Classification of neuronal spikes over the reconstructed phase space

Chan, Hsiao‐Lung; Wu, Tony; Lee, Shih‐Tseng; Fang, Shih-Chin; Chao, Pei-Kuang; Lin, Ming-An

doi:10.1016/j.jneumeth.2007.09.017

Cited by 17 publications

(16 citation statements)

References 17 publications

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“…The quantification algorithm also took into account the identification of the activity's standard waveform and the classification of probability patterns of spikes in time and frequency domains (Brown et al 2004;Jarvis and Mitra 2001;Sánchez-Campusano et al 2007) and in the phase space (Aksenova et al 2003;Chan et al 2008). Since multiunitary neuronal recordings usually contain overlapping spikes, we selected the following analytical procedure.…”

Section: Data Collection and Analysismentioning

confidence: 99%

Behavioral Characteristics, Associative Learning Capabilities, and Dynamic Association Mapping in an Animal Model of Cerebellar Degeneration

Porras-García

Sánchez-Campusano

Martı́nez-Vargas

et al. 2010

Journal of Neurophysiology

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Porras-García E, Sánchez-Campusano R, Martínez-Vargas D, Domínguez-del-Toro E, Cendelín J, Vožeh F, Delgado-García JM. Behavioral characteristics, associative learning capabilities, and dynamic association mapping in an animal model of cerebellar degeneration. J Neurophysiol 104: 346 -365, 2010. First published April 21, 2010 doi:10.1152/jn.00180.2010. Young adult heterozygous Lurcher mice constitute an excellent model for studying the role of the cerebellar cortex in motor performance-including the acquisition of new motor abilities-because of the early postnatal degeneration of almost all of their Purkinje and granular cells. Wild-type and Lurcher mice were classically conditioned for eyelid responses using a delay paradigm with or without an electrolytic lesion in the interpositus nucleus. Although the late component of electrically evoked blink reflexes was smaller in amplitude and had a longer latency in Lurcher mice than that in controls, the two groups of animals presented similar acquisition curves for eyeblink conditioning. The lesion of the interpositus nucleus affected both groups of animals equally for the generation of reflex and conditioned eyelid responses. Furthermore, we recorded the multiunitary activity at the red and interpositus nuclei during the same type of associative learning. In both nuclei, the neural firing activity lagged the beginning of the conditioned response (determined by orbicularis oculi muscle response). Although red nucleus neurons and muscle activities presented a clear functional coupling (strong correlation and low asymmetry) across conditioning, the coupling between interpositus neurons and either red nucleus neurons or muscle activities was slightly significant (weak correlation and high asymmetry). Lurcher mice presented a nonlinear coupling (high asymmetry) between red nucleus neurons and muscle activities, with an evident compensatory adjustment in the correlation of firing between interpositus and red nuclei neurons (a coupling with low asymmetry), aimed probably at compensating the absence of cerebellar cortical neurons.

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Section: Data Collection and Analysismentioning

confidence: 99%

Behavioral Characteristics, Associative Learning Capabilities, and Dynamic Association Mapping in an Animal Model of Cerebellar Degeneration

Porras-García

Sánchez-Campusano

Martı́nez-Vargas

et al. 2010

Journal of Neurophysiology

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“…PCA is an orthogonal linear transformation that projects multivariate N-dimensional data samples into a new coordinate system, so that the first coordinate exhibits the largest variance, the second coordinate exhibits the second largest variance, and so on [12]. PCA has previously been applied as a feature extraction technique in different applications [13][14][15].…”

Section: Feature Extractionmentioning

confidence: 99%

Identification of movement-related cortical potentials with optimized spatial filtering and principal component analysis

Boye

Kristiansen

Billinger

et al. 2008

Biomedical Signal Processing and Control

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“…Indeed, if one considers a sampling rate of 20 kHz and windows of about 2 ms around each spike, then one would wind up with 40-dimensional space, which could potentially be too sparsely populated to define dense clusters of spikes. Features are often extracted using spike minimum [42], spike maximum [42], spike width [42], peak-to-peak amplitude [9,87], peak-to-peak time interval [87], amplitude later in the waveform [8,71], principal component analysis (PCA) [1,15,23,26], PCA on the major portrait radius of the reconstructed phase space portrait of spikes [11], the discrete wavelet transform [41,57], the wavelet packet transform [34], autoregressive coefficients from spikes [51], the integral transform [91], discrete derivatives of the spike [24], positive and negative peaks of Brought to you by | University of Manitoba Authenticated Download Date | 6/10/15 11:13 AM the first derivative of the spike [51,88], and the positive and negative peak of the second derivative of the spike [88].…”

Section: Spike Clusteringmentioning

confidence: 99%

Review of machine learning and signal processing techniques for automated electrode selection in high-density microelectrode arrays

Dijck

Hulle

2014

Biomedical Engineering / Biomedizinische Technik

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Recently developed CMOS-based microprobes contain hundreds of electrodes on a single shaft with interelectrode distances as small as 30 µm. So far, neuroscientists manually select a subset of those electrodes depending on their appraisal of the "usefulness" of the recorded signals, which makes the process subjective but more importantly too time consuming to be useable in practice. The everincreasing number of recording electrodes on microelectrode probes calls for an automated selection of electrodes containing "good quality signals" or "signals of interest." This article reviews the different criteria for electrode selection as well as the basic signal processing steps to prepare the data to compute those criteria. We discuss three of them. The first two select the electrodes based on "signal quality." The first criterion computes the penalized signal-to-noise ratio (SNR); the second criterion models the neuroscientist's appraisal of signal quality. Last, our most recent work allows the selection of electrodes that capture particular anatomical cell types. The discussed algorithms perform what is called in the literature "electronic depth control" in contrast to the mechanical repositioning of the electrode shafts in search of "good quality signals" or "signals of interest."

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Classification of neuronal spikes over the reconstructed phase space

Cited by 17 publications

References 17 publications

Behavioral Characteristics, Associative Learning Capabilities, and Dynamic Association Mapping in an Animal Model of Cerebellar Degeneration

Behavioral Characteristics, Associative Learning Capabilities, and Dynamic Association Mapping in an Animal Model of Cerebellar Degeneration

Identification of movement-related cortical potentials with optimized spatial filtering and principal component analysis

Review of machine learning and signal processing techniques for automated electrode selection in high-density microelectrode arrays

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