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A full characterization of the Ar-NH3expanding thermal plasma source applied in industrial processes for high-rate silicon nitride deposition is presented in terms of absolute densities of reactive species such as ions and radicals. The ion composition of the plasma was identified by mass spectrometry, while absolute ion density information was obtained by Langmuir probe measurements. N radicals were detected by threshold ionization mass spectrometry, whereas NH and NH2radicals were measured by cavity ringdown spectroscopy. It was found that the ion density decreases from 1013?cm?3in a pure Ar plasma to 1010-1011?cm?3when NH3is injected, with ArH+, , , and being the most abundant ions. The densities of N and NH both saturate at a level of 3 × 1012?cm?3at NH3flows above 3?sccs while the density of NH2increases linearly with the NH3flow and reaches a level of 4 × 1012?cm?3. When the plasma source current is increased, the densities of N and NH increase linearly, while the NH2density remains approximately constant. Furthermore, it is revealed that most of the consumed NH3is converted into N2and H2in the plasma.

The present article discusses computational hypotheses on corticothalamic feedback and modulation of cortical response properties. We have recently proposed that the two phenomena are related, hypothesizing that neuronal velocity preference in the visual cortex is altered by feedback to the lateral geniculate nucleus. We now contrast the common view that response adaptation to stimuli subserves a function of redundancy reduction with the idea that it may enhance cortical representation of objects. Our arguments lead to the concept that the corticothalamic loop is involved in reducing sensory input to behaviourally relevant aspects, a pre–attentive gating.

Abstract: Two types of T helper (Th) cells have been defined on the basis of their cytokine secretion patterns. The decision of a naive T cell to differentiate into Th1 or Th2 is crucial, since to a first approximation it determines whether a cell-mediated or humoral immune response is triggered against a particular pathogen, which profoundly influences disease outcome. Here we show that the internal behaviour of the T helper system, which emerges from regulatory mechanisms ‘built into’ the T helper system, itself can usually select the appropriate T helper response. This phenomenon arises from an initial Th1 bias together with the induction of Th1 → Th2 switches when Th1 effectors do not lead to efficient antigen clearance. The occurrence of these shifts is based on the antigen dose dependence of T helper differentiation, which is a consequence of asymmetries in cross-suppression. Critical for this feature is the rate with which Th2 cells undergo antigen-induced cell death.

Helper T (Th) cells are a crucial component of the adaptive immune system and are of fundamental importance in orchestrating the appropriate response to pathogenic challenge. They fall into two broad categories defined by the cytokines each produces. Th1 cells produce interferon- γand are required for effective immunity to intracellular bacteria, viruses and protozoa whereas Th2 produce IL-4 and are required for optimal antibody production to T-dependent antigens. A great deal of experimental data on the regulation of Th1 and Th2 differentiation have been obtained but many essential features of this complex system are still not understood. Here we present a mathematical model of Th1/Th2 differentiation and cross regulation. We model Fas-mediated activation-induced cell death (AICD) as this process has been identified as an important mechanism for limiting clonal expansion and resolving T cell responses. We conclude that Th2 susceptibility to AICD is important for stabilizing the two polarized arms of the T helper response, and that cell–cell killing, not suicide, is the dominant mechanism for Fas-mediated death of Th1 effectors. We find that the combination of the anti-proliferative effect of the cytokine TGF- βand the inhibiting influence of IL-10 on T cell activation are crucial controls for Th2 populations. We see that the strengths of the activation signals for each T helper cell subset, which are dependent on the antigen dose, co-stimulatory signals and the cytokine environment, critically determine the dominant helper subset. Switches from Th1- to Th2-dominance may be important in chronic infection and we show that this phenomenon can arise from differential AICD susceptibility of T helper subsets, and asymmetries in the nature of the cross-suppressive cytokine interactions. Our model suggests that in some senses a predominantly type 2 reaction may well be the “default” pathway for an antigen-specific immune response, due to these asymmetries.

