Help with Dissn Revisions -- Basic Neuroscience Research! :)

Eva Simmons weevey at dopey.cc.utexas.edu
Mon Feb 13 17:18:57 EST 1995


(Sorry about this being a *long* posting, but I've got a lot to ask...:)

I posted recently about my dissertation revisions and my background.  I am
now posting again with a clarified and more concise explanation of what I 
need.

As I said before, I need advice on how to deal with a few points in the 
last chapter that I need to prove some way:  (1) logical arguments, (2) a 
reference somewhere, or (3) revising the model appropriately and proving or 
disproving my points.  It has been called an 'error analysis' of the model. 
Drs. Wyatt, Thomas, and Patton tabulated some points about the original 
programs relating to what they neglected and what they included.  I have 
taken either these points (or some variation on them) and I have asked the 
question of what would happen if one were to either add what was neglected 
or delete what was included.  I have given these arguments of mine to these 
questions to Dr. Rylander and he now wants more references or proof of the 
points I have made.  I am not sure where I can gather information on these 
points and I was hoping that y'all might be able to give me either specific 
references or some direction to go in when it comes to these points.  Also, 
if they are points that will *have* to be dealt with through experiments 
with the programs, please inform me of this.  I am trying to cut down on 
the number of programs that are involved in proving these points.  I have 
already put together methods for proving the points I have listed if this 
*must* be done, but I would prefer to avoid this somehow with references 
of some kind.

Here is a tabulation of the points that I am looking at and the associated
information that I already have.  It is what I've come up with upon thinking 
about the model and what data I have gathered as to how these factors would 
change things:
-----------------------------------------------------------------------------

				NEUROANATOMY
-----------------------------------------------------------------------------
 # | POINT OF DISCUSION
-----------------------------------------------------------------------------
 1 | Q: Stick neurons with 3-D axons and dendrites were used in this model.
   |    Also, 17 types of cells (10 excitatory and 7 inhibitorya as described
   |    in Table A.1.  How would this data be affected if not *so* much de-
   |    tail was used here?
   | A: Important in getting correct connections.  Would change the pattern
   |    of connections found in the model.  Specific connection types would no
   |    longer be known and the circuits would be more generalized.
   | PROGRAM -- No longer use specific models of each layer cell type, but 
   |  one generalized one for each cell type and see what happens overall.
   | PROGRAM -- Could lessen the details of cell morphology and see what 
   |  happens.  
   | PROGRAM -- Could also use simple cells of somas and very little or 
   |  nothing else, but in large numbers and see what happens.
   | LIBRARY -- Also, could examine Thomas, Patton, and Wyatt's data with 
   |  respect to this when they tried generalized cell types of limited 
   |  structure variation and non-specific naming.
   | LIBRARY -- Related to HAND-CRAFTED MODELS of NNs.  See basic theory on
   |  this in a NN textbook.
-----------------------------------------------------------------------------

				  AFFERENTS
-----------------------------------------------------------------------------
 # | POINT OF DISCUSION
-----------------------------------------------------------------------------
  3| Q: There are both X and Y afferent fibers used in this model.  Could
   |    less detail be used in modeling the afferent input and still obtain
   |    the same results? 
   | A: Distribution of these is carefully considered in the model.  If this 
   |    was neglected, then stimulation would occur, but not a properly di-
   |    stributed sites.  This would change the circuits obtained.
   | PROGRAM -- Use generalized fibers without extra details of each afferent 
   |  type and see what happens.
   | LIBRARY -- Related to HAND-CRAFTED MODELS of NNs.  See basic theory on
   |  this in a NN textbook.
-----------------------------------------------------------------------------
 4 | Q: Only dLGN afferents (no corticocortical input fibers) have been used.
   |    What influence would there be by adding these to the model in some
   |    fashion?
   | A: This would increase the number of connections in the model due to all 
   |    of the extra input coming in, which would complicate the circuits ob-
   |    tained.  (Heard this is related to some type of 'decorrelation'.  Any 
   |    references?)
   | PROGRAM -- Add more afferents and this would become obvious.  
   | LIBRARY -- Use Miikkulainen's papers and references for this one.
-----------------------------------------------------------------------------

