A photoacoustic sensor has been developed for trace-gas monitoring using a near-infrared semiconductor laser emitting in the 2ν 3 band of methane at 1.65 µm. The apparatus was designed for on-line process control in the manufacturing of the novel low-water-peak fibres developed for optical telecommunications. The importance of collisional relaxation processes in the generation of the photoacoustic signal is reported in the particular case of CH 4 detection in dry O 2 and O 2 -N 2 mixtures. The negative influence of these effects results in a strongly reduced and phase-shifted photoacoustic signal, induced by a fast resonant coupling between the vibrational states of methane and oxygen, associated with the slow relaxation of the excited oxygen molecules. An unusual parabolic response of the sensor with respect to the methane concentration has been observed and is discussed. Finally, the beneficial effect of several species, including water vapour and helium, acting as a catalyst to hasten the relaxation of the CH 4 -O 2 system, is demonstrated.PACS 42.62.Fi; 33.20.Ea; 34.50.Ez IntroductionInfrared photoacoustic spectroscopy (PAS) is widely recognized for its high performances in trace-gas monitoring and is one of the most sensitive techniques to measure low gas concentrations at atmospheric pressure [1]. The high sensitivity of PAS mainly results from its zero-background nature, which means that no signal is produced in the absence of an absorbing species. Furthermore, one of the most outstanding features of this technique is its achromaticity: the response of a photoacoustic (PA) sensor, represented by the cell constant (i.e. the PA signal normalized by the laser power and absorption coefficient), is usually independent of the laser excitation wavelength (however, a few exceptions may occur in some particular situations, as will be discussed later). It means that the same PA detector can be used with any type of laser and at any wavelength, from ultraviolet to mid infrared (MIR), with identical performances in terms of cell constant (provided that a suitable window material is used in the PA cell). This is definitively not the case for other high u Fax: +41-21-693-26-14, E-mail: stephane.schilt@epfl.ch sensitivity laser spectroscopy techniques, such as wavelengthor frequency-modulation spectroscopy (WMS/FMS), cavity ring-down spectroscopy (CRDS) or intracavity laser absorption spectroscopy (ICLAS), which all make use of optical detection that has poorer performances in the MIR region. Indeed, not only is the sensitivity of MIR photodiodes strongly reduced in comparison to near-infrared (NIR) ones, but also MIR detectors require low-temperature operation and are much more expensive than NIR detectors.For several years, there has been much interest in the use of NIR semiconductor lasers in PAS, such as distributedfeedback (DFB) lasers mass produced for the optical telecommunications market, whereas, primarily, it has been predominantly implemented using MIR gas lasers (CO or CO 2 lasers). Extreme detectio...

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Synaptic plasticity must be both competitive and stable if ongoing learning of the structure of neural inputs is to occur. In this paper, a wide class of spike-timing-dependent plasticity (STDP) models is identified that have both of these desirable properties in the case in which the input consists of subgroups of synapses that are correlated within the subgroup through the occurrence of simultaneous input spikes. The process of synaptic structure formation is studied, illustrating one particular class of these models. When the learning rate is small, multiple alternative synaptic structures are possible given the same inputs, with the outcome depending on the initial weight configuration. For large learning rates, the synaptic structure does not stabilize, resulting in neurons without consistent response properties. For learning rates in between, a unique and stable synaptic structure typically forms. When this synaptic structure exhibits a bimodal distribution, the neuron will respond selectively to one or more of the subgroups. The robustness with which this selectivity develops during learning is largely determined by the ratio of the subgroup correlation strength to the number of subgroups. The fraction of potentiated subgroups is primarily determined by the balance between potentiation and depression.

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