticity is used to fine-tune and add dynamics to the network. We use a time multiplexing approach allowing us to achieve 4096 (4k) neurons and up to 1.15 million programmable delay axons on a Virtex 6 FPGA. Test results show that the proposed neural network is capable of successfully recalling more than 95% of all spikes for 96% of the stored patterns. The tests also show that the neural network is robust to noise from random input spikes.

Temporal order detection and coding in nervous systems

This letter discusses temporal order coding and detection in nervous systems. Detection of temporal order in the external world is an adaptive function of nervous systems. In addition, coding based on the temporal order of signals can be used as an internal code. Such temporal order coding is a subset of temporal coding. We discuss two examples of processing the temporal order of external events: the auditory location detection system in birds and the visual direction detection system in flies. We then discuss how somatosensory stimulus intensities are translated into a temporal order code in the human peripheral nervous system. We next turn our attention to input order coding in the mammalian cortex. We review work demonstrating the capabilities of cortical neurons for detecting input order. We then discuss research refuting and demonstrating the representation of stimulus features in the cortex by means of input order. After some general theoretical considerations on input order detection and coding, we conclude by discussing the existing and potential use of input order coding in neuromorphic engineering.

Investigating the role of combined acoustic-visual feedback in one-dimensional synchronous brain computer interfaces, a preliminary study

Feedback plays an important role when learning to use a brain computer interface (BCI), particularly in the case of synchronous feedback that relies on the interaction subject. In this preliminary study, we investigate the role of combined auditory-visual feedback during synchronous μ rhythm-based BCI sessions to help the subject to remain focused on the selected imaginary task. This new combined feedback, now integrated within the general purpose BCI2000 software, has been tested on eight untrained and three trained subjects during a monodimensional left-right control task. In order to reduce the setup burden and maximize subject comfort, an electroencephalographic device suitable for dry electrodes that required no skin preparation was used. Quality and index of improvement was evaluated based on a personal self-assessment questionnaire from each subject and quantitative data based on subject performance. Results for this preliminary study show that the combined feedback was well tolerated by the subjects and improved performance in 75% of the naïve subjects compared with visual feedback alone.

Pregnancy detection and monitoring in cattle via combined foetus electrocardiogram and phonocardiogram signal processing

Background

Pregnancy testing in cattle is commonly invasive requiring manual rectal palpation of the reproductive tract that presents risks to the operator and pregnancy. Alternative non-invasive tests have been developed but have not gained popularity due to poor specificity, sensitivity and the inconvenience of sample handling. Our aim is to present the pilot study and proof of concept of a new non invasive technique to sense the presence and age (limited to the closest trimester of pregnancy) of the foetus by recording the electrical and audio signals produced by the foetus heartbeat using an array of specialized sensors embedded in a stand alone handheld prototype device. The device was applied to the right flank (approximately at the intercept of a horizontal line drawn through the right mid femur region of the cow and a vertical line drawn anywhere between lumbar vertebrae 3 to 5) of more than 2000 cattle from 13 different farms, including pregnant and not pregnant, a diversity of breeds, and both dairy and beef herds. Pregnancy status response is given “on the spot” from an optimized machine learning algorithm running on the device within seconds after data collection.

Results

Using combined electrical and audio foetal signals we detected pregnancy with a sensitivity of 87.6% and a specificity of 74.6% for all recorded data. Those values increase to 91% and 81% respectively by removing files with excessive noise (19%).

Foetus ageing was achieved by comparing the detected foetus heart-rate with published tables. However, given the challenging farm environment of a restless cow, correct foetus ageing was achieved for only 21% of the correctly diagnosed pregnant cows.

Conclusions

In conclusion we have found that combining ECG and PCG measurements on the right flank of cattle provides a reliable and rapid method of pregnancy testing. The device has potential to be applied by unskilled operators. This will generate more efficient and productive management of farms. There is potential for the device to be applied to large endangered quadrupeds in captive breeding programs where early, safe and reliable pregnancy diagnosis can be imperative but currently difficult to achieve.

Optimization methods for spiking neurons and networks

Spiking neurons and spiking neural circuits are finding uses in a multitude of tasks such as robotic locomotion control, neuroprosthetics, visual sensory processing, and audition. The desired neural output is achieved through the use of complex neuron models, or by combining multiple simple neurons into a network. In either case, a means for configuring the neuron or neural circuit is required. Manual manipulation of parameters is both time consuming and non-intuitive due to the nonlinear relationship between parameters and the neuron's output. The complexity rises even further as the neurons are networked and the systems often become mathematically intractable. In large circuits, the desired behavior and timing of action potential trains may be known but the timing of the individual action potentials is unknown and unimportant, whereas in single neuron systems the timing of individual action potentials is critical. In this paper, we automate the process of finding parameters. To configure a single neuron we derive a maximum likelihood method for configuring a neuron model, specifically the Mihalas-Niebur Neuron. Similarly, to configure neural circuits, we show how we use genetic algorithms (GAs) to configure parameters for a network of simple integrate and fire with adaptation neurons. The GA approach is demonstrated both in software simulation and hardware implementation on a reconfigurable custom very large scale integration chip.

