Influence of pinholes on MgO-tunnel junction barrier parameters obtained from current-voltage characteristics.

Magnetic tunnel junctions (MTJs) with thin barriers are already used as read sensors in recording media. However, the presence of pinholes across such few A thick barriers cannot be excluded and one needs to investigate their effect on the MTJ-transport properties. By applying large electrical currents we could change the electrical resistance of the studied MgO MTJs (due to pinhole-size variations), and study how pinholes influence the barrier parameters (thickness t and height phi) obtained by fitting current-voltage characteristics to Simmons' model. We found that, with decreasing resistance, the barrier thickness (height) decreases (increases). These results were well reproduced by a model of parallel-resistances, allowing us to estimate pinhole-free barrier parameters.

Computing the optimally fitted spike train for a synapse.

Experimental data have shown that synapses are heterogeneous: different synapses respond with different sequences of amplitudes of postsynaptic responses to the same spike train. Neither the role of synaptic dynamics itself nor the role of the heterogeneity of synaptic dynamics for computations in neural circuits is well understood. We present in this article two computational methods that make it feasible to compute for a given synapse with known synaptic parameters the spike train that is optimally fitted to the synapse in a certain sense. With the help of these methods, one can compute, for example, the temporal pattern of a spike train (with a given number of spikes) that produces the largest sum of postsynaptic responses for a specific synapse. Several other applications are also discussed. To our surprise, we find that most of these optimally fitted spike trains match common firing patterns of specific types of neurons that are discussed in the literature. Hence, our analysis provides a possible functional explanation for the experimentally observed regularity in the combination of specific types of synapses with specific types of neurons in neural circuits.

Efficient temporal processing with biologically realistic dynamic synapses.

Synapses play a central role in neural computation: the strengths of synaptic connections determine the function of a neural circuit. In conventional models of computation, synaptic strength is assumed to be a static quantity that changes only on the slow timescale of learning. In biological systems, however, synaptic strength undergoes dynamic modulation on rapid timescales through mechanisms such as short term facilitation and depression. Here we describe a general model of computation that exploits dynamic synapses, and use a backpropagation-like algorithm to adjust the synaptic parameters. We show that such gradient descent suffices to approximate a given quadratic filter by a rather small neural system with dynamic synapses. We also compare our network model to artificial neural networks designed for time series processing. Our numerical results are complemented by theoretical analyses which show that even with just a single hidden layer such networks can approximate a surprisingly large class of nonlinear filters: all filters that can be characterized by Volterra series. This result is robust with regard to various changes in the model for synaptic dynamics.

On the computational power of winner-take-all.

This article initiates a rigorous theoretical analysis of the computational power of circuits that employ modules for computing winner-take-all. Computational models that involve competitive stages have so far been neglected in computational complexity theory, although they are widely used in computational brain models, artificial neural networks, and analog VLSI. Our theoretical analysis shows that winner-take-all is a surprisingly powerful computational module in comparison with threshold gates (also referred to as McCulloch-Pitts neurons) and sigmoidal gates. We prove an optimal quadratic lower bound for computing winner-take-all in any feedforward circuit consisting of threshold gates. In addition we show that arbitrary continuous functions can be approximated by circuits employing a single soft winner-take-all gate as their only nonlinear operation. Our theoretical analysis also provides answers to two basic questions raised by neurophysiologists in view of the well-known asymmetry between excitatory and inhibitory connections in cortical circuits: how much computational power of neural networks is lost if only positive weights are employed in weighted sums and how much adaptive capability is lost if only the positive weights are subject to plasticity.

Neural systems as nonlinear filters.

Experimental data show that biological synapses behave quite differently from the symbolic synapses in all common artificial neural network models. Biological synapses are dynamic; their "weight" changes on a short timescale by several hundred percent in dependence of the past input to the synapse. In this article we address the question how this inherent synaptic dynamics (which should not be confused with long term learning) affects the computational power of a neural network. In particular, we analyze computations on temporal and spatiotemporal patterns, and we give a complete mathematical characterization of all filters that can be approximated by feedforward neural networks with dynamic synapses. It turns out that even with just a single hidden layer, such networks can approximate a very rich class of nonlinear filters: all filters that can be characterized by Volterra series. This result is robust with regard to various changes in the model for synaptic dynamics. Our characterization result provides for all nonlinear filters that are approximable by Volterra series a new complexity hierarchy related to the cost of implementing such filters in neural systems.

A model for fast analog computation based on unreliable synapses.

We investigate through theoretical analysis and computer simulations the consequences of unreliable synapses for fast analog computations in networks of spiking neurons, with analog variables encoded by the current firing activities of pools of spiking neurons. Our results suggest a possible functional role for the well-established unreliability of synaptic transmission on the network level. We also investigate computations on time series and Hebbian learning in this context of space-rate coding in networks of spiking neurons with unreliable synapses.

