Estimation of firing rate from instantaneous interspike intervals.

The rate coding hypothesis is the oldest and still one of the most accepted hypotheses of neural coding. Consequently, many approaches have been devised for the firing rate estimation, ranging from simple binning of the time axis to advanced statistical methods. Nonetheless the concept of firing rate, while informally understood, can be mathematically defined in several distinct ways. These definitions may yield mutually incompatible results unless implemented properly. Recently it has been shown that the notions of the instantaneous and the classical firing rates can be made compatible, at least in terms of their averages, by carefully discerning the time instant at which the neuronal activity is observed. In this paper we revisit the properties of instantaneous interspike intervals in order to derive several novel firing rate estimators, which are free of additional assumptions or parameters and their temporal resolution is 'locally self-adaptive'. The estimators are simple to implement and are numerically efficient even for very large sets of data.

Can the examination of different types of hive samples be a non-invasive method for detection and quantification of viruses in honey bee (Apis mellifera L.) colonies?

Introduction: Honey bee viruses have been shown to negatively affect the vigour and longevity of European honey bees (Apis mellifera L). In the present work, beehive materials were tested for their potential to serve as non-invasive samples for honey bee virus detection. Material and Methods: Honey, pollen, hive debris, hive grid smears and forager honey bees were collected from 24 hives at four locations in the Czech Republic. Deformed wing virus (DWV), acute bee paralysis virus (ABPV), sacbrood virus (SBV) and black queen cell virus (BQCV) were detected using a reverse transcription PCR (RT-PCR) and real-time quantitative RT-PCR and the results for bees and alternative materials compared. Results: All forager bee samples contained DWV, BQCV and SBV and 54.2% had ABPV. When comparing beehive materials to bees, the most promising results were obtained from honey and pollen samples, with BQCV and SBV detected in all honey samples and ABPV in 12.5%. Detection of SBV was achieved in 91.6% of pollen samples, detection of BQCV in 87.5% and detection of DWW in 75%. The results for debris and smears were less consistent with the viral profile of the forager samples. Conclusion: The best candidate materials for honey bee virus detection in a non-invasive technique are honey and pollen.