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Where do the basic composite parts of an umwelt – the organism and the environment – come from? Customarily, in umwelt theory, the emphasis is on their mutual co-construction or constitution through functional cycles. But another question could be added to this inquiry: what is the origin, the genesis of organisms and their environments which are now to be indivisibly united once again? On the basis of the transactional conception of relations proposed by John Dewey, the concept of schismogenesis as described by Gregory Bateson, and the process of individuation as explicated by Gilbert Simondon, it is proposed that it is the umwelt which is ontologically primary, and that it is its internal division, separation and individuation, which results in the appearance of organisms with their environments.

Review of Observing Wildlife in Tropical Forests. 1: A Geosemeiotic Approach by Nils Lindahl Elliot. Bristol: Delome Publications, 2019, 480 pp.

Introduction: Phenolic compounds garner interest in developing medicines, nutraceuticals, and cosmeceuticals based on natural products. The quantity of phenolic compounds in a sample is commonly determined via spectrophotometry; however, this instrumented technique is relatively laborious and time consuming and requires a large amount of reagents., Objective: This work aimed to develop a simple, point-of-need colorimetric sensor to rapidly determine total phenolic content (TPC) in tea extracts., Methodology: We developed a radial paper-based analytical device (PAD) for TPC determination based on the established colorimetric reaction between the Folin-Ciocâlteu reagent and phenols. The PAD was designed to enable quantitative (with image capturing device and color processing software) and semiquantitative (using a color palette reference card) determinations. Analytical performance and stability of the PAD were evaluated based on the color responses., Results: The PAD was successfully applied for the determination of phenolics in tea extracts obtained using several polar protic solvents, including water, methanol, and ethanol, with satisfactory accuracy (recovery of 95.5%-104%, 110%-116%, and 104%-110%, respectively) and precision (RSD < 9%). The obtained TPC values also agreed with those from visible spectrophotometry. Semiquantitative determination using the color reference card with three categories of TPC level (i.e., 0-100, 100-500, and 500-1000 mg gallic acid equivalent/L) provided > 95% accuracy. The devices were the most stable when stored at 4°C in a light-protected, vacuum-sealed container. The proposed PAD is promising for simple, rapid (~10-20 min), and accurate estimation of TPC in plant extracts., (© 2024 John Wiley & Sons Ltd.)

Investigation of the factors that contribute to hospital readmissions has focused largely on individual level factors. We extend the knowledge base by exploring macrolevel factors that may contribute to readmissions. We point to environmental, behavioral, and socioeconomic factors that are emerging as correlates to readmissions. Data were taken from publicly available reports provided by multiple agencies. Partial Least Squares-Structural Equation Modeling was used to test the association between economic stability and environmental factors on opioid use which was in turn tested for a direct association with hospital readmissions. We also tested whether hospital access as measured by the proportion of people per hospital moderates the relationship between opioid use and hospital readmissions. We found significant associations between Negative Economic Factors and Opioid Use, between Environmental Factors and Opioid Use, and between Opioid Use and Hospital Readmissions. We found that Hospital Access positively moderates the relationship between Opioid Use and Readmissions. A priori assumptions about factors that influence hospital readmissions must extend beyond just individualistic factors and must incorporate a holistic approach that also considers the impact of macrolevel environmental factors., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Background: Social networking site use and social network-based health information seeking behavior have proliferated to the point that the lines between seeking health information from credible social network-based sources and the decision to seek medical care or attempt to treat oneself have become blurred., Objective: We contribute to emerging research on health information seeking behavior by investigating demographic factors, social media use for health information seeking purposes, and the relationship between health information seeking and occurrences of self-treatment., Methods: Data were collected from an online survey in which participants were asked to describe sociodemographic factors about themselves, social media use patterns, perceptions about their motivations for health information seeking on social media platforms, and whether or not they attempted self-treatment after their social media-related health information seeking. We conducted a binomial logistic regression with self-treatment as a dichotomous categorical dependent variable., Results: Results indicate that significant predictors of self-treatment based on information obtained from social networking sites include race, exercise frequency, and degree of trust in the health-related information received., Conclusions: With an understanding of how sociodemographic factors might influence the decision to self-treat based on information obtained from social networking sites, health care providers can assist patients by educating them on credible social network-based sources of health information and discussing the importance of seeking medical advice from a health care provider., Competing Interests: Conflicts of Interest: None declared., (©Reginald A Silver, Chandrika Johnson. Originally published in the Online Journal of Public Health Informatics (https://ojphi.jmir.org/), 20.12.2023.)

Stereological methods for estimating the 3D particle size and density from 2D projections are essential to many research fields. These methods are, however, prone to errors arising from undetected particle profiles due to sectioning and limited resolution, known as 'lost caps'. A potential solution developed by Keiding, Jensen, and Ranek in 1972, which we refer to as the Keiding model, accounts for lost caps by quantifying the smallest detectable profile in terms of its limiting 'cap angle' (ϕ), a size-independent measure of a particle's distance from the section surface. However, this simple solution has not been widely adopted nor tested. Rather, model-independent design-based stereological methods, which do not explicitly account for lost caps, have come to the fore. Here, we provide the first experimental validation of the Keiding model by comparing the size and density of particles estimated from 2D projections with direct measurement from 3D EM reconstructions of the same tissue. We applied the Keiding model to estimate the size and density of somata, nuclei and vesicles in the cerebellum of mice and rats, where high packing density can be problematic for design-based methods. Our analysis reveals a Gaussian distribution for ϕ rather than a single value. Nevertheless, curve fits of the Keiding model to the 2D diameter distribution accurately estimate the mean ϕ and 3D diameter distribution. While systematic testing using simulations revealed an upper limit to determining ϕ, our analysis shows that estimated ϕ can be used to determine the 3D particle density from the 2D density under a wide range of conditions, and this method is potentially more accurate than minimum-size-based lost-cap corrections and disector methods. Our results show the Keiding model provides an efficient means of accurately estimating the size and density of particles from 2D projections even under conditions of a high density., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Rothman et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Neurophotonics was launched in 2014 coinciding with the launch of the BRAIN Initiative focused on development of technologies for advancement of neuroscience. For the last seven years, Neurophotonics ' agenda has been well aligned with this focus on neurotechnologies featuring new optical methods and tools applicable to brain studies. While the BRAIN Initiative 2.0 is pivoting towards applications of these novel tools in the quest to understand the brain, this status report reviews an extensive and diverse toolkit of novel methods to explore brain function that have emerged from the BRAIN Initiative and related large-scale efforts for measurement and manipulation of brain structure and function. Here, we focus on neurophotonic tools mostly applicable to animal studies. A companion report, scheduled to appear later this year, will cover diffuse optical imaging methods applicable to noninvasive human studies. For each domain, we outline the current state-of-the-art of the respective technologies, identify the areas where innovation is needed, and provide an outlook for the future directions., (© 2022 The Authors.)

