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Frontiers in Neuroanatomy
Anatomy and plasticity in large-scale brain models View all 12 Articles
Jackson C. Bittencourt
University of São Paulo, Brazil
Nuno Miguel M. Amorim Da Costa
Allen Institute for Brain Science, United States
Ping Liu
Department of Pathophysiology, School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, China
Front. Neuroanat., 26 May 2016 | https://doi.org/10.3389/fnana.2016.00057
Automatic Generation of Connectivity for Large-Scale Neuronal Network Models through Structural Plasticity
Sandra Diaz-Pier1*, Mikaël Naveau1,2, Markus Butz-Ostendorf1 and Abigail Morrison1,3,4
1Simulation Laboratory Neuroscience – Bernstein Facility for Simulation and Database Technology, Institute for Advanced Simulation, Jülich Aachen Research Alliance, Jülich Research Center, Jülich, Germany
2Serine Proteases and Pathophysiology of the Neurovascular Unit, Institut National de la Santé et de la Recherche Médicale UMR-S U919, Caen Normandy University, Groupement d'Intérêt Public (GIP) CYCERON, Caen, France
3Institute of Neuroscience and Medicine (INM-6), Computational and Systems Neuroscience, Jülich Research Centre, Jülich, Germany
4Faculty of Psychology, Institute of Cognitive Neuroscience, Ruhr-University Bochum, Bochum, Germany
With the emergence of new high performance computation technology in the last decade, the simulation of large scale neural networks which are able to reproduce the behavior and structure of the brain has finally become an achievable target of neuroscience. Due to the number of synaptic connections between neurons and the complexity of biological networks, most contemporary models have manually defined or static connectivity. However, it is expected that modeling the dynamic generation and deletion of the links among neurons, locally and between different regions of the brain, is crucial to unravel important mechanisms associated with learning, memory and healing. Moreover, for many neural circuits that could potentially be modeled, activity data is more readily and reliably available than connectivity data. Thus, a framework that enables networks to wire themselves on the basis of specified activity targets can be of great value in specifying network models where connectivity data is incomplete or has large error margins. To address these issues, in the present work we present an implementation of a model of structural plasticity in the neural network simulator NEST. In this model, synapses consist of two parts, a pre- and a post-synaptic element. Synapses are created and deleted during the execution of the simulation following local homeostatic rules until a mean level of electrical activity is reached in the network. We assess the scalability of the implementation in order to evaluate its potential usage in the self generation of connectivity of large scale networks. We show and discuss the results of simulations on simple two population networks and more complex models of the cortical microcircuit involving 8 populations and 4 layers using the new framework.
Models of large scale neural networks are an important tool for understanding the mechanics of the brain (De Garis et al., 2010; Helias et al., 2012; Eliasmith and Trujillo, 2014). Such models are created based on experimental information that has been collected for years by neuroscientists and combine mathematical methods with algorithms to reproduce observed behavior. It is known that the connectivity of the network plays an essential role in defining the way function is achieved at higher levels of activity. Nevertheless, obtaining accurate measurements of connectivity is complex, even with the most advanced experimental techniques, due to the resolution of sensors and difficult access to the target areas. The dynamics of the connectivity are also not yet well understood, although it has been shown that synaptic plasticity is fundamental for understanding how learning and memory work. Non invasive techniques such as DTI imaging and fMRI scans can provide a glimpse to the real complexity of the problem in structure and function. Higher resolution techniques like electron microscopy (Gray, 1959), photostimulation (Dantzker and Callaway, 2000) and electrophysiological recordings (Thomson et al., 2002) provide more detailed connectivity information of specific regions. Regardless, creating an exact connectivity map of even a small region of the brain is extremely challenging (Deco et al., 2008; Essen et al., 2012; Van Essen and Ugurbil, 2012; Reckfort et al., 2013). This poses a significant problem for the modeling approach, as connectivity must be specified. For small networks, parameter scans can be carried out with respect to the unknown or imprecisely known connection probabilities between populations. For larger networks, which are more costly to simulate and also potentially have many more unknown connectivity parameters, this approach is hardly feasible.
One way to address the issue of modeling connectivity within a neural network is to allow a network model to determine its own suitable connectivity to achieve target activity patterns, e.g., experimental measurements of the spiking frequency, which is easier to measure accurately than connectivity. In addition to addressing the problem of network model specification, a framework that accounts for the appearance and disappearance of synapses on the basis of network activity can provide insight into how connectivity is generated during development and learning or even on how healing after lesions takes place (De Paola et al., 2006). It can also help understand how certain structures arise as a result of exposition to adequate external stimuli during critical periods in the development of the brain (Hensch, 2005) and the mechanisms underlying experience dependent structural synaptic plasticity (Holtmaat and Svoboda, 2009).
An appropriate model of structural plasticity that incorporates the dynamic generation, deletion and rewiring of synapses within a network was presented by Butz and van Ooyen (2013). In this model, synapses are represented as connections between pre and a post synaptic elements. The growth or diminishment of these synaptic elements is an independent process for each neuron. The model is based on the idea that plasticity in cortical networks is mainly driven by the need of individual neurons to homeostatically maintain their average electrical activity. As a consequence, if activity is lower than a desired set-point, neurons will form synaptic elements, and remove them when activity becomes too high. Additionally, a minimum level of activity is needed to form synaptic elements at all. If activity falls below this level the neuron will remove synaptic elements, too. Results show that small networks of hundreds or thousands of neurons robustly grow toward a stable homeostatic equilibrium of activity and connectivity. An important advance on earlier work is that all cell types had different desired average firing rates (achieved by different homeostatic set-points) and developed connectivity accordingly. It was shown that these local rules for structural plasticity can account for network rewiring after a partial loss of external input (deafferentation) and shows remarkable similarities with biological data from network rewiring in the primary visual cortex after focal retinal lesions (Keck et al., 2008; Yamahachi et al., 2009). Further analysis by Butz et al. (2014) of changes in network topology revealed that betweenness centrality could be used as an indicator of successful brain repair, in the sense that it is related to the ability of the neurons to restore their electrical activity by network rewiring. It was concluded by the authors that structural plasticity may account for network reorganization on different spatial scales.
In this work, we provide a complete description of how the structural plasticity model proposed by Butz and van Ooyen (2013) could be implemented in the neuronal network simulator NEST (Gewaltig and Diesmann, 2007) in order to create self-organizing large scale neural networks. We evaluate the scalability of the implementation and assess the performance of the model on two use cases. We demonstrate that our implementation is capable of self-organizing the connectivity within a cortical microcircuit model consisting of 100, 000 neurons in total, starting with a fully disconnected setup. We also show the scenario where partial information of the connectivity is given as initial condition and an stable connectivity pattern is obtained in the end.
The structural plasticity extension to NEST is included in release 2.10.0 (Bos et al., 2015) and creates a novel possibility for setting up large-scale neuronal networks. While supercomputers are required for very large-scale simulation, we show that smaller networks can also be run on a personal workstation or laptop according to the NEST development philosophy. This is a fundamental advantage of this implementation of structural plasticity in terms of capacity to test different configurations, as it provides high flexibility and portability for the neuroscientist.
The corresponding extension of the Python interface of NEST (PyNEST) allows the user to set up their own structural plasticity experiments for large scale networks.
The rest of this work is divided into three major parts. The first describes the major elements of the structural plasticity algorithm and the set of tests that were designed in order to measure the performance of an implementation of this algorithm. We also present some use cases for the structural plasticity framework. In the second part we provide the results of the technical implementation in NEST and describe how the design matches the memory and speed requirements for large scale simulations. We also present results for the use cases described in the previous section. In the third part, we discuss the results of the implementation and performance tests.
Some of this material has previously been presented in abstract form (Naveau and Butz, 2014).
2.1. The Algorithm of Structural Plasticity
The original formulation of the structural plasticity algorithm defined in Butz and van Ooyen (2013) consists of three repeating steps which are described as follows:
(1) Update in electrical activity and intracellular calcium concentration. The electrical activity is calculated for each neuron on a millisecond timescale. The time-averaged level of the neuron's electrical activity drives changes in neuronal morphology. Intracellular calcium concentration is updated according to the electrical activity as follows:
dCadt={−Ca(t)τ+βif the neuron fires−Ca(t)τotherwise (1)
where τ is the calcium decay constant and β is the calcium intake constant which indicates how much calcium is accumulated each time the neuron fires. Calcium concentration is linearly proportional to average firing rate and thus is the measure that is used to guide the growth dynamics of the synaptic elements.
