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.

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.

Social insect colony as a biological regulatory system: modelling information flow in dominance networks

Social insects provide an excellent platform to investigate flow of information in regulatory systems since their successful social organization is essentially achieved by effective information transfer through complex connectivity patterns among the colony members. Network representation of such behavioural interactions offers a powerful tool for structural as well as dynamical analysis of the underlying regulatory systems. In this paper, we focus on the dominance interaction networks in the tropical social wasp Ropalidia marginata-a species where behavioural observations indicate that such interactions are principally responsible for the transfer of information between individuals about their colony needs, resulting in a regulation of their own activities. Our research reveals that the dominance networks of R. marginata are structurally similar to a class of naturally evolved information processing networks, a fact confirmed also by the predominance of a specific substructure-the `feed-forward loop'-a key functional component in many other information transfer networks. The dynamical analysis through Boolean modelling confirms that the networks are sufficiently stable under small fluctuations and yet capable of more efficient information transfer compared to their randomized counterparts. Our results suggest the involvement of a common structural design principle in different biological regulatory systems and a possible similarity with respect to the effect of selection on the organization levels of such systems. The findings are also consistent with the hypothesis that dominance behaviour has been shaped by natural selection to co-opt the information transfer process in such social insect species, in addition to its primal function of mediation of reproductive competition in the colony.

On the variety of electron diffraction patterns from quasicrystals

A new exciting era in the study of rapidly solidified alloys has been ushered in by the discovery of a quasicrystalline phase in an Al-1O%Mn alloy by Shechtman et al. (l). The fact that a quasicrystal diffracts electrons and X-rays like a single crystal provides a powerful approach for exploring the atomic configuration in these alloys. Shechtman et al deduced the icosahedral point group symmetry exhibited by quasicrystals on the basis of a set of three electron diffraction patterns showing 5-fold, 3-fold and 2-fold axes of symmetry with appropriate angular relationships. The exotic crystallography of quasicrystals has been recently reviewed by Nelson and Halperin (2).

Simple new PWM patterns for thyristor three-phase AC/DC convertors

Thyristor forced commutated AC/DC convertors are useful for improving the power factor and waveform of AC-side line current. These are controlled through pulse-width modulation schemes for best performance. However, the 3-phase versions impose restrictions on the PWM strategies that can be implemented for excellent harmonic rejection. This paper presents new PWM control strategies for the 3-phase converters and compares them along with the conventional 4-pulse PWM strategy for harmonic elimination. Finally, two new PWM strategies are shown to be the best, for which oscillograms are presented from actual implementation.

Rural energy consumption patterns - A field study

This paper is a condensed version of the final report of a detailed field study of rural energy consumption patterns in six villages located west of Bangalore in the dry belt of Karnataka State in India. The study was carried out in two phases; first, a pilot study of four villages and second, the detailed study of six villages, the populations of which varied from around 350 to about 950. The pilot survey ended in late 1976, and most of the data was collected for the main project in 1977. Processing of the collected data was completed in 1980. The aim was to carry out a census survey, rather than a sample study. Hence, considerable effort was expended in production of both a suitable questionnaire, ensuring that all respondents were contacted, and devising methods which would accurately reflect the actual energy use in various energy-utilising activities. In the end, 560 households out of 578 (97%) were surveyed. The following ranking was found for the various energy sources in order of average percentage contribution to the annual total energy requirement: firewood, 81·6%; human energy, 7·7%; animal energy, 2·7%; kerosene, 2·1%; electricity, 0·6% and all other sources (rice husks, agro-wastes, coal and diesel fuel), 5·3%. In other words commercial fuels made only a small contribution to the overall energy use. It should be noted that dung cakes are not burned in this region. The average energy use pattern, sector by sector, again on a percentage basis, was as follows: domestic, 88·3%; industry, 4·7%; agriculture, 4·3%; lighting, 2·2% and transport, 0·5%. The total annual per capita energy consumption was 12·6 ± 1·2 GJ, giving an average annual household consumption of around 78·6 GJ.