In classical theories of cerebellar cortex, high-dimensional sensorimotor representations are used to separate neuronal activity patterns, improving associative learning and motor performance. Recent experimental studies suggest that cerebellar granule cell (GrC) population activity is low-dimensional. To examine sensorimotor representations from the point of view of downstream Purkinje cell 'decoders', we used three-dimensional acousto-optic lens two-photon microscopy to record from hundreds of GrC axons. Here we show that GrC axon population activity is high dimensional and distributed with little fine-scale spatial structure during spontaneous behaviors. Moreover, distinct behavioral states are represented along orthogonal dimensions in neuronal activity space. These results suggest that the cerebellar cortex supports high-dimensional representations and segregates behavioral state-dependent computations into orthogonal subspaces, as reported in the neocortex. Our findings match the predictions of cerebellar pattern separation theories and suggest that the cerebellum and neocortex use population codes with common features, despite their vastly different circuit structures., (© 2021. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Extant research on the relationship between portal use and office visits is mixed. Some researchers have stated that there is no correlation between the two events, others have found a positive correlation, and still others have found a negative correlation between portal use and office visits. Through the use of system-generated data from two different portal systems, we demonstrate the correlation between portal visits and office visits. We also demonstrate how this correlation differs between two institutions and across three demographic attributes. We performed a retrospective bivariate correlation analysis between portal visits and office visits. The correlation analysis was followed by an application of Fisher's z transformation of the correlation coefficients to determine significant differences in correlation across ethnicity, gender, and age. We found a positive and statistically significant correlation between portal visits and office visits among both hospital patients (n = 2,594, r = .239, p < .000) and university health service patients (n = 1,233, r = .596, p < .000). This correlation varies significantly across the dimensions of ethnicity, gender, and age. Our findings support the importance of portal use in the care continuum. Future research should aim to better understand the nuance of personal characteristics on the relationship between portal visits and office visits. Knowing these nuances can assist practitioners with further promoting patient self-engagement through portal use.

Remote focusing is widely used in 3D two-photon microscopy and 3D photostimulation because it enables fast axial scanning without moving the objective lens or specimen. However, due to the design constraints of microscope optics, remote focus units are often located in non-telecentric positions in the optical path, leading to significant depth-dependent 3D field distortions in the imaging volume. To address this limitation, we characterized 3D field distortions arising from non-telecentric remote focusing and present a method for distortion precompensation. We demonstrate its applicability for a 3D two-photon microscope that uses an acousto-optic lens (AOL) for remote focusing and scanning. We show that the distortion precompensation method improves the pointing precision of the AOL microscope to < 0.5 µm throughout the 400 × 400 × 400 µm imaging volume., Competing Interests: A.M.V., F.C.N., J.E.M declare no competing interests. P.A.K. and R.A.S. are named inventors on patents owned by UCL Business relating to linear and nonlinear AOL 3D laser scanning technology. P.A.K. has a financial interest in Agile Diffraction Ltd., which aims to commercialize the AOL technology., (Published by The Optical Society under the terms of the Creative Commons Attribution 4.0 License. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.)

Inhibitory neurons orchestrate the activity of excitatory neurons and play key roles in circuit function. Although individual interneurons have been studied extensively, little is known about their properties at the population level. Using random-access 3D two-photon microscopy, we imaged local populations of cerebellar Golgi cells (GoCs), which deliver inhibition to granule cells. We show that population activity is organized into multiple modes during spontaneous behaviors. A slow, network-wide common modulation of GoC activity correlates with the level of whisking and locomotion, while faster (<1 s) differential population activity, arising from spatially mixed heterogeneous GoC responses, encodes more precise information. A biologically detailed GoC circuit model reproduced the common population mode and the dimensionality observed experimentally, but these properties disappeared when electrical coupling was removed. Our results establish that local GoC circuits exhibit multidimensional activity patterns that could be used for inhibition-mediated adaptive gain control and spatiotemporal patterning of downstream granule cells., Competing Interests: Declaration of interests R.A.S. is a named inventor on patents owned by UCL Business relating to linear and nonlinear acousto-optic lens 3D laser scanning technology., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Two-photon microscopy is widely used to investigate brain function across multiple spatial scales. However, measurements of neural activity are compromised by brain movement in behaving animals. Brain motion-induced artifacts are typically corrected using post hoc processing of two-dimensional images, but this approach is slow and does not correct for axial movements. Moreover, the deleterious effects of brain movement on high-speed imaging of small regions of interest and photostimulation cannot be corrected post hoc. To address this problem, we combined random-access three-dimensional (3D) laser scanning using an acousto-optic lens and rapid closed-loop field programmable gate array processing to track 3D brain movement and correct motion artifacts in real time at up to 1 kHz. Our recordings from synapses, dendrites and large neuronal populations in behaving mice and zebrafish demonstrate real-time movement-corrected 3D two-photon imaging with submicrometer precision.

