Psychophysical properties of odor processing can be quantitatively described by relative action potential latency patterns in mitral and tufted cells

Electrophysiological and population imaging data in rodents show that olfactory bulb (OB) activity is profoundly modulated by the odor sampling process while behavioral experiments indicate that odor discrimination can occur within a single sniff. This paper addresses the question of whether action potential (AP) latencies occurring across the mitral and tufted cell (M/TC) population within an individual sampling cycle could account for the psychophysical properties of odor processing. To determine this we created an OB model (50,000 M/TCs) exhibiting hallmarks of published in vivo properties and used a template-matching algorithm to assess stimulus separation. Such an AP latency-based scheme showed high reproducibility and sensitivity such that odor stimuli could be reliably separated independent of concentration. As in behavioral experiments we found that very dissimilar odors (“A vs. B”) were accurately and rapidly discerned while very similar odors (binary mixtures, 0.4A/0.6B vs. 0.6A/0.4B) required up to 90 ms longer. As in lesion studies we find that AP latency-based representation is rather insensitive to disruption of large regions of the OB. The AP latency-based scheme described here, therefore, captures both temporal and psychophysical properties of olfactory processing and suggests that the onset patterns of M/TC activity in the OB represent stimulus specific features of olfactory stimuli

Cortical Integration of Vestibular and Visual Cues for Navigation, Visual Processing, and Perception

Despite increasing evidence of its involvement in several key functions of the cerebral cortex, the vestibular sense rarely enters our consciousness. Indeed, the extent to which these internal signals are incorporated within cortical sensory representation and how they might be relied upon for sensory-driven decision-making, during, for example, spatial navigation, is yet to be understood. Recent novel experimental approaches in rodents have probed both the physiological and behavioral significance of vestibular signals and indicate that their widespread integration with vision improves both the cortical representation and perceptual accuracy of self-motion and orientation. Here, we summarize these recent findings with a focus on cortical circuits involved in visual perception and spatial navigation and highlight the major remaining knowledge gaps. We suggest that vestibulo-visual integration reflects a process of constant updating regarding the status of self-motion, and access to such information by the cortex is used for sensory perception and predictions that may be implemented for rapid, navigation-related decision-making

Visualizing anatomically registered data with brainrender

Three-dimensional (3D) digital brain atlases and high-throughput brain-wide imaging techniques generate large multidimensional datasets that can be registered to a common reference frame. Generating insights from such datasets depends critically on visualization and interactive data exploration, but this a challenging task. Currently available software is dedicated to single atlases, model species or data types, and generating 3D renderings that merge anatomically registered data from diverse sources requires extensive development and programming skills. Here, we present brainrender: an open-source Python package for interactive visualization of multidimensional datasets registered to brain atlases. Brainrender facilitates the creation of complex renderings with different data types in the same visualization and enables seamless use of different atlas sources. High-quality visualizations can be used interactively and exported as high-resolution figures and animated videos. By facilitating the visualization of anatomically registered data, brainrender should accelerate the analysis, interpretation, and dissemination of brain-wide multidimensional data

Threat history controls flexible escape behavior in mice.

In many instances, external sensory-evoked neuronal activity is used by the brain to select the most appropriate behavioral response. Predator-avoidance behaviors such as freezing and escape1,2 are of particular interest since these stimulus-evoked responses are behavioral manifestations of a decision-making process that is fundamental to survival.3,4 Over the lifespan of an individual, however, the threat value of agents in the environment is believed to undergo constant revision,5 and in some cases, repeated avoidance of certain stimuli may no longer be an optimal behavioral strategy.6 To begin to study this type of adaptive control of decision-making, we devised an experimental paradigm to probe the properties of threat escape in the laboratory mouse Mus musculus. First, we found that while robust escape to visual looming stimuli can be observed after 2 days of social isolation, mice can also rapidly learn that such stimuli are non-threatening. This learned suppression of escape (LSE) is extremely robust and can persist for weeks and is not a generalized adaptation, since flight responses to novel live prey and auditory threat stimuli in the same environmental context were maintained. We also show that LSE cannot be explained by trial number or a simple form of stimulus desensitization since it is dependent on threat-escape history. We propose that the action selection process mediating escape behavior is constantly updated by recent threat history and that LSE can be used as a robust model system to understand the neurophysiological mechanisms underlying experience-dependent decision-making

Dissecting cellular diversity of cortical GABAergic cells across multiple modalities: A turning point in neuronal taxonomy

Decoding the complexity of the brain requires an understanding of the architecture, function, and development of its neuronal circuits. Neuronal classifications that group neurons based on specific features/behaviors have become essential to further analyze the different subtypes in a systematic and reproducible way. A comprehensive taxonomic framework, accounting for multiple defining and quantitative features, will provide the reference to infer generalized rules for cells ascribed to the same neuronal type, and eventually predict cellular behaviors, even in the absence of experimental measures. Technologies that enable cell-type classification in the nervous system are rapidly evolving in scalability and resolution. While these approaches depict astonishing diversity in neuronal morphology, electrophysiology, and gene expression, a robust metric of the coherence between different profiling modalities leading to a unified classification is still largely missing. Focusing on GABAergic neurons of the cerebral cortex, Gouwens et al.1 pioneered the first integrated cell-type classification based on the simultaneous analysis of the transcriptional networks, the recording of intrinsic electrophysiological properties, and the reconstruction of 3D morphologies of the same cell. Their comprehensive and high-quality data provide a new framework to shed light on what may be considered a "neuronal cell type.