Rural energy consumption patterns - A field study

This paper is a condensed version of the final report of a detailed field study of rural energy consumption patterns in six villages located west of Bangalore in the dry belt of Karnataka State in India. The study was carried out in two phases; first, a pilot study of four villages and second, the detailed study of six villages, the populations of which varied from around 350 to about 950. The pilot survey ended in late 1976, and most of the data was collected for the main project in 1977. Processing of the collected data was completed in 1980. The aim was to carry out a census survey, rather than a sample study. Hence, considerable effort was expended in production of both a suitable questionnaire, ensuring that all respondents were contacted, and devising methods which would accurately reflect the actual energy use in various energy-utilising activities. In the end, 560 households out of 578 (97%) were surveyed. The following ranking was found for the various energy sources in order of average percentage contribution to the annual total energy requirement: firewood, 81A·6%; human energy, 7A·7%; animal energy, 2A·7%; kerosene, 2A·1%; electricity, 0A·6% and all other sources (rice husks, agro-wastes, coal and diesel fuel), 5A·3%. In other words commercial fuels made only a small contribution to the overall energy use. It should be noted that dung cakes are not burned in this region. The average energy use pattern, sector by sector, again on a percentage basis, was as follows: domestic, 88A·3%; industry, 4A·7%; agriculture, 4A·3%; lighting, 2A·2% and transport, 0A·5%. The total annual per capita energy consumption was 12A·6 A± 1A·2 GJ, giving an average annual household consumption of around 78A·6 GJ.

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.

An annotated checklist of amphibian of India with some insights into the patterns of species discoveries, distribution and endemism

An annotated checklist of 284 species of amphibians of India accommodated under 50 genera and 14 families is provided. Synonyms, English names, type localities, deposition of type specimens, type specimen availability and distributional records in India and outside India are provided for all the species. Among the 284 species of amphibians from India, 132 are endemic to Western Ghats; 29 to Northeastern India; and 5 to Andaman Nicobar islands. Species discovery patterns from the various biogeographic zones in India are discussed in detail. Cumulative discovery pattern with special reference to the genera Fejervarya (17 species), Nyctibatrachus (16 species), Indirana (10 species), Micrixalus (11 species), Philautus (46 species) and Gegeneophis (10 species) are also discussed.

Joint evaluation of multiple speech patterns for speech recognition and training

We are addressing the novel problem of jointly evaluating multiple speech patterns for automatic speech recognition and training. We propose solutions based on both the non-parametric dynamic time warping (DTW) algorithm, and the parametric hidden Markov model (HMM). We show that a hybrid approach is quite effective for the application of noisy speech recognition. We extend the concept to HMM training wherein some patterns may be noisy or distorted. Utilizing the concept of ``virtual pattern'' developed for joint evaluation, we propose selective iterative training of HMMs. Evaluating these algorithms for burst/transient noisy speech and isolated word recognition, significant improvement in recognition accuracy is obtained using the new algorithms over those which do not utilize the joint evaluation strategy.

Analyzing information flow in brain networks with nonparametric Granger causality

Multielectrode neurophysiological recording and high-resolution neuroimaging generate multivariate data that are the basis for understanding the patterns of neural interactions. How to extract directions of information flow in brain networks from these data remains a key challenge. Research over the last few years has identified Granger causality as a statistically principled technique to furnish this capability. The estimation of Granger causality currently requires autoregressive modeling of neural data. Here, we propose a nonparametric approach based on widely used Fourier and wavelet transforms to estimate both pairwise and conditional measures of Granger causality, eliminating the need of explicit autoregressive data modeling. We demonstrate the effectiveness of this approach by applying it to synthetic data generated by network models with known connectivity and to local field potentials recorded from monkeys performing a sensorimotor task.

Joint decoding of multiple speech patterns for robust speech recognition

We are addressing a new problem of improving automatic speech recognition performance, given multiple utterances of patterns from the same class. We have formulated the problem of jointly decoding K multiple patterns given a single Hidden Markov Model. It is shown that such a solution is possible by aligning the K patterns using the proposed Multi Pattern Dynamic Time Warping algorithm followed by the Constrained Multi Pattern Viterbi Algorithm The new formulation is tested in the context of speaker independent isolated word recognition for both clean and noisy patterns. When 10 percent of speech is affected by a burst noise at -5 dB Signal to Noise Ratio (local), it is shown that joint decoding using only two noisy patterns reduces the noisy speech recognition error rate to about 51 percent, when compared to the single pattern decoding using the Viterbi Algorithm. In contrast a simple maximization of individual pattern likelihoods, provides only about 7 percent reduction in error rate.