Recent Developments in NEURON

Abstract. We describe four recent additions to NEURON's suite of graphical tools that make it easier for users to create and manage models: an enhancement to the Channel Builder that facilitates the specification and efficient simulation of stochastic channel models; an enhancement to the Cell Builder that enables the convenient specification of spatially non-uniform properties in anatomically complex cells; the Model Viewer, which presents a browsable and quickly understood summary of the properties of models of individual cells and networks; and the Import3D tool, which simplifies conversion of detailed morphometric data into computational models of neurons

Neuron splitting in compute-bound parallel network simulations enables runtime scaling with twice as many processors

Neuron tree topology equations can be split into two subtrees and solved on different processors with no change in accuracy, stability, or computational effort; communication costs involve only sending and receiving two double precision values by each subtree at each time step. Splitting cells is useful in attaining load balance in neural network simulations, especially when there is a wide range of cell sizes and the number of cells is about the same as the number of processors. For compute-bound simulations load balance results in almost ideal runtime scaling. Application of the cell splitting method to two published network models exhibits good runtime scaling on twice as many processors as could be effectively used with whole-cell balancing

NEURON and Python

The NEURON simulation program now allows Python to be used, alone or in combination with NEURON's traditional Hoc interpreter. Adding Python to NEURON has the immediate benefit of making available a very extensive suite of analysis tools written for engineering and science. It also catalyzes NEURON software development by offering users a modern programming tool that is recognized for its flexibility and power to create and maintain complex programs. At the same time, nothing is lost because all existing models written in Hoc, including graphical user interface tools, continue to work without change and are also available within the Python context. An example of the benefits of Python availability is the use of the xml module in implementing NEURON's Import3D and CellBuild tools to read MorphML and NeuroML model specifications

Trends in Programming Languages for Neuroscience Simulations

Neuroscience simulators allow scientists to express models in terms of biological concepts, without having to concern themselves with low-level computational details of their implementation. The expressiveness, power and ease-of-use of the simulator interface is critical in efficiently and accurately translating ideas into a working simulation. We review long-term trends in the development of programmable simulator interfaces, and examine the benefits of moving from proprietary, domain-specific languages to modern dynamic general-purpose languages, in particular Python, which provide neuroscientists with an interactive and expressive simulation development environment and easy access to state-of-the-art general-purpose tools for scientific computing

Mitral cell spike synchrony modulated by dendrodendritic synapse location

On their long lateral dendrites, mitral cells of the olfactory bulb form dendrodendritic synapses with large populations of granule cell interneurons. The mitral-granule cell microcircuit operating through these reciprocal synapses has been implicated in inducing synchrony between mitral cells. However, the specific mechanisms of mitral cell synchrony operating through this microcircuit are largely unknown and are complicated by the finding that distal inhibition on the lateral dendrites does not modulate mitral cell spikes. In order to gain insight into how this circuit synchronizes mitral cells within its spatial constraints, we built on a reduced circuit model of biophysically realistic multi-compartment mitral and granule cells to explore systematically the roles of dendrodendritic synapse location and mitral cell separation on synchrony. The simulations showed that mitral cells can synchronize when separated at arbitrary distances through a shared set of granule cells, but synchrony is optimally attained when shared granule cells form two balanced subsets, each subset clustered near to a soma of the mitral cell pairs. Another constraint for synchrony is that the input magnitude must be balanced. When adjusting the input magnitude driving a particular mitral cell relative to another, the mitral-granule cell circuit served to normalize spike rates of the mitral cells while inducing a phase shift or delay in the more weakly driven cell. This shift in phase is absent when the granule cells are removed from the circuit. Our results indicate that the specific distribution of dendrodendritic synaptic clusters is critical for optimal synchronization of mitral cell spikes in response to their odor input

Glomerular and mitral-granule cell microcircuits coordinate temporal and spatial information processing in the olfactory bulb

The olfactory bulb processes inputs from olfactory receptor neurons (ORNs) through two levels: the glomerular layer at the site of input, and the granule cell level at the site of output to the olfactory cortex. The sequence of action of these two levels has not yet been examined. We analyze this issue using a novel computational framework that is scaled up, in three-dimensions (3D), with realistic representations of the interactions between layers, activated by simulated natural odors, and constrained by experimental and theoretical analyses. We suggest that the postulated functions of glomerular circuits have as their primary role transforming a complex and disorganized input into a contrast-enhanced and normalized representation, but cannot provide for synchronization of the distributed glomerular outputs. By contrast, at the granule cell layer, the dendrodendritic interactions mediate temporal decorrelation, which we show is dependent on the preceding contrast enhancement by the glomerular layer. The results provide the first insights into the successive operations in the olfactory bulb, and demonstrate the significance of the modular organization around glomeruli. This layered organization is especially important for natural odor inputs, because they activate many overlapping glomeruli

Characterization of β-amyloid peptide precursor processing by the yeast Yap3 and Mkc7 proteases

AbstractTwo proteases, denoted β- and γ-secretase, process the β-amyloid peptide precursor (APP) to yield the Aβ peptides involved in Alzheimer's disease. A third protein, α-secretase, cleaves APP near the middle of the Aβ sequence and thus prevents Aβ formation. These enzymes have defied identification. Because of its similarity to the systems of mammalian cells the yeast secretory system has provided important clues for finding mammalian processing enzymes. When expressed in Saccharomyces cerevisiae APP is processed by enzymes that possess the specificity of the α-secretases of multicellular organisms. APP processing by α-secretases occurred in sec1 and sec7 mutants, in which transport to the cell surface or to the vacuole is blocked, but not in sec17 or sec18 mutants, in which transport from the endoplasmic reticulum to the Golgi is blocked. Neutralization of the vacuole by NH4Cl did not block α-secretase action. The time course of processing of a pro-α-factor leader-APP chimera showed that processing by Kex2 protease, a Golgi protease that removes the leader, preceded processing by α-secretase. Deletions of the genes encoding the GPI-linked aspartyl proteases Yap3 and Mkc7 decreased α-secretase activity by 56 and 29%, respectively; whereas, the double deletion decreased the activity by 86%. An altered form of APP-695, in which glutamine replaced Lys-612 at the cleavage site, is cleaved by Yap3 at 5% the rate of the wild-type APP. Mkc7 protease cleaved APP (K612Q) at about 20% the rate of wild-type APP. The simplest interpretation of these results is that Yap3 and Mkc7 proteases are α-secretases which act on APP in the late Golgi. They suggest that GPI-linked aspartyl proteases should be investigated as candidate secretases in mammalian tissues