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Free C++ source code for the integrate and fire neuron model is available.
Plug-in Description
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| Figure 1. The nodes available for the realistic plug-in module. |
This plug-in simulates neural models that are more biologically realistic than those
provided in the fast neural net module. The neurons are modeled using a conductance-based, integrate-and-fire
approach. Users can define a variety of different spiking and
non-spiking chemical synapses, and
electrical
synapses. They can also use non-spiking neurons connected by electrical synapses to build simple compartmental models.
There are only two nodes that are available in this module. They are spiking neurons and non-spiking neurons.
The only difference between these two is that the non-spiking neuron has a default initial threshold that is very high.
This prevents it from spiking. Otherwise, all the properties for the two nodes are identical.
The global properties for this module are listed below, and the following pages provide more details about how
this neural model is implemented.
Plug-in Properties
You can find the global properties for the plug-in modules by clicking on
the modules tab of the neural network editor window. The properties dialog box
from figure 1 will be displayed. To edit the properties for the integrate and
Fire Neural
plug-in click on the ReaslisticNeuralNet listing in the neural modules list.
This will display the following properties for you to edit.
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| Figure 2. Realistic Plug-in Properties |
The value of the after-hyperpolarisation equilibrium potential.
This sets the equilibrium potential for the conductance increase
(presumably to potassium) which follows a spike.
Default value: -70 mV.
Acceptable range:-150 to -10 mV.
This applies the drug TTX to all neurons of this plug-in.
Cadmium blocks calcium currents. This blocks all chemical synapses
(both spiking and non-spiking). It also blocks any neuronal
calcium current defined using the the Neuron Properties window.
This will have NO effect on neurons in other neural plug-in modules.
Default value: False.
This applies the drug TTX to all neurons of this plug-in.
TTX blocks spikes in all neurons. It thus also blocks spiking synapses.
This will have NO effect on neurons in other neural plug-in modules.
Default value: False.
The value of the Ca equilibrium potential.
Note: normally Ca equilibrium potential would always be positive.
However, by setting the equilibrium potential to a negative value
and adjusting the kinetic parameters to appropriate values, it is
possible to use the Ca current as if it were a K current.
Default value: 200 mV.
Acceptable range: -100 to 500 mV.
The time in seconds when the critical period for Hebbian learning ends.
Default value: 0 s.
Acceptable range: 0 to 10000 s
The time in seconds when the critical period for Hebbian learning starts.
Default value: 0 s.
Acceptable range: 0 to 10000 s
The value of the absolute refractory period. This sets the time
period following a spike during which it is impossible to elicit another spike.
Default value: 2 ms.
Acceptable range: 1 to 50 ms.
Enter the value of the membrane potential at the peak of the spike.
Note: the spike displayed on the Results View is largely "cosmetic";
as soon as the membrane potential crosses the spike threshold, its
value briefly shifts to that set by this parameter. Hodgkin-Huxley
kinetics are not modeled. The value set for spike amplitude can
affect post-synaptic neurons when the interaction is mediated by non-spiking
chemical or electrical synapses, but when the interaction is mediated by spiking
chemical synapses the pre-synaptic spike is simply an all-or-none signal
triggering a post-synaptic response.
Default value: 0 mV.
Acceptable range: -30 to 50 mV.
Spikes drive current through electrical synapses, but because
the spike is not modeled accurately (in particular, spike
width is always just one time-bin of the simulation), it
may sometimes be necessary to increase the effective
amplitude of the spike in order to drive a more realistic
amount of current, This is accomplished by setting the spike
“strength” to some value above 1.
Default value: 1.
Acceptable range:: 1 to 1000.
Determines whether a critical period is used for Hebbian learning.
Default value: False.
The name of the file for this neural plug-in module for this organism. This is a read-only property.
This is the time step to use when simulating the neurons in this plug-in module. Each plug-in can have its
own time step defined. This allows you to run simulations with different levels of detail. For example, since the neurons
in this model are more detailed you may want to run them with a time step of 0.2 ms. But a less advanced model like the
Fast neural net plug-in may only need to run at 2.5 ms. This lets you run the less detailed model at a much faster
rate. The simulator will compare the time steps for all the modules and the physics engine and determine the lowest
time step. It will run all the other modules using some integer modulus of that minimum step.
Default value: 0.2 ms.
Acceptable range:: Greater than 0.
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