Realistic Neural Plug-in

Plug-in Description

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 moduel. 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.

1.	Neural model details.
2.	Synapse model details.
3.	Managing synaptic types.

Plug-in Properties

You can find the global properties for the plug-in modules by clicking on the modules tab of the behavioral editor window. The properties dialog box from figure 1 will be displayed. To edit the properties for the realistic neural plug-in click on the ReaslisticNeuralNet listing in the neural modules list. This will display the following properties for you to edit.

Figure 2. Realistic Plug-in Properties

AHP equilibrium potential
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.

Apply Cadmium
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.

Apply TTX
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.

Ca equilibrium potential
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.

Critical Period End
The time in seconds when the critical period for Hebbian learning ends.
Default value: 0 s.
Acceptable range: 0 to 10000 s

Critical Period Start
The time in seconds when the critical period for Hebbian learning starts.
Default value: 0 s.
Acceptable range: 0 to 10000 s

Refractory period
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.

Spike peak
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.

Spike “strength”
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.

Use Critical Period
Determines whether a critical period is used for Hebbian learning.
Default value: False.

Network Filename
The name of the file for this neural plug-in module for this organism. This is a read-only property.

Time Step
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.