Trade-offs Between Water Transport Capacity and Drought Resistance in Neotropical Canopy Liana and Tree Species

In tropical forest canopies, it is critical for upper shoots to efficiently provide water to leaves for physiological function while safely preventing loss of hydraulic conductivity due to cavitation during periods of soil water deficit or high evaporative demand. We compared hydraulic physiology of upper canopy trees and lianas in a seasonally dry tropical forest to test whether trade-offs between safety and efficiency of water transport shape differences in hydraulic function between these two major tropical woody growth forms. We found that lianas showed greater maximum stem-specific hydraulic conductivity than trees, but lost hydraulic conductivity at less negative water potentials than trees, resulting in a negative correlation and trade-off between safety and efficiency of water transport. Lianas also exhibited greater diurnal changes in leaf water potential than trees. The magnitude of diurnal water potential change was negatively correlated with sapwood capacitance, indicating that lianas are highly reliant on conducting capability to maintain leaf water status, whereas trees relied more on stored water in stems to maintain leaf water status. Leaf nitrogen concentration was related to maximum leaf-specific hydraulic conductivity only for lianas suggesting that greater water transport capacity is more tied to leaf processes in lianas compared to trees. Our results are consistent with a trade-off between safety and efficiency of water transport and may have implications for increasing liana abundance in neotropical forests

Retinal Coding of Visual Scenes— Repetitive and Redundant Too?

Visual information reaches the brain by way of a fine cable, the optic nerve. The million or so axons in the optic nerve represent an information bottleneck in the visual pathway—where the fewest number of neurons convey the visual scene. It has long been thought that to make the most of the optic nerve’s limited capacity the retina may encode visual information in an optimally efficient manner. In this issue of Neuron, Puchalla et al. report a test of this hypothesis using multielectrode recordings from retinal ganglion cells stimulated with movies of natural scenes. The authors find substantial redundancy in the retinal code and estimate that there is an ∼10-fold overrepresentation of visual information

Effect of breastfeeding on gastrointestinal infection in infants: A targeted maximum likelihood approach for clustered longitudinal data

The PROmotion of Breastfeeding Intervention Trial (PROBIT) cluster-randomized a program encouraging breastfeeding to new mothers in hospital centers. The original studies indicated that this intervention successfully increased duration of breastfeeding and lowered rates of gastrointestinal tract infections in newborns. Additional scientific and popular interest lies in determining the causal effect of longer breastfeeding on gastrointestinal infection. In this study, we estimate the expected infection count under various lengths of breastfeeding in order to estimate the effect of breastfeeding duration on infection. Due to the presence of baseline and time-dependent confounding, specialized "causal" estimation methods are required. We demonstrate the double-robust method of Targeted Maximum Likelihood Estimation (TMLE) in the context of this application and review some related methods and the adjustments required to account for clustering. We compare TMLE (implemented both parametrically and using a data-adaptive algorithm) to other causal methods for this example. In addition, we conduct a simulation study to determine (1) the effectiveness of controlling for clustering indicators when cluster-specific confounders are unmeasured and (2) the importance of using data-adaptive TMLE.Comment: Published in at http://dx.doi.org/10.1214/14-AOAS727 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

CORNN: Convex optimization of recurrent neural networks for rapid inference of neural dynamics

Advances in optical and electrophysiological recording technologies have made it possible to record the dynamics of thousands of neurons, opening up new possibilities for interpreting and controlling large neural populations in behaving animals. A promising way to extract computational principles from these large datasets is to train data-constrained recurrent neural networks (dRNNs). Performing this training in real-time could open doors for research techniques and medical applications to model and control interventions at single-cell resolution and drive desired forms of animal behavior. However, existing training algorithms for dRNNs are inefficient and have limited scalability, making it a challenge to analyze large neural recordings even in offline scenarios. To address these issues, we introduce a training method termed Convex Optimization of Recurrent Neural Networks (CORNN). In studies of simulated recordings, CORNN attained training speeds ~100-fold faster than traditional optimization approaches while maintaining or enhancing modeling accuracy. We further validated CORNN on simulations with thousands of cells that performed simple computations such as those of a 3-bit flip-flop or the execution of a timed response. Finally, we showed that CORNN can robustly reproduce network dynamics and underlying attractor structures despite mismatches between generator and inference models, severe subsampling of observed neurons, or mismatches in neural time-scales. Overall, by training dRNNs with millions of parameters in subminute processing times on a standard computer, CORNN constitutes a first step towards real-time network reproduction constrained on large-scale neural recordings and a powerful computational tool for advancing the understanding of neural computation.Comment: Accepted at NeurIPS 202

