Sudbury project (University of Muenster-Ontario Geological Survey): Origin of the polymict, allochthonous breccias of the Onaping Formation

The Sudbury structure has been interpreted as a deeply eroded remnant of a peak-ring basin. The polymict, allochthonous breccias of the Onaping Formation (OF) occur in the central part of the Sudbury structure, which is surrounded by the 1.85-Ga-old 'Sudbury Igneous Complex' (SIC). From bottom to top the OF can be divided into Basal, Gray, Green, and lower and upper Black members. The breccias were mapped in detail in the east range of the structure. The SIC and the lower part of the OF (Basal Member) are interpreted as the impact melt system. The overlying Gray Member is a breccia unit with a clastic matrix and has a sharp contact to the Basal Member. The Green Member is considered as a continuous uniform breccia layer on top of the Gray Member and comprises the former 'chlorite shard horizon'. The uppermost unit of the OF (Black Member) can be subdivided into a lower and an upper Black Member unit. The lower part (100-150 m thick) still shows petrographic features of suevitic breccias, small fragments of basement rocks, melt particles, chloritized particles, and breccia fragments in a dark, clastic matrix

Sudbury project (University of Muenster-Ontario Geological Survey): Petrology, chemistry, and origin of breccia formations

Within the Sudbury Project of the University of Muenster and the Ontario Geological Survey special emphasis was put on the breccia formations exposed at the Sudbury structure (SS) because of their crucial role for the impact hypothesis. They were mapped and sampled in selected areas of the north, east, and south ranges of the SS. The relative stratigraphic positions of these units are summarized. Selected samples were analyzed by optical microscopy, SEM, microprobe, XRF and INAA, Rb-Sr and SM-Nd-isotope geochemistry, and carbon isotope analysis. The results of petrographic and chemical analysis for those stratigraphic units that were considered the main structural elements of a large impact basin are summarized

A Physiotherapeutic Approach to Musicians' Health – Data From 614 Patients From a Physiotherapy Clinic for Musicians (INAP/O)

Currently, the treatment of musicians is an interprofessional approach. Playing-related health complaints may impact the performance of a musician. In Germany, a medical consulting hour for musicians exists, but those for athletes in sports medicine are not so common. The diagnosing and treatment procedure within the physiotherapy consultation for musicians follows a specific concept-b and requires knowledge of instruments and musician-specific complaints. Based on the consulting hour in a clinic in Osnabrueck, 614 case reports were part of this sample, of which 558 data sets were complete. The focus of the analysis is the instrument and the primary complaint. Also, the type of therapy is characterized, and the amount is calculated. Primary complaints of musicians, in general, are found most frequently in the spine and upper extremity. Musician complaints are different between instruments. Instrumentalists have a significantly higher chance to suffer from a primary complaint in the area of the upper extremity. Furthermore, the groups without an instrument (e.g., singing or dancing) are developing complaints in the anatomical area which they primarily use. Therefore, these types of therapy were used: physiotherapy, manual therapy, and osteopathy with an average of 5.9 treatment units. This study underpinned the importance of musician-specific physiotherapy as a profession to treat musicians. Also, an interdisciplinary approach is necessary to treat all aspects of complaints

Sudbury project (University of Muenster-Ontario Geological Survey): Field studies 1984-1989 - summary of results

In cooperation between the Ontario Geological Survey and the Institute of Geology and Institute of Planetology, geological, petrological, and geochemical studies were carried out on impact-related phenomena of the Sudbury structure during the last decade. The main results of the field studies are briefly reviewed. Footwall rocks, sublayer, and lower sections of the Sudbury Igneous Complex (SIC) were mainly mapped and sampled in the northern (Levack Township) and western (Trillabelle and Sultana Properties) parts of the north range. Within these mapping areas Sudbury Breccias (SB) and Footwall Breccias (FB) were studied; SB were also investigated along extended profiles beyond the north and south ranges up to 55 km from the SIC. The Onaping Formation (OF) and the upper section of the SIC were studied both in the north range (Morgan and Dowling Townships) and in the southern east range (Capreol and McLennan Townships)

Sudbury project (University of Muenster-Ontario Geological Survey): Summary of results - an updated impact model

In 1984 the Ontario Geological Survey initiated a research project on the Sudbury structure (SS) in cooperation with the University of Muenster. The project included field mapping (1984-1989) and petrographic, chemical, and isotope analyses of the major stratigraphic units of the SS. Four diploma theses and four doctoral theses were performed during the project (1984-1992). Specific results of the various investigations are reported. Selected areas of the SS were mapped and sampled: Footwall rocks; Footwall breccia and parts of the sublayer and lower section of the Sudbury Igneous Complex (SIC); Onaping Formation and the upper section of the SIC; and Sudbury breccia and adjacent Footwall rocks along extended profiles up to 55 km from the SIC. All these stratigraphic units of the SS were studied in substantial detail by previous workers. The most important characteristic of the previous research is that it was based either on a volcanic model or on a mixed volcanic-impact model for the origin of the SS. The present project was clearly directed toward a test of the impact origin of the SS without invoking an endogenic component. In general, our results confirm the most widely accepted stratigraphic division of the SS. However, our interpretation of some of the major stratigraphic units is different from most views expressed. The stratigraphy of the SS and its new interpretation is given as a basis for discussion

