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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)
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.
In this simulation we will stimulate cells in two different patterns and see if one will "win" by activating its 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). Lets 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);
We can now try stimulated 4 cells from pattern and 5 cells from pattern .
>> [v_ex,v_in] = LF_network(.01,50,350,1.5,[11 12 19 22 27 28 32 35 ],pp);
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.
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.
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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