Photon Shot Noise Limits on Optical Detection of Neuronal Spikes and Estimation of Spike Timing

AbstractOptical approaches for tracking neural dynamics are of widespread interest, but a theoretical framework quantifying the physical limits of these techniques has been lacking. We formulate such a framework by using signal detection and estimation theory to obtain physical bounds on the detection of neural spikes and the estimation of their occurrence times as set by photon counting statistics (shot noise). These bounds are succinctly expressed via a discriminability index that depends on the kinetics of the optical indicator and the relative fluxes of signal and background photons. This approach facilitates quantitative evaluations of different indicators, detector technologies, and data analyses. Our treatment also provides optimal filtering techniques for optical detection of spikes. We compare various types of Ca2+ indicators and show that background photons are a chief impediment to voltage sensing. Thus, voltage indicators that change color in response to membrane depolarization may offer a key advantage over those that change intensity. We also examine fluorescence resonance energy transfer indicators and identify the regimes in which the widely used ratiometric analysis of signals is substantially suboptimal. Overall, by showing how different optical factors interact to affect signal quality, our treatment offers a valuable guide to experimental design and provides measures of confidence to assess optically extracted traces of neural activity

Entorhinal Cortical Ocean Cells Encode Specific Contexts and Drive Context-Specific Fear Memory

Forming distinct representations and memories of multiple contexts and episodes is thought to be a crucial function of the hippocampal-entorhinal cortical network. The hippocampal dentate gyrus (DG) and CA3 are known to contribute to these functions, but the role of the entorhinal cortex (EC) is poorly understood. Here, we show that Ocean cells, excitatory stellate neurons in the medial EC layer II projecting into DG and CA3, rapidly form a distinct representation of a novel context and drive context-specific activation of downstream CA3 cells as well as context-specific fear memory. In contrast, Island cells, excitatory pyramidal neurons in the medial EC layer II projecting into CA1, are indifferent to context-specific encoding or memory. On the other hand, Ocean cells are dispensable for temporal association learning, for which Island cells are crucial. Together, the two excitatory medial EC layer II inputs to the hippocampus have complementary roles in episodic memory

The Hagedorn/Deconfinement Phase Transition in Weakly Coupled Large N Gauge Theories

We demonstrate that weakly coupled, large N, d-dimensional SU(N) gauge theories on a class of compact spatial manifolds (including S^{d-1} \times time) undergo deconfinement phase transitions at temperatures proportional to the inverse length scale of the manifold in question. The low temperature phase has a free energy of order one, and is characterized by a stringy (Hagedorn) growth in its density of states. The high temperature phase has a free energy of order N^2. These phases are separated either by a single first order transition that generically occurs below the Hagedorn temperature or by two continuous phase transitions, the first of which occurs at the Hagedorn temperature. These phase transitions could perhaps be continuously connected to the usual flat space deconfinement transition in the case of confining gauge theories, and to the Hawking-Page nucleation of AdS_5 black holes in the case of the N=4 supersymmetric Yang-Mills theory. We suggest that deconfinement transitions may generally be interpreted in terms of black hole formation in a dual string theory. Our analysis proceeds by first reducing the Yang-Mills partition function to a (0+0)-dimensional integral over a unitary matrix U, which is the holonomy (Wilson loop) of the gauge field around the thermal time circle in Euclidean space; deconfinement transitions are large N transitions in this matrix integral.Comment: harvmac, 90 pages, 14 figures, 67 footnotes. V3: added references and minor clarifications. v4: added reference, minor changes. v5: corrected figure captions. v6: small corrections and added footnot