Connection-type-specific biases make uniform random network models consistent with cortical recordings

Uniform random sparse network architectures are ubiquitous in computational neuroscience, but the implicit hypothesis that they are a good representation of real neuronal networks has been met with skepticism. Here we used two experimental data sets, a study of triplet connectivity statistics and a data set measuring neuronal responses to channelrhodopsin stimuli, to evaluate the fidelity of thousands of model networks. Network architectures comprised three neuron types (excitatory, fast spiking, and nonfast spiking inhibitory) and were created from a set of rules that govern the statistics of the resulting connection types. In a high-dimensional parameter scan, we varied the degree distributions (i.e., how many cells each neuron connects with) and the synaptic weight correlations of synapses from or onto the same neuron. These variations converted initially uniform random and homogeneously connected networks, in which every neuron sent and received equal numbers of synapses with equal synaptic strength distributions, to highly heterogeneous networks in which the number of synapses per neuron, as well as average synaptic strength of synapses from or to a neuron were variable. By evaluating the impact of each variable on the network structure and dynamics, and their similarity to the experimental data, we could falsify the uniform random sparse connectivity hypothesis for 7 of 36 connectivity parameters, but we also confirmed the hypothesis in 8 cases. Twenty-one parameters had no substantial impact on the results of the test protocols we used

Microcircuits of excitatory and inhibitory neurons in layer 2/3 of mouse barrel cortex

Avermann M, Tomm C, Mateo C, Gerstner W, Petersen CC. Microcircuits of excitatory and inhibitory neurons in layer 2/3 of mouse barrel cortex. J Neurophysiol 107: 3116-3134, 2012. First published March 7, 2012; doi:10.1152/jn.00917.2011.-Synaptic interactions between nearby excitatory and inhibitory neurons in the neocortex are thought to play fundamental roles in sensory processing. Here, we have combined optogenetic stimulation, whole cell recordings, and computational modeling to define key functional microcircuits within layer 2/3 of mouse primary somatosensory barrel cortex. In vitro optogenetic stimulation of excitatory layer 2/3 neurons expressing channelrhodopsin-2 evoked a rapid sequence of excitation followed by inhibition. Fast-spiking (FS) GABAergic neurons received large-amplitude, fast-rising depolarizing postsynaptic potentials, often driving action potentials. In contrast, the same optogenetic stimulus evoked small-amplitude, subthreshold postsynaptic potentials in excitatory and non-fast-spiking (NFS) GABAergic neurons. To understand the synaptic mechanisms underlying this network activity, we investigated unitary synaptic connectivity through multiple simultaneous whole cell recordings. FS GABAergic neurons received unitary excitatory postsynaptic potentials with higher probability, larger amplitudes, and faster kinetics compared with NFS GABAergic neurons and other excitatory neurons. Both FS and NFS GABAergic neurons evoked robust inhibition on postsynaptic layer 2/3 neurons. A simple computational model based on the experimentally determined electrophysiological properties of the different classes of layer 2/3 neurons and their unitary synaptic connectivity accounted for key aspects of the network activity evoked by optogenetic stimulation, including the strong recruitment of FS GABAergic neurons acting to suppress firing of excitatory neurons. We conclude that FS GABAergic neurons play an important role in neocortical microcircuit function through their strong local synaptic connectivity, which might contribute to driving sparse coding in excitatory layer 2/3 neurons of mouse barrel cortex in vivo

Parameter extraction and classification of three cortical neuron types reveals two distinct adaptation mechanisms

Mensi S, Naud R, Pozzorini C, Avermann M, Petersen CCH, Gerstner W. Parameter extraction and classification of three cortical neuron types reveals two distinct adaptation mechanisms. J Neurophysiol 107: 1756-1775, 2012. First published December 7, 2011; doi:10.1152/jn.00408.2011.-Cortical information processing originates from the exchange of action potentials between many cell types. To capture the essence of these interactions, it is of critical importance to build mathematical models that reflect the characteristic features of spike generation in individual neurons. We propose a framework to automatically extract such features from current-clamp experiments, in particular the passive properties of a neuron (i.e., membrane time constant, reversal potential, and capacitance), the spike-triggered adaptation currents, as well as the dynamics of the action potential threshold. The stochastic model that results from our maximum likelihood approach accurately predicts the spike times, the subthreshold voltage, the firing patterns, and the type of frequency-current curve. Extracting the model parameters for three cortical cell types revealed that cell types show highly significant differences in the time course of the spike-triggered currents and moving threshold, that is, in their adaptation and refractory properties but not in their passive properties. In particular, GABAergic fast-spiking neurons mediate weak adaptation through spike-triggered currents only, whereas regular spiking excitatory neurons mediate adaptation with both moving threshold and spike-triggered currents. GABAergic nonfast-spiking neurons combine the two distinct adaptation mechanisms with reduced strength. Differences between cell types are large enough to enable automatic classification of neurons into three different classes. Parameter extraction is performed for individual neurons so that we find not only the mean parameter values for each neuron type but also the spread of parameters within a group of neurons, which will be useful for future large-scale computer simulations