Enhancement of synchronization in a hybrid neural circuit by spike timing dependent plasticity

Synchronization of neural activity is fundamental for many functions of the brain. We demonstrate that spike-timing dependent plasticity (STDP) enhances synchronization (entrainment) in a hybrid circuit composed of a spike generator, a dynamic clamp emulating an excitatory plastic synapse, and a chemically isolated neuron from the Aplysia abdominal ganglion. Fixed-phase entrainment of the Aplysia neuron to the spike generator is possible for a much wider range of frequency ratios and is more precise and more robust with the plastic synapse than with a nonplastic synapse of comparable strength. Further analysis in a computational model of HodgkinHuxley-type neurons reveals the mechanism behind this significant enhancement in synchronization. The experimentally observed STDP plasticity curve appears to be designed to adjust synaptic strength to a value suitable for stable entrainment of the postsynaptic neuron. One functional role of STDP might therefore be to facilitate synchronization or entrainment of nonidentical neurons

Stability of Negative Image Equilibria in Spike-Timing Dependent Plasticity

We investigate the stability of negative image equilibria in mean synaptic weight dynamics governed by spike-timing dependent plasticity (STDP). The neural architecture of the model is based on the electrosensory lateral line lobe (ELL) of mormyrid electric fish, which forms a negative image of the reafferent signal from the fish's own electric discharge to optimize detection of external electric fields. We derive a necessary and sufficient condition for stability, for arbitrary postsynaptic potential functions and arbitrary learning rules. We then apply the general result to several examples of biological interest.Comment: 13 pages, revtex4; uses packages: graphicx, subfigure; 9 figures, 16 subfigure

Potent E. coli M‑17 Growth Inhibition by Ultrasonically Complexed Acetylsalicylic Acid−ZnO−Graphene Oxide Nanoparticles

A single-step ultrasonic method (20 kHz) is demonstrated for the complexation of acetylsalicylic acid (ASA)−ZnO− graphene oxide (GO) nanoparticles with an average size of <70 nm in aqueous solution. ASA−ZnO−GO more e ffi ciently inhibits the growth of probiotic Escherichia coli strain M-17 and exhibits enhanced antioxidant properties than free ASA and ASA−ZnO in neutralization of hydroxyl radicals in the electro-Fenton process. This improved function of ASA in the ASA −ZnO GO can be attributed to the well-de fi ned cone-shaped morphology, the surface structure containing hydroxyl and carboxylate groups of ZnO−GO nanoparticles, which facilitated the complexation with ASA

Dynamical principles in neuroscience

Dynamical modeling of neural systems and brain functions has a history of success over the last half century. This includes, for example, the explanation and prediction of some features of neural rhythmic behaviors. Many interesting dynamical models of learning and memory based on physiological experiments have been suggested over the last two decades. Dynamical models even of consciousness now exist. Usually these models and results are based on traditional approaches and paradigms of nonlinear dynamics including dynamical chaos. Neural systems are, however, an unusual subject for nonlinear dynamics for several reasons: (i) Even the simplest neural network, with only a few neurons and synaptic connections, has an enormous number of variables and control parameters. These make neural systems adaptive and flexible, and are critical to their biological function. (ii) In contrast to traditional physical systems described by well-known basic principles, first principles governing the dynamics of neural systems are unknown. (iii) Many different neural systems exhibit similar dynamics despite having different architectures and different levels of complexity. (iv) The network architecture and connection strengths are usually not known in detail and therefore the dynamical analysis must, in some sense, be probabilistic. (v) Since nervous systems are able to organize behavior based on sensory inputs, the dynamical modeling of these systems has to explain the transformation of temporal information into combinatorial or combinatorial-temporal codes, and vice versa, for memory and recognition. In this review these problems are discussed in the context of addressing the stimulating questions: What can neuroscience learn from nonlinear dynamics, and what can nonlinear dynamics learn from neuroscience?This work was supported by NSF Grant No. NSF/EIA-0130708, and Grant No. PHY 0414174; NIH Grant No. 1 R01 NS50945 and Grant No. NS40110; MEC BFI2003-07276, and Fundación BBVA

Network 'small-world-ness': a quantitative method for determining canonical network equivalence

