826 research outputs found
Mesoscopic theory for inhomogeneous mixtures
Mesoscopic density functional theory for inhomogeneous mixtures of sperical
particles is developed in terms of mesoscopic volume fractions by a systematic
coarse-graining procedure starting form microscopic theory. Approximate
expressions for the correlation functions and for the grand potential are
obtained for weak ordering on mesoscopic length scales. Stability analysis of
the disordered phase is performed in mean-field approximation (MF) and beyond.
MF shows existence of either a spinodal or a -surface on the
volume-fractions - temperature phase diagram. Separation into homogeneous
phases or formation of inhomogeneous distribution of particles occurs on the
low-temperature side of the former or the latter surface respectively,
depending on both the interaction potentials and the size ratios between
particles of different species. Beyond MF the spinodal surface is shifted, and
the instability at the -surface is suppressed by fluctuations. We
interpret the -surface as a borderline between homogeneous and
inhomogeneous (containing clusters or other aggregates) structure of the
disordered phase. For two-component systems explicit expressions for the MF
spinodal and -surfaces are derived. Examples of interaction potentials
of simple form are analyzed in some detail, in order to identify conditions
leading to inhomogeneous structures.Comment: 6 figure
Irregular Persistent Activity Induced by Synaptic Excitatory Feedback
Neurophysiological experiments on monkeys have reported highly irregular persistent activity during the performance of an oculomotor delayed-response task. These experiments show that during the delay period the coefficient of variation (CV) of interspike intervals (ISI) of prefrontal neurons is above 1, on average, and larger than during the fixation period. In the present paper, we show that this feature can be reproduced in a network in which persistent activity is induced by excitatory feedback, provided that (i) the post-spike reset is close enough to threshold , (ii) synaptic efficacies are a non-linear function of the pre-synaptic firing rate. Non-linearity between pre-synaptic rate and effective synaptic strength is implemented by a standard short-term depression mechanism (STD). First, we consider the simplest possible network with excitatory feedback: a fully connected homogeneous network of excitatory leaky integrate-and-fire neurons, using both numerical simulations and analytical techniques. The results are then confirmed in a network with selective excitatory neurons and inhibition. In both the cases there is a large range of values of the synaptic efficacies for which the statistics of firing of single cells is similar to experimental data
Admit your weakness: Verifying correctness on TSO architectures
“The final publication is available at http://link.springer.com/chapter/10.1007%2F978-3-319-15317-9_22 ”.Linearizability has become the standard correctness criterion for fine-grained non-atomic concurrent algorithms, however, most approaches assume a sequentially consistent memory model, which is not always realised in practice. In this paper we study the correctness of concurrent algorithms on a weak memory model: the TSO (Total Store Order) memory model, which is commonly implemented by multicore architectures. Here, linearizability is often too strict, and hence, we prove a weaker criterion, quiescent consistency instead. Like linearizability, quiescent consistency is compositional making it an ideal correctness criterion in a component-based context. We demonstrate how to model a typical concurrent algorithm, seqlock, and prove it quiescent consistent using a simulation-based approach. Previous approaches to proving correctness on TSO architectures have been based on linearizabilty which makes it necessary to modify the algorithm’s high-level requirements. Our approach is the first, to our knowledge, for proving correctness without the need for such a modification
Working Memory Cells' Behavior May Be Explained by Cross-Regional Networks with Synaptic Facilitation
Neurons in the cortex exhibit a number of patterns that correlate with working memory. Specifically, averaged across trials of working memory tasks, neurons exhibit different firing rate patterns during the delay of those tasks. These patterns include: 1) persistent fixed-frequency elevated rates above baseline, 2) elevated rates that decay throughout the tasks memory period, 3) rates that accelerate throughout the delay, and 4) patterns of inhibited firing (below baseline) analogous to each of the preceding excitatory patterns. Persistent elevated rate patterns are believed to be the neural correlate of working memory retention and preparation for execution of behavioral/motor responses as required in working memory tasks. Models have proposed that such activity corresponds to stable attractors in cortical neural networks with fixed synaptic weights. However, the variability in patterned behavior and the firing statistics of real neurons across the entire range of those behaviors across and within trials of working memory tasks are typical not reproduced. Here we examine the effect of dynamic synapses and network architectures with multiple cortical areas on the states and dynamics of working memory networks. The analysis indicates that the multiple pattern types exhibited by cells in working memory networks are inherent in networks with dynamic synapses, and that the variability and firing statistics in such networks with distributed architectures agree with that observed in the cortex
The effect of a large proximal haemodialysis arterio-venous fistula on weaning off cardiopulmonary bypass: case report
An increasing number of renal dialysis-dependent patients with Arterio-Venous fistulae are undergoing cardiac surgery
Binary Willshaw learning yields high synaptic capacity for long-term familiarity memory
We investigate from a computational perspective the efficiency of the
Willshaw synaptic update rule in the context of familiarity discrimination, a
binary-answer, memory-related task that has been linked through psychophysical
experiments with modified neural activity patterns in the prefrontal and
perirhinal cortex regions. Our motivation for recovering this well-known
learning prescription is two-fold: first, the switch-like nature of the induced
synaptic bonds, as there is evidence that biological synaptic transitions might
occur in a discrete stepwise fashion. Second, the possibility that in the
mammalian brain, unused, silent synapses might be pruned in the long-term.
