Assignment: The Linear Pulse-Step generator

Assignment: The Linear Pulse-Step generator#

Learning Goals#

In this computer exercise we will investigate the main properties of the saccade model by means of numerical simulations with Simulink (a graphical simulation package that runs under Matlab). By interactively changing the parameters in the model, we will gain a further understanding of:

  • some elementary concepts of the systems-theoretical approach: linearity, nonlinearity, feedback, ….

  • the role of different brainstem nuclei in saccade generation.

  • the effect on saccades of specific brainstem lesions.

Computer exercises with the linear PulseStep generator model#

To gain more insight into the concept of Pulse-Step Generation, and also to get more acquainted with the ideas behind Linear System’s Theory, a simple model of the so-called ‘Final Common Pathway’ of the oculomotor system (Fig. Model of the Final Common Pathway of the Oculomotor System. For saccades, this embodies the Pulse-Step Generator. The output of this pathway (i.e. eye position) is subsequently fed through an Inverse Model of the Plant, to enable a reconstruction of the motoneuron signal (see also Figs. {ref}`fig:omnreconstruction) is also available under Simulink as file \mcode{pulsestep.mdl} (Figs. fig:psgsimulink and fig:psgscope). This simulation model (compare it to the final part of the \mcode{saccade.mdl} model), allows you to interactively set parameters of:

  • The saccadic burst from the EBNs/IBNs (this is simply modeled as a pulse, having a height, \(P\), and duration \(D\).

  • The neural integrator: setting its gain to zero mimics total loss of the integrator, whereas a gain of one means normal operation.

  • The direct path: feeds the pulse directly to the oculomotor neurons. When its gain is set to zero, you actually simulate the effect of a Step input to the OMNs.

  • The Plant, with its two time constants, \(T_1\) and \(T_2\)

../_images/psgmodel.png

Fig. 49 Model of the Final Common Pathway of the Oculomotor System. For saccades, this embodies the Pulse-Step Generator. The output of this pathway (i.e. eye position) is subsequently fed through an Inverse Model of the Plant, to enable a reconstruction of the motoneuron signal (see also Figs. {ref}`fig:omnreconstruction#

```{figure images/simulink_pulsestep.png :name: fig:psgsimulink PulseStep Simulink Model. Compare to Fig. {ref}fig:psgmodel


```{figure` images/simulink_scopes.png
:name: fig:psgscope
Scopes in the PulseStep Model.

\begin{exercise}[Amplitude] How do you determine the amplitude of a saccade? \end{exercise}

\begin{exercise}[Parameters] First play with this model by changing the parameters of:

[label=(\alph*)]

  • the Burst (Burst Generator: Pulse Duration and Pulse Height)

  • the Neural Integrator (Integrator Gain)

  • the Forward Gain (Direct Path:Gain)

  • the Oculomotor Plant (Long Time Constant [T1] and Short Time Constant [T2])

Every time you change one parameter, make sure that the others are at their default value. Note what happens with each change in parameters. For example, if burst height changes (e.g. from 700 spikes/s to 300 spikes/s):

  • how large is saccade amplitude?

  • What is peak velocity?

  • What is saccade duration?

  • how did the pulse-step change (if any)?

\end{exercise}

\begin{exercise}[Linearity] Verify that the model is linear.

Hint: what is a linear system? A system that obeys the superposition principle (Def. def:superpositionprinciple). This theoretically means that you have to verify for all signals \(x\) and all weights \(a\) at all times \(t\), whether this holds. You will not have to do that.

You have to verify that the saccades (= the output signal) behave in a linear fashion for five heights of the burst (= the input signal; 167, 333, 500, 667 and 833 spikes/s; see also Exc. exc:psgparameters). Do so by \mcode{plot}ting relevant saccade parameters as a function of burst height (label the axes). Briefly explain (see also: (see Eqn. eqn:superpositionprinciple and exc:linearoculomotorsystem).

\end{exercise}

\begin{exercise}[Step response] Determine the Step response of the oculomotor plant.

  • How can you achieve this (which parameter of which block should be set to 0)?

  • What do you see (e.g. amplitude, velocity)? Check all signals in the Scopes and compare.

\end{exercise}

\begin{exercise}[Pulse-Step mismatch - Pulse Gain] Start with the default model parameters. Vary the strength of the Forward Gain (Direct Path) in the PulseStep model (from the default 0.15 to 0.05 and 0.25).

[label=(\alph*)]

  • We first concentrate on 20-deg saccades only. How can you make sure a 20-deg saccade is made? See (or revisit) your answers to Exercise exc:psgparameters and exc:psglinearity.

  • Describe the effect of these changes on the saccade.

  • Briefly explain.

  • Do the same effects also occur for saccades of different amplitudes (10, 25, 35 deg)?

\end{exercise}

\begin{exercise}[Pulse-Step mismatch - Oculomotor Plant] Again keep your default parameter set. Now only vary the long time constant of the plant, \(T_1\) (from the default 0.15 to 0.05 and 0.25).

[label=\alph*)]

  • Describe the effects on the saccade, and explain.

  • What do you conclude when you combine these effects with the previous exercise?

  • Test your hypothesis by introducing variations in both parameters, such that the saccade is unaffected (\mcode{plot}).

  • Does your parameter choice depend on the saccade amplitude?

\end{exercise}

\begin{exercise}[Pulse-Step mismatch - The Neural Position Integrator] By changing the gain of the Neural Integrator between 0 and 1, you may study the effects of lesions to the neural integrators on the saccades (for values of 0.1, 0.5, and 0.8 and for the rest keep your default parameter set).

[label=\alph*)]

  • Describe these effects, and explain.

  • What do you conclude when you combine these effects with the previous exercise?

  • Test your hypothesis by introducing variations in both parameters (Gain and T1), such that the saccade is unaffected (\mcode{plot}).

\end{exercise}

\begin{exercise}[Pulse-Step reconstruction] An additional feature of this simple simulation model is the possibility to reconstruct the actual net motoneuron input to the plant, on the basis of the (simulated) eye position signal. To that means, an inverse model of the plant is incorporated (see Fig. Model of the Final Common Pathway of the Oculomotor System. For saccades, this embodies the Pulse-Step Generator. The output of this pathway (i.e. eye position) is subsequently fed through an Inverse Model of the Plant, to enable a reconstruction of the motoneuron signal (see also Figs. {ref}`fig:omnreconstruction). Simulate the model with its default parameter set and note (\mcode{plot}) the (quite reasonable) resemblance between the reconstructed OMN input and the original Pulse-Step input. \end{exercise}

../_images/omnreconstruction.png

Fig. 50 By virtue of the plant’s linearity, it is possible to reconstruct an estimate of its net neural pulse-step control signal from the measured eye movement, \(E(t)\).#

\begin{exercise}[Inverse Model - Feedback] The inverse simulation is a specific circuit of subsystems, including a feedback loop ((When you open the box, (‘Look under Mask’), you’ll notice that feedback is used).

[label=\alph*)]

  • Explain how the inverse simulation is achieved, why feedback does the trick. Does this require full knowledge of the oculomotor plant parameters?

  • Do you have any idea why actual pulse-step and reconstructed pulse-step are not exactly identical?

\end{exercise}