Background: Our study addresses a gap in the modern information systems (IS) use literature by investigating factors that explain patient portal satisfaction (SWP) and perceptions about health-seeking behavior (HSB). A novel feature of our study is the incorporation of actual portal use data rather than the perceptions of use intention, which prevails in the modern IS literature., Objective: This study aimed to empirically validate factors that influence SWP as an influencing agent on portal use and HSB. Our population segment was comprised of college students with active patient portal accounts., Methods: Using web-based survey data from a population of portal users (n=1142) in a university health center, we proposed a theoretical model that adapts constructs from the Technology Acceptance Model by Davis, the revised Technology Adoption Model by Venkatesh, the Unified Theory of the Acceptance and Use of Technology 2, and the Health Belief Model by Rosenstock et al. We validated our model using structural equation modeling techniques., Results: Our model explained nearly 65% of the variance in SWP (R 2 =0.6499), nearly 33% of the variance in portal use (R 2 =0.3250), and 29% of the variance in HSB (R 2 =0.2900). Statistically significant antecedents of SWP included social influence (beta=.160, t 499 =6.145), habit (beta=.114, t 499 =4.89), facilitating conditions (beta=.062, t 499 =2.401), effort expectancy (beta=.311, t 499 =11.149), and performance expectancy (beta=.359, t 499 =11.588). SWP influenced HSB (beta=.505, t 499 =19.705) and portal use (beta=.050, t 499 =2.031). We did not find a statistically significant association between portal use and HSB (beta=.015, t 499 =0.513). Perceived severity significantly influenced HSB (beta=.129, t 499 =4.675) but not portal use (beta=.012, t 499 =.488)., Conclusions: Understanding the importance of SWP and the role it plays in influencing HSB may point to future technology design considerations for information technology developers and health care providers. We extend current Expectancy Confirmation Theory research by finding a positive association between SWP and portal use., (©Reginald A Silver, Chandrasekar Subramaniam, Antonis Stylianou. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 27.03.2020.)

Computational models are powerful tools for exploring the properties of complex biological systems. In neuroscience, data-driven models of neural circuits that span multiple scales are increasingly being used to understand brain function in health and disease. But their adoption and reuse has been limited by the specialist knowledge required to evaluate and use them. To address this, we have developed Open Source Brain, a platform for sharing, viewing, analyzing, and simulating standardized models from different brain regions and species. Model structure and parameters can be automatically visualized and their dynamical properties explored through browser-based simulations. Infrastructure and tools for collaborative interaction, development, and testing are also provided. We demonstrate how existing components can be reused by constructing new models of inhibition-stabilized cortical networks that match recent experimental results. These features of Open Source Brain improve the accessibility, transparency, and reproducibility of models and facilitate their reuse by the wider community., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

When animals interact with complex environments, their neural circuits must separate overlapping patterns of activity that represent sensory and motor information. Pattern separation is thought to be a key function of several brain regions, including the cerebellar cortex, insect mushroom body, and dentate gyrus. However, recent findings have questioned long-held ideas on how these circuits perform this fundamental computation. Here, we re-evaluate the functional and structural mechanisms underlying pattern separation. We argue that the dimensionality of the space available for population codes representing sensory and motor information provides a common framework for understanding pattern separation. We then discuss how these three circuits use different strategies to separate activity patterns and facilitate associative learning in the presence of trial-to-trial variability., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Geppetto is an open-source platform that provides generic middleware infrastructure for building both online and desktop tools for visualizing neuroscience models and data and managing simulations. Geppetto underpins a number of neuroscience applications, including Open Source Brain (OSB), Virtual Fly Brain (VFB), NEURON-UI and NetPyNE-UI. OSB is used by researchers to create and visualize computational neuroscience models described in NeuroML and simulate them through the browser. VFB is the reference hub for Drosophila melanogaster neural anatomy and imaging data including neuropil, segmented neurons, microscopy stacks and gene expression pattern data. Geppetto is also being used to build a new user interface for NEURON, a widely used neuronal simulation environment, and for NetPyNE, a Python package for network modelling using NEURON. Geppetto defines domain agnostic abstractions used by all these applications to represent their models and data and offers a set of modules and components to integrate, visualize and control simulations in a highly accessible way. The platform comprises a backend which can connect to external data sources, model repositories and simulators together with a highly customizable frontend.This article is part of a discussion meeting issue 'Connectome to behaviour: modelling C. elegans at cellular resolution'., (© 2018 The Authors.)

Acquisition, analysis and simulation of electrophysiological properties of the nervous system require multiple software packages. This makes it difficult to conserve experimental metadata and track the analysis performed. It also complicates certain experimental approaches such as online analysis. To address this, we developed NeuroMatic, an open-source software toolkit that performs data acquisition (episodic, continuous and triggered recordings), data analysis (spike rasters, spontaneous event detection, curve fitting, stationarity) and simulations (stochastic synaptic transmission, synaptic short-term plasticity, integrate-and-fire and Hodgkin-Huxley-like single-compartment models). The merging of a wide range of tools into a single package facilitates a more integrated style of research, from the development of online analysis functions during data acquisition, to the simulation of synaptic conductance trains during dynamic-clamp experiments. Moreover, NeuroMatic has the advantage of working within Igor Pro, a platform-independent environment that includes an extensive library of built-in functions, a history window for reviewing the user's workflow and the ability to produce publication-quality graphics. Since its original release, NeuroMatic has been used in a wide range of scientific studies and its user base has grown considerably. NeuroMatic version 3.0 can be found at http://www.neuromatic.thinkrandom.com and https://github.com/SilverLabUCL/NeuroMatic.

