Hi guys,
One knows, an ionic channel can be either open or closed  if we take a look at N channels, the probability that n channels are open needs to follow a binomial distribution.... now the standarddeviation is proportional to the square root of the considered channels  therefore : more channels are producing noise proportional to the square root .... I read (in some papers) it should be proportional to the inverse of the squareroot... but I don't see the logic behind  could you please give me a short explanation or do you know papers explaining this certain issue ?
Thank you in advance and kind regards
Channel noise

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Re: Channel noise
Time to learn about statistics and stochastic processes.

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 Joined: Wed Aug 15, 2018 8:44 am
Re: Channel noise
Dear Ted,
I know an stochastic approach with Markov chains does exist, but is the question answerable due to simple statistics ?
If we take a look at N channels  the probability that n channels are open follows a binomial distribution Bin(n,p)  so the standard deviation is proportional to the square root of n.
If we take a look at the relative frequency it is proportional to the reciprocal value.
Is this the answer in a very intuitive way ?
Kind regards,
I know an stochastic approach with Markov chains does exist, but is the question answerable due to simple statistics ?
If we take a look at N channels  the probability that n channels are open follows a binomial distribution Bin(n,p)  so the standard deviation is proportional to the square root of n.
If we take a look at the relative frequency it is proportional to the reciprocal value.
Is this the answer in a very intuitive way ?
Kind regards,

 Site Admin
 Posts: 5531
 Joined: Wed May 18, 2005 4:50 pm
 Location: Yale University School of Medicine
 Contact:
Re: Channel noise
That's it.

 Posts: 3
 Joined: Wed Aug 15, 2018 8:44 am
Re: Channel noise
Hi,
I would like to propose an other approach:
lets assume we divide a membrane into n parts, which are represented by random variables X_1,...,X_n  the "value" of this random variables is representing the current fluctuations (without specifying the distribution, but maybe we can assume a N(0,1) distribution.)
When summing the n parts, the standard deviation of
\sum_{I=1}^{n}X_i should be growing proportional to the square root of n.
I would like to describe the standard deviation of this sum under the condition that the total membrane potential V(t) is influencing the fluctuations of the X_i.
Do you know how to model this ?
kind regards
I would like to propose an other approach:
lets assume we divide a membrane into n parts, which are represented by random variables X_1,...,X_n  the "value" of this random variables is representing the current fluctuations (without specifying the distribution, but maybe we can assume a N(0,1) distribution.)
When summing the n parts, the standard deviation of
\sum_{I=1}^{n}X_i should be growing proportional to the square root of n.
I would like to describe the standard deviation of this sum under the condition that the total membrane potential V(t) is influencing the fluctuations of the X_i.
Do you know how to model this ?
kind regards