We consider the problem of estimating the parameters of a linear autoregressive model with sub-Gaussian innovations from a limited sequence of consecutive observations. Assuming that the parameters are compressible, we analyze the performance of the ℓ1-regularized least squares as well as a greedy estimator of the parameters and characterize the sampling trade-offs required for stable recovery in the non-asymptotic regime. Our results extend those of compressed sensing for linear models where the covariates are i.i.d. and independent of the observation history to autoregressive processes with highly inter-dependent covariates. We also derive sufficient conditions on the sparsity level that guarantee the minimax optimality of the ℓ1-regularized least squares estimate. Applying these techniques to simulated data as well as real-world datasets from crude oil prices and traffic speed data confirm our predicted theoretical performance gains in terms of estimation accuracy and model selection.

The nociceptive flexor withdrawal reflex has an august place in the history of neuroscience. In this issue of Neuron, Hilde et al. (2016) advance our understanding of this reflex by characterizing the molecular identity and circuit connectivity of component interneurons. They assess how a DNA-binding factor Satb2 controls cell position, molecular identity, pre-and postsynaptic targeting, and function of a population of inhibitory sensory relay interneurons that serve to integrate both proprioceptive and nociceptive afferent information.

The nociceptive flexor withdrawal reflex has an august place in the history of neuroscience. In this issue of Neuron, Hilde et al. (2016) advance our understanding of this reflex by characterizing the molecular identity and circuit connectivity of component interneurons. They assess how a DNA-binding factor Satb2 controls cell position, molecular identity, pre-and postsynaptic targeting, and function of a population of inhibitory sensory relay interneurons that serve to integrate both proprioceptive and nociceptive afferent information.

Most people have great difficulty in recalling unrelated items. For example, in free recall experiments, lists of more than a few randomly selected words cannot be accurately repeated. Here we introduce a phenomenological model of memory retrieval inspired by theories of neuronal population coding of information. The model predicts nontrivial scaling behaviors for the mean and standard deviation of the number of recalled words for lists of increasing length. Our results suggest that associative information retrieval is a dominating factor that limits the number of recalled items.

It is well known that light-sheet illumination can enable optically sectioned wide-field imaging of macroscopic samples. However, the optical sectioning capacity of a light-sheet macroscope is undermined by sample-induced scattering or aberrations that broaden the thickness of the sheet illumination. We present a technique to enhance the optical sectioning capacity of a scanning light-sheet microscope by out-of-focus background rejection. The technique, called HiLo microscopy, makes use of two images sequentially acquired with uniform and structured sheet illumination. An optically sectioned image is then synthesized by fusing high and low spatial frequency information from both images. The benefits of combining light-sheet macroscopy and HiLo background rejection are demonstrated in optically cleared whole mouse brain samples, using both green fluorescent protein (GFP)-fluorescence and dark-field scattered light contrast.

Neural circuits, governed by a complex interplay between excitatory and inhibitory neurons, are the substrate for information processing, and the organization of synaptic connectivity in neural network is an important determinant of circuit function. Here, we analyzed the fine structure of connectivity in hippocampal CA1 excitatory and inhibitory neurons innervated by Schaffer collaterals (SCs) using mGRASP in male mice. Our previous study revealed spatially structured synaptic connectivity between CA3-CA1 pyramidal cells (PCs). Surprisingly, parvalbumin-positive interneurons (PVs) showed a significantly more random pattern spatial structure. Notably, application of Peters' Rule for synapse prediction by random overlap between axons and dendrites enhanced structured connectivity in PCs, but, by contrast, made the connectivity pattern in PVs more random. In addition, PCs in a deep sublayer of striatum pyramidale appeared more highly structured than PCs in superficial layers, and little or no sublayer specificity was found in PVs. Our results show that CA1 excitatory PCs and inhibitory PVs innervated by the same SC inputs follow different connectivity rules. The different organizations of fine scale structured connectivity in hippocampal excitatory and inhibitory neurons provide important insights into the development and functions of neural networks.Understanding how neural circuits generate behavior is one of the central goals of neuroscience. An important component of this endeavor is the mapping of fine-scale connection patterns that underlie, and help us infer, signal processing in the brain. Here, using our recently developed synapse detection technology (mGRASP and neuTube), we provide detailed profiles of synaptic connectivity in excitatory (CA1 pyramidal) and inhibitory (CA1 parvalbumin-positive) neurons innervated by the same presynaptic inputs (CA3 Schaffer collaterals). Our results reveal that these two types of CA1 neurons follow different connectivity patterns. Our new evidence for differently structured connectivity at a fine scale in hippocampal excitatory and inhibitory neurons provides a better understanding of hippocampal networks and will guide theoretical and experimental studies.