(2) Update in synaptic elements. The detailed morphology of the synaptic elements is abstracted, and is represented in this formulation only by the number of possible synaptic contacts on axons (axonal elements representing axonal boutons: senders of synaptic activity) and on dendrites (dendritic elements representing dendritic spines: receivers of synaptic activity) collectively called synaptic elements. Synaptic elements are created or deleted according to a homeostatic rule. In general, the homeostatic rule will create synaptic elements when the activity is lower than the desired setpoint and delete them when the activity is higher until the desired activity level is achieved. This homeostasis is represented by a curve which defines how quickly new elements are created or deleted according to the current level of electrical activity. The original work considers two types of growth curve, linear and Gaussian:
dzdt=ν(1−1ϵCa(t))
where ν is the growth rate and ϵ is the target level of calcium concentration that the neuron should achieve.
Gaussian:
dzdt=ν(2exp(−Ca(t)−ξζ)−1)
where ξ = (η + ϵ)∕2, ζ=(ϵ-η)∕2ln2 and ν is the growth rate as before. In this Gaussian growth curve, η represents the minimum amount of calcium concentration that the neuron must have in order to start creating new synaptic elements. Same as in the linear growth, ϵ represents the target level of calcium concentration that the neuron should achieve.
A synaptic element is formed (or deleted) when the rounded down z value increase (or decrease) by one. Newly-formed synaptic elements are initially vacant and available for synapse formation.
(3) Update in connectivity. In every connectivity update, available synaptic elements allow the formation of new synapses and deleted synaptic elements dictate synapse breaking. Every available synaptic element has the same probability to be randomly chosen for a new connection. Synaptic elements to be deleted are also chosen in a uniformly random manner out from the pool of already connected elements. It is important to notice that in this algorithm when a synapse breaks due to the deletion of one synaptic element, the counterpart remains and becomes vacant again. This remaining counterpart can form a new synapse at the next update in connectivity. This effect models network rewiring by re-routing of axons or dendrites.
An important characteristic of this algorithm, is that it relies on global communication to update the connectivity in the network, as available compatible synaptic elements must be matched during the simulation to create new connections. This must be taken into consideration for the design of any implementation of this model.
2.2. Scalability
To assess the scalability of the framework, we designed strong and weak scaling tests of the structural plasticity implementation. For all tests, networks with 80% excitatory and 20% inhibitory neurons were created. The growth rate for synaptic elements in the simulation was set to 4.0 × 10−4elements∕ms for the excitatory elements of the inhibitory population and 1.0 × 10−4elements∕ms for all the other elements. The set point for desired calcium concentration in the excitatory population was defined as 0.05 Ca2+, while in the inhibitory population it was set to 0.2 Ca2+. The calcium concentration intake constant was set to β = 0.001 and the calcium concentration decay constant to τ = 10000.0 for all neurons. The post synaptic amplitude of individual synapses was set to 1.0 mV. External input was provided using a Poisson generator with a frequency of 104 Hz. The post synaptic amplitude of individual synaptic input was set to 0.01 mV. The simulation was run for 100 s, with a step size for the numerical integration of 0.1 ms. The updates in the network connectivity were performed every 10 ms. These values were chosen as they proved to be one parameter combination that allowed for stable self-organizing growth of the network toward the homeostatic equilibrium (See Section 3.3.1 for additional comments on the selection of this parameter set).
Weak scaling tests were performed for networks with 5000 neurons per node and settings of 1, 2, 4, 8, and 16 nodes, each node using 28 cores. Strong scaling tests were performed with a network of 100, 000 on the same hardware configurations as the weak scaling tests. Only physical cores were used, no simultaneous multithreading was enabled. A hybrid optimization approach was chosen, in which MPI is used for communication between nodes and OpenMP for intra node communication. All measurements were performed on the JUROPATEST cluster, which provides up to 70 nodes (T-Platforms V210s Blades), each with 2 × Intel(R) Xeon(R) CPU E5−2695 v3 (Haswell) with 14-core processors (2.30 GHz) and 128 GB DDR memory, running with Scientific Linux release 6.5 (Carbon).
2.3. Use Cases for the Structural Plasticity Framework
The main objective of the structural plasticity framework is to provide the user with a tool to model the dynamic creation and deletion of synapses between neurons of a neural network in a scalable manner. There are several applications in which structural plasticity can be used. In this section we detail two use cases as examples. The first use case shows the basic functionality of the framework and how it can be used to study the relationship between connectivity and activity. We also show how this simple set-up can model critical development periods in the network connectivity. The second example is a more complicated case with several populations, where the objective is to show how connectivity can be self-generated in a network by using the synaptic element growth curves as connectivity fitness rules. All simulations were carried out with NEST version 2.8.0 extended by our structural plasticity implementation.
2.3.1. A Simple Two Population Network
In this initial use case, we generate a network with a total of 1000 leaky integrate and fire neurons, 80% excitatory and 20% inhibitory. For the excitatory neurons, η = 0.0, ϵ = 0.05 and ν = 1.0 × 10−4 elements∕ms. For the inhibitory neurons, η = 0.0, ϵ = 0.2 and ν = 1.0 × 10−4elements∕ms, except for the excitatory elements which had ν = 4.0 × 10−4elements∕ms. The connectivity in the system was allowed to evolve using a Gaussian growth curve for 3000 s, with an integration step of 0.1 ms and a delay of the connectivity update equal to 100 integration steps. The simulations were performed on a workstation with 8 Intel core i7 − 4770@3.4 GHz CPUs running openSUSE 13.1.
2.3.2. The Cortical Microcircuit Network
In this second use case, we create a four layer network based on the model of the cortical microcircuit proposed by Potjans and Diesmann (2014). Each layer contains one inhibitory and one excitatory population of leaky integrate and fire neurons. In the simulations presented here, the network starts with the same number of neurons in each population as in the previous study, but without any synaptic connections. For each population, we define a level of desired mean electrical activity based on experimental literature and a growth curve which defines the dynamics of the variation in the number of pre- and post-synaptic elements. These are Gaussian shaped curves with two intersections with the x-axis that determine the minimum amount of electrical activity required to form any synapse (η), and the target mean calcium concentration for the neuron (ϵ). The curves are illustrated in Figure 1.
Figure 1. Growth curves for each synaptic element in each layer of the cortical microcircuit model. The growth curves define the rate at which synaptic elements are created depending on the amount of calcium concentration in the cell at the moment. Red curves are for neurons in the excitatory population. Blue curves are for neurons in the inhibitory population. Solid lines are for the excitatory synaptic elements and dotted lines represent inhibitory synaptic elements. The vertical purple line defines the target level of calcium concentration for excitatory neurons and the vertical cyan line represents the target level of calcium concentration for inhibitory neurons. It is important to highlight that all synaptic elements of the same neuron must have a growth curve with the same target level of calcium concentration, otherwise equilibrium will never be reached.
In the first example, we tune the growth rate to achieve an stable growth regime for the network connectivity. This means that the structural plasticity algorithm will stop creating and deleting synaptic connections when the desired mean activity is reached, and that this mean activity is actually reached on average in each population. In a second example, the growth rate provided leads to an unstable connectivity pattern, where the target mean electrical activities are never reached by all populations. A table containing the parameters for both cases can be seen in Appendix in Supplementary Material.
A third example was run to illustrate a more common situation where there are some assumption about the connectivity in a network and where the structural plasticity framework can help to find a suitable balance between excitation and inhibition in the network. Here we used the original model of Potjans and Diesmann (2014) and enable the structural plasticity after an initial stabilization period of 30 s.
Simulations were performed on JUROPATEST (70 nodes with 2 × 14-core processors Intel(R) Xeon(R) CPU E5−2695 v3 (Haswell) at 2.30 GHz and 128 GB DDR memory, running with Scientific Linux release 6.5) and JURECA (with 260 compute nodes with Intel Xeon E5−2680 v3 Haswell CPUs with 2 × 12 cores per CPU, 128 GB of RAM per node and running on CentOS 7 Linux distribution).
3.1. Implementation of the Structural Plasticity Model into NEST
The implementation of the structural plasticity algorithm described in this work is based on the version 2.8 of the NEST software Eppler et al. (2015). In accordance with the original formalization described in Section 2.1, the algorithm consists of three repeating parts which can be visualized in a general form in Figure 2 and described as follows:
1. Update in electrical activity and intracellular calcium concentration. The Archiving_Node class, which is the general interface for all neurons, was modified and new variables to store the of the intracellular calcium concentration, the calcium decay and calcium intake constants were added. The Archiving_Node::set_spiketime method was also modified to update the calcium concentration according to the first case defined in (1), which is performed at every time the neuron spikes. The Node class was modified by adding the method Archiving_Node::update_synaptic_element. This method updates the calcium concentration according to the second case defined in (1). This method is called by the Scheduler class when every synaptic update interval is reached.
