Analyzing multiple spike trains with nonparametric granger causality

Simultaneous recordings of spike trains from multiple single neurons are becoming commonplace. Understanding the interaction patterns among these spike trains remains a key research area. A question of interest is the evaluation of information flow between neurons through the analysis of whether one spike train exerts causal influence on another. For continuous-valued time series data, Granger causality has proven an effective method for this purpose. However, the basis for Granger causality estimation is autoregressive data modeling, which is not directly applicable to spike trains. Various filtering options distort the properties of spike trains as point processes. Here we propose a new nonparametric approach to estimate Granger causality directly from the Fourier transforms of spike train data. We validate the method on synthetic spike trains generated by model networks of neurons with known connectivity patterns and then apply it to neurons limultaneously recorded from the thalamus and the primary somatosensory cortex of a squirrel monkey undergoing tactile stimulation.

Statistical significance of sequential firing patterns in multi-neuronal spike trains

Sequential firings with fixed time delays are frequently observed in simultaneous recordings from multiple neurons. Such temporal patterns are potentially indicative of underlying microcircuits and it is important to know when a repeatedly occurring pattern is statistically significant. These sequences are typically identified through correlation counts. In this paper we present a method for assessing the significance of such correlations. We specify the null hypothesis in terms of a bound on the conditional probabilities that characterize the influence of one neuron on another. This method of testing significance is more general than the currently available methods since under our null hypothesis we do not assume that the spiking processes of different neurons are independent. The structure of our null hypothesis also allows us to rank order the detected patterns. We demonstrate our method on simulated spike trains.

Inferring neuronal network connectivity from spike data: A temporal data mining approach

Understanding the functioning of a neural system in terms of its underlying circuitry is an important problem in neuroscience. Recent d evelopments in electrophysiology and imaging allow one to simultaneously record activities of hundreds of neurons. Inferring the underlying neuronal connectivity patterns from such multi-neuronal spike train data streams is a challenging statistical and computational problem. This task involves finding significant temporal patterns from vast amounts of symbolic time series data. In this paper we show that the frequent episode mining methods from the field of temporal data mining can be very useful in this context. In the frequent episode discovery framework, the data is viewed as a sequence of events, each of which is characterized by an event type and its time of occurrence and episodes are certain types of temporal patterns in such data. Here we show that, using the set of discovered frequent episodes from multi-neuronal data, one can infer different types of connectivity patterns in the neural system that generated it. For this purpose, we introduce the notion of mining for frequent episodes under certain temporal constraints; the structure of these temporal constraints is motivated by the application. We present algorithms for discovering serial and parallel episodes under these temporal constraints. Through extensive simulation studies we demonstrate that these methods are useful for unearthing patterns of neuronal network connectivity.

Optical, radiative, and source characteristics of aerosols at Minicoy, a remote island in the southern Arabian Sea

Extensive measurements of aerosol radiative and microphysical properties were made at an island location, Minicoy (8.3 degrees N, 73.04 degrees E) in the southern Arabian Sea. A large variability in aerosol characteristics associated with changes in air mass and precipitation characteristics was observed. Six distinct transport pathways were identified on the basis of cluster analysis. The Indo-Gangetic Plain, along with the northern Arabian Sea and west Asia (NWA), was identified to be the region having the highest potential for aerosol mass loading at the island. This estimate is based on the concentration weighted trajectory as well as cluster analysis. Dust transport from the NWA region was found to make a substantial contribution to the supermicron mass fraction. The black carbon mass mixing ratios observed were the lowest compared to previous measurements over this region. Consequently, the atmospheric radiative forcing efficiency was low and was in the range 10-28 W m(-2).

Formation of a novel surface structure encoded by Salmonella Pathogenicity Island 2.

The type III secretion system (T3SS) encoded by Salmonella Pathogenicity Island 2 (SPI2) is essential for virulence and intracellular proliferation of Salmonella enterica. We have previously identified SPI2-encoded proteins that are secreted and function as a translocon for the injection of effector proteins. Here, we describe the formation of a novel SPI2-dependent appendage structure in vitro as well as on the surface of bacteria that reside inside a vacuole of infected host cells. In contrast to the T3SS of other pathogens, the translocon encoded by SPI2 is only present singly or in few copies at one pole of the bacterial cell. Under in vitro conditions, appendages are composed of a filamentous needle-like structure with a diameter of 10 nm that was sheathed with secreted protein. The formation of the appendage in vitro is dependent on acidic media conditions. We analyzed SPI2-encoded appendages in infected cells and observed that acidic vacuolar pH was not required for induction of SPI2 gene expression, but was essential for the assembly of these structures and their function as translocon for delivery of effector proteins.

Functional transfer of Salmonella pathogenicity island 2 to Salmonella bongori and Escherichia coli.