                ARCHIETECTURE -- DISTRIBUTION AND CONNECTIONS
-----------------------------------------------------------------------------
 # | POINT OF DISCUSION
-----------------------------------------------------------------------------
  5| Q: The distribution of cell types in the cortical layers is according to 
   |    experimental values, as could be obtained.  What would happen if the
   |    distribution pattern was changed?  How would this influence the data
   |    obtained?
   | A: Would change the pattern of connections found in the model.  Specific 
   |    connection types would no longer be known and the circuits would be 
   |    more generalized.
   | PROGRAM -- Change distributions around and look at the effect.
   | LIBRARY -- Related to HAND-CRAFTED MODELS of NNs.  See basic theory on
   |  this in a NN textbook.
-----------------------------------------------------------------------------
  7| Q: The model is set up so that exceptions are made for the somatic 
   |    connections of basket and chandelier cells since this is specified
   |    in the literature.  These connections to these cells are converted to 
   |    dendritic connections.  What would happen if these exceptions were
   |    not accounted for?
   | A:  
   | PROGRAM -- Eliminate exception rules and see what happens.
-----------------------------------------------------------------------------
  9| Q: Both dendritic and somatic connections are possible.  What would
   |    happen if only one or the other were used in this model?
   | A: If eliminate one or the other, the questions would be simplified.  
   |    This effect is interrelated to the factor of including separate den-
   |    dritic and somatic synaptic delay times.  (See LESS DETAIL IN 
   |    DYNAMICS - #4)
   | PROGRAM -- Drop one or the other and change equations acordingly and see 
   |  what happens.
-----------------------------------------------------------------------------
 10| Q: Multiple connections are possible between pairs of neurons.  What 
   |    influence would using only one connection for groups of connections
   |    have on the data produced by the model?
   | A: This allows for detail in synaptic connections amongst cells to get 
   |    the properties right related to activation or not.  Essential in de-
   |    termining whether a particular cell will be activated by the stimula-
   |    tion it receives, i.e., whether enough cells have stimulated it enough.
   |    If this factor were left out, the connection strength would become 
   |    especially important.
   | PROGRAM -- Use single connections in program and this will be proven.  
   |  Rather logical, so not sure *why* this is needed...?
-----------------------------------------------------------------------------

	             COMMUNICATION AND CONNECTION STRENGTH
-----------------------------------------------------------------------------
 # | POINT OF DISCUSION
-----------------------------------------------------------------------------
 11| Q: There are both excitatory and inhibitory connections.  Are both of
   |    these types of connections necessary or can the model run just fine
   |    without one or the other?
   | A: Important to keep the connections right and for the model to work 
   |    properly.
   | LIBRARY -- Logical.  Also, if all excitatory then like epileptic sei-
   |  zures (ref?).  If all inhibitory, won't work -- no activity (ref?).
-----------------------------------------------------------------------------
 12| Q: Fixed, nonplastic connection strengths are used.  Is this proper for
   |    this model and the studies being done here or should the connection
   |    strengths change with time?
   | A: Only a factor if the model is run for a longer period of time.
   | PROGRAM -- Could set up program to vary connection strengths with time 
   |  according to either some version of Hebb's Rule or some other simpli-
   |  fied method.  
   | MY DATA -- Evidence somewhat seen in various data sets with different 
   |  connection strength values.
-----------------------------------------------------------------------------

				EQUATIONS
-----------------------------------------------------------------------------
 # | POINT OF DISCUSION
-----------------------------------------------------------------------------
 14| Q: The Hodgkin-Huxley type equations have not been integrated for each
   |    neuron.  What influence would these equations and membrane channels
   |    have on the model's function if they were used?
   | A: Would make the model more biologically correct with the inclusion of
   |    large, brief excursions in conductances to sodium and potassium and 
   |    establishing the ionic picture of the basis of potentials (MacGregor,
   |    1987).  This would, in my opinion, be too much detail for the 
   |    current research and would slow down the model considerably. 
   | LIBRARY OR PROGRAM -- Find more information on H-H equations and either 
   |  run them or find data in articles in support of this.
-----------------------------------------------------------------------------
 15| Q: The neurons operate in either a graded or an impulse mode for realism.
   |    The impulse mode involves the 'pasting' of waveforms in for a cell 
   |    when it is above threshold.  it saves some time by not having to
   |    integrate those difference-differential equations for a bit.  Are
   |    both of these modes necessary or can one be eliminated?
   | A: Yes, the impulse mode could be eliminated since it is mainly used as
   |    a shortcut.  But, if the purpose of changes in the model is for more
   |    efficiency and more realism, then this factor should probably be 
   |    kept.
   | PROGRAM -- Eliminate a mode and see what happens.
-----------------------------------------------------------------------------
 16| Q: When cell activity is below threshold integration for the system of 
   |    nonlinear, differential-difference equations for cell voltages, Vi(t), 
   |    is done.  Are these equations reasonable in their construction and
   |    as compared to other equations that are used in these kind of models?
   | A: Important for making the model as realistic as possible and to connect 
   |    the activities of all of the cells to one another so they are working 
   |    as a synchronous unit.
   | LIBRARY -- Rather logical and makes sense based on the physiology pre-
   |  sented in other models and also the H-H equations.  (References in proof 
   |  of this?)
-----------------------------------------------------------------------------