2. Update in synaptic elements. The first step taken in order to design and develop a framework for synaptic elements (e.g., axonal boutons and dendritic spines) was to redefine synapses in such a way that they can now be described using connection elements. This description can be applied to every available neuron model in NEST for generating electrical activity. The design also considers that the users can define their own synaptic elements and their corresponding growth dynamics. The class SynapticElement was created in order to represent the connection points for the neurons. The class GrowthCurve was also created in order to define the homeostatic rules which guide the creation and deletion of synaptic elements. Currently, the available growth dynamics are based on either a linear or a Gaussian growth curve. The linear growth curve uses an exact integration method to update the number of synaptic elements, while the Gaussian growth curve uses a forward Euler integration method. The framework can be further extended by the user to incorporate more complex element growth dynamics models. An example of such curves is shown in Figure 1, where independent dynamics for each type of element in a network of 8 populations (see use case on cortical microcircuit) have been defined. Synaptic elements are used as a discrete value, the actual number of available synaptic elements is an integer truncated from the float variable used to represent them. The Archiving_Node class now incorporates a map data structure to store the synaptic elements. The method Archiving_Node::update_synaptic_element takes care of updating the number of each of the synaptic elements in the map using the value of the calcium concentration at the time of the call and the corresponding growth curve. The method Archiving_Node::decay_synaptic_element_vacant takes care of deleting a percentage of the unused or vacant synaptic elements on every call. Both methods are called by the Scheduler at the end of every synapse update interval.
3. Update in connectivity. To coordinate the changes in the structure of the network, a new StructuralPlasticityManager class was implemented. At the end of every synapse update interval, the Scheduler calls the the newly implemented structural plasticity connectivity manager via the Network class. The StructuralPlasticityManager determines, for each neuron, how many vacant synaptic elements are available for new synapse formation and how many deleted synaptic elements caused synapse breaking. Then it makes use of the ConnBuilder in order to create or delete connections. For this, the ConnBuilder was extended to include the new methods ConnBuilder::sp_disconnect_ and ConnBuilder::sp_connect_. Once new synapses are formed, synaptic elements are tagged from "vacant" to "connected." It is important to notice that when a synapse breaks due to the deletion of one synaptic element, the counterpart remains and becomes vacant again. This remaining counterpart can form a new synapse at the next update in connectivity. This effect preserves the network rewiring capabilities of the original formulation. A detailed diagram of how the new calls are integrated into the normal simulation flow of NEST can be seen in Figure 3.
Figure 2. Diagram of the implementation of the structural plasticity model in NEST. In (1) the number of synaptic elements is calculated depending on the electrical activity of the neuron. These calculations are optimized using MPI and OpenMP. In (2) the structural plasticity manager gathers the number of synaptic elements per neuron using MPI directives and in (3) creates or deletes synapses to update the connections between neurons using MPI and OpenMP.
Figure 3. Integration of the new structural plasticity calls into the normal simulation flow of NEST.
An important feature that we implemented to simulate structural plasticity in NEST is the ability to create and delete synapses during the simulation time. Our new implementation of the connection management overcomes the limitation of the NEST simulator that currently models networks with a fixed connectivity. We have implemented the dynamic creation and deletion of synapses using the new connection framework released in version 2.6.0. The new connection framework improves memory usage to store connection data and reduces the computation time needed to create a connection.
The main limitation of the structural plasticity algorithm described by Butz and van Ooyen (2013) is that it requires global knowledge of the synaptic elements of the entire network. Fortunately, the MPI global communications, also used by the NEST kernel to communicate the electrical activity between the neurons during the simulation, do not pose a substantial bottleneck since changes in connectivity are assumed to take place on average around a factor of 100 times slower than changes in electrical activity. Therefore selecting a biologically realistic growth rate of around 10−4elements∕ms will result in an exchange of data that is sufficiently low rate so as not to impact the scalability of the simulator as a whole. At the end of each connectivity update step, the number of created/deleted synaptic elements per neuron are communicated to all MPI processes and a global shuffle subsequently assigns the new pairs of neurons that should be connected, and likewise chooses existing connections for deletion. In the current implementation, no topological constraints are taken into account while deciding which neurons will be connected. The probability of two neurons connecting to each other depends solely on the number of available compatible synaptic elements between them. The actual creation and deletion of the synapses is finally done in parallel using the NEST connection framework. As stated before, a single update in connectivity should not produce a major modification of the network. That means that only a small part of the neurons should create or delete a synaptic element between two updates in connectivity.
It is important to highlight that the usage of global communication is a characteristic of the technical implementation of the algorithm and is not related to the functionality of the model. If topology was to be taken into account, the ability of a neuron to connect to any other would be limited by the constraints imposed by its relative position to others. Global communication would still be used by the implementation, but only relevant information would be taken into account to define the connectivity. The local homeostatic rules only define the creation or deletion of synaptic elements per neuron. The number of available synaptic elements is transmitted globaly and the synaptic plasticity manager takes care of forming new synapses or deleting existing ones based on this information.
The update of electrical activity and of the number of synaptic elements is performed by every individual neuron and therefore benefits from the parallel framework already implemented in NEST. Indeed, the NEST software has already demonstrated its high scaling properties on supercomputer, including the JUQUEEN system (Helias et al., 2012; Kunkel et al., 2014).
Finally, the Python interface of NEST (PyNEST) was extended to allow users to easily set up the structural plasticity parameters. It is important to highlight that the user can enable structural plasticity inside the simulation and then disable it when the network has achieved a desired connectivity pattern or activity level. The user can now also delete synapses even without enabling structural plasticity, in a similar way as the connect functions work in NEST.
3.1.1. Setting up a Network in NEST with Structural Plasticity
In this section we will introduce the high level functions that are introduced into NEST with the structural plasticity framework using PyNEST.
In order to set up the network using structural plasticity, one first needs to define the time at which updates in the structure of the network should take place as follows:
nest.SetStructuralPlasticityStatus({
'structural_plasticity_update_
interval':
update_interval,
The next step is to define the synapses which can be dynamically modified by the structural plasticity manager during the simulation. This is achieved by:
'structural_plasticity_synapses': {
'structural_plasticity_synapse_
ex': {
'model': 'structural_
plasticity_synapse_ex',
'post_synaptic_element'
: 'Den_ex',
'pre_synaptic_element'
: 'Axon_ex',
in': {
plasticity_synapse_in',
: 'Den_in',
: 'Axon_in',
Here, two types of synapses are being defined, one for the excitatory synapses and another one for the inhibitory synapses. It is important to notice that in this definition, a name for the post and pre synaptic elements is also specified. This allows the structural plasticity manager to create new synapses of the type specified in model when synaptic elements related to this label become available. This way of setting up the dynamic synapses also allows the user to define static connectivity constraints in the network. This can be achieved by using one synapse model which is not registered for structural plasticity to define this fixed connectivity. For the moment, no other constraints in connectivity like indegree or outdegree ranges can be specified. Nevertheless, thanks to its flexible design, the model can be extended to add new constraints.
Next step involves defining the growth curves for the synaptic elements defined above. This is done as follows:
growth_curve_e_e = {
'growth_curve': "gaussian",
'growth_rate': 0.0001,
'continuous': False,
'eta': 0.0,
'eps': 0.05,
This is an example of a Gaussian growth curve where the minimum level of calcium concentration required to start generating synaptic elements is η = 0.0 Ca2+, and the desired calcium concentration is set to ϵ = 0.5 Ca2+. Finally, the rate at which the synaptic elements will grow is ν = 1 × 10−4elements/ms. Independent growth curves can be created for each synaptic element.
Now that we have defined the growth curve, we can assign this growth curve to the synaptic elements that each neuron will be able to grow. After that, we create the neurons and let NEST know that these synaptic elements are linked to the neurons:
synaptic_elements = {
'Den_ex': growth_curve_e_e,
'Den_in': growth_curve_e_i,
'Axon_ex': growth_curve_e_e,
nodes = nest.Create('iaf_neuron',
number_excitatory_neurons)
nest.SetStatus(nodes, 'synaptic_
elements', synaptic_elements)
In this case we are creating the neurons pertaining to the excitatory population. Each neuron has three types of synaptic elements, one dendritic excitatory, one dendritic inhibitory and one axonal excitatory.
The final step is to enable structural plasticity and simulate:
nest.EnableStructuralPlasticity()
nest.Simulate(t_sim)
A complete PyNEST example which describes how to create a network with two populations, enable structural plasticity and simulate the network is available as Supplementary Material for this paper.
While the update in electrical activity has been proven to scale up to 109 neurons, it is important to verify that updating the number of elements and the deletion and formation of synapses does not restrict the expected scaling, at least in the desired regime of up to 106 neurons. Updates in synaptic elements and connectivity make use of MPI's "AllGather" communication scheme to communicate the data. This collective communication is also used by the NEST kernel to communicate the spiking activity between the neurons during the simulation. Although AllGather implements communication between all processes, it is very unlikely that a huge amount of data has to be communicated when a reasonable growth rate of around 10−4elements∕ms because updating the number of synaptic elements and the connectivity are very slow processes compared to the update in electrical activity.