Besides the usual pattern and network capacities, we calculate the synaptic
capacity of the model, a recently proposed measure where only the functional
subset of synapses is taken into account. We find that in terms of network
capacity, Willshaw learning is strongly affected by the pattern coding rates,
which have to be kept fixed and very low at any time to achieve a non-zero
capacity in the large network limit. The information carried per functional
synapse, however, diverges and is comparable to that of the pattern association
case, even for more realistic moderately low activity levels that are a
function of network size.Comment: 20 pages, 4 figure
Mean-field cooperativity in chemical kinetics
We consider cooperative reactions and we study the effects of the interaction
strength among the system components on the reaction rate, hence realizing a
connection between microscopic and macroscopic observables. Our approach is
based on statistical mechanics models and it is developed analytically via
mean-field techniques. First of all, we show that, when the coupling strength
is set positive, the model is able to consistently recover all the various
cooperative measures previously introduced, hence obtaining a single unifying
framework. Furthermore, we introduce a criterion to discriminate between weak
and strong cooperativity, based on a measure of "susceptibility". We also
properly extend the model in order to account for multiple attachments
phenomena: this is realized by incorporating within the model -body
interactions, whose non-trivial cooperative capability is investigated too.Comment: 25 pages, 4 figure
d=3 Bosonic Vector Models Coupled to Chern-Simons Gauge Theories
We study three dimensional O(N)_k and U(N)_k Chern-Simons theories coupled to
a scalar field in the fundamental representation, in the large N limit. For
infinite k this is just the singlet sector of the O(N) (U(N)) vector model,
which is conjectured to be dual to Vasiliev's higher spin gravity theory on
AdS_4. For large k and N we obtain a parity-breaking deformation of this
theory, controlled by the 't Hooft coupling lambda = 4 \pi N / k. For infinite
N we argue (and show explicitly at two-loop order) that the theories with
finite lambda are conformally invariant, and also have an exactly marginal
(\phi^2)^3 deformation.
For large but finite N and small 't Hooft coupling lambda, we show that there
is still a line of fixed points parameterized by the 't Hooft coupling lambda.
We show that, at infinite N, the interacting non-parity-invariant theory with
finite lambda has the same spectrum of primary operators as the free theory,
consisting of an infinite tower of conserved higher-spin currents and a scalar
operator with scaling dimension \Delta=1; however, the correlation functions of
these operators do depend on lambda. Our results suggest that there should
exist a family of higher spin gravity theories, parameterized by lambda, and
continuously connected to Vasiliev's theory. For finite N the higher spin
currents are not conserved.Comment: 34 pages, 29 figures. v2: added reference
Balanced Input Allows Optimal Encoding in a Stochastic Binary Neural Network Model: An Analytical Study
Recent neurophysiological experiments have demonstrated a remarkable effect of attention on the underlying neural activity that suggests for the first time that information encoding is indeed actively influenced by attention. Single cell recordings show that attention reduces both the neural variability and correlations in the attended condition with respect to the non-attended one. This reduction of variability and redundancy enhances the information associated with the detection and further processing of the attended stimulus. Beyond the attentional paradigm, the local activity in a neural circuit can be modulated in a number of ways, leading to the general question of understanding how the activity of such circuits is sensitive to these relatively small modulations. Here, using an analytically tractable neural network model, we demonstrate how this enhancement of information emerges when excitatory and inhibitory synaptic currents are balanced. In particular, we show that the network encoding sensitivity -as measured by the Fisher information- is maximized at the exact balance. Furthermore, we find a similar result for a more realistic spiking neural network model. As the regime of balanced inputs has been experimentally observed, these results suggest that this regime is functionally important from an information encoding standpoint
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