Neurons within cortical microcircuits are interconnected with recurrent excitatory synaptic connections that are thought to amplify signals (Douglas and Martin, 2007), form selective subnetworks (Ko et al., 2011), and aid feature discrimination. Strong inhibition (Haider et al., 2013) counterbalances excitation, enabling sensory features to be sharpened and represented by sparse codes (Willmore et al., 2011). This balance between excitation and inhibition makes it difficult to assess the strength, or gain, of recurrent excitatory connections within cortical networks, which is key to understanding their operational regime and the computations that they perform. Networks that combine an unstable high-gain excitatory population with stabilizing inhibitory feedback are known as inhibition-stabilized networks (ISNs) (Tsodyks et al., 1997). Theoretical studies using reduced network models predict that ISNs produce paradoxical responses to perturbation, but experimental perturbations failed to find evidence for ISNs in cortex (Atallah et al., 2012). Here, we reexamined this question by investigating how cortical network models consisting of many neurons behave after perturbations and found that results obtained from reduced network models fail to predict responses to perturbations in more realistic networks. Our models predict that a large proportion of the inhibitory network must be perturbed to reliably detect an ISN regime robustly in cortex. We propose that wide-field optogenetic suppression of inhibition under promoters targeting a large fraction of inhibitory neurons may provide a perturbation of sufficient strength to reveal the operating regime of cortex. Our results suggest that detailed computational models of optogenetic perturbations are necessary to interpret the results of experimental paradigms. SIGNIFICANCE STATEMENT Many useful computational mechanisms proposed for cortex require local excitatory recurrence to be very strong, such that local inhibitory feedback is necessary to avoid epileptiform runaway activity (an "inhibition-stabilized network" or "ISN" regime). However, recent experimental results suggest that this regime may not exist in cortex. We simulated activity perturbations in cortical networks of increasing realism and found that, to detect ISN-like properties in cortex, large proportions of the inhibitory population must be perturbed. Current experimental methods for inhibitory perturbation are unlikely to satisfy this requirement, implying that existing experimental observations are inconclusive about the computational regime of cortex. Our results suggest that new experimental designs targeting a majority of inhibitory neurons may be able to resolve this question., (Copyright © 2017 Sadeh et al.)

Pattern separation is a fundamental function of the brain. The divergent feedforward networks thought to underlie this computation are widespread, yet exhibit remarkably similar sparse synaptic connectivity. Marr-Albus theory postulates that such networks separate overlapping activity patterns by mapping them onto larger numbers of sparsely active neurons. But spatial correlations in synaptic input and those introduced by network connectivity are likely to compromise performance. To investigate the structural and functional determinants of pattern separation we built models of the cerebellar input layer with spatially correlated input patterns, and systematically varied their synaptic connectivity. Performance was quantified by the learning speed of a classifier trained on either the input or output patterns. Our results show that sparse synaptic connectivity is essential for separating spatially correlated input patterns over a wide range of network activity, and that expansion and correlations, rather than sparse activity, are the major determinants of pattern separation.

Understanding how neural circuits process information requires rapid measurements of activity from identified neurons distributed in 3D space. Here we describe an acousto-optic lens two-photon microscope that performs high-speed focusing and line scanning within a volume spanning hundreds of micrometers. We demonstrate its random-access functionality by selectively imaging cerebellar interneurons sparsely distributed in 3D space and by simultaneously recording from the soma, proximal and distal dendrites of neocortical pyramidal cells in awake behaving mice.

Encoding continuous sensory variables requires sustained synaptic signalling. At several sensory synapses, rapid vesicle supply is achieved via highly mobile vesicles and specialized ribbon structures, but how this is achieved at central synapses without ribbons is unclear. Here we examine vesicle mobility at excitatory cerebellar mossy fibre synapses which sustain transmission over a broad frequency bandwidth. Fluorescent recovery after photobleaching in slices from VGLUT1(Venus) knock-in mice reveal 75% of VGLUT1-containing vesicles have a high mobility, comparable to that at ribbon synapses. Experimentally constrained models establish hydrodynamic interactions and vesicle collisions are major determinants of vesicle mobility in crowded presynaptic terminals. Moreover, models incorporating 3D reconstructions of vesicle clouds near active zones (AZs) predict the measured releasable pool size and replenishment rate from the reserve pool. They also show that while vesicle reloading at AZs is not diffusion-limited at the onset of release, diffusion limits vesicle reloading during sustained high-frequency signalling., Competing Interests: The authors declare that no competing interests exist.

The strength and variability of electrical synaptic connections between GABAergic interneurons are key determinants of spike synchrony within neuronal networks. However, little is known about how electrical coupling strength is determined due to the inaccessibility of gap junctions on the dendritic tree. We investigated the properties of gap junctions in cerebellar interneurons by combining paired somato-somatic and somato-dendritic recordings, anatomical reconstructions, immunohistochemistry, electron microscopy, and modeling. By fitting detailed compartmental models of Golgi cells to their somato-dendritic voltage responses, we determined their passive electrical properties and the mean gap junction conductance (0.9 nS). Connexin36 immunofluorescence and freeze-fracture replica immunogold labeling revealed a large variability in gap junction size and that only 18% of the 340 channels are open in each plaque. Our results establish that the number of gap junctions per connection is the main determinant of both the strength and variability in electrical coupling between Golgi cells., (Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.)

Chemical synapses enable neurons to communicate rapidly, process and filter signals and to store information. However, studying their functional properties is difficult because synaptic connections typically consist of multiple synaptic contacts that release vesicles stochastically and exhibit time-dependent behavior. Moreover, most central synapses are small and inaccessible to direct measurements. Estimation of synaptic properties from responses recorded at the soma is complicated by the presence of nonuniform release probability and nonuniform quantal properties. The presence of multivesicular release and postsynaptic receptor saturation at some synapses can also complicate the interpretation of quantal parameters. Multiple-probability fluctuation analysis (MPFA; also known as variance-mean analysis) is a method that has been developed for estimating synaptic parameters from the variance and mean amplitude of synaptic responses recorded at different release probabilities. This statistical approach, which incorporates nonuniform synaptic properties, has become widely used for studying synaptic transmission. In this chapter, we describe the statistical models used to extract quantal parameters and discuss their interpretation when applying MPFA.

