Intrinsic variability of latency to first-spike

The assessment of the variability of neuronal spike timing is fundamental to gain understanding of latency coding. Based on recent mathematical results, we investigate theoretically the impact of channel noise on latency variability. For large numbers of ion channels, we derive the asymptotic distribution of latency, together with an explicit expression for its variance. Consequences in terms of information processing are studied with Fisher information in the Morris–Lecar model. A competition between sensitivity to input and precision is responsible for favoring two distinct regimes of latencies.     

Elimination of response latency variability in neuronal spike trains

 Neuronal activity in the mammalian cortex exhibits a considerable amount of trial-by-trial variability. This may be reflected by the magnitude of the activity as well as by the response latency with respect to an external event, such as the onset of a sensory stimulus, or a behavioral event. Here we present a novel nonparametric method for estimating trial-by-trial differences in response latency from neuronal spike trains. The method makes use of the dynamic rate profile for each single trial and maximizes their total pairwise correlation by appropriately shifting all trials in time. The result is a new alignment of trials that largely eliminates the variability in response latency and provides a new internal trigger that is independent of experiment time. To calibrate the method, we simulated spike trains based on stochastic point processes using a parametric model for phasic response profiles. We illustrate the method by an application to simultaneous recordings from a pair of neurons in the motor cortex of a behaving monkey. It is demonstrated how the method can be used to study the temporal relation of the neuronal response to the experiment, to investigate whether neurons share the same dynamics, and to improve spike correlation analysis. Differences between this and other previously published methods are discussed. Received: 8 April 2002 / Accepted: 26 November 2002 / Published online: 7 April 2003 Correspondence to: Stefan Rotter (e-mail: rotter@biologie.uni-freiburg.de), Tel.: +49-761-2032862, Fax: +49-761-2032860 Acknowledgements. We are grateful to Alexa Riehle for providing us with the monkey data and for valuable discussions. We also thank Felix Kümmell, Hiroyuki Nakahara, and Shun-ichi Amari for helpful discussions. Partial funding was received by the Deutsche Forschungsgemeinschaft (DFG, SFB 505) and the German-Israeli Foundation (GIF). Additional support was provided by the RIKEN Brain Science Institute.     

Intrinsic structural variability in GNRA-like tetraloops: insight from molecular dynamics simulation

The structure of a hairpin loop—in particular its large accessible surface area and its exposed hydrogen-bonding edges—facilitate an inherent possibility for interactions. Just like higher-order RNA macromolecules, pre-microRNAs possess a hairpin loop, and it plays a crucial role in miRNA biogenesis. Upon inspecting the crystal structures of RNAs with various functions, we noticed that, along with a fairly long double helix, the RNAs contained sequentially different hairpin loops comprising four residues. We therefore applied molecular dynamics simulation to analyze six of these previously unexplored tetraloops, along with GNRA (where N is any nucleotide and R is a purine nucleotide) tetraloops, to understand their structural and functional characteristics. A number of analyses quantifying loop stability by examining base–base stacking, base–sugar and base–phosphate hydrogen bonding, and backbone variability were performed. Importantly, we determined the different interbase stacking preferences of the single-stranded unpaired bases of the hairpin loops, which had not previously been quantified in any form. Furthermore, our study indicates that canonical GNRA structural properties are exhibited by some structures containing non-GNRA loop sequences.

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Graphical abstract Stacking overlap at loop region

     

Estimate of physiological variability of peak latency in single sweep P300

Among single sweep records of event-related potentials (ERPs), the peak latency of P300, which is one of the most prominent positive peaks in the ERP obtained in the oddball paradigm, may vary depending on the conditions of the subject. In the analysis of characteristics of the variability in the peak latency, it is important to know to what extent the variability of the measured peak latency (measured variability) is actually caused by physiological factors (physiological variability). In our previous study, a method was developed for judging whether the physiological variability really exists or not, and if it does exist, the developed method extracts the physiological variability from the measured variability based on a limited number of single sweep records. In the present study, based on the P300 waveforms which were detected by blinded visual inspection of the ERP data obtained by an auditory oddball paradigm from 12 healthy adults, the physiological variability was shown to exist with a confidence level of 95% for all subjects. Furthermore, its interval estimate was calculated by subtracting noise variability from the measured variability with a confidence level of 80%, and it was found to range from 17 to 57 ms for all subjects.     

Intrinsic and extrinsic causes of spatial variability across scales in a metacommunity

The relative importance of extrinsic and intrinsic causes of variability is among the oldest unresolved problems in ecology. However, the interaction between large-scale intrinsic variability in species abundance and environmental heterogeneity is still unknown. We use a metacommunity model with disturbance-recovery dynamics to resolve the interaction between scales of environmental heterogeneity, biotic processes and of intrinsic variability. We explain how population density increases with environmental variability only when its scale matches that of intrinsic patterns of abundance, through their ability to develop in heterogeneous environments. Succession dynamics reveals how the strength of local species interactions, through its control of intrinsic variability, can in turn control the scale of metapopulation response to environmental scales. Our results show that the environment and species density might fail to show any correlation despite their strong causal association. They more generally suggest that the spatial scale of ecological processes might not be sufficient to build a predictive framework for spatially heterogeneous habitats, including marine reserve networks.     

Intrinsic fluctuations within cortical systems account for intertrial variability in human behavior

The resting brain is not silent, but exhibits organized fluctuations in neuronal activity even in the absence of tasks or stimuli. This intrinsic brain activity persists during task performance and contributes to variability in evoked brain responses. What is unknown is if this intrinsic activity also contributes to variability in behavior. In the current fMRI study, we identify a relationship between human brain activity in the left somatomotor cortex and spontaneous trial-to-trial variability in button press force. We then demonstrate that 74% of this brain-behavior relationship is attributable to ongoing fluctuations in intrinsic activity similar to those observed during resting fixation. In addition to establishing a functional and behavioral significance of intrinsic brain activity, these results lend new insight into the origins of variability in human behavior.     

Single-trial latency variability does not contribute to fast habituation of the long-latency averaged auditory evoked potential in the albino rat

Fas habituation (FH) is defined as a general reduction in long-latency, vertex-recorded, averaged auditory evoked potential (AEP) amplitude that occurs in response to the second of a pair of acoustic stimuli. Our laboratory has been studying FH in a variety of human populations with different paradigms and has interpreted it to be a measure of neural attentional mechanism(s) and/or resource allocation related to the processing of cognitive information. We have also reported an analogous phenomenon in the rat. In the present investigation, we examined the relationship between FH (viz., averaged AEP component amplitude decrement) and the single-trial latency variability of the AEP peaks comprising that component. Specifically, AEPs were obtained to 60 paired-tone stimuli from unanesthetized and restrained albino rats previously implanted with chronic skull electrodes. Using a template-matching algorithm similar to that used by Michalewski et al. (Electroenceph. clin. Neurophysiol., 1986, 65:59–71), the latency variability for each animal was computed for the N1 and P2 peaks of the single-trial AEPs that were used to compose the averaged wave form. Findings indicated that (a) there was no difference in single-trial latency variability for these peaks either within or across tones, and (b) there was no relationship between single-trial latency variability for either the N1 or the P2 peaks and the overall peak-to-peak amplitude (N1-P2) of the averaged wave form in response to the second tone. Thus, FH of the N1-P2 (i.e. Peak 2) amplitude in the rat is not due to an increase in latency variability across tones.