The type III secretion system (T3SS) encoded by the Salmonella pathogenicity island 2 (SPI2) has a central role in systemic infections by Salmonella enterica and for the intracellular phenotype. Intracellular S. enterica uses the SPI2-encoded T3SS to translocate a set of effector proteins into the host cell, which modify host cell functions, enabling intracellular survival and replication of the bacteria. We sought to determine whether specific functions of the SPI2-encoded T3SS can be transferred to heterologous hosts Salmonella bongori and Escherichia coli Mutaflor, species that lack the SPI2 locus and loci encoding effector proteins. The SPI2 virulence locus was cloned and functionally expressed in S. bongori and E. coli. Here, we demonstrate that S. bongori harboring the SPI2 locus is capable of secretion of SPI2 substrate proteins under culture conditions, as well as of translocation of effector proteins under intracellular conditions. An SPI2-mediated cellular phenotype was induced by S. bongori harboring the SPI2 if the sifA locus was cotransferred. An interference with the host cell microtubule cytoskeleton, a novel SPI2-dependent phenotype, was observed in epithelial cells infected with S. bongori harboring SPI2 without additional effector genes. S. bongori harboring SPI2 showed increased intracellular persistence in a cell culture model, but SPI2 transfer was not sufficient to confer to S. bongori systemic pathogenicity in a murine model. Transfer of SPI2 to heterologous hosts offers a new tool for the study of SPI2 functions and the phenotypes of individual effectors.

Salmonella pathogenicity island 2 mediates protection of intracellular Salmonella from reactive nitrogen intermediates.

Salmonella typhimurium causes an invasive disease in mice that has similarities to human typhoid. A type III protein secretion system encoded by Salmonella pathogenicity island 2 (SPI2) is essential for virulence in mice, as well as survival and multiplication within macrophages. Reactive nitrogen intermediates (RNI) synthesized by inducible nitric oxide synthase (iNOS) are involved in the control of intracellular pathogens, including S. typhimurium. We studied the effect of Salmonella infection on iNOS activity in macrophages. Immunofluorescence microscopy demonstrated efficient colocalization of iNOS with bacteria deficient in SPI2 but not wild-type Salmonella, and suggests that the SPI2 system interferes with the localization of iNOS and Salmonella. Furthermore, localization of nitrotyrosine residues in the proximity was observed for SPI2 mutant strains but not wild-type Salmonella, indicating that peroxynitrite, a potent antimicrobial compound, is excluded from Salmonella-containing vacuoles by action of SPI2. Altered colocalization of iNOS with intracellular Salmonella required the function of the SPI2-encoded type III secretion system, but not of an individual "Salmonella translocated effector." Inhibition of iNOS increased intracellular proliferation of SPI2 mutant bacteria and, to a lesser extent, of wild-type Salmonella. The defect in systemic infection of a SPI2 mutant strain was partially restored in iNOS(-/-) mice. In addition to various strategies to detoxify RNI or repair damage due to RNI, avoidance of colocalization with RNI is important in adaptation of a pathogen to an intracellular life style.

Conditional Probability-Based Significance Tests for Sequential Patterns in Multineuronal Spike Trains

We consider the problem of detecting statistically significant sequential patterns in multineuronal spike trains. These patterns are characterized by ordered sequences of spikes from different neurons with specific delays between spikes. We have previously proposed a data-mining scheme to efficiently discover such patterns, which occur often enough in the data. Here we propose a method to determine the statistical significance of such repeating patterns. The novelty of our approach is that we use a compound null hypothesis that not only includes models of independent neurons but also models where neurons have weak dependencies. The strength of interaction among the neurons is represented in terms of certain pair-wise conditional probabilities. We specify our null hypothesis by putting an upper bound on all such conditional probabilities. We construct a probabilistic model that captures the counting process and use this to derive a test of significance for rejecting such a compound null hypothesis. The structure of our null hypothesis also allows us to rank-order different significant patterns. We illustrate the effectiveness of our approach using spike trains generated with a simulator.

Shrewd alliances: mixed foraging associations between treeshrews,greater racket-tailed drongos and sparrowhawks on Great Nicobar Island, India

Mixed-species foraging associations may form to enhance feeding success or to avoid predators. We report the costs and consequences of an unusual foraging association between an endemic foliage gleaning tupaid (Nicobar treeshrew Tupaia nicobarica) and two species of birds; one an insectivorous commensal (greater racket-tailed drongo Dicrurus paradiseus) and the other a diurnal raptor and potential predator (Accipiter sp.). In an alliance driven, and perhaps engineered, by drongos, these species formed cohesive groups with predictable relationships. Treeshrew breeding pairs were found more frequently than solitary individuals with sparrowhawks and were more likely to tolerate sparrowhawks in the presence of drongos. Treeshrews maintained greater distances from sparrowhawks than drongos, and permitted the raptors to come closer when drongos were present. Treeshrew foraging rates declined in the presence of drongos; however, the latter may provide them predator avoidance benefits. The choice of the raptor to join the association is intriguing; particular environmental resource states may drive the evolution of such behavioural strategies. Although foraging benefits seem to be the primary driver of this association, predator avoidance also influences interactions, suggesting that strategies driving the formation of flocks may be complex and context dependent with varying benefits for different actors.