Synaptic efficacy and precision are influenced by the coupling of voltage-gated Ca(2+) channels (VGCCs) to vesicles. But because the topography of VGCCs and their proximity to vesicles is unknown, a quantitative understanding of the determinants of vesicular release at nanometer scale is lacking. To investigate this, we combined freeze-fracture replica immunogold labeling of Cav2.1 channels, local [Ca(2+)] imaging, and patch pipette perfusion of EGTA at the calyx of Held. Between postnatal day 7 and 21, VGCCs formed variable sized clusters and vesicular release became less sensitive to EGTA, whereas fixed Ca(2+) buffer properties remained constant. Experimentally constrained reaction-diffusion simulations suggest that Ca(2+) sensors for vesicular release are located at the perimeter of VGCC clusters (<30 nm) and predict that VGCC number per cluster determines vesicular release probability without altering release time course. This "perimeter release model" provides a unifying framework accounting for developmental changes in both synaptic efficacy and time course., (Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.)

Computational models are increasingly important for studying complex neurophysiological systems. As scientific tools, it is essential that such models can be reproduced and critically evaluated by a range of scientists. However, published models are currently implemented using a diverse set of modeling approaches, simulation tools, and computer languages making them inaccessible and difficult to reproduce. Models also typically contain concepts that are tightly linked to domain-specific simulators, or depend on knowledge that is described exclusively in text-based documentation. To address these issues we have developed a compact, hierarchical, XML-based language called LEMS (Low Entropy Model Specification), that can define the structure and dynamics of a wide range of biological models in a fully machine readable format. We describe how LEMS underpins the latest version of NeuroML and show that this framework can define models of ion channels, synapses, neurons and networks. Unit handling, often a source of error when reusing models, is built into the core of the language by specifying physical quantities in models in terms of the base dimensions. We show how LEMS, together with the open source Java and Python based libraries we have developed, facilitates the generation of scripts for multiple neuronal simulators and provides a route for simulator free code generation. We establish that LEMS can be used to define models from systems biology and map them to neuroscience-domain specific simulators, enabling models to be shared between these traditionally separate disciplines. LEMS and NeuroML 2 provide a new, comprehensive framework for defining computational models of neuronal and other biological systems in a machine readable format, making them more reproducible and increasing the transparency and accessibility of their underlying structure and properties.

The synaptic connectivity within neuronal networks is thought to determine the information processing they perform, yet network structure-function relationships remain poorly understood. By combining quantitative anatomy of the cerebellar input layer and information theoretic analysis of network models, we investigated how synaptic connectivity affects information transmission and processing. Simplified binary models revealed that the synaptic connectivity within feedforward networks determines the trade-off between information transmission and sparse encoding. Networks with few synaptic connections per neuron and network-activity-dependent threshold were optimal for lossless sparse encoding over the widest range of input activities. Biologically detailed spiking network models with experimentally constrained synaptic conductances and inhibition confirmed our analytical predictions. Our results establish that the synaptic connectivity within the cerebellar input layer enables efficient lossless sparse encoding. Moreover, they provide a functional explanation for why granule cells have approximately four dendrites, a feature that has been evolutionarily conserved since the appearance of fish., (Copyright © 2014 Elsevier Inc. All rights reserved.)

In vivo, cortical pyramidal cells are bombarded by asynchronous synaptic input arising from ongoing network activity. However, little is known about how such 'background' synaptic input interacts with nonlinear dendritic mechanisms. We have modified an existing model of a layer 5 (L5) pyramidal cell to explore how dendritic integration in the apical dendritic tuft could be altered by the levels of network activity observed in vivo. Here we show that asynchronous background excitatory input increases neuronal gain and extends both temporal and spatial integration of stimulus-evoked synaptic input onto the dendritic tuft. Addition of fast and slow inhibitory synaptic conductances, with properties similar to those from dendritic targeting interneurons, that provided a 'balanced' background configuration, partially counteracted these effects, suggesting that inhibition can tune spatio-temporal integration in the tuft. Excitatory background input lowered the threshold for NMDA receptor-mediated dendritic spikes, extended their duration and increased the probability of additional regenerative events occurring in neighbouring branches. These effects were also observed in a passive model where all the non-synaptic voltage-gated conductances were removed. Our results show that glutamate-bound NMDA receptors arising from ongoing network activity can provide a powerful spatially distributed nonlinear dendritic conductance. This may enable L5 pyramidal cells to change their integrative properties as a function of local network activity, potentially allowing both clustered and spatially distributed synaptic inputs to be integrated over extended timescales.

NeuroML is an XML-based model description language, which provides a powerful common data format for defining and exchanging models of neurons and neuronal networks. In the latest version of NeuroML, the structure and behavior of ion channel, synapse, cell, and network model descriptions are based on underlying definitions provided in LEMS, a domain-independent language for expressing hierarchical mathematical models of physical entities. While declarative approaches for describing models have led to greater exchange of model elements among software tools in computational neuroscience, a frequent criticism of XML-based languages is that they are difficult to work with directly. Here we describe two Application Programming Interfaces (APIs) written in Python (http://www.python.org), which simplify the process of developing and modifying models expressed in NeuroML and LEMS. The libNeuroML API provides a Python object model with a direct mapping to all NeuroML concepts defined by the NeuroML Schema, which facilitates reading and writing the XML equivalents. In addition, it offers a memory-efficient, array-based internal representation, which is useful for handling large-scale connectomics data. The libNeuroML API also includes support for performing common operations that are required when working with NeuroML documents. Access to the LEMS data model is provided by the PyLEMS API, which provides a Python implementation of the LEMS language, including the ability to simulate most models expressed in LEMS. Together, libNeuroML and PyLEMS provide a comprehensive solution for interacting with NeuroML models in a Python environment.

