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An Information Theoretic approach is used for studying the effect of noise on various spiking neural systems. Detailed statistical analyses of neural behavior under the influence of stochasticity are carried out and their results related to other work and also biological neural networks. The neurocomputational capabilities of the neural systems under study are put on an absolute scale. A proof of-concept algorithm is designed, based on information theory and the coding fraction, which optimises noise through maximising information throughput. The algorithm is applied with success to a single neuron and then generalised to an entire neural population with various structural characteristics (feedforward, lateral, recurrent connections). It is shown that there are certain positive and persistent phenomena due to noise in spiking neural networks and that these phenomena can be observed even under simplified conditions and therefore exploited. The transition is made from detailed and computationally expensive tools to efficient approximations. These phenomena are shown to be persistent and exploitable under a variety of circumstances.

An Information Theoretic approach is used for studying the effect of noise on various spiking neural systems. Detailed statistical analyses of neural behavior under the influence of stochasticity are carried out and their results related to other work and also biological neural networks. The neurocomputational capabilities of the neural systems under study are put on an absolute scale. A proof of-concept algorithm is designed, based on information theory and the coding fraction, which optimises noise through maximising information throughput. The algorithm is applied with success to a single neuron and then generalised to an entire neural population with various structural characteristics (feedforward, lateral, recurrent connections). It is shown that there are certain positive and persistent phenomena due to noise in spiking neural networks and that these phenomena can be observed even under simplified conditions and therefore exploited. The transition is made from detailed and computationally expensive tools to efficient approximations. These phenomena are shown to be persistent and exploitable under a variety of circumstances.

Random fluctuations in population dynamics are fundamentally important in pure and applied ecology. This book introduces demographic and environmental stochasticity, and illustrates statistical methods for estimating them from field data. The concept of long-run growth rate of a population is explained and extended to age-structured populations. Diffusion approximations show how stochastic factors affect extinction in single populations and metapopulations. Delayed density dependence in populations with discrete annual reproduction is estimated from time series of adult numbers combined with basic life history data. The spatial scale of population fluctuations and local extinction risk depend on the scales of spatial environmental autocorrelation and individual dispersal, and the strength of density dependence. Stochastic dynamics and statistical uncertainty in population parameters are incorporated in Population Viability Analysis and sustainable harvesting. Statistics of species diversity measures and species abundance distributions are described, with implications for rapid assessments of biodiversity, and methods are developed for partitioning species diversity into components. Analysis of stochastic community dynamics indicates that real communities are far from neutral.

Random fluctuations in population dynamics are fundamentally important in pure and applied ecology. This book introduces demographic and environmental stochasticity, and illustrates statistical methods for estimating them from field data. The concept of long-run growth rate of a population is explained and extended to age-structured populations. Diffusion approximations show how stochastic factors affect extinction in single populations and metapopulations. Delayed density dependence in populations with discrete annual reproduction is estimated from time series of adult numbers combined with basic life history data. The spatial scale of population fluctuations and local extinction risk depend on the scales of spatial environmental autocorrelation and individual dispersal, and the strength of density dependence. Stochastic dynamics and statistical uncertainty in population parameters are incorporated in Population Viability Analysis and sustainable harvesting. Statistics of species diversity measures and species abundance distributions are described, with implications for rapid assessments of biodiversity, and methods are developed for partitioning species diversity into components. Analysis of stochastic community dynamics indicates that real communities are far from neutral.