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Interim Report/Lessons Learned on a Simulation Model of the Olfactory Bulb

Harry Erwin
January 1, 1995

As a graduate school project during the last quarter, I've been developing
a computational and compartmental model of a small but biologically
realistic subset of the olfactory bulb. It owes its primary inspiration to
the work done by Walter Freeman and James Skinner, but its many errors
naturally remain the responsibility of the author. 

I'm posting this report for anyone who might provide useful critical
feedback. The simulation consists of a number of small compartmental
models of sensory, tufted/mitral, periglomerular, and granule cell neurons
structured to provide insight into architectural details of the olfactory
bulb. Crucial omissions include reafference from the anterior olfactory
nucleus, the pyriform cortex, and the locus coeruleus and the effect of
changes in the chloride gradiant in the glomeruli. 

The model is written in C++ and represents 32 tufted/mitral cells and the
associated periglomerular and granule cells. The code is efficient, and
performance is excellent, both on a Macintosh IIfx and on a Silicon
Graphics Iris Workstation. On the SGI, approximately 200 milliseconds of
activity is modeled in 8 minutes of CPU time. The model has been rehosted
on PVM 3 and will eventually be moved to the Paragon supercomputer. 

Lessons learned in developing this simulation include the following:

1. Errors in the equations for neural dynamics--a number of errors were
noted in recent papers. My take is that it is unsafe to rely on the
equations in published papers, and any equations used should be rederived.
I didn't notice this until I made my initial runs and got weird results 
(highly at variance with the biological data). 

2. Instability in explicit compartmental models--compartmental neural
models are advective with all the problems associated with such models. 
This becomes clear if one reviews Wilfred Rall's 1989 paper, "Cable theory
for dendritic neurons," in Koch and Segev, Methods in Neuronal Modeling. 
Since both the shape and strength of the signals between compartments are
biologically important, the preferred approach would be a high-order
adaptive scheme using implicit solution techniques. The system of
equations appears to be stiff, with high long-range connectivity, making
matrix inversions computationally expensive. My exploratory modeling used
a low-order explicit code, and so can only be regarded as suggestive. 

3. Transmission rates in compartmental models--some workers publishing
compartmental models appear to assume that transmission delays between
compartments are unimportant. That leads to modeling difficulties. The 
equations used should be formally correct.

4. The true role of 'inhibitory' neurotransmitters--GABA and glycine are
_not_ inverted excitatory neurotransmitters. Instead they serve to
increase the 'inertia' of the neuron by reducing its sensitivity to
excitation. The reversal potential for chloride channels is in the
vicinity of -70 mV, close to the resting potential of the neuron and also
close to the reversal potential for potassium channels. This means that
GABA can depolarize as well as hyperpolarize a neuron, depending on the
chloride gradient. The model for release of GABA at a synapse must take
that into account. 

5. Synaptic release models in most published compartmental models are (to
be polite about it) simplistic, typically strongly influenced by the
artificial neural network model of the spiking neuron. Very little work
appears to have been done on the mechanism by which a depolarization level
on the presynaptic side results in vesicle release, followed either
depolarization or buffering against depolarization on the postsynaptic
side. What appears in most analyses are "all-or-nothing" presynaptic
spikes and postsynaptic responses, and that does not address the detailed
dynamics that actually occur. In particular, electrical synapses and 
chemical synapses implementing graded potentials are given short-shrift 
by this model.

6. The crucial role of active tuning in producting the observed EEG
patterns--to get the observed EEG patterns, the olfactory bulb has to be
actively tuned in sensitivity. I modeled this by adjusting the trigger
potential for spiking by active conductances, and over a range of 10 mV, I
went from fixed point dynamics to completely chaotic dynamics. To
reproduce the dynamics seen in vivo would thus seem to require sensitive
tuning in near-real-time. 

7. The role of periglomerular cells in the system--the periglomerular
cells clearly normalize the bulb input as part of this tuning process. 
Initially during a breath, they are relatively inactive, so that all the
tufted/mitral cells are allowed explore the input, but later in the
breath, the periglomerular cells emerge more strongly, eventually allowing
the neural cellular assembly (NCA) best classifying the afferent input to
dominate. Tuning of the periglomerular cell response is a key aspect to
modeling the time constant of this process. 

8. The role of granule cells in the system--these appear to force the
system into a Hopf bifurcation and only work right if the system is
actively tuned, since they do not appear to be adaptive. Whether they have
active conductances is a major issue for my model. Active conductances
appear to overdrive them, since there is no evidence for afferent GABA or
glycine synapses. 

9. The role of attention in creating and maintaining neural cellular
assemblies--see Gary Aston-Jones's work and Gray, Skinner, and Freeman,
1986, in Behavioral Neuroscience, 100(4):585-596. Norepinephrine appears
to have a role in vigilance, by modulating the sensitivity of the
olfactory bulb. This is much like the modulation by the periglomerular
neurons, but on a more global scale, adjusting the percentage of the
existing neural cellular assemblies (NCAs) that respond to afferent
signals and facilitating NCA assembly, disassembly. I intend to
investigate this further. 

10. The mechanism of the 'H' synapses in the glomeruli and the reciprocal 
synapses between the tufted/mitral cells and the granule cells remains 
unclear. I suspect they produce some sort of difference signal.

--
Harry Erwin
Internet: herwin@gmu.edu