Background: Two-photon microscopy is widely used to study brain function, but conventional microscopes are too slow to capture the timing of neuronal signalling and imaging is restricted to one plane. Recent development of acousto-optic-deflector-based random access functional imaging has improved the temporal resolution, but the utility of these technologies for mapping 3D synaptic activity patterns and their performance at the excitation wavelengths required to image genetically encoded indicators have not been investigated., New Method: Here, we have used a compact acousto-optic lens (AOL) two-photon microscope to make high speed [Ca(2+)] measurements from spines and dendrites distributed in 3D with different excitation wavelengths (800-920 nm)., Results: We show simultaneous monitoring of activity from many synaptic inputs distributed over the 3D arborisation of a neuronal dendrite using both synthetic as well as genetically encoded indicators. We confirm the utility of AOL-based imaging for fast in vivo recordings by measuring, simultaneously, visually evoked responses in 100 neurons distributed over a 150 μm focal depth range. Moreover, we explore ways to improve the measurement of timing of neuronal activation by choosing specific regions within the cell soma., Comparison With Existing Methods: These results establish that AOL-based 3D random access two-photon microscopy has a wider range of neuroscience applications than previously shown., Conclusions: Our findings show that the compact AOL microscope design has the speed, spatial resolution, sensitivity and wavelength flexibility to measure 3D patterns of synaptic and neuronal activity on individual trials., (Copyright © 2013 The Authors. Published by Elsevier B.V. All rights reserved.)

In this chapter, we describe how to create mathematical models of synaptic transmission and integration. We start with a brief synopsis of the experimental evidence underlying our current understanding of synaptic transmission. We then describe synaptic transmission at a particular glutamatergic synapse in the mammalian cerebellum, the mossy fiber to granule cell synapse, since data from this well-characterized synapse can provide a benchmark comparison for how well synaptic properties are captured by different mathematical models. This chapter is structured by first presenting the simplest mathematical description of an average synaptic conductance waveform and then introducing methods for incorporating more complex synaptic properties such as nonlinear voltage dependence of ionotropic receptors, short-term plasticity, and stochastic fluctuations. We restrict our focus to excitatory synaptic transmission, but most of the modeling approaches discussed here can be equally applied to inhibitory synapses. Our data-driven approach will be of interest to those wishing to model synaptic transmission and network behavior in health and disease., (© 2014 Elsevier Inc. All rights reserved.)

Rapid information processing in our nervous system relies on high-frequency fusion of transmitter-filled vesicles at chemical synapses. Some sensory synapses possess prominent electron-dense ribbon structures that provide a scaffold for tethering synaptic vesicles at the active zone (AZ), enabling sustained vesicular release. Here, we review functional data indicating that some central and neuromuscular synapses can also sustain vesicle-fusion rates that are comparable to those of ribbon-type sensory synapses. Comparison of the ultrastructure across these different types of synapses, together with recent work showing that cytomatrix proteins can tether vesicles and speed vesicle reloading, suggests that filamentous structures may play a key role in vesicle supply. We discuss potential mechanisms by which vesicle tethering could contribute to sustained high rates of vesicle fusion across ribbon-type, central, and neuromuscular synapses., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

The cerebellar cortex coordinates movements and maintains balance by modifying motor commands as a function of sensory-motor context, which is encoded by mossy fiber (MF) activity. MFs exhibit a wide range of activity, from brief precisely timed high-frequency bursts, which encode discrete variables such as whisker stimulation, to low-frequency sustained rate-coded modulation, which encodes continuous variables such as head velocity. While high-frequency MF inputs have been shown to activate granule cells (GCs) effectively, much less is known about sustained low-frequency signaling through the GC layer, which is impeded by a hyperpolarized resting potential and strong GABA(A)-mediated tonic inhibition of GCs. Here we have exploited the intrinsic MF network of unipolar brush cells to activate GCs with sustained low-frequency asynchronous MF inputs in rat cerebellar slices. We find that low-frequency MF input modulates the intrinsic firing of Purkinje cells, and that this signal transmission through the GC layer requires synaptic activation of Mg²⁺-block-resistant NMDA receptors (NMDARs) that are likely to contain the GluN2C subunit. Slow NMDAR conductances sum temporally to contribute approximately half the MF-GC synaptic charge at hyperpolarized potentials. Simulations of synaptic integration in GCs show that the NMDAR and slow spillover-activated AMPA receptor (AMPAR) components depolarize GCs to a similar extent. Moreover, their combined depolarizing effect enables the fast quantal AMPAR component to trigger action potentials at low MF input frequencies. Our results suggest that the weak Mg²⁺ block of GluN2C-containing NMDARs enables transmission of low-frequency MF signals through the input layer of the cerebellar cortex.

Measurement of the release time course (RTC) and of the quantal content is important for quantifying synaptic precision and understanding the molecular basis of the release process at central synapses. In theory, the RTC can be determined directly from the histogram of first latencies of quantal events only if a maximum of one vesicle is released per trial, but at most synapses multiple vesicles are released. Traditionally, first latency histograms have been corrected for multiple releases using a simple correction, derived by Barrett and Stevens (BS; 1972b) for quantifying release at the neuromuscular junction. This correction has also been used to quantify release at central synapses. We show, by combining an analytical approach and numerical simulations of stochastic quantal release, that the BS correction gives a biased estimate for RTC and quantal content. The bias increases with release probability, and is therefore particularly problematic for central synapses. We show that this is due to assuming infinite availability of releasable vesicles and we derive a formula for estimating the RTC from first latencies without this assumption. The resulting 'binomial correction' requires knowledge of the maximum number of quanta that can be released following an action potential (N), which can be estimated with variance-mean analysis. We show with simulations that estimating RTC and quantal content from first latencies using the binomial correction is robust in the presence of noise and when release probability is non-uniform. We also provide an alternative method for estimating RTC from the first latencies when N cannot be determined., (Copyright © 2012 Elsevier B.V. All rights reserved.)

