When do correlations increase with firing rates in recurrent networks?

A central question in neuroscience is to understand how noisy firing patterns are used to transmit information. Because neural spiking is noisy, spiking patterns are often quantified via pairwise correlations, or the probability that two cells will spike coincidentally, above and beyond their baseline firing rate. One observation frequently made in experiments, is that correlations can increase systematically with firing rate. Theoretical studies have determined that stimulus-dependent correlations that increase with firing rate can have beneficial effects on information coding; however, we still have an incomplete understanding of what circuit mechanisms do, or do not, produce this correlation-firing rate relationship. Here, we studied the relationship between pairwise correlations and firing rates in recurrently coupled excitatory-inhibitory spiking networks with conductance-based synapses. We found that with stronger excitatory coupling, a positive relationship emerged between pairwise correlations and firing rates. To explain these findings, we used linear response theory to predict the full correlation matrix and to decompose correlations in terms of graph motifs. We then used this decomposition to explain why covariation of correlations with firing rate—a relationship previously explained in feedforward networks driven by correlated input—emerges in some recurrent networks but not in others. Furthermore, when correlations covary with firing rate, this relationship is reflected in low-rank structure in the correlation matrix

Reallocation, Firm Turnover, and Efficiency: Selection on Productivity or Profitability?

There is considerable evidence that producer-level churning contributes substantially to aggregate (industry) productivity growth, as more productive businesses displace less productive ones. However, this research has been limited by the fact that producer-level prices are typically unobserved; thus within-industry price differences are embodied in productivity measures. If prices reflect idiosyncratic demand or market power shifts, high "productivity" businesses may not be particularly efficient, and the literature's findings might be better interpreted as evidence of entering businesses displacing less profitable, but not necessarily less productive, exiting businesses. In this paper, we investigate the nature of selection and productivity growth using data from industries where we observe producer-level quantities and prices separately. We show there are important differences between revenue and physical productivity. A key dissimilarity is that physical productivity is inversely correlated with plant-level prices while revenue productivity is positively correlated with prices. This implies that previous work linking (revenue-based) productivity to survival has confounded the separate and opposing effects of technical efficiency and demand on survival, understating the true impacts of both. We further show that young producers charge lower prices than incumbents, and as such the literature understates the productivity advantage of new producers and the contribution of entry to aggregate productivity growth.

THE INVESTMENT CLIMATE IN 16 INDIAN STATES

In this paper, the author attempts to identify the characteristics of the business climate in India that can help explain the different performance of individual states in terms of investment and growth. The paper develops a new Investment Climate Index aimed at summarizing the aspects of the business environment that entrepreneurs consider when deciding whether to invest. Using this index, the author explores the investment climate in several typologies of Indian states and identify the key features of a poor business environment in India. The analysis shows that infrastructure and institutions remain the main bottlenecks in the country's private sector development. More specifically, power, transportation, corruption, tax regulations, and theft are major factors explaining the poor business environment in some Indian states. Infrastructure appears to be the single most important constraint, as it is particularly binding in states that show low levels of domestic investment and GDP growth.affiliated organizations; bank financing; bottlenecks; business climate; business environment; business regulations; collateral; Cost of finance; data availability; domestic investment;

Using a modified DEA model to estimate the importance of objectives. An application to agricultural economics.

This paper shows a connection between Data Envelopment Analysis (DEA) and the methodology proposed by Sumpsi et al. (1997) to estimate the weights of objectives for decision makers in a multiple attribute approach. This connection gives rise to a modified DEA model that allows to estimate not only efficiency measures but also preference weights by radially projecting each unit onto a linear combination of the elements of the payoff matrix (which is obtained by standard multicriteria methods). For users of Multiple Attribute Decision Analysis the basic contribution of this paper is a new interpretation of the methodology by Sumpsi et al. (1997) in terms of efficiency. We also propose a modified procedure to calculate an efficient payoff matrix and a procedure to estimate weights through a radial projection rather than a distance minimization. For DEA users, we provide a modified DEA procedure to calculate preference weights and efficiency measures which does not depend on any observations in the dataset. This methodology has been applied to an agricultural case study in Spain.Multicriteria Decision Making, Goal Programming, Weights, Preferences, Data Envelopment Analysis.

Information-geometric method for multiple neuronal spike data analysis

This dissertation explores a novel statistical technique—information geometric method for theory and its application in analysis of multiple neuronal spike data. The previous studies have indicated that information-geometric method provides a powerful tool of estimating neuronal interactions from observed spiking data. However, these studies were conducted based on simplified neural network structure, which has limitations in the real brain. We systematically extended the previous studies by using intensive mathematical analysis and numerical simulations of realistic and complex neural network. The studies show that information geometric approach provide robust estimation for the sum of the connection weights between neuronal pairs in a complex recurrent network, providing a way of investigating the underlying network structures from neuronal spike data.Alberta Innovates Technology Futures (SCH001),National Science Foundation(CRCNS-1010172),Alberta Innovates Health Solution

The specificity and robustness of long-distance connections in weighted, interareal connectomes

Brain areas' functional repertoires are shaped by their incoming and outgoing structural connections. In empirically measured networks, most connections are short, reflecting spatial and energetic constraints. Nonetheless, a small number of connections span long distances, consistent with the notion that the functionality of these connections must outweigh their cost. While the precise function of these long-distance connections is not known, the leading hypothesis is that they act to reduce the topological distance between brain areas and facilitate efficient interareal communication. However, this hypothesis implies a non-specificity of long-distance connections that we contend is unlikely. Instead, we propose that long-distance connections serve to diversify brain areas' inputs and outputs, thereby promoting complex dynamics. Through analysis of five interareal network datasets, we show that long-distance connections play only minor roles in reducing average interareal topological distance. In contrast, areas' long-distance and short-range neighbors exhibit marked differences in their connectivity profiles, suggesting that long-distance connections enhance dissimilarity between regional inputs and outputs. Next, we show that -- in isolation -- areas' long-distance connectivity profiles exhibit non-random levels of similarity, suggesting that the communication pathways formed by long connections exhibit redundancies that may serve to promote robustness. Finally, we use a linearization of Wilson-Cowan dynamics to simulate the covariance structure of neural activity and show that in the absence of long-distance connections, a common measure of functional diversity decreases. Collectively, our findings suggest that long-distance connections are necessary for supporting diverse and complex brain dynamics.Comment: 18 pages, 8 figure