Dynamic stochastic synapses as computational units.

In most neural network models, synapses are treated as static weights that change only with the slow time scales of learning. It is well known, however, that synapses are highly dynamic and show use-dependent plasticity over a wide range of time scales. Moreover, synaptic transmission is an inherently stochastic process: a spike arriving at a presynaptic terminal triggers the release of a vesicle of neurotransmitter from a release site with a probability that can be much less than one. We consider a simple model for dynamic stochastic synapses that can easily be integrated into common models for networks of integrate-and-fire neurons (spiking neurons). The parameters of this model have direct interpretations in terms of synaptic physiology. We investigate the consequences of the model for computing with individual spikes and demonstrate through rigorous theoretical results that the computational power of the network is increased through the use of dynamic synapses.

Analog neural nets with gaussian or other common noise distribution cannot recognize arbitrary regular languages.

We consider recurrent analog neural nets where the output of each gate is subject to gaussian noise or any other common noise distribution that is nonzero on a sufficiently large part of the state-space. We show that many regular languages cannot be recognized by networks of this type, and we give a precise characterization of languages that can be recognized. This result implies severe constraints on possibilities for constructing recurrent analog neural nets that are robust against realistic types of analog noise. On the other hand, we present a method for constructing feedforward analog neural nets that are robust with regard to analog noise of this type.

A simple model for neural computation with firing rates and firing correlations.

A simple extension of standard neural network models is introduced which provides a model for neural computations that involve both firing rates and firing correlations. Such an extension appears to be useful since it has been shown that firing correlations play a significant computational role in many biological neural systems. Standard neural network models are only suitable for describing neural computations in terms of firing rates. The resulting extended neural network models are still relatively simple, so that their computational power can be analysed theoretically. We prove rigorous separation results, which show that the use of firing correlations in addition to firing rates can drastically increase the computational power of a neural network. Furthermore, one of our separation results also throws new light on a question that involves just standard neural network models: we prove that the gap between the computational power of high-order and first-order neural nets is substantially larger than shown previously.

NA1/NA2 antisera inhibit FcgammaRI- but not FcgammaRII- mediated phagocytosis.

BACKGROUND AND OBJECTIVES: Alloantibodies against the granulocyte-specific NA antigens play an important role in alloimmune neonatal neutropenia. As the NA system is located on the FcgammaRIIIb, the influence of NA-specific antibodies on granulocyte function is of special interest. MATERIALS AND METHODS: We tested alloantisera specific for NA1 and NA2 for their ability to influence FcgammaR-mediated phagocytosis of polymorphonuclear neutrophils by use of different FcgammaR-specific targets. Red blood cells coated with human IgG anti-D served as specific targets for FcgammaRI-mediated phagocytosis while mouse IgG1 anti-glycophorin A was used to coat red blood cells (RBCs) to obtain FcgammaRII specific targets. To test for a hypothetical induction of phagocytosis by FcgammaRIIIb we used D-- RBCs coated with human monoclonal anti-D as target cells for unprimed neutrophils. RESULTS: Granulocyte phagocytosis was directly induced by FcgammaRI and FcgammaRII but not by FcgammaRIIIb. NA1 alloantisera significantly inhibited FcgammaRI-mediated phagocytosis of IFN-gamma-stimulated neutrophils if the corresponding antigen was expressed. Conversely, NA2 alloantisera inhibited FcgammaRI-mediated phagocytosis in NA2-positive individuals. There was no effect of NA1- and NA2-specific alloantibodies on FcgammaRII-mediated phagocytosis. CONCLUSION: NA-specific alloantisera inhibit the FcgammaRI-induced phagocytosis in primed neutrophils, but they do not significantly inhibit their FcgammaRIIa-specific phagocytosis of mIgG1-coated RBCs.

Fast sigmoidal networks via spiking neurons.

We show that networks of relatively realistic mathematical models for biological neurons in principle can simulate arbitrary feedforward sigmoidal neural nets in a way that has previously not been considered. This new approach is based on temporal coding by single spikes (respectively by the timing of synchronous firing in pools of neurons) rather than on the traditional interpretation of analog variables in terms of firing rates. The resulting new simulation is substantially faster and hence more consistent with experimental results about the maximal speed of information processing in cortical neural systems. As a consequence we can show that networks of noisy spiking neurons are "universal approximators" in the sense that they can approximate with regard to temporal coding any given continuous function of several variables. This result holds for a fairly large class of schemes for coding analog variables by firing times of spiking neurons. This new proposal for the possible organization of computations in networks of spiking neurons systems has some interesting consequences for the type of learning rules that would be needed to explain the self-organization of such networks. Finally, the fast and noise-robust implementation of sigmoidal neural nets by temporal coding points to possible new ways of implementing feedforward and recurrent sigmoidal neural nets with pulse stream VLSI.

