Computational neuroscience has produced a diversity of software for simulations of networks of spiking neurons, with both negative and positive consequences. On the one hand, each simulator uses its own programming or confi guration language, leading to considerable diffi culty in porting models from one simulator to another. This impedes communication between investigators and makes it harder to reproduce and build on the work of others. On the other hand, simulation results can be cross-checked between different simulators, giving greater confi dence in their correctness, and each simulator has different optimizations, so the most appropriate simulator can be chosen for a given modelling task. A common programming interface to multiple simulators would reduce or eliminate the problems of simulator diversity while retaining the benefi ts. PyNN is such an interface, making it possible to write a simulation script once, using the Python programming language, and run it without modifi cation on any supported simulator (currently NEURON, NEST, PCSIM, Brian and the Heidelberg VLSI neuromorphic hardware). PyNN increases the productivity of neuronal network modelling by providing high-level abstraction, by promoting code sharing and reuse, and by providing a foundation for simulator-agnostic analysis, visualization and data-management tools. PyNN increases the reliability of modelling studies by making it much easier to check results on multiple simulators. PyNN is open-source software and is available from http://neuralensemble.org/PyNN. Keywords: Python, interoperability, large-scale models, simulation, parallel computing, reproducibility, computational neuroscience, translation compiler standards and simulators develop. Another is that model source code is often not written with reuse and extension in mind, and so considerable rewriting to modularize the code is necessary. Probably the most important barrier is that code written for one simulator is not compatible with any other simulator.Although many computational models in neuroscience are written from the ground up in a general purpose programming language such as C++ or Fortran, probably the majority use a special purpose simulator that allows models to be expressed in terms of neuroscience-specifi c concepts such as neurons, ion channels, synapses; the simulator takes care of translating these concepts into a system of equations and of numerically solving the equations. A large number of such simulators are available (reviewed in Brette et al., 2007), mostly as open-source software, and each has its own programming language, confi guration syntax and/or graphical interface, which creates considerable diffi culty in translating models from one simulator to another, or even in understanding someone else's code, with obvious negative consequences for communication between investigators, reproducibility of others' models and building on existing models.However, the diversity of simulators also has a number of positive consequences: (i) it allows cross-checking -t...

Emerging evidence suggests that connexin mediated gap junctional intercellular communication contributes to many aspects of bone biology including bone development, maintenance of bone homeostasis and responsiveness of bone cells to diverse extracellular signals. Deletion of connexin 43, the predominant gap junction protein in bone, is embryonic lethal making it challenging to examine the role of connexin 43 in bone in vivo. However, transgenic murine models in which only osteocytes and osteoblasts are deficient in connexin 43, and which are fully viable, have recently been developed. Unfortunately, the bone phenotype of different connexin 43 deficient models has been variable. To address this issue, we used an osteocalcin driven Cre-lox system to create osteoblast and osteocyte specific connexin 43 deficient mice. These mice displayed bone loss as a result of increased bone resorption and osteoclastogenesis. The mechanism underlying this increased osteoclastogenesis included increases in the osteocytic, but not osteoblastic, RANKL/OPG ratio. Previous in vitro studies suggest that connexin 43 deficient bone cells are less responsive to biomechanical signals. Interestingly, and in contrast to in vitro studies, we found that connexin 43 deficient mice displayed an enhanced anabolic response to mechanical load. Our results suggest that transient inhibition of connexin 43 expression and gap junctional intercellular communication may prove a potentially powerful means of enhancing the anabolic response of bone to mechanical loading.

Organisms are projected to face unprecedented rates of change in future ocean conditions due to anthropogenic climate-change. At present, marine life encounters a wide range of environmental heterogeneity from natural fluctuations to mean climate change. Manipulation studies suggest that biota from more variable marine environments have more phenotypic plasticity to tolerate environmental heterogeneity. Here, we consider current strategies employed by a range of representative organisms across various habitats - from short-lived phytoplankton to long-lived corals - in response to environmental heterogeneity. We then discuss how, if and when organismal responses (acclimate/migrate/adapt) may be altered by shifts in the magnitude of the mean climate-change signal relative to that for natural fluctuations projected for coming decades. The findings from both novel climate-change modelling simulations and prior biological manipulation studies, in which natural fluctuations are superimposed on those of mean change, provide valuable insights into organismal responses to environmental heterogeneity. Manipulations reveal that different experimental outcomes are evident between climate-change treatments which include natural fluctuations vs. those which do not. Modelling simulations project that the magnitude of climate variability, along with mean climate change, will increase in coming decades, and hence environmental heterogeneity will increase, illustrating the need for more realistic biological manipulation experiments that include natural fluctuations. However, simulations also strongly suggest that the timescales over which the mean climate-change signature will become dominant, relative to natural fluctuations, will vary for individual properties, being most rapid for CO2 (~10 years from present day) to 4 decades for nutrients. We conclude that the strategies used by biota to respond to shifts in environmental heterogeneity may be complex, as they will have to physiologically straddle wide-ranging timescales in the alteration of ocean conditions, including the need to adapt to rapidly rising CO2 and also acclimate to environmental heterogeneity in more slowly changing properties such as warming.

Computational neuroscience as a scientific discipline must provide for the ready testing of published models by others in the field. Unfortunately this has rarely been fulfilled. When exact reproduction of a model simulation is achieved, it is often a long and difficult process. Too often, missing or typographically incorrect equations and parameter values have made it difficult to explore or build upon published models. Compounding this difficulty is the proliferation of platforms and operating systems that are incompatible with the author's original computing environment.Because of these problems, most models are never subjected to the rigorous testing by others in the field that is a hallmark of the scientific method. This not only impedes validation of a model, but also prevents a deeper understanding of its inner workings, especially through modification of the parameters. Furthermore, modular pieces of the model, e.g. ion channels or the morphology of a cell, cannot be reused to build new models and propel research forward.ModelDB (http://senselab.med.yale.edu/modeldb) is intended to address these issues (Peterson et al, 1996;Shepherd et al, 1998). ModelDB is a database of computational models, either classics in the field or published in recent years. It focuses on models for different types of neurons, and presently contains over 60 models for 15 neuron types. In addition to compartmental models, it contains models covering from ion channels and receptors through axons and dendrites through neurons to networks. Models can be accessed by author, model name, neuron type, concept, e.g. synaptic plasticity, pattern recognition, etc, or by simulation environment.ModelDB is a member of a major neuroscience database collection called SenseLab. Each SenseLab database has an easily extensible structure achieved through the EAV/CR (EntityAttribute-Value with Classes and Relationships) data schema , Miller et al 2001. ModelDB is integrated with NeuronDB (Marenco et al 1999), another SenseLab database that stores neuronal properties derived from the neuroscience literature (http:// senselab.med.yale.edu/senselab/NeuronDB). Use of the models is free to all. Contributing to the database is also open to all. Contributions are tested for quality-control purposes before being made public. Here we describe how to find, run, and submit models to ModelDB. Browsing ModelDBThe use of ModelDB typically starts with a computational neuroscientist who wishes to test the results of a simulation by a published model, and use that as a starting point for further research. Instead of recreating the model from scratch, the user goes to the ModelDB home page (Fig.1, top), to find the model by any of the various ways already mentioned. All the information about a model is shown on a single page (Fig.1, bottom), which also contains tools for finding related models in the database.The files required to run a simulation are stored in a compressed archive in zip format and can be browsed and/or downloaded (Fig.1, bottom, lower left column)...

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