Limiting factors in acoustic separation of carbon particles in air

Particles suspended in a fluid that is exposed to an acoustic standing wave experience a time-averaged force that drives them to either the pressure nodes or anti-nodes of the wave. Several filter designs have been successfully implemented using this force to filter small particles in liquids with low flow rates and small cross-sectional areas. It has been suggested that the filtration of small solid particles out of a gas, such as carbon in air (smoke), would be a possible application of acoustic standing wave based particle separation. This study shows the limiting factors, in both power requirements and design factors, of an acoustic filter designed for filtering smoke particles across large cross-sectional areas. It is shown that while filtration is possible, the power needed is impractical. It is also shown that operating the filter within certain settling time parameters optimizes the energy usage of the filter.

Code-division-multiplexed electrical impedance tomography spectroscopy

Electrical impedance tomography uses multiple impedance measurements to image the internal conductivity of an object, such as the human body. Code-division multiplexing is proposed as a new method that can provide simultaneous impedance measurements of the multiple channels. Code division provides clear advantages of a wide frequency range at reduced cost and reduced complexity of sources. A potential drawback is the lack of perfectly orthogonal code sets. This caused an increase of 0.62% in root-mean-square spectral error when two codes were used to record two impedance channels simultaneously on a low-pass filter network. The method described provides images and spectra which are equivalent to the conventional time-multiplexed method, with increases in frequency resolution and measurement speed which may be of benefit in some applications of electrical impedance tomography spectroscopy.

A first-order nonhomogeneous Markov model for the response of spiking neurons stimulated by small phase-continuous signals

We present a first-order nonhomogeneous Markov model for the interspike-interval density of a continuously stimulated spiking neuron. The model allows the conditional interspike-interval density and the stationary interspike-interval density to be expressed as products of two separate functions, one of which describes only the neuron characteristics and the other of which describes only the signal characteristics. The approximation shows particularly clearly that signal autocorrelations and cross-correlations arise as natural features of the interspike-interval density and are particularly clear for small signals and moderate noise. We show that this model simplifies the design of spiking neuron cross-correlation systems and describe a four-neuron mutual inhibition network that generates a cross-correlation output for two input signals.

An active 2-d silicon cochlea

In this paper, we present an analog integrated circuit design for an active 2-D cochlea and measurement results from a fabricated chip. The design includes a quality factor control loop that incorporates some of the nonlinear behavior exhibited in the real cochlea. This control loop varies the gain and the frequency selectivity of each cochlear resonator based on the amplitude of the input signal.

High power resonant tracking amplifier using admittance locking

A high power resonance tracking ultrasonic amplifier is described. The amplifier is a class D type inverter, configured as a half-bridge in which the output MOSFETs are driven into saturation when on. The resonance tracking system makes use of a new method of frequency locking; admittance locking is used to track the optimum power conversion frequency for the transducer. This new arrangement offers some advantages over phase locking and motional feedback methods. The system is capable of delivering up to 3 kW at up to 25 kHz in resonance tracking operation.

A novel phase locked cavity resonator for B/A measurements in fluids

A new technique for the measurement in fluids of the acoustic non-linearity parameter B/A is presented, together with measured B/A values for several fluids. The non-linearity parameter is measured by phase locking radial modes within a PZT cylinder. The system, which implements the isentropic phase technique, uses continuous wave phase locking to measure the change in sound velocity that is typically associated with a change in ambient pressure under constant entropy. The method provides a means of measuring B/A in vitro both accurately and simply without the typical problems involved in time-of-flight systems. Fluid samples can remain small due to the nature of the cavity resonator, so the system is well suited to small volume, biological samples.

Listening to local voices: adapting rapid appraisal to assess health and social needs in general practice

OBJECTIVES

To explore the use of rapid appraisal in defining the health and social needs of a community and to formulate joint action plans between the residents and service providers.

DESIGN

Collection of data by an extended primary care team from three sources: existing documents about the neighbourhood, interviews with a range of informants, and direct observations to build a profile of the community.

SETTING

Council estate of 670 homes in Edinburgh.

MAIN OUTCOME MEASURES

Perceived problems of the community and suggestions for change.

RESULTS

The interviews and focus groups identified six priorities for change, many of which were not health related. These changes have been or are being implemented.

CONCLUSIONS

An expanded primary care team can use rapid appraisal as a first step in identifying and meeting local health needs. It facilitates a multidisciplinary approach and complements quantitative methods of assessing need.

Immunoperoxidase techniques and histology in the diagnosis of rhabdomyolysis related acute renal failure

A case of rhabdomyolysis associated acute renal failure (RM-ARF) occurring as a result of strenuous exercise is presented. Diagnostic renal biopsy was performed. The histological appearances, combined with immunoperoxidase staining for myoglobin, allowed a positive diagnosis of RM-ARF to be made and excluded the possibility of glomerulonephritis. The patient recovered completely after a stormy clinical course.