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Free C++ source code for the firing rate neuron model is available.
Plug-in Description
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| Figure 1. The nodes available for the firing rate neural net plug-in module. |
This firing rate neural model plug-in performs simulations that are more biologically realistic than standard connectionist
schemes, but it is still abstract enough to be able to run much more quickly than more detailed
integrate-and-fire or
compartmental models like Neuron or Genesis. It is a firing rate model based on work originally done by
Beer. The
neuron is modeled as a simple RC circuit that is integrated using the
Euler method. The firing frequency of
each neuron ranges from 0 to its normalized maximum of 1. Synapses between neurons have a weight that is the
maximum amount of current that will be injected into the post-synaptic neuron which is proportional to the firing
frequency of the pre-synaptic neuron.
Modulatory and gated synapses are also provided to allow the firing of
one neuron to alter the gain of the output of another neuron. No learning is currently implemented in this model.
The following pages provide more details about how this
neural model is implemented, and the types of neurons and
synapses that are available.
Also, the global properties for this module are listed in the next section.
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 firing rate
neural
plug-in click on the FastNeuralNet listing in the neural modules list.
This will display the following properties for you to edit.
The name of the file for this neural plug-in module for this organism. This is a read-only property.
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| Figure 2. firing rate neural Net Plug-in Properties |
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
firing rate 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: 2.5 ms.
Acceptable range:: Greater than 0.
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