Classifying continuous, real-time e-nose sensor data using a bio-inspired spiking network modelled on the insect olfactory system

In many application domains, conventional e-noses are frequently outperformed in both speed and accuracy by their biological counterparts. Exploring potential bio-inspired improvements, we note a number of neuronal network models have demonstrated some success in classifying static datasets by abstracting the insect olfactory system. However, these designs remain largely unproven in practical settings, where sensor data is real-time, continuous, potentially noisy, lacks a precise onset signal and accurate classification requires the inclusion of temporal aspects into the feature set. This investigation therefore seeks to inform and develop the potential and suitability of biomimetic classifiers for use with typical real-world sensor data. Taking a generic classifier design inspired by the inhibition and competition in the insect antennal lobe, we apply it to identifying 20 individual chemical odours from the timeseries of responses of metal oxide sensors. We show that four out of twelve available sensors and the first 30 s(10%) of the sensors’ continuous response are sufficient to deliver 92% accurate classification without access to an odour onset signal. In contrast to previous approaches, once training is complete, sensor signals can be fed continuously into the classifier without requiring discretization. We conclude that for continuous data there may be a conceptual advantage in using spiking networks, in particular where time is an essential component of computation. Classification was achieved in real time using a GPU-accelerated spiking neural network simulator developed in our group

Models wagging the dog: are circuits constructed with disparate parameters?

In a recent article, Prinz, Bucher, and Marder (2004) addressed the fundamental question of whether neural systems are built with a fixed blueprint of tightly controlled parameters or in a way in which properties can vary largely from one individual to another, using a database modeling approach. Here, we examine the main conclusion that neural circuits indeed are built with largely varying parameters in the light of our own experimental and modeling observations. We critically discuss the experimental and theoretical evidence, including the general adequacy of database approaches for questions of this kind, and come to the conclusion that the last word for this fundamental question has not yet been spoken

Neuronal synchrony: peculiarity and generality

Synchronization in neuronal systems is a new and intriguing application of dynamical systems theory. Why are neuronal systems different as a subject for synchronization? (1) Neurons in themselves are multidimensional nonlinear systems that are able to exhibit a wide variety of different activity patterns. Their “dynamical repertoire” includes regular or chaotic spiking, regular or chaotic bursting, multistability, and complex transient regimes. (2) Usually, neuronal oscillations are the result of the cooperative activity of many synaptically connected neurons (a neuronal circuit). Thus, it is necessary to consider synchronization between different neuronal circuits as well. (3) The synapses that implement the coupling between neurons are also dynamical elements and their intrinsic dynamics influences the process of synchronization or entrainment significantly. In this review we will focus on four new problems: (i) the synchronization in minimal neuronal networks with plastic synapses (synchronization with activity dependent coupling), (ii) synchronization of bursts that are generated by a group of nonsymmetrically coupled inhibitory neurons (heteroclinic synchronization), (iii) the coordination of activities of two coupled neuronal networks (partial synchronization of small composite structures), and (iv) coarse grained synchronization in larger systems (synchronization on a mesoscopic scale

Modelling the atmosphere of the carbon-rich Mira RU Vir

Context. We study the atmosphere of the carbon-rich Mira RU Vir using the mid-infrared high spatial resolution interferometric observations from VLTI/MIDI. Aims. The aim of this work is to analyse the atmosphere of the carbon-rich Mira RU Vir, with state of the art models, in this way deepening the knowledge of the dynamic processes at work in carbon-rich Miras. Methods. We compare spectro-photometric and interferometric measurements of this carbon-rich Mira AGB star, with the predictions of different kinds of modelling approaches (hydrostatic model atmospheres plus MOD-More Of Dusty, self-consistent dynamic model atmospheres). A geometric model fitting tool is used for a first interpretation of the interferometric data. Results. The results show that a joint use of different kind of observations (photometry, spectroscopy, interferometry) is essential to shed light on the structure of the atmosphere of a carbon-rich Mira. The dynamic model atmospheres fit well the ISO spectrum in the wavelength range {\lambda} = [2.9, 25.0] {\mu}m. Nevertheless, a discrepancy is noticeable both in the SED (visible), and in the visibilities (shape and level). A possible explanation are intra-/inter-cycle variations in the dynamic model atmospheres as well as in the observations. The presence of a companion star and/or a disk or a decrease of mass loss within the last few hundred years cannot be excluded but are considered unlikely.Comment: 15 pages. Accepted in A&

Multi-neuronal refractory period adapts centrally generated behaviour to reward

Oscillating neuronal circuits, known as central pattern generators (CPGs), are responsible for generating rhythmic behaviours such as walking, breathing and chewing. The CPG model alone however does not account for the ability of animals to adapt their future behaviour to changes in the sensory environment that signal reward. Here, using multi-electrode array (MEA) recording in an established experimental model of centrally generated rhythmic behaviour we show that the feeding CPG of Lymnaea stagnalis is itself associated with another, and hitherto unidentified, oscillating neuronal population. This extra-CPG oscillator is characterised by high population-wide activity alternating with population-wide quiescence. During the quiescent periods the CPG is refractory to activation by food-associated stimuli. Furthermore, the duration of the refractory period predicts the timing of the next activation of the CPG, which may be minutes into the future. Rewarding food stimuli and dopamine accelerate the frequency of the extra-CPG oscillator and reduce the duration of its quiescent periods. These findings indicate that dopamine adapts future feeding behaviour to the availability of food by significantly reducing the refractory period of the brain's feeding circuitry