Browsing by Author "Sun, X."

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Fine Frequency-Modulation Trigger Features of Midbrain Auditory Neurons Extracted by the Progressive Thresholding Method - A Preliminary Study
(2010-12-31)
Chang, T. R.
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Chiu, T. W.
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Sun, X.
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Poon, Paul W. F.
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生物科技學系
Spectro-temporal receptive fields (STRFs) are commonly used to characterize response properties of central auditory neurons and for visualizing 'trigger features'. However, trigger features in STRF maps typically have a blurry appearance. Therefore it is unclear what details could be embedded in them. To investigate this, we developed a new method called 'progressive thresholding' to resolve fine structures in the STRFs, and applied the method to FM responses recorded from single units at the auditory midbrain of anesthetized rats. Random FM tones of a narrow frequency range (similar to 0.5 octave) were first presented to evoked spike responses at the cell's best frequency. Perispike modulating time waveforms collected (50 msec long, n = 1,500 to 4,000 tracings) were used to generate STRF based on spike-triggered-averaging. After supra-threshold areas of pixel counts had been determined through a step of progressive thresholding in the map, those pen-spike modulating waveforms passing through each area were dejittered systematically. At what seemed to be an optimal threshold, multiple trigger features (up to a maximum of 4 fine bands) were extracted from the initially simple-looking STRF. Results show that fine FM trigger features are present in STRFs and that they can be resolved with the present method of analysis.
Modeling complex responses of FM-sensitive cells in the auditory midbrain using a committee machine
(2013-11-06)
Chang, T. R.
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Chiu, T. W.
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Sun, X.
;
Poon, Paul W. F.
;
生物科技學系
Frequency modulation (FM) is an important building block of complex sounds that include speech signals. Exploring the neural mechanisms of FM coding with computer modeling could help understand how speech sounds are processed in the brain. Here, we modeled the single unit responses of auditory neurons recorded from the midbrain of anesthetized rats. These neurons displayed spectral temporal receptive fields (STRFs) that had multiple-trigger features, and were more complex than those with single-trigger features. Their responses have not been modeled satisfactorily with simple artificial neural networks, unlike neurons with simple-trigger features. To improve model performance, here we tested an approach with the committee machine. For a given neuron, the pen-stimulus time histogram (PSTH) was first generated in response to a repeated random FM tone, and peaks in the PSTH were segregated into groups based on the similarity of their pre-spike FM trigger features. Each group was then modeled using an artificial neural network with simple architecture, and, when necessary, by increasing the number of neurons in the hidden layer. After initial training, the artificial neural networks with their optimized weighting coefficients were pooled into a committee machine for training. Finally, the model performance was tested by prediction of the response of the same cell to a novel FM tone. The results showed improvement over simple artificial neural networks, supporting that trigger-feature-based modeling can be extended to cells with complex responses. (C) 2013 Elsevier B.V. All rights reserved.
Modeling frequency modulated responses of midbrain auditory neurons based on trigger features and artificial neural networks
(2012-01-24)
Chang, T. R.
;
Chiu, T. W.
;
Sun, X.
;
Poon, Paul W. F.
;
生物科技學系
Frequency modulation (FM) is an important building block of communication signals for animals and human. Attempts to predict the response of central neurons to FM sounds have not been very successful, though achieving successful results could bring insights regarding the underlying neural mechanisms. Here we proposed a new method to predict responses of FM-sensitive neurons in the auditory midbrain. First we recorded single unit responses in anesthetized rats using a random FM tone to construct their spectro-temporal receptive fields (STRFs). Training of neurons in the artificial neural network to respond to a second random FM tone was based on the temporal information derived from the STRF. Specifically, the time window covered by the presumed trigger feature and its delay time to spike occurrence were used to train a finite impulse response neural network (FIRNN) to respond to this random FM. Finally we tested the model performance in predicting the response to another similar FM stimuli (third random FM tone). We found good performance in predicting the time of responses if not also the response magnitudes. Furthermore, the weighting function of the FIRNN showed temporal 'bumps' suggesting temporal integration of synaptic inputs from different frequency laminae. This article is part of a Special Issue entitled: Neural Coding. (C) 2011 Elsevier B.V. All rights reserved.