		     FACTORS RELATED TO THE EQUATIONS		
-----------------------------------------------------------------------------
 # | POINT OF DISCUSION
-----------------------------------------------------------------------------
 19| Q: Is the difference between somatic and dendritic connections enough? 
   |    Should there be little changes due to travelling relatively so fast 
   |    and the signal doesn't change relative to the soma of other classes 
   |    based on the locations of neurons?
   | A: For most models this is enough.  This assures that the connections 
   |    matter very much.
   | PROGRAM -- (b) Add a way to vary seed and delay in the model in some 
   |  simplified form.
-----------------------------------------------------------------------------
 20| Q: The voltages are not found in axonal or dendrtic components, just a 
   |    single voltage for the whole cell.  Is this okay?
   | A: This detail wouldn't contribute that much if the original voltages 
   |    that were used for the overall cell were reasonable.
   | LIBRARY -- See more references using this kind of model method and 
   |  analyze their data.  
   | PROGRAM -- Also use those programs (articles) to vary model and test 
   |  this out.
-----------------------------------------------------------------------------
 21| Q: There are uncertainties about conduction velocities in axons.  How
   |    important are these uncertainties?  How would varying them a bit
   |    influence the model's function?
   | A: This detail would change things extensively if the uncertainties are 
   |    large about the conduction velocities, but hopefully they are not.
   | PROGRAM -- Could also vary conduction velocity ranges and see what 
   |  happens to the model overall.
-----------------------------------------------------------------------------
 22| Q: Axonal and synaptic delay times are included.  Are both of these
   |    necessary or can an average one be used?
   | A: The effect would be limited if possibly an average synaptic delay 
   |    were used instead of separate ones for dendritic and somatic.
   | PROGRAM -- Try eliminating these and using an average one instead of two 
   |  different ones and see what happens.
-----------------------------------------------------------------------------
 23| Q: An axonal propagation time is included.  Is this necessary?
   | A: Important because axons carry signals at a slower rate than den-
   |    drities.  Adds reality to the timing of the patterns of the connec-
   |    tions.
   | PROGRAM -- Drop and see what happens.
   | LIBRARY -- MENON dissertation may have something on this topic.
-----------------------------------------------------------------------------
 24| Q: A fixed firing threshold and bursting patterns, which are indepen-
   |    dent of firing history, have been used.  How would adding this factor
   |    influence this model?  Is this detail necessary under the circum-
   |    stances of this research?
   | A: This detail would be important if one were to run the model for a 
   |    longer period of time than is being used here.  Those values would 
   |    change with time, but how *much* time?
   | PROGRAM -- Vary these factors over time (longer running model?) and see 
   |  what happens to the data.
   | LOGICAL ARGUMENT -- Long-term related -- not related to my studies of
   |  20 ms.  But, could vary with time to see what happens for the heck of
   |  of it.  Like a whole *new* experiment?  How do you compare the data
   |  obtained?  Related to Hebbian learning.
-----------------------------------------------------------------------------
 25| Q: Firing threshold (single or multiple-spike) and post-firing hyperpo-
   |    larization have been included.  What would happen if the firing
   |    threshold were:  (a) nonexistent or (b) infinite?  What would happen
   |    if there was no post-firing hyperpolarization?
   | A: Important because one can not have a large group firing at the same 
   |    time, as found in epilepsy.  Must be more cells or all controlled in 
   |    order to process data properly.
   | PROGRAM -- Drop these and see what happens.
   | LOGICAL ARGUMENT -- Define these terms and their purposes in neurons.
   |  By thinking about these, it should become *obvious* that a THRESHOLD
   |  is a *NECESSITY* and *CAN'T* be done without!  Therefore, if one of 
   |  these were to be dropped it would have to be the post-firing hyper-
   |  polarization.  Logically, what effect would this have on neuronal
   |  activity??  (Need refs!!  Probably in a basic Neural Science book.)
   | LIBRARY -- MENON dissertation may have something on this topic.
-----------------------------------------------------------------------------

Please go over these and tell me what you think and if you happen to know
of any references.  

					Thanks,

					  Eva S. Simmons

-- 
************************ THE SIMMONS FACTOR **********************************
Eva Sabrina Simmons   University of Texas at Austin       Ph.D. (new)
 Theor P-Chem -- Field: Computational Neurobiology  weevey at dopey.cc.utexas.edu
****************** WATCH IT, OR IT MIGHT ATTACK!! ****************************



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