Attractor networks and memory replay of phase coded spike patterns

We analyse the storage and retrieval capacity in a recurrent neural network of spiking integrate and fire neurons. In the model we distinguish between a learning mode, during which the synaptic connections change according to a Spike-Timing Dependent Plasticity (STDP) rule, and a recall mode, in which connections strengths are no more plastic. Our findings show the ability of the network to store and recall periodic phase coded patterns a small number of neurons has been stimulated. The self sustained dynamics selectively gives an oscillating spiking activity that matches one of the stored patterns, depending on the initialization of the network.Comment: arXiv admin note: text overlap with arXiv:1210.678

Associative memory of phase-coded spatiotemporal patterns in leaky Integrate and Fire networks

We study the collective dynamics of a Leaky Integrate and Fire network in which precise relative phase relationship of spikes among neurons are stored, as attractors of the dynamics, and selectively replayed at differentctime scales. Using an STDP-based learning process, we store in the connectivity several phase-coded spike patterns, and we find that, depending on the excitability of the network, different working regimes are possible, with transient or persistent replay activity induced by a brief signal. We introduce an order parameter to evaluate the similarity between stored and recalled phase-coded pattern, and measure the storage capacity. Modulation of spiking thresholds during replay changes the frequency of the collective oscillation or the number of spikes per cycle, keeping preserved the phases relationship. This allows a coding scheme in which phase, rate and frequency are dissociable. Robustness with respect to noise and heterogeneity of neurons parameters is studied, showing that, since dynamics is a retrieval process, neurons preserve stablecprecise phase relationship among units, keeping a unique frequency of oscillation, even in noisy conditions and with heterogeneity of internal parameters of the units

Storage of phase-coded patterns via STDP in fully-connected and sparse network: a study of the network capacity

We study the storage and retrieval of phase-coded patterns as stable dynamical attractors in recurrent neural networks, for both an analog and a integrate-and-fire spiking model. The synaptic strength is determined by a learning rule based on spike-time-dependent plasticity, with an asymmetric time window depending on the relative timing between pre- and post-synaptic activity. We store multiple patterns and study the network capacity. For the analog model, we find that the network capacity scales linearly with the network size, and that both capacity and the oscillation frequency of the retrieval state depend on the asymmetry of the learning time window. In addition to fully-connected networks, we study sparse networks, where each neuron is connected only to a small number z << N of other neurons. Connections can be short range, between neighboring neurons placed on a regular lattice, or long range, between randomly chosen pairs of neurons. We find that a small fraction of long range connections is able to amplify the capacity of the network. This imply that a small-world-network topology is optimal, as a compromise between the cost of long range connections and the capacity increase. Also in the spiking integrate and fire model the crucial result of storing and retrieval of multiple phase-coded patterns is observed. The capacity of the fully-connected spiking network is investigated, together with the relation between oscillation frequency of retrieval state and window asymmetry

Rattler-induced aging dynamics in jammed granular systems

Granular materials jam when developing a network of contact forces able to resist the applied stresses. Through numerical simulations of the dynamics of the jamming process, we show that the jamming transition does not occur when the kinetic energy vanishes. Rather, as the system jams, the kinetic energy becomes dominated by rattlers particles, that scatter withing their cages. The relaxation of the kinetic energy in the jammed configuration exhibits a double power-law decay, which we interpret in terms of the interplay between backbone and rattlers particles.Comment: The paper has been accepted by the journal "Soft Matter

Non-monotonic dependence of the friction coefficient on heterogeneous stiffness

The complexity of the frictional dynamics at the microscopic scale makes difficult to identify all of its controlling parameters. Indeed, experiments on sheared elastic bodies have shown that the static friction coefficient depends on loading conditions, the real area of contact along the interfaces and the confining pressure. Here we show, by means of numerical simulations of a 2D Burridge-Knopoff model with a simple local friction law, that the macroscopic friction coefficient depends non-monotonically on the bulk elasticity of the system. This occurs because elastic constants control the geometrical features of the rupture fronts during the stick-slip dynamics, leading to four different ordering regimes characterized by different orientations of the rupture fronts with respect to the external shear direction. We rationalize these results by means of an energetic balance argument

Uncertainty Analysis for the Classification of Multispectral Satellite Images Using SVMs and SOMs

