[Comp-neuro] oscillations synchrony and noise.

Nathan Urban nurban at cmu.edu
Mon Jul 28 17:06:47 CEST 2008


A few points regarding this very interesting recent discussion.

1) I think that there have been several useful points made regarding the 
definition of "noise".  First, noise can be something that is 
unpredicted given one's knowledge of the conditions of the experiment - 
such as the variation of a neuron's firing rate when the experimenter 
presents "the same" stimulus repeatedly.  Clearly, lots is going on for 
the animal besides the stimulus that the experimenter presents.  Thus, 
variation is top be expected.  Second, noise can be something that is 
undesirable given what one is trying to measure at a given time.  These 
two definitions are often quite closely related, although (as pointed 
out by Jim's example in the case of cosmic background radiation) this 
can cause serious problems.  The third meaning (and the one that we use 
in the review) is that noise refers to a fluctuating signal (e.g. an 
output or an input) that has certain statistical properties - like being 
well described as having every point drawn randomly from a Gaussian 
distribution.  This third usage of the term is agnostic to the cause of 
these fluctuations.  This is the only definition that makes sense in the 
context of  experiments in which we are delivering noisy inputs to a 
cell or a whole animal.  None of these are at al llike thermal noise - 
which variation in a variable such as temperature that has a particular 
distribution (e.g. boltzmann) that is due basic physical laws, but where 
the value of the variable at any given time is not predictable.  This is 
a sort of non-deterministic or essentially stochastic system.  In my 
mind it is this kind of noise that people usually refer to when they say 
that neurons are in fact not "noisy".  My bias is that neurons are not 
noisy in this way.  I think that the vast majority of spikes copuld be 
predicted if only we knew all the inputs and all the properties of a 
neuron.  However, in fact, I don't think that it matters a lot whether 
neurons are noisy in this way because in most "noise" due to our 
uncertainty about the inputs and properties is so dominant.   

2) I agree with Jim that approaching the study of the brain with 
preconceived notions is dangerous.  Thus I think that  starting from the 
view that oscillations must be important is to be avoided, but so is 
starting from the point of view that oscillations are most likely 
epiphenomena because so many systems oscillate also is to be avoided.  
My perspective is that many different brain areas show local field 
potential oscillations - and in many cases these oscillations changes in 
a way that is correlated with changes in state or behavior of the 
animal.  If we are going to determine whether oscillations are useful in 
coding, or are analogous to changes in local temperature, we must 
determine the mechanism of these oscillations so that we can 
disrupt/augment them.  That is, in my mind we must test empirically 
whether these phenomena functionally interesting or not and the only way 
to do this is to understand their mechanism.  

My gut reaction is that in some circumstances the brain cares about  the 
relative timing of spikes across neurons, and that in some cases 
synchronous spikes are more effective in depolarizing postsynaptic 
targets than non-synchronous spikes.  Thus, I am interested in 
mechanisms that allow neurons to coordinate their activity at short time 
scales - such as the noise-induced synchronization that we have described. 

Nathan

-- 
____________________________________________
Nathan Urban, Ph.D. 
Associate Professor
Department of Biological Sciences
and Center for the Neural Basis of Cognition
Carnegie Mellon University
4400 Fifth Ave
Pittsburgh PA 15213
ph. 412-268-5122
fax 412-268-8423
http://www.andrew.cmu.edu/user/nurban/Lab_pages/





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