3.2.1. Weak Scaling
Figure 4A shows the efficiency, defined as the speed-up divided by the number of nodes, of the implementation as measured by a weak scaling test with 28 OMP threads running on each node. It is visible that, as the number of neurons increases, so does the total number of synapses. The presence of new synapses leads to an increase of communication between neurons, which leads to a decrease in the efficiency of the simulation.
Figure 4. Results of the scalability tests performed with structural plasticity. (A) Efficiency as a function of the number of nodes for 5000 neurons in the weak scaling test. The network was allowed to grow synapses following the structural plasticity rules during a simulation of 100 s of biological time. (B) Simulation time (red curve) as a function of the number of nodes for a network of 100, 000 neurons and in the strong scaling test. The blue curve indicates ideal linear scaling. (C) Efficiency as a function of the number of nodes for a network of 100, 000 neurons in the strong scaling test. The peak scaling efficiency is marked with a star.
3.2.2. Strong Scaling for a Network of 100,000 Neurons
Figure 4B shows the computation times of the strong scaling tests for a network of 100, 000 neurons, and Figure 4C shows the efficiency, defined as speed-up divided by the number of nodes, of the strong scaling test. The peak efficiency is achieved with 4 nodes and 112 cores. These results show supra-linear scaling for this network. In Morrison et al. (2005) and Plesser et al. (2007), supra linear scaling for biological neural networks on NEST was demonstrated due to increasingly efficient caching.
These results show that the introduction of the new structural plasticity framework into NEST has no impact in the scalability of the simulation up to a network size close to that of a cortical column if a suitably low growth rate is selected.
3.3. Performance on the Use Cases
The upper panel of Figure 5 shows the evolution of the calcium concentration and total number of connections for the two population model described in 2.3.1.
Figure 5. Upper panel: Calcium concentration and numbers of connections as functions of time in a simple two population network. The cyan and black curves show the calcium concentration measured in the inhibitory and excitatory populations, respectively. The paler horizontal lines indicate the corresponding target levels ϵ. The blue and red dashed curves indicate the total number of connections in the inhibitory and excitatory populations, respectively. Vertical gray lines indicate the times of the snapshots displayed in the lower panel. Lower panel (A–F): Evolution of the connectivity in the two population network visualized using MSPViz. Images show half of the total amount of neurons in the network, where triangles represent excitatory neurons and circles inhibitory neurons. Red lines indicate excitatory connections while blue lines indicate inhibitory connections.
The lower panel of Figure 5 shows a graphical representation of the evolution of the connectivity in the network. During the first 30 s of the simulation, mostly excitatory connections are created (Figure 5A). This allows the calcium concentration to increase in both populations. When the target mean electrical activity is reached and overshoots in the excitatory population (Figure 5B), the number of excitatory connections starts to decrease (Figure 5C) until the desired level of calcium concentration is achieved and stabilized in the excitatory population. However, both pre- and post-synaptic elements in the inhibitory population are still being created because it has not yet reached its target mean electrical activity. It is important to remember that neurons have no information regarding the global status of the network and the evolution of their synaptic elements depends solely on the predefined homeostatic local rules. At around 40 s (Figure 5D), an increment in excitatory connections is triggered by the enhanced levels of inhibition. This leads to a complete rewiring of the network (Figure 5E). The trend is preserved until the mean electrical activity in the inhibitory population is also reached (Figure 5F).
In this network setting, the inhibitory population has a higher level of activity than the excitatory population. It is important to remember that the probability of two neurons connecting depends only on the number of available compatible synaptic elements between them. At the start of the simulation, the inhibitory population must offer more post-synaptic elements for excitatory synapses than the excitatory population, otherwise the excitatory population would reach equilibrium first and cease to create excitatory pre-synaptic elements. As a result, not enough excitatory synapses would be created to the inhibitory population and it would never reach the desired level of activity. It is important to remember that the structural plasticity parameter space is broad and a certain amount of exploration is required to discover combinations for each synaptic element which take the network to equilibrium. However, there is in general no unique combination of parameters leading to equilibrium, and different equilibrium combinations will typically produce different connectivity patterns. At this point, biological constraints must be applied to choose between them.
In the case of the cortical microcircuit model described in 2.3.2, Figure 6 shows the changes in calcium concentration, while Figure 7 shows the evolution of connectivity among layers as the simulation runs. In this case, parameters which lead to stable network connectivity were chosen. Reaching stable connectivity in the networks takes around 700 biological seconds of simulation, which takes 24 h using 25 nodes and 24 cores per node in the JURECA cluster to simulate. It is visible that during the first 20–30 s of simulation, connectivity highly increases on every layer. After the initial overshoot, a smoother approximation toward the desired activity levels is achieved. As seen only from the calcium concentration diagram, the evolution of the network appears to be quite stable. Regardless, the connectivity plots show a continuous dynamical reorganization. While neurons on some layers might start deleting connections due to excess of activity, the post-synaptic neurons must then create new connections in order to compensate for missing activity in case they have not reached their setpoint yet. This leads to a continuous search for compensating excitation and inhibition which must satisfy the requirements of all 8 populations. From Figure 7 it can be seen that outgoing connections from excitatory populations on layers IV, V, and VI are quite stable. On the other hand, layer II/III exhibits the highest amount of reorganization, both from the excitatory and inhibitory populations. This might be due to the fact that their reduced target levels of activity might be easily influenced by variations in all other layers. Inhibitory populations on all layers in general exhibit a higher degree of reorganization during the whole simulation.
Figure 6. Evolution of calcium concentration in each layer of the cortical microcircuit model. Pale horizontal lines indicate the target concentration of the corresponding population. (A) Excitatory populations in layers II/III (red), IV (blue), V (black), and VI (orange). (B) Inhibitory populations in layers II/III (brown), IV (cyan), V (gray,) and VI (purple).
Figure 7. Evolution of connectivity in the microcircuit model resolved by source and target population. Panels on the left and right illustrate efferent connections from excitatory and inhibitory populations, respectively, while the vertical arrangement indicates the layer of the source neurons. In each panel, the numbers of connections to each of the eight population in the model are shown as a function of time. The color of the curves indicates the target population, as in Figure 6: connections to excitatory populations in layers II/III (red), IV (blue), V (black), and VI (orange) are shown as solid curves, and connections to inhibitory populations in layers II/III (brown), IV (cyan), V (gray), and VI (purple) are shown as dashed curves.
The search space of connectivity parameters for this model of the cortical microcircuit is large as each setup requires 64 values to be defined. If a brute force exploration would be performed on these parameters by simulating each combination for 1 biological second, only 1−2 values per parameter could be considered before more biological seconds would be simulated than using the structural plasticity approach. When adequate synaptic element growth curves are defined, the structural plasticity framework allows a progressive exploration of the space in which the dynamics of the the 8 populations are balanced at every step, thus providing an efficient way to find stable connectivity combinations.
Figure 8 presents a comparison between the proportional values of connectivity among layers between the results obtained from the simulation using structural plasticity and the original values reported by Potjans and Diesman. The average error in percentual connectivity is of 1.058 ± 1.175.
Figure 8. Comparison of the normalized connectivity in the microcircuit model between the results obtained with the structural plasticity framework (red) and the values reported by Potjans and Diesmann (2014) (blue). The radius of the circle represents the linearly normalized value of the percentage of connections between layers.
A second case was also explored, in which parameters lead to unstable network activity are chosen. Figure 9 shows the evolution of connectivity among layers and Figure 10 shows the changes in calcium concentration in each layer for this scenario. Overshoots in the connectivity, are originated by a choice of higher rate in the creation of synaptic elements. The system behaves as a feedback control system, with a delay which is defined by the time between updates in connectivity and the synaptic element creation rate. The synaptic element growth rate determines the steepness of the growth curve, and influences the speed at which control changes are made. The instability in the connectivity is reflected in the calcium concentration, never reaching the desired levels. A stable setting involves finding a suitable balance between the speed in the creation of excitatory and inhibitory connections related to the desired level of activity for each layer.
Figure 9. Evolution of connectivity through time for each layer in the cortical microcircuit model with an unstable set of parameters. Panels on the left illustrate connections incoming from excitatory populations. On the right, connections incoming from inhibitory populations. Rows show connections incoming from layers II/III, IV, V, and VI from top to bottom, respectively. On every panel, connections to excitatory populations in layers II/III (red), IV (blue), V (black), and VI (orange) are shown as solid curves, and connections to inhibitory populations in layers II/III (brown), IV (cyan), V (gray), and VI (purple) are shown as dashed curves.
Figure 10. Evolution of calcium concentration in each layer of the cortical microcircuit model with an unstable set of parameters. (A) Shows the calcium concentration in the excitatory populations in layers II/III (red), IV (blue), V (black), and VI (orange). (B) Shows the calcium concentration in the inhibitory populations in layers II/III (brown), IV (cyan), V (gray), and VI (purple).
