NEST (NEural Simulation Tool) - Scholarpedia. Dr. Marc- Oliver Gewaltig, Blue Brain Project, . Markus Diesmann, Institute of Neuroscience and Medicine (INM- 6), Computational and Systems Neuroscience, J. NEST is best suited for models that focus on the dynamics, size, and structure of neural systems rather than on the detailed morphological and biophysical properties of individual. The user builds a neural system by creating one or more model neurons and connecting them via synapses to. NEST is optimized for large networks of spiking neurons and can represent spikes in continuous time (Morrison et al. It is possible to combine different types of model neurons in one network. Abstract for the 1993 Neuroscience Meeting A NEURON simulation program. Michael Hines & John W. Neurobiology, Duke Univ. Durham, NC 27710 NEURON is a nerve simulation program which is designed to solve. Program Committee; Future Meetings. NEST is a simulation software for large-scale neuronal network simulations. Simulation environement for empirically-based simulations of neurons and networks of neurons. Different synapse models implement synaptic mechanisms such as spike- timing dependent plasticity (STDP) or short- term plasticity. It is possible to structure networks into layers, areas, and sub- networks. Special nodes, called devices, are responsible for providing input to or obtaining measurements from the network. Neuron Simulation Program With IntegratedNEST can take advantage of multiprocessor (multicore) computers and computer clusters to. In parallel simulations, NEST scales better than linearly (Morisson et al. Nodes can be neurons, devices, and sub- networks. Nodes can send and receive events of different types, for example spikes or currents. Among them are. NEST has commands to define and structure large networks. Sub- networks help to organize a large network model into manageable pieces. Commands exist to create a large number of network elements in. Other. commands automatically create multi- dimensional network structures. The weight determines how strong a signal is and the delay how long it takes for the signal to travel from one node to the. Each connection can carry one type of event. Synapse models define how incoming events affect the post- synaptic neuron. The simulation engine of NEST efficiently represents the heterogeneity of. The simplest synapse model delivers the connection weight to the post- synaptic neuron. The NEURON Simulation Environment 1181 . However, each synapse can define its own dynamics. NEST also. has synapse models for spike- timing dependent plasticity (STDP). The algorithm for STDP was documented by Morrison et al. These include convergent or divergent connections to/from an. Each event carries the time- stamp. The time- stamp. is combined with the delay of the connection to determine when the. Each neuron model implements. The most important types are. Spike. Event, to carry spike times. On computer clusters, NEST distributes the network. Each computer creates its own part of. This. not only reduces the run- time of the simulation supralinearly, it also. NEST. To reduce the simulation time at least. NEST exploits the fact. MPI controls the distributed NEST processes on many computers. On each computer individual processors can run independent threads. NEST ensures that for a given number of virtual processes, the results of a simulation are. MPI processes. During this period, all nodes are effectively decoupled. Lamport 1. 97. 8). There is no global event queue. Nodes that live in the same virtual process directly exchange and queue their events. Between virtual processes, NEST does not transmit individual events, as there are far too many. Instead, for each node that produced an event, NEST transmits the identifier of the sender node and the time at which the event occurred. All other connection parameters, such as the long list of weights, delays and targets, are available at each virtual process. With this information, the virtual processes re- construct the actual events and deliver them to their destinations. Aspects of NEST's update algorithm are available (Guerrero- Rivera et al. FPGAhardware implementations of neural networks. NEST has several mechanisms to avoid this. NEST does not have a global equation solver. Since in a heterogeneous network each node may have its own set of state equations, each node can update its state with the most accurate algorithms for its equations. Neuron Simulation Program StructureThis technique uses the matrix exponential to integrate systems of linear time- invariant differential equations. NEST solves this problem by handling spikes in continuous time within a globally time- driven simulation (Morrison et al. The syntax is simple: Numbers, arrays, or character strings are put on a stack. Functions take their arguments from the stack and return their results back to it. The numbers 1,2, and 3 are entered and the interpreter shows the size of the stack in brackets after the prompt. The command stack shows the contents of the stack. SLI . The following examples both assign the value \(1. The leading slash indicates that tau is an argument for the assignment and protects the name from being replaced by its value. They store lists of name/value pairs, enclosed in the delimiters < < and > >. The following example defines a dictionary with three entries and assigns it to the variable /phonebook. Thomas (+4. 