New experimental data on ternary K-Rb-Cs and Na-Rb-Cs alloy systems, including binary Na-Cs and Na-Rb alloys, are presented. The ternary alloys were chosen because they offer the intrinsic possibility of constructing alloy systems for which the Knight shift of one of the components, or the atomic volume, is independent of concentration. The experimental data are discussed in terms of an extended single-APW model and are also compared with the results of recently published pseudopotential calculations. A survey of existing estimations for the spin susceptibility of alkali metals is given. Finally, the possibilities for a consistent description of the Knight shift, involving both the nuclear contact density and the susceptibility, are investigated.

In the first part of this paper we discuss a technical visual sensory system, which——in analogy with the retina——includes some preprocessing of visual information. In so doing, we use an information-theoretic criterion, the infomax ansatz, to optimize the response of the sensory system. In particular, it is shown that the lattice structure of the photoreceptor array has to be taken into account. By a discrete Fourier transformation on a triangular lattice we derive the frequency response of the infomax filter within the first Brillouin zone. To illustrate the response properties, infomax filters adapted to different noise levels are applied to images with different signal-to-noise ratios. This clearly demonstrates the necessity of adaptation of the filter properties to the given noise level. Furthermore, it is shown how to efficiently implement infomax-like filters by simple networks with only nearest-neighbour interactions. A two-layered network topology proves to be very advantageous in implementing the desired high-pass or low-pass properties. The network topology allows for adaption of the network to low and high noise levels by simply adjusting the nearest-neighbour couplings.In the second part of this paper, we compare the previously described information-theoretic requirements on a visual sensory system with biological facts known from the vertebrate retina. The substantial physiological response properties of the vertebrate retina are in agreement with the main features of the infomax filter. Since available experimental data lacks information which is necessary for a more quantitative comparison, we present suggestions for future experiments. Some key anatomical features of the retina of many vertebrates compare well with our two-layered implementation of the infomax filter. The analogy, in particular, concerns the adaption mechanism. To illustrate this point, we summarize some recent experiments which demonstrate that in the retina of some species adaption is based on the release of the neuromodulator dopamine by the interplexiform cells. This causes the horizontal cells to decouple. On the basis of recently gained understanding of the outer plexiform layer of the retina some further hypotheses about the functionality of the retina become obvious and possible future experiments to verify or refute them are suggested.Finally, we discuss the infomax approach from a more general point of view. In particular, we show that redundancy is essential to obtaining noise robustness of an internal representation of the environment as it is produced by a sensory system such as the retina.

1. A model of Ca2+ dynamics in spines of CA1 hippocampal neurons is presented. In contrast to traditional models, which concentrate on the effects of Ca2+ influx, diffusion, buffering, and extrusion, we also consider the additional effect of intracellular Ca2+ stores. 2. It is shown that traditional models without Ca2+ stores cannot account for the time course of long-term potentiation (LTP) induction as found in recent experiments. Experimental data suggest that the intracellular Ca2+ concentration should be elevated for up to 2 s, whereas the Ca2+ concentration in standard models of Ca2+ dynamics decays much faster. 3. When intracellular Ca2+ stores are taken into account, a much slower decay is found. In particular, a model simulation with a stimulation paradigm consisting of two bursts of four impulses at 100 Hz each and variable interburst intervals can reproduce experimental results found for primed or theta-burst stimulation. 4. In our model, Ca2+ release from the store has a nonlinear, bell-shaped dependence on the intracellular Ca2+ concentration, similar to the one found for inositoltrisphosphate and ryanodine receptors. These receptors are known to control calcium release from intracellular stores. 5. Our model suggests an important role of intracellular calcium stores in the induction of LTP. The stores serve as a long-term calcium source that can sustain an intracellular Ca2+ concentration above the resting level for 1-2 s.

The Hopfield network provides a simple model of an associative memory in a neuronal structure. It is, however, based on highly artificial assumptions, especially the use of formal two-state neurons or graded-response neurons. The authors address the question of what happens if formal neurons are replaced by a model of ‘‘spiking’’ neurons. They do so in two steps. First, they show how to include refractoriness and noise into a simple threshold model of neuronal spiking. The spike trains resulting from such a model reproduce the distribution of interspike intervals and gain functions found in real neurons. In a second step they connect the model neurons so as to form a large associative memory system. The spike transmission is described by a synaptic kernel which includes axonal delays, ‘‘Hebbian’’ synaptic efficacies, and a realistic postsynaptic response. The collective behaviour of the system is predicted by a set of dynamical equations which are exact in the limit of a large and fully connected network that has to store a finite number of patterns. The authors show that in a stationary retrieval state the statistics of the spiking dynamics is completely wiped out and the system reduces to a network of graded-response neurons. In the case of an oscillatory retrieval state, however, the spiking noise and the internal time constants of the neurons become important and determine the behaviour of the system.

