The flavoenzyme Ero1p produces disulfide bonds for oxidative protein folding in the endoplasmic reticulum. Disulfides generated de novo within Ero1p are transferred to protein disulfide isomerase and then to substrate proteins by dithiol-disulfide exchange reactions. Despite this key role of Ero1p, little is known about the mechanism by which this enzyme catalyzes thiol oxidation. Here, we present the X-ray crystallographic structure of Ero1p, which reveals the molecular details of the catalytic center, the role of a CXXCXXC motif, and the spatial relationship between functionally significant cysteines and the bound cofactor. Remarkably, the Ero1p active site closely resembles that of the versatile thiol oxidase module of Erv2p, a protein with no sequence homology to Ero1p. Furthermore, both Ero1p and Erv2p display essential dicysteine motifs on mobile polypeptide segments, suggesting that shuttling electrons to a rigid active site using a flexible strand is a fundamental feature of disulfide-generating flavoenzymes.

The range of natural inputs encoded by a neuron often exceeds its dynamic range. To overcome this limitation, neural populations divide their inputs among different cell classes, as with rod and cone photoreceptors, and adapt by shifting their dynamic range. We report that the dynamic behavior of retinal ganglion cells in salamanders, mice, and rabbits is divided into two opposing forms of short-term plasticity in different cell classes. One population of cells exhibited sensitization—a persistent elevated sensitivity following a strong stimulus. This novel dynamic behavior compensates for the information loss caused by the known process of adaptation occurring in a separate cell population. The two populations divide the dynamic range of inputs, with sensitizing cells encoding weak signals, and adapting cells encoding strong signals. In the two populations, the linear, threshold and adaptive properties are linked to preserve responsiveness when stimulus statistics change, with one population maintaining the ability to respond when the other fails.

Executing memory-guided behavior requires storage of information about experience and later recall of that information to inform choices. Awake hippocampal replay, when hippocampal neural ensembles briefly reactivate a representation related to prior experience, has been proposed to critically contribute to these memory-related processes. However, it remains unclear whether awake replay contributes to memory function by promoting the storage of past experiences, facilitating planning based on evaluation of those experiences, or both. We designed a dynamic spatial task that promotes replay before a memory-based choice and assessed how the content of replay related to past and future behavior. We found that replay content was decoupled from subsequent choice and instead was enriched for representations of previously rewarded locations and places that had not been visited recently, indicating a role in memory storage rather than in directly guiding subsequent behavior.

Computation in the brain involves multiple types of neurons, yet the organizing principles for how these neurons work together remain unclear. Information theory has offered explanations for how different types of neurons can maximize the transmitted information by encoding different stimulus features. However, recent experiments indicate that separate neuronal types exist that encode the same filtered version of the stimulus, but then the different cell types signal the presence of that stimulus feature with different thresholds. Here we show that the emergence of these neuronal types can be quantitatively described by the theory of transitions between different phases of matter. The two key parameters that control the separation of neurons into subclasses are the mean and standard deviation (SD) of noise affecting neural responses. The average noise across the neural population plays the role of temperature in the classic theory of phase transitions, whereas the SD is equivalent to pressure or magnetic field, in the case of liquid-gas and magnetic transitions, respectively. Our results account for properties of two recently discovered types of salamander Off retinal ganglion cells, as well as the absence of multiple types of On cells. We further show that, across visual stimulus contrasts, retinal circuits continued to operate near the critical point whose quantitative characteristics matched those expected near a liquid-gas critical point and described by the nearest-neighbor Ising model in three dimensions. By operating near a critical point, neural circuits can maximize information transmission in a given environment while retaining the ability to quickly adapt to a new environment.neural coding | information theory | phase transitions | scaling N eural circuits use populations composed of multiple cell types to perform complex computations. Theoretical arguments, based upon the maximization of information transmitted about incoming stimuli, have proved successful in accounting for properties of single neurons (1-4) or populations of neurons encoding either one (5-9) or several different visual features (10-13). However, recent experiments in the retina have discovered types of neurons whose responses are triggered by the presence of the same visual feature in the stimulus but differ in the threshold value by which they detect and report the presence of that feature, where threshold is defined as 50% spiking probability in a binary neuron (Fig. 1A) (14). These neurons may be described by having the same linear properties (reflecting the match in their relevant stimulus features) but different nonlinear properties, such as different threshold values. Here we sought to develop a framework to explain the existence of such neuronal types as a function of the average noise across the neural population and differences in the noise between neural classes. We use the retina as a tractable system to study how neural responses in heterogeneous populations might be coordinated to efficiently encode complex sensory inp...