Electrically coupled inhibitory interneurons dynamically control network excitability, yet little is known about how chemical and electrical synapses regulate their activity. Using two-photon glutamate uncaging and dendritic patch-clamp recordings, we found that the dendrites of cerebellar Golgi interneurons acted as passive cables. They conferred distance-dependent sublinear synaptic integration and weakened distal excitatory inputs. Gap junctions were present at a higher density on distal dendrites and contributed substantially to membrane conductance. Depolarization of one Golgi cell increased firing in its neighbors, and inclusion of dendritic gap junctions in interneuron network models enabled distal excitatory synapses to drive network activity more effectively. Our results suggest that dendritic gap junctions counteract sublinear dendritic integration by enabling excitatory synaptic charge to spread into the dendrites of neighboring inhibitory interneurons.

Two-photon (2P) microscopy is widely used in neuroscience, but the optical properties of brain tissue are poorly understood. We have investigated the effect of brain tissue on the 2P point spread function (PSF₂p) by imaging fluorescent beads through living cortical slices. By combining this with measurements of the mean free path of the excitation light, adaptive optics and vector-based modeling that includes phase modulation and scattering, we show that tissue-induced wavefront distortions are the main determinant of enlargement and distortion of the PSF₂p at intermediate imaging depths. Furthermore, they generate surrounding lobes that contain more than half of the 2P excitation. These effects reduce the resolution of fine structures and contrast and they, together with scattering, limit 2P excitation. Our results disentangle the contributions of scattering and wavefront distortion in shaping the cortical PSF₂p, thereby providing a basis for improved 2P microscopy.

Significant inroads have been made to understand cerebellar cortical processing but neural coding at the output stage of the cerebellum in the deep cerebellar nuclei (DCN) remains poorly understood. The DCN are unlikely to just present a relay nucleus because Purkinje cell inhibition has to be turned into an excitatory output signal, and DCN neurons exhibit complex intrinsic properties. In particular, DCN neurons exhibit a range of rebound spiking properties following hyperpolarizing current injection, raising the question how this could contribute to signal processing in behaving animals. Computer modeling presents an ideal tool to investigate how intrinsic voltage-gated conductances in DCN neurons could generate the heterogeneous firing behavior observed, and what input conditions could result in rebound responses. To enable such an investigation we built a compartmental DCN neuron model with a full dendritic morphology and appropriate active conductances. We generated a good match of our simulations with DCN current clamp data we recorded in acute slices, including the heterogeneity in the rebound responses. We then examined how inhibitory and excitatory synaptic input interacted with these intrinsic conductances to control DCN firing. We found that the output spiking of the model reflected the ongoing balance of excitatory and inhibitory input rates and that changing the level of inhibition performed an additive operation. Rebound firing following strong Purkinje cell input bursts was also possible, but only if the chloride reversal potential was more negative than -70 mV to allow de-inactivation of rebound currents. Fast rebound bursts due to T-type calcium current and slow rebounds due to persistent sodium current could be differentially regulated by synaptic input, and the pattern of these rebounds was further influenced by HCN current. Our findings suggest that active properties of DCN neurons could play a crucial role for signal processing in the cerebellum.

Sustained rate-coded signals encode many types of sensory modalities. Some sensory synapses possess specialized ribbon structures, which tether vesicles, to enable high-frequency signaling. However, central synapses lack these structures, yet some can maintain signaling over a wide bandwidth. To analyze the underlying molecular mechanisms, we investigated the function of the active zone core component Bassoon in cerebellar mossy fiber to granule cell synapses. We show that short-term synaptic depression is enhanced in Bassoon knockout mice during sustained high-frequency trains but basal synaptic transmission is unaffected. Fluctuation and quantal analysis as well as quantification with constrained short-term plasticity models revealed that the vesicle reloading rate was halved in the absence of Bassoon. Thus, our data show that the cytomatrix protein Bassoon speeds the reloading of vesicles to release sites at a central excitatory synapse., (Copyright © 2010 Elsevier Inc. All rights reserved.)

Electrical synapses between interneurons contribute to synchronized firing and network oscillations in the brain. However, little is known about how such networks respond to excitatory synaptic input. To investigate this, we studied electrically coupled Golgi cells (GoC) in the cerebellar input layer. We show with immunohistochemistry, electron microscopy, and electrophysiology that Connexin-36 is necessary for functional gap junctions (GJs) between GoC dendrites. In the absence of coincident synaptic input, GoCs synchronize their firing. In contrast, sparse, coincident mossy fiber input triggered a mixture of excitation and inhibition of GoC firing and spike desynchronization. Inhibition is caused by propagation of the spike afterhyperpolarization through GJs. This triggers network desynchronization because heterogeneous coupling to surrounding cells causes spike-phase dispersion. Detailed network models predict that desynchronization is robust, local, and dependent on synaptic input properties. Our results show that GJ coupling can be inhibitory and either promote network synchronization or trigger rapid network desynchronization depending on the synaptic input., ((c) 2010 Elsevier Inc. All rights reserved.)

The vast computational power of the brain has traditionally been viewed as arising from the complex connectivity of neural networks, in which an individual neuron acts as a simple linear summation and thresholding device. However, recent studies show that individual neurons utilize a wealth of nonlinear mechanisms to transform synaptic input into output firing. These mechanisms can arise from synaptic plasticity, synaptic noise, and somatic and dendritic conductances. This tool kit of nonlinear mechanisms confers considerable computational power on both morphologically simple and more complex neurons, enabling them to perform a range of arithmetic operations on signals encoded ina variety of different ways.