Learning and associative memory are concerned with storing and retrieving activity patterns of a neuronal net. It is considered to be a minimal requirement that the number of patterns that can be stored faithfully is extensive, i.e. is at least proportional to the number of neighbours each neuron interacts with. The main drawback of Hebbian learning, and of any one-shot local learning procedure, is that it cannot store extensively many patterns with activities which vary from pattern to pattern because, being local, it cannot discern global correlations. We critically review the performance of Hebbian unlearning——also proposed as a model of REM sleep——in a network of formal neurons with a distribution of axonal delays. Hebbian unlearning, though as local and unsupervised as Hebbian learning, eliminates undesirable global correlations, handles any spatio-temporal pattern, and improves the network performance greatly——sometimes even saturating a theoretical upper bound. Furthermore, it is shown that unlearning has an optimal and critical number of unlearning loops (‘‘dreams’’) at and above which, respectively, storage and retrieval are optimal and break down completely. In applications, ‘‘blinking’’ is a most useful property: after having unlearned stationary patterns, the network, when presented with a noisy pattern, either converges to the original one or to a blinking state signalling that the final pattern has not been learned. In a daily context, data acquisition and REM sleep alternate. Numerical simulations have shown that, alternating between learning bundles of patterns and unlearning, a network only stores and retrieves the most recently learned data and ‘‘forgets’’ the past. A key property of unlearning being the elimination of undesirable correlations, the process would give a straightforward explanation of Freudian condensation. In other words, the unlearning viewpoint offers a comprehensive perspective.

The radiosensitization by oxygen of biologically active bacteriophage DNA in bacterial extracts has been studied. The oxygen-effect in such a system appeared not to be due or only to a minor extent to the presence of endogenous sulphydryl compounds. The components in a cell extract which enable oxygen and other sensitizers to sensitize DNA could not be destroyed by extremely high doses of γ-radiation.

Hebbian learning allows a network of spiking neurons to store and retrieve spatio-temporal patterns with a time resolution of 1 ms, despite the long postsynaptic and dendritic integration times. To show this, we introduce and analyze a model of spiking neurons, the spike response model, with a realistic distribution of axonal delays and with realistic postsynaptic potentials. Learning is performed by a local Hebbian rule which is based on the synchronism of presynaptic neurotransmitter release and some short-acting postsynaptic process. The time window of this synchronism determines the temporal resolution of pattern retrieval, which can be initiated by applying a short external stimulus pattern. Furthermore, a rate quantization is found in dependence upon the threshold value of the neurons, i.e., in a given time a pattern runs n times as often as learned, where n is a positive integer (n ⩾ 0). We show that all information about the spike pattern is lost if only mean firing rates (temporal average) or ensemble activities (spatial average) are considered. An average over several retrieval runs in order to generate a post-stimulus time histogram may also deteriorate the signal. The full information on a pattern is contained in the spike raster of a single run. Our results stress the importance, and advantage, of coding by spatio-temporal spike patterns instead of firing rates and average ensemble activity. The implications regarding modelling and experimental data analysis are discussed.

A model of an associative network of spiking neurons with stationary states, globally locked oscillations, and weakly locked oscillatory states is presented and analyzed. The network is close to biology in the following sense. First, the neurons spike and our model includes an absolute refractory period after each spike. Second, we consider a distribution of axonal delay times. Finally, we describe synaptic signal transmission by excitatory and inhibitory potentials (EPSP and IPSP) with a realistic shape, that is, through a response kernel. During retrieval of a pattern, all active neurons exhibit periodic spike bursts which may or may not be synchronized (‘locked’) into a coherent oscillation. We derive an analytical condition of locking and calculate the period of collective activity during oscillatory retrieval. In a stationary retrieval state, the overlap assumes a constant value proportional to the mean firing rate of the neurons. It is argued that in a biological network an intermediate scenario of “weak locking” is most likely.