To study the behavior of the structural plasticity algorithm on partially pre-connected networks, another simulation was set in which the initial conditions in connectivity for the structural plasticity algorithm were those specified in the original model of Potjans and Diesman. The network was simulated without plasticity for an initial period of 30 s in order to allow the calcium concentration reach an initial stable value. The evolution of the calcium concentration in all layers after the structural plasticity algorithm was enabled can be seen in Figures 11A,B. The stability point is reached a lot faster than in the scenario with no initial connections, at around 400 s. A final simulation was set in which the connectivity was specified with a 10% error margin from the original setup reported by Potjans and Diesman. The evolution of the calcium concentration in all layers after plasticity was enabled can be seen in Figures 11C,D. The structural plasticity algorithm is able to find a suitable balance between excitation and inhibition. The initial overshoot in electrical activity is a reflection of the initial stronger reconfigurations of the network connectivity. It is important to highlight that a suitable growth scheme is required for the algorithm to reach this stability. Not all setups will become stable or find a solution, this depends on the initial conditions, the desired set points, the shape of the growth curve and the growth rate.
Figure 11. Evolution of calcium concentration in each layer of the cortical microcircuit model with partially pre-connected initial conditions. Pale horizontal lines indicate the target concentration of the corresponding population. Left pannels show excitatory populations in layers II/III (red), IV (blue), V (black), and VI (orange). Right pannels show inhibitory populations in layers II/III (brown), IV (cyan), V (gray), and VI (purple). (A,B) Show the scenario where the network was started with the connectivity as specified in the original work by Potjans and Diesman. (C,D) Show the same scenario but with a 10% error in the initial connectivity setup.
In this paper we have described the implementation of a framework of structural plasticity for the neural network simulator NEST. We show that the framework is scalable and can be used to model the dynamical creation and deletion of synapses inside a large scale network guided by simple homeostatic rules.
This work also presents some use cases for the framework and some of its potential applications. Researchers can now use structural plasticity in NEST to generate the connectivity of a network from scratch, defining homeostatic rules, in form of synaptic element growth curves, which may vary according to their needs. The shape of the growth curve defines the speed with which new synaptic elements are created, and as a result, defines the acceleration at which calcium is stored inside the neuron. The relationship between the growth speed at certain level of calcium concentration of excitatory and inhibitory elements is fundamental to achieve stable setups under the model of structural plasticity. As is has been shown, some parameter combinations lead to unstable activity in the network. There are cases where the desired average electrical activity will never be reached by the system. In other cases the average electrical activity will oscillate continuously or suddenly go out of bounds. This relationship depends also on the size of the network and the neuron model used. As a consequence, some care is required in navigating the parameter space in order to achieve desired results.
The example of the two population network illustrates how this framework can be used to understand the interaction between activity and the creation of synapses. The behavior observed in the simulation can be used to model how inhibition triggers critical periods of connectivity during development of neural networks (Hensch, 2005). During this window, external stimuli can also be used to shape the formation of the new connections. Together with the performance measurements, these results show that our implementation of structural plasticity is suitable to study the development of connectivity patterns inside a neural network in an efficient and scalable manner.
In the specific case of the cortical microcircuit presented in this work, we are able to see some similarities and differences between the results obtained by simulating with the structural plasticity framework and the data reported in Potjans and Diesmann (2014). One of the most visible differences is the smaller amount of recurrent connections generated in the simulation for layer 2/3. This layer has a very low target electrical activity, which is initially almost reached by external input. This means that very few synapses are required to reach this target. This fact limits the creation of synapses for this layer. Note that the results shown in this paper were obtained only by defining target activity levels; no other connectivity constraints were specified. A more elaborate simulation could incorporate tailored growth curves for each layer, and implement additional connectivity restrictions which promote recurrent connections and other connectivity patterns that do not emerge naturally from the current approach.
Another visible difference is that the excitatory population of layers 5 and 6 show a higher number of connections than the ones shown in the original work. On the other hand, connections from and to the inhibitory population of layer 5 and layer 2/3 are well fit. Except from connections between the inhibitory and excitatory populations of layer 4, connections from and to layer 4 are also well predicted.
In this paper we describe a framework which can be used to study structural network dynamics. The focus of this paper is on the technical implementation. It is not the scope of the present work to perform a deep analysis of the biological results that can be obtained using this framework. However, we provide some examples of how the framework can be used, its capacities and limitations. This implementation gives researchers flexibility to explore complex connectivity dynamics by extending the synaptic elements growth rules. As our implementation is integrated into NEST, simulations using structural plasticity can also be combined with other features available in the simulator. For example, the user may take into account dynamic synaptic weights by mixing this framework with synaptic plasticity. The framework can also be further extended using the current topology framework in NEST in order to constrain connectivity by relative position.
We also show that the structural plasticity algorithm is able to solve the complex balance of interaction between layers with different levels of electrical activity when partial information of the connectivity is available. This result is very promising, as it shows that given the right growth rules, it would now be possible to reconstruct connectivity inside a network without having exact anatomical information. We therefore conclude that our approach represents a novel and useful technique to close the current gaps in information about the connectivity in certain regions of the brain.
SD worked on the implementation of the structural plasticity framework in NEST, simulated the use cases and performed the scalability tests. MN did most of the implementation of the structural plasticity framework in NEST and assessed the simulations. MB gave the theoretical guidance regarding the structural plasticity algorithm and the use cases. AM gave scientific and theoretical guidance on the simulation and implementation of the framework.
This work was supported by the Helmholtz Association through the Helmholtz Portfolio Theme "Supercomputing and Modeling for the Human Brain" and its Initiative and Networking Fund, and by the Jülich Aachen Research Alliance (JARA).
We would like to thank Juan Pedro Brito Méndez and Benjamin Weyers for their collaboration in the development of the MSPViz tool to visualize the evolution of neural networks using our implementation of structural plasticity in NEST.
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/article/10.3389/fnana.2016.00057
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Keywords: structural plasticity, large scale neural networks, high performance computing, homeostatic growth, self-organizing network
Citation: Diaz-Pier S, Naveau M, Butz-Ostendorf M and Morrison A (2016) Automatic Generation of Connectivity for Large-Scale Neuronal Network Models through Structural Plasticity. Front. Neuroanat. 10:57. doi: 10.3389/fnana.2016.00057
Received: 30 November 2015; Accepted: 06 May 2016;
Jackson Cioni Bittencourt, University of Sao Paulo, Brazil
Nuno Miguel M. Amorim Da Costa, University of Zurich (UZH) and Swiss Federal Institute of Technology Zurich (ETH), Switzerland
Ping Liu, University of Connecticut Health Center, USA
Copyright © 2016 Diaz-Pier, Naveau, Butz-Ostendorf and Morrison. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Sandra Diaz-Pier, s.diaz@fz-juelich.de


Warning! GuruFocus has detected 2 Severe warning signs with ORCL. Click here to check it out.
Oracle sells a wide range of enterprise IT solutions, including databases, middleware, applications, and hardware. While software licenses, support, and maintenance continue to represent roughly 70% of revenue, the firm is undergoing a mix shift toward cloud-based subscriptions that should necessitate continued heavy investment in the business model transition.


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DOI: 10.2174/2211544711201020132
Current Catalysis
Title:Jet Fuel Hydrocarbons Production from Catalytic Cracking and Hydrocracking of Soybean Oil
Author(s): Huali Wang, Shuli Yan, Manhoe Kim, Steven O. Salley and K. Y. Simon Ng
Keywords: Jet fuel hydrocarbons, catalytic cracking, hydrocracking, renewable fuels, transesterification, CME, SME, biomass, hydroisomerization, hydrocracking, isotherms, thermal conductivity detectors , flame ionization, OLP, chromatogram
Abstract: Hydrocarbons representative of jet fuel were produced by both catalytic cracking and one-step catalytic hydrocracking of soybean oil in a lab-scale flow reactor. The yield of kerosene/jet fuel hydrocarbons by catalytic cracking over a commercialized ZSM-5 was as high as 21%. Both temperature and space velocity exhibited irregular effects on the product distribution. Steady state was not attained and significant amounts of tar and coke were generated during the reaction. A bifunctional hydrocracking catalyst with 1.1% ruthenium supported on ZSM-5, prepared by an impregnation method, produced a 16% yield of jet fuel, which is comparable to yields over commercialized sulfided NiMo catalysts while at a much lower pressure of 650 psi. Compared to the catalytic cracking process over ZSM-5, steady-state flow reaction was obtained with less tar and polymerized products.