Novel Approaches to Monitor and Manipulate Single Neurons In Vivo

The complexity of the vertebrate brain poses an enormous challenge to experimental neuroscience. One way of dealing with this complexity has been to investigate different aspects of brain function in widely different preparations, each best suited to address a particular question. Accordingly, cellular questions are typically addressed with intracellular recordings in in vitro preparations such as brain slices or neuronal cultures, whereas network behavior and sensory or motor response properties are analyzed in vivo, often with extracellular recordings. This division of labor has proved to be an experimentally effective strategy. However, although there seems to be no limit to the wealth of data that can be generated in this way, integrating results derived in different preparations comes with its own set of challenges. The enormous difficulties encountered when one attempts to link cellular phenomena such as synaptic plasticity to systems properties such as spatial memory (Martin et al., 2000) have shown us that close collaboration between molecular−cellular and systems neuroscience is required (Tonegawa et al., 2003) and that we need more convergence of experimental techniques to analyze the cellular basis of neural function under more natural conditions. Studying neurons under naturalistic conditions is, however, easier said than done. A return to in vivo preparations will only be successful if we are able to solve the technical problems that led previous researchers to abandon the study of intact brains in the first place. Thus, studying neurons at the cellular level in vertebrate brains is today first and foremost a technological challenge. Here we highlight recent efforts to improve our ability to analyze functions of single neurons in vivo. Given th

Widespread Vestibular Activation of the Rodent Cortex

Much of our understanding of the neuronal mechanisms of spatial navigation is derived from chronic recordings in rodents in which head-direction, place, and grid cells have all been described. However, despite the proposed importance of self-reference information to these internal representations of space, their congruence with vestibular signaling remains unclear. Here we have undertaken brain-wide functional mapping using both fMRI and electrophysiological methods to directly determine the spatial extent, strength, and time course of vestibular signaling across the rat forebrain. We find distributed activity throughout thalamic, limbic, and particularly primary sensory cortical areas in addition to known head-direction pathways. We also observe activation of frontal regions, including infralimbic and cingulate cortices, indicating integration of vestibular information throughout functionally diverse cortical regions. These whole-brain activity maps therefore suggest a widespread contribution of vestibular signaling to a self-centered framework for multimodal sensorimotor integration in support of movement planning, execution, spatial navigation, and autonomic responses to gravito-inertial changes

Functional and multiscale 3D structural investigation of brain tissue through correlative in vivo physiology, synchrotron microtomography and volume electron microscopy

Understanding the function of biological tissues requires a coordinated study of physiology and structure, exploring volumes that contain complete functional units at a detail that resolves the relevant features. Here, we introduce an approach to address this challenge: Mouse brain tissue sections containing a region where function was recorded using in vivo 2-photon calcium imaging were stained, dehydrated, resin-embedded and imaged with synchrotron X-ray computed tomography with propagation-based phase contrast (SXRT). SXRT provided context at subcellular detail, and could be followed by targeted acquisition of multiple volumes using serial block-face electron microscopy (SBEM). In the olfactory bulb, combining SXRT and SBEM enabled disambiguation of in vivo-assigned regions of interest. In the hippocampus, we found that superficial pyramidal neurons in CA1a displayed a larger density of spine apparati than deeper ones. Altogether, this approach can enable a functional and structural investigation of subcellular features in the context of cells and tissues

Neuronal Oscillations Enhance Stimulus Discrimination by Ensuring Action Potential Precision

Although oscillations in membrane potential are a prominent feature of sensory, motor, and cognitive function, their precise role in signal processing remains elusive. Here we show, using a combination of in vivo, in vitro, and theoretical approaches, that both synaptically and intrinsically generated membrane potential oscillations dramatically improve action potential (AP) precision by removing the membrane potential variance associated with jitter-accumulating trains of APs. This increased AP precision occurred irrespective of cell type and—at oscillation frequencies ranging from 3 to 65 Hz—permitted accurate discernment of up to 1,000 different stimuli. At low oscillation frequencies, stimulus discrimination showed a clear phase dependence whereby inputs arriving during the trough and the early rising phase of an oscillation cycle were most robustly discriminated. Thus, by ensuring AP precision, membrane potential oscillations dramatically enhance the discriminatory capabilities of individual neurons and networks of cells and provide one attractive explanation for their abundance in neurophysiological systems

Probabilistic identification of cerebellar cortical neurones across species.

Despite our fine-grain anatomical knowledge of the cerebellar cortex, electrophysiological studies of circuit information processing over the last fifty years have been hampered by the difficulty of reliably assigning signals to identified cell types. We approached this problem by assessing the spontaneous activity signatures of identified cerebellar cortical neurones. A range of statistics describing firing frequency and irregularity were then used, individually and in combination, to build Gaussian Process Classifiers (GPC) leading to a probabilistic classification of each neurone type and the computation of equi-probable decision boundaries between cell classes. Firing frequency statistics were useful for separating Purkinje cells from granular layer units, whilst firing irregularity measures proved most useful for distinguishing cells within granular layer cell classes. Considered as single statistics, we achieved classification accuracies of 72.5% and 92.7% for granular layer and molecular layer units respectively. Combining statistics to form twin-variate GPC models substantially improved classification accuracies with the combination of mean spike frequency and log-interval entropy offering classification accuracies of 92.7% and 99.2% for our molecular and granular layer models, respectively. A cross-species comparison was performed, using data drawn from anaesthetised mice and decerebrate cats, where our models offered 80% and 100% classification accuracy. We then used our models to assess non-identified data from awake monkeys and rabbits in order to highlight subsets of neurones with the greatest degree of similarity to identified cell classes. In this way, our GPC-based approach for tentatively identifying neurones from their spontaneous activity signatures, in the absence of an established ground-truth, nonetheless affords the experimenter a statistically robust means of grouping cells with properties matching known cell classes. Our approach therefore may have broad application to a variety of future cerebellar cortical investigations, particularly in awake animals where opportunities for definitive cell identification are limited