Data-driven models of neurons and circuits are important for understanding how the properties of membrane conductances, synapses, dendrites, and the anatomical connectivity between neurons generate the complex dynamical behaviors of brain circuits in health and disease. However, the inherent complexity of these biological processes makes the construction and reuse of biologically detailed models challenging. A wide range of tools have been developed to aid their construction and simulation, but differences in design and internal representation act as technical barriers to those who wish to use data-driven models in their research workflows. NeuroML, a model description language for computational neuroscience, was developed to address this fragmentation in modeling tools. Since its inception, NeuroML has evolved into a mature community standard that encompasses a wide range of model types and approaches in computational neuroscience. It has enabled the development of a large ecosystem of interoperable open-source software tools for the creation, visualization, validation, and simulation of data-driven models. Here, we describe how the NeuroML ecosystem can be incorporated into research workflows to simplify the construction, testing, and analysis of standardized models of neural systems, and supports the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles, thus promoting open, transparent and reproducible science.

Many human cells fire plateau action potentials, from the endocrine system to the heart. For example, cardiac myocites may present perturbations of the plateau that are linked to the long QT syndrome and cardiac arrhythmia. Here, we provide a unified and minimalist dynamic explanation for some of the known forms of the loss of stability of the plateau, which are linked to these pathologies. One of these forms is a newly found non-chaotic aperiodic oscillation mode of autonomous plateau spikes. This happens via an infinite period bifurcation of the interspike interval. We accomplish this through a simple map model of the action potential containing two fast and one slow variables. This reveals a small set of ingredients for the observation of plateau anomalies, and allows us to propose different ways to recover from them by adjusting slow-fast timescales and the fast-fast coupling conductance. This integrated and generic view might provide insights for complex models with many variables and parameters. One of the predictions of the model is the existence of a critical value in the fast-fast coupling conductance below which cardiac spike anomalies completely disappear, regardless of other currents. Notwithstanding, excitable cardiac spikes stay away from prolongation if their slow variable time scales ratio between drive and recovery is less than one. These findings might help the future development of innovative treatments for the related diseases.

Experiments on human auditory perception have shown that interaural time difference (ITD) is sufficient to generate spatial percepts, even though stimuli containing only the ITD cue are perceived as being emitted from inside the head instead of from external locations at specific azimuths. These experiments are thus interpreted as "lateralization" instead of "localization" tasks. In fact, lateralized spatial perception has been quantified using tasks in which participants have to report their estimates by selecting a putative location inside the head, or matching the perceived position to sounds with a given interaural level difference. Therefore, these estimates are made with respect to internal frames of reference, but it is unclear whether these percepts have any significance for the more ecological problem of locating an external sound source. In order to investigate the link between internalized spatial percepts and sound localization, we designed a new task in which subjects are instructed to report externalized azimuthal location for sounds containing only ITD cues. Despite the mismatch between an internalized percept having to be reported as emanating from an external location, subjects were able to estimate azimuths consistently. Furthermore, normalized estimates were indistinguishable from those obtained using traditional lateralization tasks. Our results revealed a direct relationship between perceived azimuths and ITD, which deviates from that obtained from acoustical analysis of binaural recordings, revealing estimation biases. Intriguingly, these results indicate that externalized percepts are not required for the generation of azimuthal percepts.

The Wilson-Cowan model constitutes a paradigmatic approach to understanding the collective dynamics of networks of excitatory and inhibitory units. It has been profusely used in the literature to analyze the possible phases of neural networks at a mean-field level, e.g., assuming large fully connected networks. Moreover, its stochastic counterpart allows one to study fluctuation-induced phenomena, such as avalanches. Here we revisit the stochastic Wilson-Cowan model paying special attention to the possible phase transitions between quiescent and active phases. We unveil eight possible types of such transitions, including continuous ones with scaling behavior belonging to known universality classes-such as directed percolation and tricritical directed percolation-as well as six distinct ones. In particular, we show that under some special circumstances, at a so-called "Hopf tricritical directed percolation" transition, rather unconventional behavior is observed, including the emergence of scaling breakdown. Other transitions are discontinuous and show different types of anomalies in scaling and/or exhibit mixed features of continuous and discontinuous transitions. These results broaden our knowledge of the possible types of critical behavior in networks of excitatory and inhibitory units and are, thus, of relevance to understanding avalanche dynamics in actual neuronal recordings. From a more general perspective, these results help extend the theory of nonequilibrium phase transitions into quiescent or absorbing states.

In self-organized criticality (SOC) models, as well as in standard phase transitions, criticality is only present for vanishing external fields h→0. Considering that this is rarely the case for natural systems, such a restriction poses a challenge to the explanatory power of these models. Besides that, in models of dissipative systems like earthquakes, forest fires, and neuronal networks, there is no true critical behavior, as expressed in clean power laws obeying finite-size scaling, but a scenario called "dirty" criticality or self-organized quasi-criticality (SOqC). Here, we propose simple homeostatic mechanisms which promote self-organization of coupling strengths, gains, and firing thresholds in neuronal networks. We show that with an adequate separation of the timescales for the coupling strength and firing threshold dynamics, near criticality (SOqC) can be reached and sustained even in the presence of significant external input. The firing thresholds adapt to and cancel the inputs (h decreases towards zero). Similar mechanisms can be proposed for the couplings and local thresholds in spin systems and cellular automata, which could lead to applications in earthquake, forest fire, stellar flare, voting, and epidemic modeling.