Wang Huali, Yan Shuli, Kim Manhoe, O. Salley Steven and Y. Simon Ng K., Jet Fuel Hydrocarbons Production from Catalytic Cracking and Hydrocracking of Soybean Oil, Current Catalysis 2012; 1(2) . https://dx.doi.org/10.2174/2211544711201020132
https://dx.doi.org/10.2174/2211544711201020132 Print ISSN
Innovations in Corrosion and Materials Science (Discontinued)
Metallic Oxynitride Thin Films by Reactive Sputtering and Related Deposition Methods: Process, Properties and Applications
Chemical Modification of Solid Surfaces by the Use of Additives

redpajama_set_name: RedPajamaCommonCrawl


Fridays can sometimes be a slow news day but that was not the case in the transit world as he were hit with a bombshell. MTA CEO & Chairman Jospeh Lhota resigned from his posts effective immediately days after Gov. Andrew Cuomo won a third term.
Sudden departures are nothing new for Lhota who stepped down at the end of 2012 after helping the agency through Hurricane Sandy to run for mayor of New York City.
During his second stint at the agency, his biggest accomplishments were getting more than $800M into the agency for repair work & new hires. However all was not well as he recently was receiving harsh criticism from watchdogs for having multiple jobs including sitting on the board at MSG which could be seen as a huge conflict of interest.
After working with you to save the transit system following Superstorm Sandy and returning for a second tour as Chairman, Joe Lhota is leaving the MTA today. We are extremely grateful for the steady leadership he provided during his tenure. Joe launched the Subway Action Plan, reinvigorated the MTA and set us on the path to success. His focus on delivering better daily service for our customers and prioritizing cost containment and procurement reform initiatives and working to create long-term sustainable funding sources were the hallmarks of his time leading the board.
While Joe may be departing – our relentless focus on our customers remains unchanged.
Thanks to your hard work, the MTA is well-positioned for a renaissance. New Yorkers recognize the value of reinvestment in our transit infrastructure and the groundwork has been laid for a long-overdue renewal. At the same time, at each of your agencies and at MTA Headquarters we are finding new and innovative ways to do our jobs and deliver results for the region. That work goes on and our commitment to support you is steadfast.
Every day you help millions of people get to work, school, and anywhere else they want or need to go. You may not realize it but you are the engine that makes the entire New York metropolitan region run. So thank you for all that you do now and what we will all accomplish together in the future.
"Joe Lhota has dedicated decades of his life to public service culminating in two tours of duty at the helm of the MTA. He stabilized the subway system, appointed a new leadership structure to completely overhaul the MTA, and led with a steady hand during some of the agency's most challenging moments. In short, Joe demonstrated time and again why he was the right person for the job. I am deeply grateful for his service to the State of New York.
"I want to take this opportunity to thank Governor Cuomo for the trust and support he has given me. His non-stop drive, enormous energy and vision are re-creating a better and stronger MTA. The Governor understands the over-arching importance of mass transit for the people and the economy of New York. His commitment and robust support to enhance and modernize the MTA into an integrated 21st century transportation system is unmatched and unwavering.
"In late spring 2017, following a well-documented period of rapid deterioration of transit services, I volunteered to become MTA chairman with the sole purpose of halting the decline of service and stabilizing the system for my fellow New Yorkers.
"The Subway Action Plan was developed in my first month at the MTA and it has successfully arrested the subway's decline. The plan has produced a 34.8% decline in major subway incidents causing delays (a comparison of 9/2017 to 9/2018). In September 2018 the number of total train delays fell to the lowest point since February 2016. There is still a long way to go to achieve the performance that New Yorkers demand and deserve. The proposed Fast Forward plan provides the roadmap for modernizing the entire system.
"When I agreed to return to the MTA it was with the understanding that I would maintain my private sector positions and delegate day-to-day responsibility to a new team. Accordingly, I created the Office of the Chairman for the purpose of managing the MTA. This office includes the managing director, president, chief development officer and chief financial officer. In addition, during my tenure I have appointed new leadership at the operating agencies by selecting new presidents at NYC Transit, Long Island Rail Road and Metro-North Railroad, a new general counsel and a new MTA chief safety officer. Together, they work every second of every day to further stabilize and enhance the MTA for the benefit of all New Yorkers.
As of now, Fernando Ferrer will once again take over until a new leader is in place which begs to question, who wants to take this job considering how unpopular the position is?
One thing that is definitely needed is the best person for the job regardless of their gender or race unlike the completely asinine virtue signaling from some who want to force feed their agenda on what the hire should be. Hopefully the decision will be made without the consideration of the virtue signaling & clueless clowns out there who clearly rather push their agenda over the needs of the agency.


Why are you running? I am running for the future of our children, we need to reform TIFs in an attempt to correct a system that began with good intent, but now plays to the powerful developers in an abusive manner. This system now inflicts damage on the average homeowner by unfairly increasing their home values and their tax burden. We need to strengthen the American dream for the young starting out, the elderly living in quiet and the middle aged wanting to provide a safe and happy home for their family. We need to stop borrowing and spending tomorrow's money today, stop putting more debt on the backs of our children. We need to start taking care of our cities, our state and our country first.
What is the most important issue facing Mascoutah? How would you approach it? The city of Mascoutah has lost its sense of direction and its role as a city. Its lack of transparency with the citizens, its abuse of using TlFs for fleecing of citizens property taxes, without consenting with them. The only members on the TIF-Joint Review Board are members of existing tax bodies that are recipients of some of the TIF taxes taken. And they are: Georgia Costello, Mark Kern, Gerald Daugherty, Nancy Larson, Debra Moil and Craig Fiegel not one citizen from any TIF zone. Today we should be concerned about the false sense of prosperity, that TIFs give us. With the inflated assessments they have created,Homeowners are paying more in taxes and insurance while living under the delusion that their bloated home EAV (assessments) have brought them prosperity. While our infrastructure is crumbling and the ability to sustain the American Dream is being eroded.
If you were writing a job description for the position you're seeking, what would it say? Mayor: integrity, with transparency, no more or any less, knowing that the city and its citizens come first, along with its infrastructure.
How would you keep costs down or reduce costs in your municipality? The city's infrastructure is crumbling; surface water protection district needs help for the pig mud river running under the downtown, it's crumbling, as are the streets and sidewalks. What's the city's debt ratio? These need to be addressed today. But because of the lack of transparency on where the TIF money has gone, we have no information as to how much money has been borrowed against the TIFs property, or by grants, general tax revenue, utility taxes, motor fuel tax and general revenue bonds. The city is making payments to seven different banks. The only way I can find out is to join the club; run for mayor.
In St. Clair County, there will be votes on two sales tax referendums. One would benefit school facility costs, one would benefit public safety. Do you support either or both of the referendums? Why or why not? I would make a point to get involved with all the taxing bodies that are related to our community, we should all work together for a common goal, a better community and more bang for the buck.
Why should people vote for you? For change, stop the·fleecing of our taxes and do what a city is supposed to do, take care of the city and its citizens first. Mascoutah now has 3 TIFs, and if you look at TIF 1 and TIF 2B zones they look today just like they did 16 years ago, the only big change is that property taxes are three times higher.


Often overlooked as one of the most important pioneer scientists and inventors, Nikola Tesla is a 20th-century Serbian-American well known for creating the alternating (AC) electricity system and being a rival to Thomas Edison.
Of the 300 patents he filed for his inventions during his lifetime, only a few would garner the attention they deserved and Tesla died alone without the world ever properly recognizing him for his ability to independently create a vision for breakthrough technology and then bring that vision to life.
Many reports indicate that he would see a visual image of a type of technology and then he would create that technology in reality without straying from the vision he saw in his mind. These created visions led the world to the basis of some of our most treasured technological advances to date.
In the 1920's Tesla also described a future of interconnected communication that could be what we experience today as the internet and social media. In 1901 he attempted to create a tower that would facilitate this electrical communication system but was unable to complete it due to lack of funding.
Among other visions for the future that he believed would come to pass Tesla was also quoted as describing a future where women would be viewed as superior to men.
While I am not a believer in the concept of superiority based on gender, I can appreciate the idea that the progress of humankind will be elevated tremendously when women are encouraged to recognize their greatness and contribute without hindrance to the advancement of the human race.
Thank you for this, I knew Nikola Tesla was a much overlooked inventor but I had not realised that he was also such a perceptive social visionary. Even if the superiority of the female gender in so many ways is now so obvious, in Tesla's time and culture this was a huge leap in perception.


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Obama Nominates Merrick Garland For SCOTUS, Gives Zero F*cks
Today, President Obama nominated DC Circuit Court of Appeals Judge Merrick Garland to the Supreme Court.
Rebekah KuschmiderJenni Berrett
And lo! From the Rose Garden did second term President Obama speak and tell the world that he has zero fucks to give!
He did bring forth a Supreme Court Nominee with shining angel wings and a bipartisan pedigree who will make the stonewalling Senate Republicans look like total schmucks.