9 6. 91. Michael (+3. 0 2. Sue (+4. 4 7. 10. This example uses character strings to representing the phone numbers. Dictionaries are used to set and retrieve the parameters of models and nodes (see below). New commands are defined by assigning procedures to names. The following code defines a command that prints Hello World! The most important are. The command ifelse distinguishes the two cases. Procedures can be. The command if tests whether the argument is greater than 1. The Boolean is returned by the comparison command gt, which returns true if the value at stack level 1 is greater than the value at level 0, and false otherwise. Models are defined in the dictionary modeldict, which must be opened to use the models. The command Create expects the name of a neuron or device model and returns a global identifier number (GID) as a handle for the new node. The following example creates one integrate- and- fire neuron. It then returns the handle of the last node. There are many different models for neurons and synapses. The most important are. Model name. Description subnet Subnetworks are used to structure large models and can be nested to define hierarchical models. The dimensions are given by an array and the total size is only limited by the computer. This example shows that layers are represented as nested sub- networks. It expects the global identifiers (GIDs) of the pre- synaptic and post- synaptic nodes, and optionally the weight and delay for the connection. SLI has functions to create many. Convergent. Connect connects a group of neurons to one target neuron. The command Get. Leaves retrieves the identifiers of all neurons in the layer net. The two populations are modelled as Poisson processes whose rates are equal to the number of neurons times the mean neuronal firing rate. The program tries to find the firing rate of the inhibitory population such that the target neuron fires at the same rate as the excitatory population. Examples of more complex network models are described in (Brette et al 2. NEST programs can be downloaded at Model. DB. A dictionary is a list of name/value. The command Connect accepts different sets of parameters. To connect the neuron to the spike detector, neither weight nor delay is needed. This is repeated, until the rate of the target neuron matches the rate of the neurons in the excitatory population. This algorithm is implemented in two steps. The function Output. Rate measures the firing rate of the target neuron for a given firing rate of the inhibitory neurons. The argument is stored in the variable guess and the inhibitory Poisson generator is configured accordingly. Next, the spike- counter of the spike detector (/events) is set to zero and the network is simulated for 1. The command Simulate takes the desired simulation time in milliseconds and advances the network for this amount of time. During simulation, the spike detector counts the spikes of the target neuron and after the simulation period the result is extracted. Find. Root takes four arguments: First, the function whose zero crossing is to be determined: the firing rate of the target neuron should equal the firing rate of the neurons of the excitatory population. The final argument prec is the desired precision of the zero crossing. Running it in NEST produces the following. SLI . The first value is by definition (upper+lower)/2. The final value is the position where the function crosses zero within the error bound prec. This result is left as the return value on the stack. Circles represent times for multiple threads in one process, crosses multiple MPI processes. The gray- line indicates linear scaling. The network had 1. The figure above demonstrates the performance of NEST for a network of 1. The connections used alpha- shaped current injecting synapses with a delay of 1 ms. The total number of synapses was 1. Panel (a) shows how the absolute run- time decreases with the number of processors used. Panel (b) shows the speedup, computed from the same numbers. For four processors, the simulation is more than four times as fast. It is possible to add modules and to tailor NEST to particular problems. NEST is extensible in different ways. At the level of the simulation kernel, it is possible to add new models for neurons and devices. In 2. 01. 2, The NEST Initiative re- constituted itself as an association under Swiss Federal Law with seat in Ecublens Switzerland. Which are available under the GNU General Public License at http: //www. GNU gcc) and a POSIX compatible operating system. To use the Python interfaces Py. NEST or Py. NN, a Python installation is required. M, Diesmann M, Morrison A, Goodman P. H, Harris Jr F C, Zirpe M, Natschlaeger T, Pecevski D, Ermentrout B, Djurfeldt M, Lansner A, Rochel O, Vieville T, Muller E, Davison A P, El Boustani S, and Destexhe A. Simulation of networks of spiking neurons: A review of tools and strategies. Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons. Journal of Computational Neuroscience. Diesmann M and Gewaltig M- O. NEST: An Environment for Neural Systems Simulations.
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