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Sigmanest 9 1 Keygen |VERIFIED| Generator



See Auto- and crosscorrelation functions for spike trains[cross_check_mip_corrdet.py] in pynest/examples. SLI /s1 /spike_generator Create def /s2 /spike_generator Create def s1 > SetStatus s2 > SetStatus /cd /correlation_detector Create def cd > SetStatus s1 cd > Connect s2 cd > Connect 10 Simulate cd [/n_events] get == --> [# 5 7 #] cd [/histogram] get == --> [. 0 3 3 1 4 3 2 6 1 2 2 .] cd > SetStatus cd [/histogram] get == --> [. 0 0 0 0 0 0 0 0 0 0 0 .]




Sigmanest 9 1 Keygen Generator



Example: /s1 /spike_generator Create def/s2 /spike_generator Create defs1 > SetStatuss2 > SetStatus/cm /correlomatrix_detector Create defcm > SetStatuss1 cm > Connects2 cm > Connect10 Simulatecm [/n_events] get == --> [# 5 7 #]cm [/count_covariance] get == --> [[ ] [ ]]cm > SetStatuscm [/count_covariance] get == --> [[ ] [ ]]


See also examples/nest/correlospinmatrix_detector.sli for a basic example in sli. /sg1 /spike_generator Create def/sg2 /spike_generator Create def/sg3 /spike_generator Create def/csd /correlospinmatrix_detector Create defcsd > SetStatussg1 > SetStatussg2 > SetStatus% one final event needed so that last down transition will be detectedsg3 > SetStatussg1 csd > Connectsg2 csd > Connectsg3 csd > Connect100. Simulate


Examples: SLIThe dc current can be altered in the following way:/dc_generator Create /dc_gen Set % Creates a dc_generator, which is a nodedc_gen GetStatus info % View properties (amplitude is 0)dc_gen SetStatusdc_gen GetStatus info % amplitude is now 1500.0


The dc_generator is rather inefficient, since it needs to send the same current information on each time step. If you only need a constant bias current into a neuron, you should set it directly in the neuron, e.g., dc_generator.


Description: The inhomogeneous Poisson generator provides Poisson spike trains at a piecewise constant rate to the connected node(s). The rate of the process is changed at the specified times. The unit of the instantaneous rate is spikes/s. By default, each target of the generator will receive a different spike train.


The mip_generator generates correlated spike trains using an Multiple Interaction Process (MIP) as described in [1]. Underlying principle is a Poisson mother process with rate r, the spikes of which are copied into the child processes with a certain probability p. Every node the mip_generator is connected to receives a distinct child process as input, whose rate is p*r. The value of the pairwise correlation coefficient of two child processes created by a MIP process equals p.


The MIP generator may emit more than one spike through a child process during a single time step, especially at high rates. If this happens, the generator does not actually send out n spikes. Instead, it emits a single spike with n-fold synaptic weight for the sake of efficiency. Furthermore, note that as with the Poisson generator, different threads have their own copy of a MIP generator. By using the same mother_seed it is ensured that the mother process is identical for each of the generators.


As the noise generator provides a different current for each of its targets, the current recorded represents the instantaneous average of all the currents computed. When there exists only a single target, this would be equivalent to the actual current provided to that target.


A Poisson generator may, especially at high rates, emit more than one spike during a single time step. If this happens, the generator does not actually send out n spikes. Instead, it emits a single spike with n-fold synaptic weight for the sake of efficiency.


The design decision to implement the Poisson generator as a device which sends spikes to all connected nodes on every time step and then discards the spikes that should not have happened generating random numbers at the recipient side via an event hook is twofold.


On the other hand, a proper implementation of the Poisson generator needs to provide two basic features: (a) generated spike trains should be IID processes w.r.t. target neurons to which the generator is connected and (b) as long as virtual_num_proc is constant, each neuron should receive an identical Poisson spike train in order to guarantee reproducibility of the simulations across varying machine numbers.


Therefore, first, as Network::get_network().send sends spikes to all the recipients, differentiation has to happen in the hook, second, the hook can use the RNG from the thread where the recipient neuron sits, which explains the current design of the generator. For details, refer to:


The ppd_sup_generator generator simulates the pooled spike train of a population of neurons firing independently with Poisson process with dead time statistics. The rate parameter can also be sine-modulated. The generator does not initialize to equilibrium in this case, initial transients might occur.


The pulsepacket_generator produces a spike train contains Gaussian pulse packets centered about given times. A Gaussian pulse packet is a given number of spikes with normal distributed random displacements from the center time of the pulse. It resembles the output of synfire groups of neurons.


Individual spike trains vs single spike train: By default, the generator sends a different spike train to each of its targets. If /individual_spike_trains is set to false using either SetDefaults or CopyModel before a generator node is created, the generator will send the same spike train to all of its targets.


SeeAlso: sinusoidal_poisson_generator, gamma_sup_generator AC Gamma Generator. Generates AC-modulated inhomogeneous gamma process. NoteThe simulator works by calculating the hazard h(t) for each time step and comparing h(t) dt to a [0,1)-uniform number. The hazard is given by \[ h(t) = \fraca \lambda(t) \Lambda(t)^a-1 e^-\Lambda(t)\Gamma(a, \Lambda(t)) \] with \[ \lambda(t) = dc + ac \sin ( 2 \pi f t + \phi ) \] \[ \Lambda(t) = a \int_t_0^t \lambda(s) ds \] and the incomplete Gamma function \( Gamma(a,z) \); \( a \) is the order of the gamma function and \(t_0\) the time of the most recent spike.


Note that GetStatus will report the spike times that the spike_generator will actually use, i.e., for grid-based simulation the spike times rounded to the appropriate point on the time grid. This means that GetStatus may return different /spike_times values at different resolutions.


Assume that NEST works with default resolution (step size) of 0.1ms and default tic length of 0.001ms. Then, spikes times not falling onto the grid will be handled as follows for different option settings: /spike_generator Create---> spikes at steps 10 (==1.0ms), 20 (==2.0ms) and 30 (==3.0ms)/spike_generator Create---> error, spike time 1.05 not within tic/2 of step/spike_generator Create---> spikes at steps 10, 11 (mid-step time rounded up), 30 (time within tic/2 of step moved to step)/spike_generator Create---> spikes at step 10, offset 0.0; step 11, offset -0.05; step 31, offset -0.0999Assume we have simulated 10.0ms and simulation times is thus 10.0 (step 100).Then, any spike times set, at this time, must be later than step 100./spike_generator Create---> spike time is within tic/2 of step 100, rounded down to 100 thus not in the future, spike will not be emitted/spike_generator Create---> spike at step 101, offset -0.0999 is in the future/spike_generator Create---> spike at step 101, spike shifted into the future, and spike at step 110, not shifted, since it is in the future anyways


Example: spike_generator SetStatusInstructs the spike generator to generate an event with weight 5.0at 1.0 ms, and an event with weight -8.0 at 2.0 ms, relative tothe device-timer origin.spike_generator SetStatusInstructs the spike generator to generate events at 1.0, 2.0, and3.0 milliseconds, and use the weight of the connection.


The current can be altered in the following way: /step_current_generator Create /sc Setsc SetStatusThe amplitude of the DC will be 0.0 pA in the time interval [0, 0.2),2.0 pA in the interval [0.2, 0.5) and 4.0 from then on.


The rate_generator provides a piecewise constant rate input to the connected rate unit(s). Please note that this input is handled in the same way as input from any other rate unit, i.e. it is processed by the input function of the receiving rate unit. The amplitude of the rate is changed at the specified times. The unit of the rate is Hz.


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