Today, President Obama nominated DC Circuit Court of Appeals Judge Merrick Garland to the Supreme Court. Garland has a long resume that looks like something Aaron Sorkin would have invented in creating the character of an unblemished nominee.
He's a 60-something white guy, so no one can whine about affirmative action. He served as an Assistant U.S. Attorney for the District of Columbia from 1989 to 1992, and as Deputy Assistant Attorney General in the Criminal Division of the U.S. Department of Justice from 1993 to 1994 — so he has ample prosecutorial experience. Then the pièce de résistance: Garland was the chief prosecutor of the Oklahoma City bombing.
BOOM, GOP. Try talking smack about the guy who got the death penalty for homegrown terrorist, Timothy McVeigh. Dare ya!
Garland was appointed to the DC Circuit by President Bill Clinton in 1995. Then-Senate Judiciary Chair Orrin Hatch was instrumental in ushering his appointment through the Senate and he had mused about seeing Garland on the Supreme Court someday.
Hatch still sits on the Judiciary Committee. Your move, Senator.
Obama went on to talk about how Garland tutors DC students and how much everyone who works with him likes him. He finished the announcement by scolding the Senate about politicking on the Supreme Court and admonished them to be fair.
Stayed tuned for the GOP response, kids. This is going to be fun.
Merrick Garland
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It's not Duck Dynasty--it's just the opposite, regardless of appearances.
Tattoos. Unwed pregnancy. Giving up on shaving…showering…and employment. These used to be signatures of a trashy individual. Now they're the new norm. What happened to etiquette, hygiene, and self restraint? Charlotte Hays, Southern gentlewoman extraordinaire, takes a humorous look at the spread of white trash culture to all levels of American society.
As White Trash values have traveled upwards in society, it is not surprising that the tide has engulfed the churches. It used to be that being a Pentecostal or a snake handler in Appalachia made you White Trash. But that's so yesterday. Anyway, you've got to hand it to the snake fellows—they weren't half as ignorant as our nouveau White Trash.
Indeed, I'd bet on the serpent handler any day of the week over my friend—let's call her Jane, and let's just say you'd probably be impressed with her educational credentials—if they could both get on Jeopardy! When was the last time somebody had to say to a snake handler, "Bubba, honey, Jesus weren't crucified on Ash Wednesday." Jane resisted at first, but with my silver tongue I finally managed to swing her over to the Good Friday position. She's an Episcopalian.
You do not need an Ivy League degree to be White Trash in your religious orientation these days, but there is no denying that it helps. I am thinking of another friend, a magazine writer who initially thought that Epiphany Church in Georgetown was named after a boutique. He thought this was cool. Then I ruined it by telling him about the Magi.
This is White Trash religion in a nutshell: proud, ignorant, and messy. Just like in Appalachia—only now it's everywhere! The bon mot about "organized" religion, by the way, is inevitably delivered with an air of superiority. But you know what? Hit's pure White Trash.
A neighbor of mine is a scruffy man with a goatee dyed blue to match his tattoos (yes, I keep noticing them). A dabbler in Buddhism and other Eastern spiritualities—who also belongs to a gay-friendly Episcopal church near Dupont Circle—he has no inkling that it isn't the height of originality when he says, "I just don't like organized religion." Apparently, a really disorganized ashram is just the ticket. He adds without a soupçon of self-knowledge, "I hope I am not overintellectualizing this." I set his mind at rest.
Despite the pretensions of its practitioners, all this yoga and ersatz Buddhist spirituality is nothing more than an updated version of some Snopes floozy in a Faulkner novel too lazy to get out of her dirty bed in her awful cabin to get dressed and go to church on Sunday morning, while the respectable Sartoris grandmother has made sure her grandchildren are scrubbed and dressed to within an inch of their lives and marched them into the pew to insure that they'll end up public-spirited contributing members of the community. We'll get to the God aspect of religion later.
Whatever churchgoers believed pre–White Trash Normal—and God knows, my sister and I have wondered many times what on earth our mother, no scholar, taught her Sunday school classes—nice people got up, got dressed, and sat in a pew every Sunday or, if Jewish, on Saturday. (Mosque wasn't much of an option in Mississippi in those days.) Having a religious affiliation was part of what made nice people nice.
A by-product of this was at least a glancing familiarity with ideas and concepts that had built Western civilization. Charlemagne? Got him. You could absorb a lot about history and art just by going to church when I was growing up. On Sunday nights, I frequented St. James Episcopal Church in Greenville, Mississippi, for Evensong and hot teen gossip, not necessarily in that order. It was when St. James sent us out one evening, two by two, to help complete a religious survey of the town that I encountered for the first time a man who didn't belong to a church.
We kept trying to reframe the question so he'd spill the beans and let us get back to St. James and scarf down hamburgers.
But not belonging to a church—well, I never!
Ironically, it's the old line WASPs, a people whose very identify is tied to their religious heritage, who have let the team down most.
What bothered me was not the fate of the man's soul but the sheer tawdriness of not having a religious affiliation, even a casual one. Where would his poor kids learn to sing "Onward Christian Soldiers" or acquire basic (very basic, if you happened to be an Episcopalian) knowledge of the Bible? You may not eat Squirt Cheese on saltines, but if you don't know at least the first verse of "Oh, God, Our Help in Ages Past," you may be White Trash. Ironically, it's the old line WASPs, a people whose very identify is tied to their religious heritage, who have let the team down most. The dear old things may have been a tad dull at times, but they dutifully got their children to Sunday school every Sunday morning before the big Sunday lunch of overcooked roast beef and creamed peas in pastry shells. I'm sure I'm not the only WASP manqué (I later moved on to an even more organized church) who has enough Crown and Cross decorations (you got a pin, a wreath, and then a bar for every year of perfect attendance) to cross-dress as a Latin American dictator.
But nowadays the churches once frequented by such people are offering yoga classes or "Eat, Pray, Love" study groups in place of Isaac Watts's old hymns. And it is not working. Average Sunday attendance dropped 23 percent for Episcopalians in the last decade, and the Methodists, Lutherans, and Presbyterians have seen similar declines. Ross Douthat wrote a book entitled "Bad Religion," which, undoubtedly unbeknownst to Mr. Douthat, is a guide to White Trash religion. "America's problem isn't too much religion, or too little of it," Douthat wrote. "It's bad religion: the slow-motion collapse of traditional Christianity and the rise of a variety of pseudo-Christianities in its wake." Often one finds pseudo-Christianity in high places. When the Episcopal bishop of Washington, D.C., steps into the pulpit of the National Cathedral, the premiere church of the Episcopal Church, and reads a poem by New Age poet David Whyte, that's literally Whyte Trash in a once-great house of worship. "It doesn't interest me if there is one God or many gods," Whyte once wrote. "I want to know if you belong—or feel abandoned." Yuck.
Not sending your children to Sunday school is worse for posterity than having a dead tractor in the front yard.
You thought the Rapture crowd would believe anything? Wrong. Neo–White Trash religion takes gullibility to a new height. White Trash religion embraces not only pseudo-Christianities but pseudo-scholarship with a simple faith that is almost touching. One of the White Trash notions afoot—and it's among the general populace, not just White Trash academics—is that the early history of the Church is just a one long series of power struggles between men and the women they sought to oppress and impose their odious patriarchal views on. Unfortunately for this point of view, the early Christians were often poor and too busy getting themselves martyred to do much in the way of oppressing women or building up earthly power. St. John the Divine was the only Apostle to reach old age and die in his bed. If there was a power struggle going on, it was with the pagan authorities, not ditzy broads who wanted to dance the Eucharist. The older WASP had some appreciation of this history, but his grandchildren—likely named Apple, Bodhi, and Thor—don't. They were not fortified against such foolishness by the simple expedient of being sent to Sunday school every Sunday. Not sending your children to Sunday school is worse for posterity than having a dead tractor in the front yard.
Some of the new White Trash religions people concoct are parody-worthy but at the same time not a laughing matter. Goddess worship is all the rage, and its devotees fondly believe they are following something quite ancient. But they are deluded. For one thing, they got their goddess all wrong. The girls on the popular '90s TV show Friends called on the goddess for help getting dates. Feminist goddess worshippers go howl at the moon, or some such foolishness, to invoke her.
They should count their lucky stars the goddess doesn't appear. Most of the goddesses in the ancient world made Yahweh at his plague-wielding worst look like a pushover. My own personal favorite is Cybele, who insisted that her male worshippers become do-it-yourself John Wayne Bobbitts. I'm told that Cybele has a certain following in transgender circles, and that it is believed that Christianity suppressed her cult because of its "fear" of LGBTQ people. So we should all relax! Why worry if young men are turning themselves into eunuchs for no good reason?
It's not surprising that the press doesn't know the niceties, but it is surprising that they don't know they don't know—and therefore never think to ask somebody.
