A potential role for the cerebellar nuclei in absence seizures

© 2013 Alva et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Poster presented ar CNS 2013Non peer reviewe

Stability Analysis of Asynchronous States in Neuronal Networks with Conductance-Based Inhibition

Oscillations in networks of inhibitory interneurons have been reported at various sites of the brain and are thought to play a fundamental role in neuronal processing. This Letter provides a self-contained analytical framework that allows numerically efficient calculations of the population activity of a network of conductance-based integrate-and-fire neurons that are coupled through inhibitory synapses. Based on a normalization equation this Letter introduces a novel stability criterion for a network state of asynchronous activity and discusses its perturbations. The analysis shows that, although often neglected, the reversal potential of synaptic inhibition has a strong influence on the stability as well as the frequency of network oscillations

Proximity effects and Andreev reflection in mesoscopic SNS junction with perfect NS interfaces

Low temperature transport measurements on superconducting film - normal metal wire - superconducting film (SNS) junctions fabricated on the basis of 6 nm thick superconducting polycrystalline PtSi films are reported. The structures with the normal metal wires of two different lengths L=1.5 $\mu$m and L=6$\mu$m and the same widths W=0.3$\mu$m are studied. Zero bias resistance dip related to pair current proximity effect is observed for all junctions whereas the subharmonic energy gap structure originating from phase coherent multiple Andreev reflections have occurs only in the SNS junctions with short wires.Comment: ReVTex, 4 pages, 4 eps figures include

EEG microstates: Functional significance and short-term test-retest reliability

Appendix A: Supplementary data to this article can be found online at https://doi. org/10.1016/j.ynirp.2022.100089.Copyright /© 2022 The Authors. EEG signal power, may have clinical relevance; however, their functional significance and test-retest reliability remain unclear. To investigate the functional significance of the canonical EEG microstate classes and their pairwise transitions, and to establish their within-session test-retest reliability, we recorded 36-channel EEGs in 20 healthy volunteers during three eyes-closed conditions: mind-wandering, verbalization (silently repeating the word ‘square’ every 2 s), and visualization (visualizing a square every 2 s). Each condition lasted 3 min and the sequence of three conditions was repeated four times (two runs of two sequence repetitions). The participants' alertness and their sense of effort during the experiment were rated using visual-analogue scales. The EEG data were 2–20 Hz bandpass-filtered and analysed into the four canonical microstate classes: A, B, C, and D. EEG microstate classes C and D were persistently more dominant than classes A and B in all conditions. Of the first-order microstate parameters, average microstate duration was most reliable. The duration of class D microstate was longer during mind-wandering (106.8 ms) than verbalization (102.2 ms) or visualization (99.8 ms), with a concomitantly higher coverage (36.4% vs. 34.7% and 35.2%), but otherwise there was no clear association of the four microstate classes to particular mental states. The test-retest reliability was higher at the beginning of each run, together with higher average alpha power and subjective ratings of alertness. Only the transitions between classes C and D (from C to D in particular) were significantly higher than what would be expected from the respective microstates' occurrences. The transition probabilities, however, did not distinguish between conditions, and their test-retest reliability was overall lower than that of the first-order parameters such as duration and coverage. Further studies are needed to establish the functional significance of the canonical EEG microstate classes. This might be more fruitfully achieved by looking at their complex syntax beyond pairwise transitions. To ensure greater test-retest reliability of microstate parameters, study designs should allow for shorter experimental runs with regular breaks, particularly when using EEG microstates as clinical biomarkers.BIAL Foundation (grant number: 183/16)

The effect of non-specific LTD on pattern recognition in cerebellar Purkinje cells

© 2010 Safaryan et al; licensee BioMed Central Ltd.Poster presented at CNS 2010Peer reviewe

Endogenous cholinergic inputs and local circuit mechanisms govern the phasic mesolimbic dopamine response to nicotine