We describe a high speed 3D Acousto-Optic Lens Microscope (AOLM) for femtosecond 2-photon imaging. By optimizing the design of the 4 AO Deflectors (AODs) and by deriving new control algorithms, we have developed a compact spherical AOL with a low temporal dispersion that enables 2-photon imaging at 10-fold lower power than previously reported. We show that the AOLM can perform high speed 2D raster-scan imaging (>150 Hz) without scan rate dependent astigmatism. It can deflect and focus a laser beam in a 3D random access sequence at 30 kHz and has an extended focusing range (>137 mum; 40X 0.8NA objective). These features are likely to make the AOLM a useful tool for studying fast physiological processes distributed in 3D space.

Biologically detailed single neuron and network models are important for understanding how ion channels, synapses and anatomical connectivity underlie the complex electrical behavior of the brain. While neuronal simulators such as NEURON, GENESIS, MOOSE, NEST, and PSICS facilitate the development of these data-driven neuronal models, the specialized languages they employ are generally not interoperable, limiting model accessibility and preventing reuse of model components and cross-simulator validation. To overcome these problems we have used an Open Source software approach to develop NeuroML, a neuronal model description language based on XML (Extensible Markup Language). This enables these detailed models and their components to be defined in a standalone form, allowing them to be used across multiple simulators and archived in a standardized format. Here we describe the structure of NeuroML and demonstrate its scope by converting into NeuroML models of a number of different voltage- and ligand-gated conductances, models of electrical coupling, synaptic transmission and short-term plasticity, together with morphologically detailed models of individual neurons. We have also used these NeuroML-based components to develop an highly detailed cortical network model. NeuroML-based model descriptions were validated by demonstrating similar model behavior across five independently developed simulators. Although our results confirm that simulations run on different simulators converge, they reveal limits to model interoperability, by showing that for some models convergence only occurs at high levels of spatial and temporal discretisation, when the computational overhead is high. Our development of NeuroML as a common description language for biophysically detailed neuronal and network models enables interoperability across multiple simulation environments, thereby improving model transparency, accessibility and reuse in computational neuroscience.

To act as computational devices, neurons must perform mathematical operations as they transform synaptic and modulatory input into output firing rate. Experiments and theory indicate that neuronal firing typically represents the sum of synaptic inputs, an additive operation, but multiplication of inputs is essential for many computations. Multiplication by a constant produces a change in the slope, or gain, of the input-output relationship, amplifying or scaling down the sensitivity of the neuron to changes in its input. Such gain modulation occurs in vivo, during contrast invariance of orientation tuning, attentional scaling, translation-invariant object recognition, auditory processing and coordinate transformations. Moreover, theoretical studies highlight the necessity of gain modulation in several of these tasks. Although potential cellular mechanisms for gain modulation have been identified, they often rely on membrane noise and require restrictive conditions to work. Because nonlinear components are used to scale signals in electronics, we examined whether synaptic nonlinearities are involved in neuronal gain modulation. We used synaptic stimulation and the dynamic-clamp technique to investigate gain modulation in granule cells in acute slices of rat cerebellum. Here we show that when excitation is mediated by synapses with short-term depression (STD), neuronal gain is controlled by an inhibitory conductance in a noise-independent manner, allowing driving and modulatory inputs to be multiplied together. The nonlinearity introduced by STD transforms inhibition-mediated additive shifts in the input-output relationship into multiplicative gain changes. When granule cells were driven with bursts of high-frequency mossy fibre input, as observed in vivo, larger inhibition-mediated gain changes were observed, as expected with greater STD. Simulations of synaptic integration in more complex neocortical neurons suggest that STD-based gain modulation can also operate in neurons with large dendritic trees. Our results establish that neurons receiving depressing excitatory inputs can act as powerful multiplicative devices even when integration of postsynaptic conductances is linear.

Precise representation of the timing of sensory stimuli is essential for rapid motor coordination, a core function of the cerebellum. Feedforward inhibition has been implicated in precise temporal signaling in several regions of the brain, but little is known about this type of inhibitory circuit within the input layer of the cerebellar cortex. We investigated the synaptic properties of feedforward inhibition at near physiological temperatures (35 degrees C) in rat cerebellar slices. We establish that the previously uncharacterized mossy fiber-Golgi cell-granule cell pathway can act as a functional feedforward inhibitory circuit. The synchronous activation of four mossy fibers, releasing a total of six quanta onto a Golgi cell, can reset spontaneous Golgi cell firing with high temporal precision (200 mus). However, only modest increases in Golgi cell firing rate were observed during trains of high-frequency mossy fiber stimulation. This decoupling of Golgi cell activity from mossy fiber firing rate was attributable to a strong afterhyperpolarization after each action potential, preventing mossy fiber-Golgi cell signaling for approximately 50 ms. Feedforward excitation of Golgi cells induced a temporally precise inhibitory conductance in granule cells that curtailed the excitatory action of the mossy fiber EPSC. The synaptic and cellular properties of this feedforward circuit appear tuned to trigger a fast inhibitory conductance in granule cells at the onset of stimuli that produce intense bursts of activity in multiple mossy fibers, thereby conserving the temporal precision of the initial granule cell response.

The extent to which synaptic activity can signal a sensory stimulus limits the information available to a neuron. We determined the contribution of individual synapses to sensory representation by recording excitatory postsynaptic currents (EPSCs) in cerebellar granule cells during a time-varying, quantifiable vestibular stimulus. Vestibular-sensitive synapses faithfully reported direction and velocity, rather than position or acceleration of whole-body motion, via bidirectional modulation of EPSC frequency. The lack of short-term synaptic dynamics ensured a highly linear relationship between velocity and charge transfer, and as few as 100 synapses provided resolution approaching psychophysical limits. This indicates that highly accurate stimulus representation can be achieved by small networks and even within single neurons.