Stimulus encoding during the early stages of olfactory processing: A modeling study using an artificial olfactory system

Pearce, Timothy Charles; Verschure, Paul F. M. J.; White, Joel; Kauer, JS

doi:10.1016/s0925-2312(01)00455-6

Cited by 15 publications

(6 citation statements)

References 12 publications

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“…A number of computational olfactory models have been applied to offline processing of real-world chemosensor input (Pearce et al, 2001; Gill and Pearce, 2003; Gutiérrez-Gálvez and Gutiérrez Osuna, 2006; Perera et al, 2006; Raman et al, 2006; Muezzinoglu et al, 2008, 2009; Karout et al, 2011; Martinelli et al, 2011; Ayhan and Müştak, 2012; Yamani et al, 2012a,b) as reviewed by Marco and Gutierrez-Gálvez (2009) and more recently as a Frontiers in Neural Engineering Research Topic (Huerta and Nowotny, 2012). Some of these bio-inspired and biologically constrained models demonstrate clear advantages for chemosensor array processing to overcome the manifold challenges inherent to machine olfaction, beyond existing engineered signal processing solutions.…”

Section: Discussionmentioning

confidence: 99%

“…LNs and PNs within the MGC may also be sexually dimorphic (Homberg et al, 1988, 1989), further suggesting a specialized role in female produced pheromone processing in males. Pheromone specific olfactory receptor neuron (ORN) axons on the periphery project exclusively to the MGC region, with those ORNs expressing identical pheromone sensitive olfactory receptors converging on a single macroglomerulus, thought to enhance signal-to-noise ratio to optimize pheromone sensitivity (Pearce et al, 2001). …”

Section: Introductionmentioning

confidence: 99%

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Rapid processing of chemosensor transients in a neuromorphic implementation of the insect macroglomerular complex

Pearce¹,

Karout²,

Rácz³

et al. 2013

Front. Neurosci.

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We present a biologically-constrained neuromorphic spiking model of the insect antennal lobe macroglomerular complex that encodes concentration ratios of chemical components existing within a blend, implemented using a set of programmable logic neuronal modeling cores. Depending upon the level of inhibition and symmetry in its inhibitory connections, the model exhibits two dynamical regimes: fixed point attractor (winner-takes-all type), and limit cycle attractor (winnerless competition type) dynamics. We show that, when driven by chemosensor input in real-time, the dynamical trajectories of the model's projection neuron population activity accurately encode the concentration ratios of binary odor mixtures in both dynamical regimes. By deploying spike timing-dependent plasticity in a subset of the synapses in the model, we demonstrate that a Hebbian-like associative learning rule is able to organize weights into a stable configuration after exposure to a randomized training set comprising a variety of input ratios. Examining the resulting local interneuron weights in the model shows that each inhibitory neuron competes to represent possible ratios across the population, forming a ratiometric representation via mutual inhibition. After training the resulting dynamical trajectories of the projection neuron population activity show amplification and better separation in their response to inputs of different ratios. Finally, we demonstrate that by using limit cycle attractor dynamics, it is possible to recover and classify blend ratio information from the early transient phases of chemosensor responses in real-time more rapidly and accurately compared to a nearest-neighbor classifier applied to the normalized chemosensor data. Our results demonstrate the potential of biologically-constrained neuromorphic spiking models in achieving rapid and efficient classification of early phase chemosensor array transients with execution times well beyond biological timescales.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Rapid processing of chemosensor transients in a neuromorphic implementation of the insect macroglomerular complex

Pearce¹,

Karout²,

Rácz³

et al. 2013

Front. Neurosci.

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“…The neuron's electrical activity parameters occurring during learning associated with its excitability were simulated, and they showed that the neuronal model exhibited different excitability after the learning procedure relative to the different input signals corresponding to the experimental data. Pearce et al (2001) modeled the efficiency of odour stimulus encoding within the early stages of an artificial olfactory system using a spiking neuronal model driven by fluorescent microbead chemosensors. The specific objective of the modeling study was to investigate how a rate-coding scheme compared to the direct transmission of graded potentials in terms of the accuracy of the estimate that an ideal observer may make about the stimulus.…”

Section: Computer Modeling Of Olfactory Systemsmentioning

confidence: 99%

Synthesizing Neurophysiology, Genetics, Behaviour and Learning to Produce Whole-Insect Programmable Sensors to Detect Volatile Chemicals

Rains

Kulasiri

Zhou

et al. 2009

Biotechnology and Genetic Engineering Reviews

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Insects have extremely sensitive systems of olfaction. These systems have been explored as potential sensors for odourants associated with forensics, medicine, security, and agriculture application. Most sensors based on insect olfaction utilize associative learning to "program" the insects to exhibit some form of behavioural response to a target odourant. To move to the next stage of development with whole-insect programmable sensors, an examination of how odourants are captured, processed and used to create behaviour is necessary. This review article examines how the neurophysiological, molecular, genetic and behavioural system of olfaction works and how an understanding of these systems should lead the way to future developments in whole-insect programmable sensors.

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“…Choice of the most efficient sensor array for solution of a specific problem is one of the most important tasks in optimization of EN-type devices. Various generalized mathematical models [2][3][4], statistical approaches [5,6], estimations of information content via the Fisher information [7,8], predictions based on system dynamic behavior [9] etc. have been proposed in this area and successfully demonstrated in many cases [9].…”

Section: Introductionmentioning

confidence: 99%

Methods of cluster analysis in sensor engineering: advantages and faults

Burlachenko¹

2010

Semicond. phys. quantum electron. optoelectron.

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We consider the crisp and fuzzy partitioning techniques of cluster analysis bearing in mind their application for classification of data obtained with chemical sensor arrays. The advantage of the cluster analysis techniques is existence of a parameter S(i). This parameter gives quantitative efficiency of classification and can be used as optimization criterion for sensor system as a whole as well as the measurement procedure. The crisp and fuzzy techniques give practically the same result when analyzing the data that cluster uniquely. It is shown that big value of the parameter S(i) is not sufficient for adequate data partitioning into cluster in more complicated cases, and the results of clusterization for the above techniques may diverge. In this case, one should apply both techniques concurrently, checking the correctness of partitioning into clusters against the principal component analysis.

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Stimulus encoding during the early stages of olfactory processing: A modeling study using an artificial olfactory system

Cited by 15 publications

References 12 publications

Rapid processing of chemosensor transients in a neuromorphic implementation of the insect macroglomerular complex

Rapid processing of chemosensor transients in a neuromorphic implementation of the insect macroglomerular complex

Synthesizing Neurophysiology, Genetics, Behaviour and Learning to Produce Whole-Insect Programmable Sensors to Detect Volatile Chemicals

Methods of cluster analysis in sensor engineering: advantages and faults

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