Background: Many technological, biological, social, and information networks fall into the broad class of 'small-world' networks: they have tightly interconnected clusters of nodes, and a shortest mean path length that is similar to a matched random graph (same number of nodes and edges). This semi-quantitative definition leads to a categorical distinction ('small/not-small') rather than a quantitative, continuous grading of networks, and can lead to uncertainty about a network's small-world status. Moreover, systems described by small-world networks are often studied using an equivalent canonical network model-the Watts-Strogatz (WS) model. However, the process of establishing an equivalent WS model is imprecise and there is a pressing need to discover ways in which this equivalence may be quantified. Methodology/Principal Findings: We defined a precise measure of 'small-world-ness' S based on the trade off between high local clustering and short path length. A network is now deemed a 'small-world' if S. 1-an assertion which may be tested statistically. We then examined the behavior of S on a large data-set of real-world systems. We found that all these systems were linked by a linear relationship between their S values and the network size n. Moreover, we show a method for assigning a unique Watts-Strogatz (WS) model to any real-world network, and show analytically that the WS models associated with our sample of networks also show linearity between S and n. Linearity between S and n is not, however, inevitable, and neither is S maximal for an arbitrary network of given size. Linearity may, however, be explained by a common limiting growth process. Conclusions/Significance: We have shown how the notion of a small-world network may be quantified. Several key properties of the metric are described and the use of WS canonical models is placed on a more secure footing

Heteroclinic Ratchets in a System of Four Coupled Oscillators

We study an unusual but robust phenomenon that appears in an example system of four coupled phase oscillators. We show that the system can have a robust attractor that responds to a specific detuning between certain pairs of the oscillators by a breaking of phase locking for arbitrary positive detunings but not for negative detunings. As the dynamical mechanism behind this is a particular type of heteroclinic network, we call this a 'heteroclinic ratchet' because of its dynamical resemblance to a mechanical ratchet

Photoactive Properties of Transport Sol-Gel Layers Based on Strontium Titanate for Perovskite Solar Cells

In this work, we have investigated the photocurrent and spectral sensitivity of the silicon/SrTiO3:xNb/perovskite structures. The sol–gel method carried out the deposition of undoped SrTiO3 layers as well as niobium-doped (SrTiO3:Nb) layers at atomic concentrations of 3 and 6% Nb. The perovskite layer, CH3NH3PbI3_xClx, has been deposited by the vacuum co-evaporation technique. The layers have been characterized by scanning electron microscopy and X-ray diffraction measurements. The volt–ampere characteristics and spectral sensitivity of the fabricated samples have been measured under illumination with selective wavelengths of 405, 450, 520, 660, 780, 808, 905, 980, and 1064 nm of laser diodes. We have shown that for different configurations of applied voltage between silicon, SrTiO3:xNb, and CH3NH3PbI3_xClx, the structures are photosensitive ones with a variation of photocurrent from microamperes to milliamperes depending on Nb concentration in SrTiO3, and the highest photocurrent and spectral sensitivity values are observed when a SrTiO3:Nb layer with 3 at.% of Nb is used. A possible application of the proposed structure with a SrTiO3:Nb layer for perovskite solar cells and photodetectors is being discussed

Fabrication and simulation of silver nanostructures on different types of porous silicon for surface enhanced Raman spectroscopy

In this paper, we propose a systematic approach to controllably fabricate silver nanoparticles, dendrites and nanovoids on porous template based on silicon and two-step wet process. Geometry of metallic structures was managed by variation of dopant type of silicon, regimes of template formation and deposition of silver. General models of each structure were developed and studied for distribution and strength of electric field arising in them under 473, 633 and 785 nm laser excitation. Simulation results revealed reasons of variable activity of the fabricated structures in surface enhanced Raman spectroscopy, which allowed to define optimal conditions of analysis of target molecules

Formation of feedforward networks and frequency synchrony by spike-timing-dependent plasticity

Spike-timing-dependent plasticity (STDP) with asymmetric learning windows is commonly found in the brain and useful for a variety of spike-based computations such as input filtering and associative memory. A natural consequence of STDP is establishment of causality in the sense that a neuron learns to fire with a lag after specific presynaptic neurons have fired. The effect of STDP on synchrony is elusive because spike synchrony implies unitary spike events of different neurons rather than a causal delayed relationship between neurons. We explore how synchrony can be facilitated by STDP in oscillator networks with a pacemaker. We show that STDP with asymmetric learning windows leads to self-organization of feedforward networks starting from the pacemaker. As a result, STDP drastically facilitates frequency synchrony. Even though differences in spike times are lessened as a result of synaptic plasticity, the finite time lag remains so that perfect spike synchrony is not realized. In contrast to traditional mechanisms of large-scale synchrony based on mutual interaction of coupled neurons, the route to synchrony discovered here is enslavement of downstream neurons by upstream ones. Facilitation of such feedforward synchrony does not occur for STDP with symmetric learning windows.Comment: 9 figure

An optical screen for light guiding in the vertical direction

Display devices with light guiding in the vertical direction with respect to the screen surface could be used for information security. Also the vertical light guiding could be used for interchip optical interconnects. We have developed the optical screen based on macroporous alumina or macroporous silicon membrane which can provide the light propagation in the vertical direction while the other directions are prohibited