Neural Comput - Information-geometric measures for estimation of connection weight under correlated inputs.

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Resumo

The brain processes information in a highly parallel manner. Determination of the relationship between neural spikes and synaptic connections plays a key role in the analysis of electrophysiological data. Information geometry (IG) has been proposed as a powerful analysis tool for multiple spike data, providing useful insights into the statistical interactions within a population of neurons. Previous work has demonstrated that IG measures can be used to infer the connection weight between two neurons in a neural network. This property is useful in neuroscience because it provides a way to estimate learning-induced changes in synaptic strengths from extracellular neuronal recordings. A previous study has shown, however, that this property would hold only when inputs to neurons are not correlated. Since neurons in the brain often receive common inputs, this would hinder the application of the IG method to real data. We investigated the two-neuron-IG measures in higher-order log-linear models to overcome this limitation. First, we mathematically showed that the estimation of uniformly connected synaptic weight can be improved by taking into account higher-order log-linear models. Second, we numerically showed that the estimation can be improved for more general asymmetrically connected networks. Considering the estimated number of the synaptic connections in the brain, we showed that the two-neuron IG measure calculated by the fourth- or fifth-order log-linear model would provide an accurate estimation of connection strength within approximately a 10% error. These studies suggest that the two-neuron IG measure with higher-order log-linear expansion is a robust estimator of connection weight even under correlated inputs, providing a useful analytical tool for real multineuronal spike data.

Resumo Limpo

brain process inform high parallel manner determin relationship neural spike synapt connect play key role analysi electrophysiolog data inform geometri ig propos power analysi tool multipl spike data provid use insight statist interact within popul neuron previous work demonstr ig measur can use infer connect weight two neuron neural network properti use neurosci provid way estim learninginduc chang synapt strength extracellular neuron record previous studi shown howev properti hold input neuron correl sinc neuron brain often receiv common input hinder applic ig method real data investig twoneuronig measur higherord loglinear model overcom limit first mathemat show estim uniform connect synapt weight can improv take account higherord loglinear model second numer show estim can improv general asymmetr connect network consid estim number synapt connect brain show twoneuron ig measur calcul fourth fifthord loglinear model provid accur estim connect strength within approxim error studi suggest twoneuron ig measur higherord loglinear expans robust estim connect weight even correl input provid use analyt tool real multineuron spike data

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