Ido Golding and colleagues examine transcriptional time series in Escherichia coli, characterizing mRNA copy number statistics for 20 promoters at single transcript resolution. They find that the degree of burstiness depends primarily on the level of gene expression. Gene activity is described by the time series of discrete, stochastic mRNA production events. This transcriptional time series shows intermittent, bursty behavior. One consequence of this temporal intricacy is that gene expression can be tuned by varying different features of the time series. Here we quantify copy-number statistics of mRNA from 20 Escherichia coli promoters using single-molecule fluorescence in situ hybridization in order to characterize the general properties of these transcriptional time series. We find that the degree of burstiness is correlated with gene expression level but is largely independent of other parameters of gene regulation. The observed behavior can be explained by the underlying variation in the duration of bursting events. Using Shannon's mutual information function, we estimate the mutual information transmitted between an outside stimulus, such as the extracellular concentration of inducer molecules, and intracellular levels of mRNA. This suggests that the outside stimulus transmits information reflected in the properties of transcriptional time series.

Information is carried in the brain by the joint activity patterns of large groups of neurons. Understanding the structure and function of population neural codes is challenging because of the exponential number of possible activity patterns and dependencies among neurons. We report here that for groups of ~100 retinal neurons responding to natural stimuli, pairwise-based models, which were highly accurate for small networks, are no longer sufficient. We show that because of the sparse nature of the neural code, the higher-order interactions can be easily learned using a novel model and that a very sparse low-order interaction network underlies the code of large populations of neurons. Additionally, we show that the interaction network is organized in a hierarchical and modular manner, which hints at scalability. Our results suggest that learnability may be a key feature of the neural code.

To understand a neural circuit completely requires simultaneous recording from most of the neurons in that circuit. Here we report recording and spike sorting techniques that enable us to record from all or nearly all of the ganglion cells in a patch of the retina. With a dense multi-electrode array, each ganglion cell produces a unique pattern of activity on many electrodes when it fires an action potential. Signals from all of the electrodes are combined with an iterative spike sorting algorithm to resolve ambiguities arising from overlapping spike waveforms. We verify that we are recording from a large fraction of ganglion cells over the array by labeling the ganglion cells with a retrogradely transported dye and by comparing the number of labeled and recorded cells. Using these methods, we show that about 60 receptive fields of ganglion cells cover each point in visual space in the salamander, consistent with anatomical findings.