Spike sorting using locality preserving projection with gap statistics and landmark-based spectral clustering

Understanding neural functions requires knowledge from analysing electrophysiological data. The process of assigning spikes of a multichannel signal into clusters, called spike sorting, is one of the important problems in such analysis. There have been various automated spike sorting techniques with both advantages and disadvantages regarding accuracy and computational costs. Therefore, developing spike sorting methods that are highly accurate and computationally inexpensive is always a challenge in the biomedical engineering practice.

Classification of neural action potentials using mean shift clustering

 Understanding neural functions requires the observation of the activities of single neurons that are represented via electrophysiological data. Processing and understanding these data are challenging problems in biomedical engineering. A microelectrode commonly records the activity of multiple neurons. Spike sorting is a process of classifying every single action potential (spike) to a particular neuron. This paper proposes a combination between diffusion maps (DM) and mean shift clustering method for spike sorting. DM is utilized to extract spike features, which are highly capable of discriminating different spike shapes. Mean shift clustering provides an automatic unsupervised clustering, which takes extracted features from DM as inputs. Experimental results show a noticeable dominance of the features extracted by DM compared to those selected by wavelet transformation (WT). Accordingly, the proposed integrated method is significantly superior to the popular existing combination of WT and superparamagnetic clustering regarding spike sorting accuracy.

Neural signal analysis by landmark-based spectral clustering with estimated number of clusters

Spike sorting plays an important role in analysing electrophysiological data and understanding neural functions. Developing spike sorting methods that are highly accurate and computationally inexpensive is always a challenge in the biomedical engineering practice. This paper proposes an automatic unsupervised spike sorting method using the landmark-based spectral clustering (LSC) method in connection with features extracted by the locality preserving projection (LPP) technique. Gap statistics is employed to evaluate the number of clusters before the LSC can be performed. Experimental results show that LPP spike features are more discriminative than those of the popular wavelet transformation (WT). Accordingly, the proposed method LPP-LSC demonstrates a significant dominance compared to the existing method that is the combination between WT feature extraction and the superparamagnetic clustering. LPP and LSC are both linear algorithms that help reduce computational burden and thus their combination can be applied into realtime spike analysis.

Magnetic moments in amorpous palladium-cobalt-silicon alloys

The magnetic moments of amorphous ternary alloys containing Pd, Co and Si in atomic concentrations corresponding to Pd_(80-x)Co_xSi_(20) in which x is 3, 5, 7, 9, 10 and 11, have been measured between 1.8 and 300°K and in magnetic fields up to 8.35 kOe. The alloys were obtained by rapid quenching of a liquid droplet and their structures were analyzed by X-ray diffraction. The measurements were made in a null-coil pendulum magnetometer in which the temperature could be varied continuously without immersing the sample in a cryogenic liquid. The alloys containing 9 at.% Co or less obeyed Curie's Law over certain temperature ranges, and had negligible permanent moments at room temperature. Those containing 10 and 11 at.% Co followed Curie's Law only above approximately 200°K and had significant permanent moments at room temperature. For all alloys, the moments calculated from Curie's Law were too high to be accounted for by the moments of individual Co atoms. To explain these findings, a model based on the existence of superparamagnetic clustering is proposed. The cluster sizes calculated from the model are consistent with the rapid onset of ferromagnetism in the alloys containing 10 and 11 at.% Co and with the magnetic moments in an alloy containing 7 at.% Co heat treated in such a manner as to contain a small amount of a crystalline phase. In alloys containing 7 at.% Co or less, a maximum in the magnetization vs temperature curve was observed around 10°K. This maximum was eliminated by cooling the alloy in a magnetic field, and an explanation for this observation is suggested.

Cepstrum based unsupervised spike classification

In this research, we study the effect of feature selection in the spike detection and sorting accuracy.We introduce a new feature representation for neural spikes from multichannel recordings. The features selection plays a significant role in analyzing the response of brain neurons. The more precise selection of features leads to a more accurate spike sorting, which can group spikes more precisely into clusters based on the similarity of spikes. Proper spike sorting will enable the association between spikes and neurons. Different with other threshold-based methods, the cepstrum of spike signals is employed in our method to select the candidates of spike features. To choose the best features among different candidates, the Kolmogorov-Smirnov (KS) test is utilized. Then, we rely on the superparamagnetic method to cluster the neural spikes based on KS features. Simulation results demonstrate that the proposed method not only achieve more accurate clustering results but also reduce computational burden, which implies that it can be applied into real-time spike analysis.

Hidden Markov model neurons classification based on Mel-frequency cepstral coefficients

In neuroscience, the extracellular actions potentials of neurons are the most important signals, which are called spikes. However, a single extracellular electrode can capture spikes from more than one neuron. Spike sorting is an important task to diagnose various neural activities. The more we can understand neurons the more we can cure more neural diseases. The process of sorting these spikes is typically made in some steps which are detection, feature extraction and clustering. In this paper we propose to use the Mel-frequency cepstral coefficients (MFCC) to extract spike features associated with Hidden Markov model (HMM) in the clustering step. Our results show that using MFCC features can differentiate between spikes more clearly than the other feature extraction methods, and also using HMM as a clustering algorithm also yields a better sorting accuracy.

On the synthesis and magnetic properties of multiwall carbon nanotube– superparamagnetic iron oxide nanoparticle nanocomposites

Multiwall carbon nanotubes (MWCNTs) possessing an average inner diameter of 150 nm were synthesized by template assisted chemical vapor deposition over an alumina template. Aqueous ferrofluid based on superparamagnetic iron oxide nanoparticles (SPIONs) was prepared by a controlled co-precipitation technique, and this ferrofluid was used to fill the MWCNTs by nanocapillarity. The filling of nanotubes with iron oxide nanoparticles was confirmed by electron microscopy. Selected area electron diffraction indicated the presence of iron oxide and graphitic carbon from MWCNTs. The magnetic phase transition during cooling of the MWCNT–SPION composite was investigated by low temperature magnetization studies and zero field cooled (ZFC) and field cooled experiments. The ZFC curve exhibited a blocking at ∼110 K. A peculiar ferromagnetic ordering exhibited by the MWCNT–SPION composite above room temperature is because of the ferromagnetic interaction emanating from the clustering of superparamagnetic particles in the constrained volume of an MWCNT. This kind of MWCNT–SPION composite can be envisaged as a good agent for various biomedical applications