Abstract: Classification of multispectral remotely sensed data with textural features is investigated with a special focus on uncertainty analysis in the produced land-cover maps. Much effort has already been directed into the research of satisfactory accuracy-assessment techniques in image classification, but a common approach is not yet universally adopted. We look at the relationship between hard accuracy and the uncertainty on the produced answers, introducing two measures based on maximum probability and a quadratic entropy. Their impact differs depending on the type of classifier. In this paper, we deal with two different classification strategies, based on support vector machines (SVMs) and Kohonen's self-organizingmaps (SOMs), both suitably modified to give soft answers. Once the multiclass probability answer vector is available for each pixel in the image, we studied the behavior of the overall classification accuracy as a function of the uncertainty associated with each vector, given a hard-labeled test set. The experimental results show that the SVM with one-versus-one architecture and linear kernel clearly outperforms the other supervised approaches in terms of overall accuracy. On the other hand, our analysis reveals that the proposed SOM-based classifier, despite its unsupervised learning procedure, is able to provide soft answers which are the best candidates for a fusion with supervised results

447. AP20187-Inducible Insulin-Like Effects in Diabetic Muscle and Liver Transduced with AAV

Diabetes Mellitus, characterized by insulin deficiency (type I) or resistance (type II), derives from insulin action impairments in hormone target tissues: muscle, liver and adipocytes. Insulin regulates metabolism and glucose homeostasis through binding to a specific membrane receptor (IR) with tyrosine kinase activity. Induction of the insulin receptor signaling in hormone target cells may represent a tool to rescue glucose homeostasis in both insulin and insulin receptor deficiencies. Recently we have described that homodimerization of the chimeric insulin receptor LFv2IRE induced by the small dimerizer drug AP20187 results in insulin like actions in hepatocytes trasduced with adeno-associated viral vectors (AAV)

Clozapine Impairs Insulin Action by Up-Regulating Akt Phosphorylation and Ped/Pea-15 Protein Abundance

Clinical and experimental evidence indicates that atypical antipsychotics impair glucose metabolism. We investigated whether clozapine may directly affect insulin action by analyzing insulin signaling in vitro and in vivo. Clozapine reduced insulin-stimulated glucose uptake in PC12 and in L6 cells, representative models of neuron and skeletal muscle, respectively. Consistently, clozapine reduced insulin effect on insulin receptor (IR) by 40% and on IR substrate-1 (IRS1) tyrosine phosphorylation by 60%. Insulin-stimulated Akt phosphorylation was also reduced by about 40%. Moreover, insulin-dependent phosphorylation of protein kinase C-ζ (PKC-ζ) was completely blunted in clozapine-treated cells. Interestingly, clozapine treatment was accompanied by an insulin-independent increase of Akt phosphorylation, with no change of IR, IRS1, and PKC-ζ basal phosphorylation. The cellular abundance of Ped/Pea-15, an Akt substrate and inducer of insulin resistance, was also increased following clozapine exposure, both in the absence and in the presence of cyclohexymide, a protein synthesis inhibitor. Similar as in cellular models, in the caudate–putamen and in the tibialis muscle of clozapine-treated C57/BL/KsJ mice, Akt phosphorylation and Ped/Pea-15 protein levels were increased and PKC-ζ phosphorylation was decreased. Thus, in these experimental models, clozapine deranged Akt function and up-regulated Ped/Pea-15, thereby inhibiting insulin stimulation of PKC-ζ and of glucose uptake. J. Cell. Physiol. 227: 1485–1492, 2012. © 2011 Wiley Periodicals, Inc

The climate in the European Union and the enlarged European Region is a determinant of the COVID-19 case fatality ratio

Climate could influence the COVID-19 pandemic, but while no evidence has been advanced on the influence of colder climates, some studies have provided data to support a possible heat-related protective factor. The objective is to verify whether areas with a Cold Temperate Climate (TC) have a higher Case Fatality Ratio (CFR) for COVID-19 than areas with a Cold Climate (CC) or with a Mediterranean Climate (MC) in the European Union and the Enlarged European Region. Countries or regions were subdivided into 3 groups according to the Köppen climate classification system: TC (Cfa, Cfb and Cfc in the Köppen system); MC (Csa, Csb); CC (D and E in the Köppen system). The total number of cases and the total number of deaths were detected on 13 August 2020 on the COVID-19 Map-Johns Hopkins Coronavirus Resource Center-the CFR was thus calculated by area. Living in TC areas is strongly associated with risk of a high Case Fatality Ratio for COVID-19, OR for MC =0.42, IC 95% 0.41-0.43; OR for CC=0.33, IC 95% 0.33-0.35. The results are confirmed in the EU, OR per MC=0.85, CI 95% 0.84-0.87; OR per CC=0.63, IC 95% 0.61-0.65.The study found that the IC in a humid temperate climate is associated with higher CFR with respect to the coldest and warmest temperate climates in Europe. This does not appear to be the only determinant of the pandemic