A Wavelet based Teager Energy Operator for Spike Detection in Microelectrode Array Recordings

Spike detection in neural recordings is the initial step in the creation of brain machine interfaces. The Teager energy operator (TEO) treats a spike as an increase in the `local' energy and detects this increase. The performance of TEO in detecting action potential spikes suffers due to its sensitivity to the frequency of spikes in the presence of noise which is present in microelectrode array (MEA) recordings. The multiresolution TEO (mTEO) method overcomes this shortcoming of the TEO by tuning the parameter k to an optimal value m so as to match to frequency of the spike. In this paper, we present an algorithm for the mTEO using the multiresolution structure of wavelets along with inbuilt lowpass filtering of the subband signals. The algorithm is efficient and can be implemented for real-time processing of neural signals for spike detection. The performance of the algorithm is tested on a simulated neural signal with 10 spike templates obtained from [14]. The background noise is modeled as a colored Gaussian random process. Using the noise standard deviation and autocorrelation functions obtained from recorded data, background noise was simulated by an autoregressive (AR(5)) filter. The simulations show a spike detection accuracy of 90%and above with less than 5% false positives at an SNR of 2.35 dB as compared to 80% accuracy and 10% false positives reported [6] on simulated neural signals.

Impact of energy quantisation in single electron transistor island on hybrid complementary metal oxide semiconductor-single electron transistor integrated circuits

For the first time, the impact of energy quantisation in single electron transistor (SET) island on the performance of hybrid complementary metal oxide semiconductor (CMOS)-SET transistor circuits has been studied. It has been shown through simple analytical models that energy quantisation primarily increases the Coulomb Blockade area and Coulomb Blockade oscillation periodicity of the SET device and thus influences the performance of hybrid CMOS-SET circuits. A novel computer aided design (CAD) framework has been developed for hybrid CMOS-SET co-simulation, which uses Monte Carlo (MC) simulator for SET devices along with conventional SPICE for metal oxide semiconductor devices. Using this co-simulation framework, the effects of energy quantisation have been studied for some hybrid circuits, namely, SETMOS, multiband voltage filter and multiple valued logic circuits. Although energy quantisation immensely deteriorates the performance of the hybrid circuits, it has been shown that the performance degradation because of energy quantisation can be compensated by properly tuning the bias current of the current-biased SET devices within the hybrid CMOS-SET circuits. Although this study is primarily done by exhaustive MC simulation, effort has also been put to develop first-order compact model for SET that includes energy quantisation effects. Finally, it has been demonstrated that one can predict the SET behaviour under energy quantisation with reasonable accuracy by slightly modifying the existing SET compact models that are valid for metallic devices having continuous energy states.

Some new results regarding spikes and a heuristic for spike construction

his paper addresses the problem of minimizing the number of columns with superdiagonal nonzeroes (viz., spiked columns) in a square, nonsingular linear system of equations which is to be solved by Gaussian elimination. The exact focus is on a class of min-spike heuristics in which the rows and columns of the coefficient matrix are first permuted to block lower-triangular form. Subsequently, the number of spiked columns in each irreducible block and their heights above the diagonal are minimized heuristically. We show that ifevery column in an irreducible block has exactly two nonzeroes, i.e., is a doubleton, then there is exactly one spiked column. Further, if there is at least one non-doubleton column, there isalways an optimal permutation of rows and columns under whichnone of the doubleton columns are spiked. An analysis of a few benchmark linear programs suggests that singleton and doubleton columns can abound in practice. Hence, it appears that the results of this paper can be practically useful. In the rest of the paper, we develop a polynomial-time min-spike heuristic based on the above results and on a graph-theoretic interpretation of doubleton columns.

Noise-spike-induced escape in a bistable system driven by colored noise: Noise with long correlation times

An escape mechanism in a bistable system driven by colored noise of large but finite correlation time (tau) is analyzed. It is shown that the fluctuating potential theory [Phys. Rev. A 38, 3749 (1988)] becomes invalid in a region around the inflection points of the bistable potential, resulting in the underestimation of the mean first passage time at finite tau by this theory. It is shown that transitions at large but finite tau are caused by noise spikes, with edges rising and falling exponentially in a time of O(tau). Simulation of the dynamics of the bistable system driven by noise spikes of the above-mentioned nature clearly reveal the physical mechanism behind the transition.