Anatomy of a cortical simulator

Insights into brain’s high-level computational principles will lead to novel cognitive systems, computing architectures, programming paradigms, and numerous practical applications. An important step towards this end is the study of large networks of cortical spiking neurons. We have built a cortical simulator, C2, incorporating several algorithmic enhancements to optimize the simulation scale and time, through: computationally efficient simulation of neurons in a clock-driven and synapses in an event-driven fashion; memory efficient representation of simulation state; and communication efficient message exchanges. Using phenomenological, single-compartment models of spiking neurons and synapses with spike-timing dependent plasticity, we represented a rat-scale cortical model (55 million neurons, 442 billion synapses) in 8TB memory of a 32,768processor BlueGene/L. With 1 millisecond resolution for neuronal dynamics and 1-20 milliseconds axonal delays, C2 can simulate 1 second of model time in 9 seconds per Hertz of average neuronal firing rate. In summary, by combining state-of-the-art hardware with innovative algorithms and software design, we simultaneously achieved unprecedented time-to-solution on an unprecedented problem size. 1

Systematic review of the current status of cadaveric simulation for surgical training

Background: There is growing interest in and provision of cadaveric simulation courses for surgical trainees. This is being driven by the need to modernize and improve the efficiency of surgical training within the current challenging training climate. The objective of this systematic review is to describe and evaluate the evidence for cadaveric simulation in postgraduate surgical training. Methods: A PRISMA‐compliant systematic literature review of studies that prospectively evaluated a cadaveric simulation training intervention for surgical trainees was undertaken. All relevant databases and trial registries were searched to January 2019. Methodological rigour was assessed using the widely validated Medical Education Research Quality Index (MERSQI) tool. Results: A total of 51 studies were included, involving 2002 surgical trainees across 69 cadaveric training interventions. Of these, 22 assessed the impact of the cadaveric training intervention using only subjective measures, five measured impact by change in learner knowledge, and 23 used objective tools to assess change in learner behaviour after training. Only one study assessed patient outcome and demonstrated transfer of skill from the simulated environment to the workplace. Of the included studies, 67 per cent had weak methodology (MERSQI score less than 10·7). Conclusion: There is an abundance of relatively low‐quality evidence showing that cadaveric simulation induces short‐term skill acquisition as measured by objective means. There is currently a lack of evidence of skill retention, and of transfer of skills following training into the live operating theatre

Imaging Informatics and the Human Brain Project: the Role of Structure, Review

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A biophysical observation model for field potentials of networks of leaky integrate-and-fire neurons

We present a biophysical approach for the coupling of neural network activity as resulting from proper dipole currents of cortical pyramidal neurons to the electric field in extracellular fluid. Starting from a reduced threecompartment model of a single pyramidal neuron, we derive an observation model for dendritic dipole currents in extracellular space and thereby for the dendritic field potential that contributes to the local field potential of a neural population. This work aligns and satisfies the widespread dipole assumption that is motivated by the "open-field" configuration of the dendritic field potential around cortical pyramidal cells. Our reduced three-compartment scheme allows to derive networks of leaky integrate-and-fire models, which facilitates comparison with existing neural network and observation models. In particular, by means of numerical simulations we compare our approach with an ad hoc model by Mazzoni et al. [Mazzoni, A., S. Panzeri, N. K. Logothetis, and N. Brunel (2008). Encoding of naturalistic stimuli by local field potential spectra in networks of excitatory and inhibitory neurons. PLoS Computational Biology 4 (12), e1000239], and conclude that our biophysically motivated approach yields substantial improvement.Comment: 31 pages, 4 figure

Cone-Beam Computed Tomography contrast validation of an artificial periodontal phantom for use in endodontics

