Response Functions Improving Performance in Analog Attractor Neural Networks

In the context of attractor neural networks, we study how the equilibrium analog neural activities, reached by the network dynamics during memory retrieval, may improve storage performance by reducing the interferences between the recalled pattern and the other stored ones. We determine a simple dynamics that stabilizes network states which are highly correlated with the retrieved pattern, for a number of stored memories that does not exceed $\alpha_{\star} N$, where $\alpha_{\star}\in[0,0.41]$ depends on the global activity level in the network and $N$ is the number of neurons.Comment: 13 pages (with figures), LaTex (RevTex), to appear on Phys.Rev.E (RC

Peri-prostatic fat volume measurement as a predictive tool for castration resistance in advanced prostate cancer

Background: Obesity and aggressive prostate cancer (PC) may be linked, but how local peri-prostatic fat relates to tumour response following androgen deprivation therapy (ADT) is unknown. Objective: To test if peri-prostatic fat volume (PPFV) predicts tumour response to ADT. Design, setting, and participants: We performed a retrospective study on consecutive patients receiving primary ADT. From staging pelvic magnetic resonance imaging scans, the PPFV was quantified with OsirixX 6.5 imaging software. Statistical (univariate and multivariate) analysis were performed using R Version 3.2.1. Results and limitations: Of 224 consecutive patients, 61 with advanced (≥T3 or N1 or M1) disease had (3-mm high resolution axial sections) pelvic magnetic resonance imaging scan before ADT. Median age = 75 yr; median PPFV = 24.8 cm3 (range, 7.4–139.4 cm3). PPFV was significantly higher in patients who developed castration resistant prostate cancer (CRPC; n = 31), with a median of 37.9 cm3 compared with 16.1 cm3 (p < 0.0001, Wilcoxon rank sum test) in patients who showed sustained response to ADT (n = 30). Multivariate analysis using Cox proportional hazards models were performed controlling for known predictors of CRPC. PPFV was shown to be independent of all included factors, and the most significant predictor of time to CRPC. Using our multivariate model consisting of all known factors prior to ADT, PPFV significantly improved the area under the curve of the multivariate models receiver operating characteristic analysis. The main study limitation is a relatively small cohort to account for multiple variables, necessitating a future large-scale prospective analysis of PPFV in advanced PC. Conclusions: PPFV quantification in patients with advanced PC predicts tumour response to ADT

Neuro-flow Dynamics and the Learning Processes

A new description of the neural activity is introduced by the neuro-flow dynamics and the extended Hebb rule. The remarkable characteristics of the neuro-flow dynamics, such as the primacy and the recency effect during awakeness or sleep, are pointed out.Comment: 8 pages ,10 Postscript figures, LaTeX file, to appear in Chaos, Solitons and Fractal

Chimera order in spin systems

Homogeneous populations of oscillators have recently been shown to exhibit stable coexistence of coherent and incoherent regions. Generalizing the concept of chimera states to the context of order-disorder transition in systems at thermal equilibrium, we show analytically that such complex ordering can appear in a system of Ising spins, possibly the simplest physical system exhibiting this phenomenon. We also show numerically the existence of chimera ordering in 3-dimensional spin systems that model layered magnetic materials, suggesting possible means of experimentally observing such states.Comment: 5 pages, 3 figure

Theory of Interaction of Memory Patterns in Layered Associative Networks

A synfire chain is a network that can generate repeated spike patterns with millisecond precision. Although synfire chains with only one activity propagation mode have been intensively analyzed with several neuron models, those with several stable propagation modes have not been thoroughly investigated. By using the leaky integrate-and-fire neuron model, we constructed a layered associative network embedded with memory patterns. We analyzed the network dynamics with the Fokker-Planck equation. First, we addressed the stability of one memory pattern as a propagating spike volley. We showed that memory patterns propagate as pulse packets. Second, we investigated the activity when we activated two different memory patterns. Simultaneous activation of two memory patterns with the same strength led the propagating pattern to a mixed state. In contrast, when the activations had different strengths, the pulse packet converged to a two-peak state. Finally, we studied the effect of the preceding pulse packet on the following pulse packet. The following pulse packet was modified from its original activated memory pattern, and it converged to a two-peak state, mixed state or non-spike state depending on the time interval

Learning by message-passing in networks of discrete synapses

We show that a message-passing process allows to store in binary "material" synapses a number of random patterns which almost saturates the information theoretic bounds. We apply the learning algorithm to networks characterized by a wide range of different connection topologies and of size comparable with that of biological systems (e.g. $n\simeq10^{5}-10^{6}$). The algorithm can be turned into an on-line --fault tolerant-- learning protocol of potential interest in modeling aspects of synaptic plasticity and in building neuromorphic devices.Comment: 4 pages, 3 figures; references updated and minor corrections; accepted in PR

Conductance of quantum wires: a numerical study of the effects of an impurity and interactions

We use the non-equilibrium Green's function formalism along with a self-consistent Hartree-Fock approximation to numerically study the effects of a single impurity and interactions between the electrons (with and without spin) on the conductance of a quantum wire. We study how the conductance varies with the wire length, the temperature, and the strength of the impurity and interactions. The dependence of the conductance on the wire length and temperature is found to be in rough agreement with the results obtained from a renormalization group analysis based on the Hartree-Fock approximation. For the spin-1/2 model with a repulsive on-site interaction or the spinless model with an attractive nearest neighbor interaction, we find that the conductance increases with increasing wire length or decreasing temperature. This can be qualitatively explained using the Born approximation in scattering theory. For a strong impurity, the conductance is significantly different for a repulsive and an attractive impurity; this is due to the existence of a bound state in the latter case. In general, the large density deviations at short distances have an appreciable effect on the conductance which is not captured by the renormalization group analysis.Comment: Revtex, 15 pages including 21 figures; all the numerical calculations have been re-done with a Fermi wavenumber of pi/10; this is the version published in Phys Rev