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>> [v_ex,v_in] = LF_network(.01,50,350,1.5,[19 23 28 29 45 46 47],pp);>> help viewer viewer.m takes in output from LF_network and plots voltage traces of all the cells. If you wish to only look at one cell enter that as a optional 3rd input. viewer(v_ex,v_in,cell_trace)>> viewer(v_ex,v_in)
Cells 19, 23, 28 and 29 of pattern [19 23 28 29 32 35 36 42] are stimulated as well as noise cells 45, 46, and 47. The blue traces are the excitatory cells, red are the inhibitory companions. The full assembly activates and then dies out around 250ms. The noise cells shut down before 50ms even passes.

We can get a better look by looking up close at the trace of cell 36, one of the cells in the assembly that did not receive a stimuli. Just type viewer(v _ ex,v _ in,36) into the prompt.

This is a close up of cell 36 Excitatory and Inhibitory from the simulation above. The blue trace of the excitatory cell slowly increases until it reaches a threshold where upon it fires rapidly. The interspike intervals decrease until the cell shut off. The inhibitory cell show by the red trace receives no input once the noise cells have been shut down.
Here the two C a 2 + pools are plotted for excitatory cell 36. Note the AP calcium enters quicker, the NMDA calcium has a slower time course and helps shut down the assembly.

Simulation 2 - pattern competition

In this simulation we will stimulate cells in two different patterns and see if one will "win" by activating it’s full pattern and suppressing the competing pattern. We will use the pattern from above and also another pattern.

>> find(p(6,:)==1) ans = 11 12 19 22 27 29 33 39

This pattern 6 should be interesting since it has two overlapping cells with pattern 1 (that is # 19 and # 29). Let’s run a simulation that picks one of the overlapping cells (19), 4 others from pattern 1 (28 32 35 36) and 3 more from pattern # 6 (11 12 22).

>> [v_ex,v_in] = LF_network(.01,50,350,1.5,[11 12 19 28 32 35 22 36],pp);
5 cells from pattern 1 and 4 cells from pattern 6 are stimulated. Pattern 1 resolves and shuts down around 250 ms

We can now try stimulated 4 cells from pattern # 1 and 5 cells from pattern # 6 .

>> [v_ex,v_in] = LF_network(.01,50,350,1.5,[11 12 19 22 27 28 32 35 ],pp);
4 cells from pattern 1 and 5 cells from pattern 6 are stimulated. This time Pattern 6 resolves and shuts down around 250 ms

Conclusion

This module has shown how results from the paper being followed can be reproduced. Hopefully, this will be helpful should future VIGRE students wish to pick up on these ideas. Extracting all the information out of the papers is difficult, so this module should serve as an easier way to follow the research we have done in the CAAM 499 Hippocampus Neuroscience Research Class in the Fall 07 and Spring 08 Semesters.

Acknowledgements

This work was partially supported by NSF DMS Grant 0240058. I would like to thank Dr. Steve Cox for leading our Hippocampus Computational Neuroscience research team. Also, thanks to the entire PFUG whose members included Chris Conner, Jay Raol, Tony Kellems, Charlie Peck, Jim Wang, Darren DeFreeuw, Eva Dyer, Paul Smolen, Michell Bettelheim, and Katherine Ward.

References

1. Hebb, Donald. (1949) The Organization of Behavior. (New York: John Wiley).

2. Lansner, A. and Ekeberg,Ö.. 1989. A one-layer feedback, artificial neural network with a Bayesian learning rule. Int. J. Neural Systems 1. p. 77-87.

3. Ekeberg,Ö., Wallen, P., Lansner, A., Traven, H., Brodin, L., and Grillner, S.. 1991. A computer based model for realistic simulations of neural networks. I: The single neuron and synaptic interaction. Biol. Cybern. p. 65 81-90.

4. Lansner, A. and Fransén, E.. 1992. Modelling Hebbian cell assemblies comprised of cortical neurons. Network 3. p.105-119.

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Source:  OpenStax, The art of the pfug. OpenStax CNX. Jun 05, 2013 Download for free at http://cnx.org/content/col10523/1.34
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