[Experimental studies of the evaluation of the size of infarct and perfusion defects using analysis of regional wall motion from 2-dimensional echocardiography]. / Experimentelle Untersuchungen zur Beurteilung der Infarkt- bzw. Perfusionsdefektgrösse mittels regionaler Wandbewegungsanalyse aus dem zweidimensionalen Echokardiogramm.

In an experimental study with laboratory animals we studied the relation between the extent of a disorder of regional wall motion as determined by echocardiography and size of a perfusion defect following occlusion of a coronary vessel for 5 hours. It was demonstrated that wall motion is not uniform in a normal left ventricle and that there is a wide range of variability in wall motion within a given myocardial segment. For this reason we determined the extent of a disorder of regional wall motion in two echocardiographic planes with reference to defined normal values. Analysis of interobserver and intraobserver variability showed that reproducible determinations of the circumferential extent of a disorder of regional wall motion and the ejection fraction are possible with an acceptable degree of certainty. There was a significant correlation between morphological determinations of the size of a perfusion defect in the left ventricle and the circumferential extent of a disorder of regional wall motions as demonstrated in the echocardiogram (r = 0.83). The regression curve (y = 4.26 + 0.95x) for determinations of the size of the perfusion defect approached the identity line with a standard error of estimation for the echocardiographic examination of 7.4%. Size of the zone of infarction was overestimated by an average of 8% with echocardiography (r = 0.81), with a standard error of estimation of 6.6% (y = -2.41 + 0.85x). There was no significant correlation between ejection fraction and size of the perfusion defect or the size of infarction.(ABSTRACT TRUNCATED AT 250 WORDS)

[Importance of intravenous digital subtraction angiography in the diagnosis of aneurysms in the region of the abdominal aorta]. / Die Bedeutung der intravenösen digitalen Subtraktionsangiographie zur Diagnostik von Aneurysmen im Bereich der Bauchaorta.

The arterial vessels can be examined with satisfactory relevance via intravenous DSA using a relatively small amount of contrast medium. An aneurysm first diagnosed via sonography in the region of the abdominal aorta was confirmed in this manner in 19 patients. In 7 of these cases, it was also possible to diagnose a stenosis in the renal artery region. This method can supply exact information on the size, position and extension of the aneurysm--since the equipment has been technically improved--even in the case of kinking of the vessels. It can also supply information in the relationship of the aneurysm to visceral arteries, on the degree of calcification of the vessel walls, and on the additional occurrence of vascular stenoses. Hence, we are of the opinion that intravenous DSA is the method of choice after sonography has been performed and that it is superior to CT within the framework of preoperative examinations of aneurysms of the abdominal aorta.

[Digital subtraction angiography with dynamic color function images]. / Digitale Subtraktionsangiographie mit dynamischen Farbfunktionsbildern.

The authors present for the first time the principles and initial results of a laboratory pilot plant with which parametric colour images can be produced in real time. These images are digital function images of the type originally made widely known by Höhne et al. The colour function images can be produced during conventional digital subtraction angiography (DSA) practically as a sideline, that is to say, with a minimum of time and effort, as a parallel product. Despite the fact that the course of the functional processes can be visualised dynamically, the only feature required is the storage capacity of a single digital TV screen image. Of course, the colour function image is not a substitute for exact quantitative evaluations. However, it does furnish a rapid survey of haemodynamic processes for a multitude of patients in routine diagnosis.

The effect on fungal growth of some 2,5-dihydroxy-1,4-benzoquinones.

It has been shown that 2,5-dihydroxy-1,4-benzoquinones decrease vegetative growth and inhibit spore germination of 12 species of fungi belonging to six diverse genera. The nature of are substituents at the 3 and 6 positions of the quinone ring also affected their growth-inhibitory properties; generally those substituents of lower polarity inhibited growth at lower concentrations. As in the case of cochiliodinol, chemical modification of the quinone group, or the hydroxyl groups of the quinone ring, in compounds of the polyporic acid series, also led to loss of biological activity.

Metabolism of 4-chlorobiphenyl by lichens.

Eighteen lichens from a variety of habitats were treated with 4-chlorobiphenyl (4-CB). All, as determined by means of radioactive tracers, were shown to partially convert 4-CB to 4-chloro-4'-hydroxybiphenyl. Only one species (Pseudocyphellaria crocata) produced a further major metabolite not previously reported, namely 4-chloro-4'-methoxybiphenyl. The formation of the hydroxyderivative by Cladonia rangiferina and Lasallia papulosa was proven by isolation and chemical identification. Difficulties in the recovery of both the starting material and the metabolites from Pseudocyphellaria were encountered.