Some other facets of White Trash Normal are just annoying. The elite U.S. press doesn't have the foggiest when it comes to the forms of religion. Whenever there is a ceremony in Westminster Abbey, they get all the clergy titles wrong.
Note to White Trash press covering the next royal event in the Abbey: the Archbishop of Canterbury is not called "Reverend Welby." It's not surprising that they don't know the niceties, but it is surprising that they don't know they don't know—and therefore never think to ask somebody.
When Pope Emeritus Benedict XVI wrote a book on the Infancy narratives suggesting the calendar may have Christ's birth a year or two off—hardly a matter of doctrine—the press went wild and had the pope "disputing" the gospel.
Not having a nodding acquaintance with religion, they can't distinguish between what matters and what doesn't matter.
It was considered fatal to invite an Episcopalian to church on Sword Drill night because they'd lose for your team.
What these kids needed growing up was a good, oldfashioned Baptist Sword Drill to set them straight. "Sword drills," as my Baptist organist friend puts it, "were something like a spelling bee, but using the Bible—the Sword of the Spirit." The moderator called out something from the Scripture, and the first one to locate it in the Bible stepped forward and read it aloud." It was considered fatal to invite an Episcopalian to church on Sword Drill night because they'd lose for your team. One of my friends was there the night Hebrews was tossed out. She frantically scoured the Old Testament until her Baptist hostess took pity and said, "Meredith, there are lots of Hebrews in the Old Testament, but the letter to them is in the New Testament." My sister claims to have cost many a Baptist unwise enough to ask her to church on Sunday nights many a victory in the Sword Drill. But my sister can still recite the Catechism by rote and identify the heraldic shields of all the Apostles, which were prominently displayed in our parish hall. Okay, learning the coats of arms for the Apostles is very Anglican—but you take my larger point: back then we were able to paddle a bit in the stream that is our civilization. We weren't stuck in the hollows. It is sad that so few children nowadays have the charming experience of memorizing the books of the Bible by singing "Genesis, Exodus, Leviticus" in Sunday school. But then you'd be hard-pressed to find many thriving Sunday schools these days. Like many once civilizing aspects of life, Sunday school is a casualty of divorce. Instead of a morning with Abraham, Isaac, Jacob, and the God of Israel, the modern child is more likely to spend the day visiting the "other" parent. White Trash has always been partial to immediate gratification over long-term planning. Sunday school was the ultimate in long-term planning for the next generation.
Cultural illiteracy breeds White Trash behavior. If you don't know who Adam and Eve were, you probably don't have reasoned arguments as to whether Adam and Steve should get married. Indeed, I'll go out on a limb and predict a day when a clergyman divorces his wife, comes out of the closet, takes a male lover, and then becomes the Episcopal bishop of New Hampshire. Nah, that's crazy. Things will never get that trashy. Sometimes I amuse myself by trying to picture my grandfather, a plain vanilla Episcopalian if ever there was one, "exchanging the peace." No can do. But you know what I really can't imagine? I really can't imagine him—or any of his contemporaries—sitting in a pew at the Cathedral of St. John the Divine engaging in ritual howling. Back in the day, even Episcopalians had a grip on reality.
Instead of being puffed up with self-esteem, maybe the young should be learning more about Original Sin.
And, if you think some bumptious coot who dresses like Larry the Cable Guy is full of hisself, you need to get out more. Having grown up mostly without the tempering influence of what was once mainstream religion, today's young are off the charts when it comes to self-esteem, formerly known as vanity. Several recent studies have shown that self-esteem is highest among prison inmates, neo-Nazis, and other assorted bullies. But high self-regard is on the rise among young people in general. Psychologist Jean Twenge's famous study "Generation Me: Why Today's Young Americans Are More Confident, Assertive, Entitled—and More Miserable than Ever Before" looked at the scores on the Narcissistic Personality Inventory evaluation administered to sixteen thousand American college students between the years of 1982 and 2006. The evaluation includes questions such as: "I think I am a special person." "If I ruled the world it would be a better place." "I find it easy to manipulate people." Around 65 percent of the students surveyed in 2006 scored high, a rise of 30 percent from 1982. Can you imagine how scary it would be if one of these narcissists became president? Instead of being puffed up with self-esteem, maybe the young should be learning more about Original Sin.
A beautifully educated young friend of mine possesses a fine mind and is in no way White Trash—except with regard to religion (or lack thereof). He occasionally popped into a mosque when he was required to attend worship services in school, but his contact with his family's religion is minimal.
Throwing down the gauntlet to me, he insists that the mention of dragons in Scripture shows that the Bible is nothing but myth. I can only think of how Father Mowbray, the hapless priest charged with instructing the invincibly ignorant Rex Mottram in Brideshead Revisited, characterized his charge.
"Lady Marchmain," said the despairing Jesuit, "he doesn't correspond to any degree of paganism known to the missionaries." That is the epitaph for our society.
When I attended the graveside funeral service for a friend's aunt, a decade or so ago, we were asked to recite the Twenty-Third Psalm. It was moving to see all the older people, my mother and my own aunt, able to recite the psalm from memory.
How much longer will it be possible to ask a congregation to say the Twenty-third Psalm without a printed text? One mustn't think of regular church attendance merely as a way to keep White Trash manners at bay. But it helps.
From the book When Did White Trash Become the New Normal?: A Southern Lady Asks the Impertinent Question. Reprinted by arrangement with Regnery Publishing. All rights reserved.
Charlotte Hays is the Director of Cultural Programs and Senior Editor at the Independent Women's Forum. Most recently, she is the author of When Did White Trash Become the New Normal?


When I met Catherine in college, I didn't know her older sister was friends with Mack… well, to be entirely accurate, I didn't know Mack. It wasn't until much later, when talking to my mom about the couple we'd been on a double date with, that we realized we have more of a "way back" connection than we thought. As it turns out, my mom went to church with Catherine's mom when they were in junior high! Such a fun "small world" situation, particularly since my mom ended up living in Indiana and California before she eventually made it back to Texas.
Catherine & Jason are getting married in just over 2 weeks, and we had so much fun getting to know them as a couple during this session. It was CRAZY windy the day we shot, necessitating a last minute change of location and fading light. But when we're photographing people who are this real and relaxed and just HAPPY with each other, we don't mind having to move fast.
Thanks for hanging through my wordy intro… now, please feel rewarded with some sweet portraits of this bride & groom at the Fort Worth Water Gardens!


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Movie Reviews Drama
Fences review
Katie Smith-Wong 08/02/2017 2:00pm No Comments
In 1983, playwright August Wilson released a play set in 1950s Pittsburgh, which went on to become a Tony Award winner and attracted talent such as James Earl Jones, Lenny Henry and Courtney B Vance in subsequent productions. Before Wilson's death in 2005, he managed to write a screenplay of his production, which is now a full-length feature directed by and starring Denzel Washington.
Fences follows the troubles of Troy Maxson (Washington), a binman living with his wife Rose (Viola Davis) and teenage son Cory (Jovan Adepo), which explore race relations, family life and the pressures of being an African-American in the 1950s.
On the face of it, Fences resembles a dysfunctional family drama. With its simplistic layout, emphasis on racism, and the theme of men being the head of the family, it is a film fitting of its era. However, its faithfulness to the original play makes it a little bland and underwhelming in comparison to other films that have similar issues.
It is evident that Troy's experiences of racism and past regrets have left him resentful, and despite his best intentions to his sons, he essentially prevents them from being successful. Along with the fear that they can achieve more than he can, he becomes a closed-off character whose irresponsible attitude and stubbornness further isolate him from the ones that support him. In comparison, the other characters such as Rose and Troy's mentally handicapped brother Gabe (Mykelti Williamson) are more rounded and relatable, making them more empathic to the audience.
As former cast members of the Broadway production, Davis and Washington deliver powerhouse performances. Both complement each other – Washington's loquacious, embittered Troy against Davis's stable, level-headed Rose – and they share a natural chemistry. Supporting cast members such as Williamson and Stephen Henderson, who plays Troy's friend Bono, relate to Troy's softer side and remind us that despite Troy's behaviour, they are those who care about him.
Most of the dialogue is everyday banter, but the references and occasional sporting metaphors (the language that Troy resonates with the most, as it resembles the one opportunity to rise up) reinforce the theme of moving on and becoming more in life. By drawing attention to the closed-off Troy, Wilson and co-producer Tony Kushner create a script that is both era-relevant and respectful to the themes that some may consider sensitive. The direction is simple and evident that it is adapted from the stage to the screen. In Washington's first directorial feature since Antwone Fisher in 2002, he focuses on the emotional fragility of the characters, which drives the one-dimensional narrative.
Overall, Fences feels too simplistic in production and style, but showcases intense, dramatic performances from Washington and Davis.
• This review first appeared on Musings of Guitargalchina
Katie Smith-Wong
Katie Smith-Wong is a Screenjabber contributor


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