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

Wiring cost in the organization of a biological network

To find out the role of the wiring cost in the organization of the neural network of the nematode \textit{Caenorhapditis elegans} (\textit{C. elegans}), we build the neuronal map of \textit{C. elegans} based on geometrical positions of neurons and define the cost as inter-neuronal Euclidean distance \textit{d}. We show that the wiring probability decays exponentially as a function of \textit{d}. Using the edge exchanging method and the component placement optimization scheme, we show that positions of neurons are not randomly distributed but organized to reduce the total wiring cost. Furthermore, we numerically study the trade-off between the wiring cost and the performance of the Hopfield model on the neural network

Using the NDVI vegetation index to assess the state of forest plantations on disturbed land

Проведено исследование данных спутниковых снимков высокого пространственного разрешения для оценки состояния лесных насаждений на нарушенных землях Свердловской области. Установлено, что применение вегетационного индекса NDVI позволяет успешно идентифицировать древесную растительность, произрастающую на отвалах вскрышных пород. Набор снимков в течение всего анализируемого года позволяет вычислить параметры активности вегетации древесной растительности на нарушенных землях. Объектом исследований являлась древесная растительность естественного происхождения, произрастающая на отвалах вскрышных пород ОАО «Уральский асбестовый горно-обогатительный комбинат». Отвалы формировались в период с 1991 по 1999 гг. На отвале «Восточный» заложены ПП № 1 (25,2 га) на верхней площадке и ПП № 2 (3,9 га) на склоне отвала. На отвале «Северо-Пролетарский» заложены ПП № 3 (4,9 га) на верхней площадке и ПП № 4 (7,8 га) на склоне отвала. Отсутствие травянистой растительности на изучаемых отвалах позволяет точно идентифицировать древесную растительность с помощью вегетационного индекса NDVI. В результате исследований установлено, что степень зарастания древесной растительностью составила от 61,6 до 69,4 % в зависимости от местоположения участка. Среднегодовая интенсивность вегетации лесных насаждений естественного происхождения на отвале «Восточный» характеризуется как средняя на всех высотных уровнях (ПП № 1 NDVI = 0,43; ПП № 2 NDVI = 0,33) а на отвале «Северо-Пролетарский» вегетация оценивается как высокая на склоне (ПП № 3 NDVI = 0,63) и хорошая на верхней площадке (ПП № 4 NDVI = 0,51). С помощью геоинформационных систем составлены карты и отражены зоны вегетации. Доля площади с низкой степенью вегетации (NDVI 0,2–0,3) наибольшая на склоне отвала (ПП № 2 – 38,4 %, ПП № 4 – 37,1 %). Данные о зонах с низкой степенью вегетации позволяют выявить локальные участки, лишенные растительности, для назначения мероприятий по рекультивации и планирования создания насаждений искусственным способом.The data of satellite images of high spatial resolution was studied. The assessment of the state of forest plantations on disturbed lands in the Sverdlovsk region was studied. It was found that the use of the NDVI vegetation index allows us to successfully identify woody vegetation growing on the dumps of mountain quarries. A set of satellite images allows you to calculate the parameters of vegetation activity of woody vegetation on disturbed lands. The object of research was wood vegetation of natural origin growing on the dumps of the Ural asbestos mining and processing plant. The dumps were formed between 1991 and 1999. On the «Vostochnyj» dump, there are laid out inventory plot № 1 (25,2 ha) on the upper platform and inventory plot № 2 (3,9 ha) on the slope of the dump. On the «Severo-Proletarskyj» dump, there are laid out inventory plot № 3 (4,9 ha) on the upper platform and inventory plot № 4 (7,8 ha) on the slope of the dump. The absence of grassy vegetation on the studied dumps allows for accurate identifi cation of woody vegetation using the NDVI vegetation index. As a result of research, it was found that the degree of overgrowth of woody vegetation ranged from 61,6 to 69,4 %, depending on the location of the site. The average annual vegetation intensity of forest stands of natural origin on the overburden dumps on the «Vostochnyj» dump is characterized as average at all high-altitude levels (inventory plot № 1 NDVI = 0,43; inventory plot № 2 NDVI = 0,33) and on the «Severo-Proletarskyj» dump vegetation is estimated as high on the slope (inventory plot № 3 NDVI = 0,63) and good on the upper platform (inventory plot № 4 NDVI = 0,51). With the help of geographic information systems maps have been drawn and refl ected areas of vegetation. The share of the area with a low degree of vegetation (NDVI 0,2–0,3) is highest on the slope of the dump (inventory plot № 2–38,4 %, inventory plot № 4–37,1 %). Data on areas with a low degree of vegetation allows you to identify local areas that are devoid of vegetation for the purpose of reclamation activities and planning the creation of artifi cial plantings

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

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

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

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

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

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