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Open Access Publications from the University of California

Scholarly Works (2 results)

  • Thesis
  • Peer Reviewed
UC San Diego Electronic Theses and Dissertations (2018)

Information maximization is a strong candidate for the design principles of early sensory systems. Yet, previous applications of information maximization are mostly restricted to linear or small neural systems due to difficulty in computing mutual information. To solve this problem, we developed a method that could efficiently compute mutual information provided about high dimensional inputs by responses of a large neural population.

Using our method, we first quantify information transmission by multiple overlapping retinal ganglion cell mosaics. The results reveal a transition where one high-density mosaic becomes less informative than two or more overlapping lower-density mosaics. The results explain differences in the fractions of multiple cell types and predict the existence of new retinal ganglion cell subtypes.

We then apply our method to neurons receiving time-varying stimuli and producing spike trains. Surprisingly, we found that the optimal nonlinearity for neurons receiving temporal corre- lated signal has finite slope, quantitatively explaining the ubiquitous sigmoid shape nonlinearity observed in neurons. The optimal nonlinearities we predicted agree well with experimental data without any parameters in our model.

We further investigate the optimal network connectivity for information transmission. Using olfactory system as a model, we analytically compute the optimal connectivity rate that maximize information transmission. The optimal connectivity rate has suprisingly simple expression and is inverse proportional to the input pattern sparsity. Our model also provides a feedforward solution to reconstruct odor signal. Our architecture is shown to be efficient, robust, and account for a number of experimental observations.

    Cover page: Information Maximization in Early Sensory Systems
    • Article
    • Peer Reviewed
    UC Davis Previously Published Works (2015)
    Motivation: An important property of a valid method for testing for differential expression is that the false positive rate should at least roughly correspond to the p-value cutoff, so that if 10,000 genes are tested at a p-value cutoff of 10−4, and if all the null hypotheses are true, then there should be only about 1 gene declared to be significantly differentially expressed. We tested this by resampling from existing RNA-Seq data sets and also by matched negative binomial simulations.

    Results:

    Methods we examined, which rely strongly on a negative binomial model, such as edgeR, DESeq, and DESeq2, show large numbers of false positives in both the resampled real-data case and in the simulated negative binomial case. This also occurs with a negative binomial generalized linear model function in R. Methods that use only the variance function, such as limma-voom, do not show excessive false positives, as is also the case with a variance stabilizing transformation followed by linear model analysis with limma. The excess false positives are likely caused by apparently small biases in estimation of negative binomial dispersion and, perhaps surprisingly, occur mostly when the mean and/or the dis-persion is high, rather than for low-count genes.
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