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This is a linear filter difference of Gaussians (DoG) model. The center and surround are each represented by a 2-D Gaussian function multiplied by a temporal filter.

Configuring the Model


These parameters set the basic simulation settings: The next set of parameters define the center and surround of the spatial DoG filters and the shape of the temporal filter. The temporal filter is generated by multiplying two functions, which we refer to as the kernel and the mask. The mask is a Gaussian centered on time tcent and having SD tsig. The kernel may be one of several filters, defined by the parameter tfilter, described below. The Gaussian mask reshapes the temporal kernel, for example, in can be used to balance out positive and negative lobes or to cut short a long tail.

The model can be made transient in two ways, either by using a transient temporal filter or by having spike rate adaptation (setting a positive adaptation conductance value).

Temporal Filter

The kernel may be one of several filters, controlled by the parameter tfilter, which defaults to 'ab'. The following is a list of possible options and the parameters needed to define them:

Spike Generation

dog_01 models also require a <spike_gen> object that defines the method of spike generation from the final filter output.

Model Outputs

Model Components

The following commands allow users to extract information about the DoG filter itself:

Model Responses

The following response types can be requested:

Models of This Type