Nicotine exerts its reinforcing action by stimulating nicotinic acetylcholine receptors (nAChRs) and boosting dopamine (DA) output from the ventral tegmental area (VTA). Recent data have led to a debate about the principal pathway of nicotine action: direct stimulation of the DAergic cells through nAChR activation, or disinhibition mediated through desensitization of nAChRs on GABAergic interneurons. We use a computational model of the VTA circuitry and nAChR function to shed light on this issue. Our model illustrates that the α4β2-containing nAChRs either on DA or GABA cells can mediate the acute effects of nicotine. We account for in vitro as well as in vivo data, and predict the conditions necessary for either direct stimulation or disinhibition to be at the origin of DA activity increases. We propose key experiments to disentangle the contribution of both mechanisms. We show that the rate of endogenous acetylcholine input crucially determines the evoked DA response for both mechanisms. Together our results delineate the mechanisms by which the VTA mediates the acute rewarding properties of nicotine and suggest an acetylcholine dependence hypothesis for nicotine reinforcement.Peer reviewe

A New Approach for Determining Phase Response Curves Reveals that Purkinje Cells Can Act as Perfect Integrators

Cerebellar Purkinje cells display complex intrinsic dynamics. They fire spontaneously, exhibit bistability, and via mutual network interactions are involved in the generation of high frequency oscillations and travelling waves of activity. To probe the dynamical properties of Purkinje cells we measured their phase response curves (PRCs). PRCs quantify the change in spike phase caused by a stimulus as a function of its temporal position within the interspike interval, and are widely used to predict neuronal responses to more complex stimulus patterns. Significant variability in the interspike interval during spontaneous firing can lead to PRCs with a low signal-to-noise ratio, requiring averaging over thousands of trials. We show using electrophysiological experiments and simulations that the PRC calculated in the traditional way by sampling the interspike interval with brief current pulses is biased. We introduce a corrected approach for calculating PRCs which eliminates this bias. Using our new approach, we show that Purkinje cell PRCs change qualitatively depending on the firing frequency of the cell. At high firing rates, Purkinje cells exhibit single-peaked, or monophasic PRCs. Surprisingly, at low firing rates, Purkinje cell PRCs are largely independent of phase, resembling PRCs of ideal non-leaky integrate-and-fire neurons. These results indicate that Purkinje cells can act as perfect integrators at low firing rates, and that the integration mode of Purkinje cells depends on their firing rate

Why Are Computational Neuroscience and Systems Biology So Separate?

Despite similar computational approaches, there is surprisingly little interaction between the computational neuroscience and the systems biology research communities. In this review I reconstruct the history of the two disciplines and show that this may explain why they grew up apart. The separation is a pity, as both fields can learn quite a bit from each other. Several examples are given, covering sociological, software technical, and methodological aspects. Systems biology is a better organized community which is very effective at sharing resources, while computational neuroscience has more experience in multiscale modeling and the analysis of information processing by biological systems. Finally, I speculate about how the relationship between the two fields may evolve in the near future

NeuroML: A Language for Describing Data Driven Models of Neurons and Networks with a High Degree of Biological Detail

Biologically detailed single neuron and network models are important for understanding how ion channels, synapses and anatomical connectivity underlie the complex electrical behavior of the brain. While neuronal simulators such as NEURON, GENESIS, MOOSE, NEST, and PSICS facilitate the development of these data-driven neuronal models, the specialized languages they employ are generally not interoperable, limiting model accessibility and preventing reuse of model components and cross-simulator validation. To overcome these problems we have used an Open Source software approach to develop NeuroML, a neuronal model description language based on XML (Extensible Markup Language). This enables these detailed models and their components to be defined in a standalone form, allowing them to be used across multiple simulators and archived in a standardized format. Here we describe the structure of NeuroML and demonstrate its scope by converting into NeuroML models of a number of different voltage- and ligand-gated conductances, models of electrical coupling, synaptic transmission and short-term plasticity, together with morphologically detailed models of individual neurons. We have also used these NeuroML-based components to develop an highly detailed cortical network model. NeuroML-based model descriptions were validated by demonstrating similar model behavior across five independently developed simulators. Although our results confirm that simulations run on different simulators converge, they reveal limits to model interoperability, by showing that for some models convergence only occurs at high levels of spatial and temporal discretisation, when the computational overhead is high. Our development of NeuroML as a common description language for biophysically detailed neuronal and network models enables interoperability across multiple simulation environments, thereby improving model transparency, accessibility and reuse in computational neuroscience