International audienceValidation of image processing techniques such as endodontic segmentations in cone-beam computed tomography (CBCT) is a challenging issue because of the lack of ground truth in in vivo experiments. The purpose of our study was to design an artificial surrounding tissues phantom able to provide CBCT image quality of real extracted teeth, similar to in vivo conditions. Note that these extracted teeth could be previously scanned using micro computed tomography (μCT) to access true quantitative measurements of the root canal anatomy. Different design settings are assessed in our study by comparison to in vivo images, in terms of the contrast-to-noise ratio (CNR) obtained between different anatomical structures. Concerning the root canal and the dentine, the best design setup allowed our phantom to provide a CNR difference of only 3% compared to clinical cases

A unified view on weakly correlated recurrent networks

The diversity of neuron models used in contemporary theoretical neuroscience to investigate specific properties of covariances raises the question how these models relate to each other. In particular it is hard to distinguish between generic properties and peculiarities due to the abstracted model. Here we present a unified view on pairwise covariances in recurrent networks in the irregular regime. We consider the binary neuron model, the leaky integrate-and-fire model, and the Hawkes process. We show that linear approximation maps each of these models to either of two classes of linear rate models, including the Ornstein-Uhlenbeck process as a special case. The classes differ in the location of additive noise in the rate dynamics, which is on the output side for spiking models and on the input side for the binary model. Both classes allow closed form solutions for the covariance. For output noise it separates into an echo term and a term due to correlated input. The unified framework enables us to transfer results between models. For example, we generalize the binary model and the Hawkes process to the presence of conduction delays and simplify derivations for established results. Our approach is applicable to general network structures and suitable for population averages. The derived averages are exact for fixed out-degree network architectures and approximate for fixed in-degree. We demonstrate how taking into account fluctuations in the linearization procedure increases the accuracy of the effective theory and we explain the class dependent differences between covariances in the time and the frequency domain. Finally we show that the oscillatory instability emerging in networks of integrate-and-fire models with delayed inhibitory feedback is a model-invariant feature: the same structure of poles in the complex frequency plane determines the population power spectra

A Biomechanical Investigation of Load Sharing at the Distal Forearm

Loading at the distal forearm has been previously examined under static loads, however there remains no consensus on how loading is affected by active wrist and forearm motion. This work examines load magnitudes and load sharing at the distal radius and ulna during of active wrist and forearm motion. Two instrumented implants were designed to measure in vitro loading in cadaveric specimen. The implants were evaluated and found reliable for use in further biomechanical studies. An in vitro study investigated the effect of joint angle and direction of joint motion on loads in the distal radius and ulna during active flexion-extension, radioulnar deviation and dart throw motion. Loads through the distal radius and ulna were significantly greater in extension and reverse dart throw motion than in flexion and forward dart throw motion. A subsequent study examined the effect of radial length changes, joint angle and direction of motion on distal radius and ulna loading during active forearm rotation. Load magnitudes through the distal radius were greater in supination than in pronation. Radial lengthening found to increase radial loading and decrease ulnar loading and radial shortening decreased distal radius loading and increased distal ulna loading throughout forearm rotation, in a quasilinear fashion. This work improves the understanding of forearm bone loading and will assist clinicians in the development of rehabilitation techniques, surgical protocols and implant designs

Modeling of Neuronal Growth In Vitro: Comparison of Simulation Tools NETMORPH and CX3D

We simulate the growth of neuronal networks using the two recently published tools, NETMORPH and CX3D. The goals of the work are (1) to examine and compare the simulation tools, (2) to construct a model of growth of neocortical cultures, and (3) to characterize the changes in network connectivity during growth, using standard graph theoretic methods. Parameters for the neocortical culture are chosen after consulting both the experimental and the computational work presented in the literature. The first (three) weeks in culture are known to be a time of development of extensive dendritic and axonal arbors and establishment of synaptic connections between the neurons. We simulate the growth of networks from day 1 to day 21. It is shown that for the properly selected parameters, the simulators can reproduce the experimentally obtained connectivity. The selected graph theoretic methods can capture the structural changes during growth.Peer reviewe