Open source tools for large-scale neuroscience

New technologies for monitoring and manipulating the nervous system promise exciting biology but pose challenges for analysis and computation. Solutions can be found in the form of modern approaches to distributed computing, machine learning, and interactive visualization. But embracing these new technologies will require a cultural shift: away from independent efforts and proprietary methods and toward an open source and collaborative neuroscience

Direct Imaging of Hippocampal Epileptiform Calcium Motifs Following Kainic Acid Administration in Freely Behaving Mice

Prolonged exposure to abnormally high calcium concentrations is thought to be a core mechanism underlying hippocampal damage in epileptic patients; however, no prior study has characterized calcium activity during seizures in the live, intact hippocampus. We have directly investigated this possibility by combining whole-brain electroencephalographic (EEG) measurements with microendoscopic calcium imaging of pyramidal cells in the CA1 hippocampal region of freely behaving mice treated with the pro-convulsant kainic acid (KA). We observed that KA administration led to systematic patterns of epileptiform calcium activity: a series of large-scale, intensifying flashes of increased calcium fluorescence concurrent with a cluster of low-amplitude EEG waveforms. This was accompanied by a steady increase in cellular calcium levels (>5 fold increase relative to the baseline), followed by an intense spreading calcium wave characterized by a 218% increase in global mean intensity of calcium fluorescence (n = 8, range [114 - 349%], p<10-4; t-test). The wave had no consistent EEG phenotype and occurred before the onset of motor convulsions. Similar changes in calcium activity were also observed in animals treated with 2 different proconvulsant agents, N-methyl-D-aspartate (NMDA) and pentylenetetrazol (PTZ), suggesting the measured changes in calcium dynamics are a signature of seizure activity rather than a KA-specific pathology. Additionally, despite reducing the behavioral severity of KA-induced seizures, the anticonvulsant drug valproate (VA, 300 mg/kg) did not modify the observed abnormalities in calcium dynamics. These results confirm the presence of pathological calcium activity preceding convulsive motor seizures and support calcium as a candidate signaling molecule in a pathway connecting seizures to subsequent cellular damage. Integrating in vivo calcium imaging with traditional assessment of seizures could potentially increase translatability of pharmacological intervention, leading to novel drug screening paradigms and therapeutics designed to target and abolish abnormal patterns of both electrical and calcium excitation

Stable sulforaphane protects against gait anomalies and modifies bone microarchitecture in the spontaneous STR/Ort model of osteoarthritis

Osteoarthritis (OA), affecting joints and bone, causes physical gait disability with huge socio-economic burden; treatment remains palliative. Roles for antioxidants in protecting against such chronic disorders have been examined previously. Sulforaphane is a naturally occurring antioxidant. Herein, we explore whether SFX-01®, a stable synthetic form of sulforaphane, modifies gait, bone architecture and slows/reverses articular cartilage destruction in a spontaneous OA model in STR/Ort mice. Sixteen mice (n = 8/group) were orally treated for 3 months with either 100 mg/kg SFX-01® or vehicle. Gait was recorded, tibiae were microCT scanned and analysed. OA lesion severity was graded histologically. The effect of SFX-01® on bone turnover markers in vivo was complemented by in vitro bone formation and resorption assays. Analysis revealed development of OA-related gait asymmetry in vehicle-treated STR/Ort mice, which did not emerge in SFX-01®-treated mice. We found significant improvements in trabecular and cortical bone. Despite these marked improvements, we found that histologically-graded OA severity in articular cartilage was unmodified in treated mice. These changes are also reflected in anabolic and anti-catabolic actions of SFX-01® treatment as reflected by alteration in serum markers as well as changes in primary osteoblast and osteoclast-like cells in vitro. We report that SFX-01® improves bone microarchitecture in vivo, produces corresponding changes in bone cell behaviour in vitro and leads to greater symmetry in gait, without marked effects on cartilage lesion severity in STR/Ort osteoarthritic mice. Our findings support both osteotrophic roles and novel beneficial gait effects for SFX-01® in this model of spontaneous OA