We present a novel machine learning architecture for classification suggested by experiments on the insect olfactory system . The network separates odors via a winnerless competition network, then classifies objects by projection into a high dimensional space where a support vector machine provides more precision in classification. We build this network using biophysical models of neurons with our results showing high discrimination among inputs and exceptional robustness to noise. The same circuitry accurately identifies the amplitudes of mixtures of the odors on which it has been trained .
We present a novel machine learning architecture for classification suggested by experiments on olfactory systems . The network separates input stimuli, represented as spatially distinct currents, via winnerless competition---a process based on the intrinsic sequential dynamics of the neural system---then uses a support vector machine (SVM) to provide precision to the space-time separation of the output. The combined network uses biophysical models of neurons and shows high discrimination among inputs and robustness to noise. While using the SVM alone does not permit determination of the components of mixtures of classified inputs, the combined network is able to tell the precise concentrations of the constituent parts .
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the insect olfactory system
olfactory systems
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odors via a winnerless competition network, then classifies objects by projection into a high dimensional space where a
input stimuli, represented as spatially distinct currents, via winnerless competition---a process based on the intrinsic sequential dynamics of the neural system---then uses a
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provides more precision in classification. We build this network using
(SVM) to provide precision to the space-time separation of the output. The combined network uses
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with our results showing
and shows
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exceptional
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The same circuitry accurately identifies the amplitudes of mixtures of the odors on which it has been trained
While using the SVM alone does not permit determination of the components of mixtures of classified inputs, the combined network is able to tell the precise concentrations of the constituent parts