Biomedical subjects

D C Tam

Publications and source records attributed to D C Tam.

4 recordsLinked to original sources

PubMed1999

A joint interspike interval difference stochastic spike train analysis: detecting local trends in the temporal firing patterns of single neurons.

We introduce a stochastic spike train analysis method called joint interspike interval difference (JISID) analysis. By design, this method detects changes in firing interspike intervals (ISIs), called local trends, within a 4-spike pattern in a spike train. This analysis classifies 4-spike patterns that have similar incremental changes. It characterizes the higher-order serial dependence in spike firing relative to changes in the firing history. Mathematically, this spike train analysis describes the statistical joint distribution of consecutive changes in ISIs, from which the serial dependence of the changes in higher-order intervals can be determined. It is similar to the joint interspike interval (JISI) analysis, except that the joint distribution of consecutive ISI differences (ISIDs) is quantified. The graphical location of points in the JISID scatter plot reveals the local trends in firing (i.e., monotonically increasing, monotonically decreasing, or transitional firing). The trajectory of these points in the serial-JISID plot traces the time evolution of these trends represented by a 5-spike pattern, while points in the JISID scatter plot represent trends of a 4-spike pattern. We provide complete theoretical interpretations of the JISID analysis. We also demonstrate that this method indeed identifies firing trends in both simulated spike trains and spike trains recorded from cultured neurons.

Animals↗

PubMed1998

A cross-interval spike train analysis: the correlation between spike generation and temporal integration of doublets.

A stochastic spike train analysis technique is introduced to reveal the correlation between the firing of the next spike and the temporal integration period of two consecutive spikes (i.e., a doublet). Statistics of spike firing times between neurons are established to obtain the conditional probability of spike firing in relation to the integration period. The existence of a temporal integration period is deduced from the time interval between two consecutive spikes fired in a reference neuron as a precondition to the generation of the next spike in a compared neuron. This analysis can show whether the coupled spike firing in the compared neuron is correlated with the last or the second-to-last spike in the reference neuron. Analysis of simulated and experimentally recorded biological spike trains shows that the effects of excitatory and inhibitory temporal integration are extracted by this method without relying on any subthreshold potential recordings. The analysis also shows that, with temporal integration, a neuron driven by random firing patterns can produce fairly regular firing patterns under appropriate conditions. This regularity in firing can be enhanced by temporal integration of spikes in a chain of polysynaptically connected neurons. The bandpass filtering of spike firings by temporal integration is discussed. The results also reveal that signal transmission delays may be attributed not just to conduction and synaptic delays, but also to the delay time needed for temporal integration.

Action Potentials↗

PubMed1997

Multi-unit spike discrimination using wavelet transforms.

A new spike discrimination procedure addressing the specific problem of spike superposition is described. The method, based on a shift-invariant wavelet transform and its amplitude-and-phase representation, has the advantage of both reducing the effect of noise present in the data and correcting the latency of specific components in a waveform. When spikes overlap and produce unknown patterns, the procedure extracts the constituent spikes and also estimates their exact time of occurrence. Fast implementation algorithms, having complexity of at most O (N log N), allow the use of the method in real-time applications.

Action Potentials↗

PubMed1988

Conditional cross-interval correlation analyses with applications to simultaneously recorded cerebellar Purkinje neurons.

Two conditional cross-correlation techniques are described for the analysis of two simultaneously recorded neuronal spike trains. The conditional interspike interval histogram describes the distribution of interspike intervals of a neuron conditioned by a preceding spike in another neuron. The conditional cross-interval histogram describes the distribution of cross-intervals of two neurons conditioned by a preceding spike in one of the neurons. These techniques could be used to reveal the temporal coupling in the discharge of two neurons recorded simultaneously. The techniques augment the description of the correlation obtained with conventional cross-correlation measures. When applied to the simple spike discharge of simultaneously recorded cerebellar Purkinje neurons, the methods reveal temporal interactions between neurons that are not readily apparent from conventional cross-correlograms. The patterns observed suggest a tightly coupled, temporal surround-inhibition among nearby Purkinje neurons.

Animals↗