We propose a statistical method for modeling the non-Poisson variability of spike trains observed in a wide range of brain regions. Central to our approach is the assumption that the variance and the mean of interspike intervals are related by a power function characterized by two parameters: the scale factor and exponent. It is shown that this single assumption allows the variability of spike ...

In principle, the state space of a chaotic attractor can be partially or wholly reconstructed from interspike intervals recorded from experiment. Under certain conditions, the quality of a partial reconstruction, as measured by the spike train prediction error, can be increased by adding noise to the spike creation process. This phenomenon for chaotic systems is an analogue of stochastic resona...

The subject of neural coding has generated much debate. A key issue is whether the nervous system uses coarse or fine coding. Each has different strengths and weaknesses and, therefore, different implications for how the brain computes. For example, the strength of coarse coding is that it is robust to fluctuations in spike arrival times; downstream neurons do not have to keep track of the deta...

Spike train variability is of fundamental importance for understanding how information is encoded and processed in the nervous system. Most studies in this area have focused on short-term variability, as characterized by the coefficient of variation of the interspike interval distribution. Here we discuss the importance of extending the analysis of spike train variability to longer time scales ...

ON-OFF transient ganglion cells of the turtle retina show distinct spike-event patterns in response to abrupt intensity changes, such as during saccadic eye movements. These patterns consist of two main spike events, with the latency of each event showing a systematic dependency on stimulus contrast. Whereas the latency of the first event decreases monotonically with increasing contrast, as exp...

Spike train coherence is used to characterize common inputs that drive motor unit synchronization. However, data segmentation, overlap, and taper can affect coherence magnitude, thereby influencing the incidence at which significant coherence is detected. Also, the effect of spike train firing rate and common input variability on the detection of significant coherence is unknown. We used a pool...

Reliable accounts of the variability observed in neural spike trains are a prerequisite for the proper interpretation of neural dynamics and coding principles. Models that accurately describe neural variability over a wide range of stimulation and response patterns are therefore highly desirable, especially if they can explain this variability in terms of basic neural observables and parameters...

As Texas Parks and Wildlife biologists, we often receive calls from hunters or land managers asking if they should or should not harvest spike bucks. The caller has usually read an article that says it is genetics or nutrition or being late born that causes deer to produce spike antlers. “If poor nutrition causes spikes,” the article says, “then you shouldn’t harvest spikes.” The article may st...