Interaural Time Difference (ITD) is the main cue for azimuthal auditory perception in humans. ITDs at each frequency contribute differently to azimuth discrimination, which can be quantified by their azimuthal Fisher Information. Consistently, human ITD discrimination thresholds are predicted by the azimuthal information. However, this prediction is poor for frequencies below 500 Hz. Such poor prediction could be ascribed to the strategy of quantifying azimuthal information using HRTFs obtained in unnaturalistic anechoic chambers or by using a direct method which does not incorporate the delay lines proposed by the Jeffress-Colburn model. In the present study, we obtained ITD discrimination thresholds from extensive sampling across frequency and ITD, and applied multiple strategies for quantifying azimuthal information. These strategies employed HRTFs obtained in realistic and anechoic chambers, with and without considering delay lines. We found that ITD discriminability thresholds across the complete range of frequencies are better predicted by azimuthal information conveyed by ITD cues when (1) we use naturalistic high-noise HRTFs, and (2) ITD delay compensation is not applied. Our results support that auditory perception is shaped by natural environments, which include high reverberation in low frequencies. Moreover, we also suggest that delay lines are not a crucial feature for determining ITD discrimination thresholds in the human auditory system. GRAPHICAL ABSTRACT

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.

Computational models are powerful tools for investigating brain function in health and disease. However, biologically detailed neuronal and circuit models are complex and implemented in a range of specialized languages, making them inaccessible and opaque to many neuroscientists. This has limited critical evaluation of models by the scientific community and impeded their refinement and widespread adoption. To address this, we have combined advances in standardizing models, open source software development and web technologies to develop Open Source Brain, a platform for visualizing, simulating, disseminating and collaboratively developing standardized models of neurons and circuits from a range of brain regions. Model structure and parameters can be visualized and their dynamical properties explored through browser-controlled simulations, without writing code. Open Source Brain makes neural models transparent and accessible and facilitates testing, critical evaluation and refinement, thereby helping to improve the accuracy and reproducibility of models, and their dissemination to the wider community.

The dynamics of a parametric simple pendulum submitted to an arbitrary angle of excitation ϕ was investigated experimentally by simulations and analytically. Analytical calculations for the loci of saddle-node bifurcations corresponding to the creation of resonant orbits were performed by applying Melnikov's method. However, this powerful perturbative method cannot be used to predict the existence of odd resonances for a vertical excitation within first order corrections. Yet, we showed that period-3 resonances indeed exist in such a configuration. Two degenerate attractors of different phases, associated with the same loci of saddle-node bifurcations in parameter space, are reported. For tilted excitation, the degeneracy is broken due to an extra torque, which was confirmed by the calculation of two distinct loci of saddle-node bifurcations for each attractor. This behavior persists up to ϕ≈7π/180, and for inclinations larger than this, only one attractor is observed. Bifurcation diagrams were constructed experimentally for ϕ=π/8 to demonstrate the existence of self-excited resonances (periods smaller than three) and hidden oscillations (for periods greater than three).

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'.

Advances in experimental techniques and computational power allowing researchers to gather anatomical and electrophysiological data at unprecedented levels of detail have fostered the development of increasingly complex models in computational neuroscience. Large-scale, biophysically detailed cell models pose a particular set of computational challenges, and this has led to the development of a number of domain-specific simulators. At the other level of detail, the ever growing variety of point neuron models increases the implementation barrier even for those based on the relatively simple integrate-and-fire neuron model. Independently of the model complexity, all modeling methods crucially depend on an efficient and accurate transformation of mathematical model descriptions into efficiently executable code. Neuroscientists usually publish model descriptions in terms of the mathematical equations underlying them. However, actually simulating them requires they be translated into code. This can cause problems because errors may be introduced if this process is carried out by hand, and code written by neuroscientists may not be very computationally efficient. Furthermore, the translated code might be generated for different hardware platforms, operating system variants or even written in different languages and thus cannot easily be combined or even compared. Two main approaches to addressing this issues have been followed. The first is to limit users to a fixed set of optimized models, which limits flexibility. The second is to allow model definitions in a high level interpreted language, although this may limit performance. Recently, a third approach has become increasingly popular: using code generation to automatically translate high level descriptions into efficient low level code to combine the best of previous approaches. This approach also greatly enriches efforts to standardize simulator-independent model description languages. In the past few years, a number of code generation pipelines have been developed in the computational neuroscience community, which differ considerably in aim, scope and functionality. This article provides an overview of existing pipelines currently used within the community and contrasts their capabilities and the technologies and concepts behind them.

Perception seems so simple.I look out of the window to see houses, trees, people walking past, the sky above, the grass below.I hear birds in the trees, cars going past, the distant sound of an alarm.The world is full of objects that make their presence known to me through my senses -what could be more simple?Yet the efficacy of perceptual experience hides a host of questions for which we do not yet have the answers.Information reaching our senses is generally incomplete, ambiguous, distributed in space and time and not neatly sorted according to its source, so a key function of our perceptual systems is to discover the likely causes of our sensations.Perception as inference or hypothesis testing, formalised in the predictive coding theory, offers an attractive framework for exploring these issues.From this perspective, regularities or patterns provide perceptual systems with some traction, allowing the formation of expectations and a basis for decomposing the world into discrete objects.But in the dynamic world which we inhabit, object representations must be similarly dynamic, and need to form and dissolve, dominate and yield, in a way that facilitates veridical perception.In this talk I will discuss auditory scene analysis in the context of predictive coding using experimental data, exemplar models, and the phenomenon of perceptual multistability.

Our aim is to unveil how resonances of parametric systems are affected when symmetry is broken. We showed numerically and experimentally that odd resonances indeed come about when the pendulum is excited along a tilted direction. Applying the Melnikov subharmonic function, we not only determined analytically the loci of saddle-node bifurcations delimiting resonance regions in parameter space but also explained these observations by demonstrating that, under the Melnikov method point of view, odd resonances arise due to an extra torque that appears in the asymmetric case.