Many scientific software platforms provide plugin mechanisms that simplify the integration, deployment, and execution of externally developed functionality. One of the most widely used platforms in the imaging space is Fiji, a popular open-source application for scientific image analysis. Fiji incorporates and builds on the ImageJ and ImageJ2 platforms, which provide a powerful plugin architecture used by thousands of plugins to solve a wide variety of problems. This capability is a major part of Fiji's success, and it has become a widely used biological image analysis tool and a target for new functionality. However, a plugin-based software architecture cannot unify disparate platforms operating on incompatible data structures; interoperability necessitates the creation of adaptation or "bridge" layers to translate data and invoke functionality. As a result, while platforms like Fiji enable a high degree of interconnectivity and extensibility, they were not fundamentally designed to integrate across the many data types, programming languages, and architectural differences of various software help address this challenge, we present SciJava Ops, a foundational software library for expressing algorithms as plugins in a unified and extensible way. Continuing the evolution of Fiji's SciJava plugin mechanism, SciJava Ops enables users to harness algorithms from various software platforms within a central execution environment. In addition, SciJava Ops automatically adapts data into the most appropriate structure for each algorithm, allowing users to freely and transparently combine algorithms from otherwise incompatible tools. While SciJava Ops is initially distributed as a Fiji update site, the framework does not require Fiji, ImageJ, or ImageJ2, and would be suitable for integration with additional image analysis platforms.

Does the C. elegans nervous system contain multi-neuron computational modules that perform stereotypical functions? We attempt to answer this question by searching for recurring multi-neuron inter-connectivity patterns in the C. elegans nervous system’s wiring diagram.

In a recent experiment, functional magnetic resonance imaging blood oxygen level-dependent (fMRI BOLD) signals were compared in different cortical areas (primary-visual and associative), when subjects were required covertly to name images in two protocols: sequences of images, with and without intervening delays. The amplitude of the BOLD signal in protocols with delay was found to be closer to that without delays in associative areas than in primary areas. The present study provides an exploratory proposal for the identification of the neural activity substrate of the BOLD signal in quasi-realistic networks of spiking neurons, in networks sustaining selective delay activity (associative) and in networks responsive to stimuli, but whose unique stationary state is one of spontaneous activity (primary). A variety of observables are 'recorded' in the network simulations, applying the experimental stimulation protocol. The ratios of the candidate BOLD signals, in the two protocols, are compared in networks with and without delay activity. There are several options for recovering the experimental result in the model networks. One common conclusion is that the distinguishing factor is the presence of delay activity. The effect of NMDAr is marginal. The ultimate quantitative agreement with the experiment results depends on a distinction of the baseline signal level from its value in delay-period spontaneous activity. This may be attributable to the subjects' attention. Modifying the baseline results in a quantitative agreement for the ratios, and provided a definite choice of the candidate signals. The proposed framework produces predictions for the BOLD signal in fMRI experiments, upon modification of the protocol presentation rate and the form of the response function.

Secretins form megadalton bacterial-membrane channels in at least four sophisticated multiprotein systems that are crucial for translocation of proteins and assembled fibers across the outer membrane of many species of bacteria. Secretin subunits contain multiple domains, which interact with numerous other proteins, including pilotins, secretion-system partner proteins, and exoproteins. Our understanding of the structure of secretins is rapidly progressing, and it is now recognized that features common to all secretins include a cylindrical arrangement of 12-15 subunits, a large periplasmic vestibule with a wide opening at one end and a periplasmic gate at the other. Secretins might also play a key role in the biogenesis of their cognate secretion systems.