<p>ρ(t) in Equation 1.1 cannot be summed and averaged because the idealized spikes are infinitesimally narrow and the chance of two spikes from different trials occurring at the same time t is zero.</p>

<p>In Equation 1.2, the idea of convolving each idealized spike with a "well behaved function" h(t). If you use a rectangular function around zero as h(t), with a reasonable width (say 1ms), then Equation 1.2 will give you a timeseries that can be averaged. This is because the chances of two nonzero values from different trials occurring at the same t will become a more reasonable (nonzero) value. </p>

<p>If you have enough trials, then your trial-averaged neural response function using this more reasonable representation of spikes will give you a good sense of the average spike density.</p>

CC BY-SA 3.0
2016-03-16T23:59:26.157Z
2016-03-16T23:59:26.157Z
K A
<p>i'am currently reading " theoretical neuroscience by dayan abbott" and it says </p>

<p>" If we ignore the brief duration of an action potential (about
1 ms), an action potential sequence can be characterized simply by a list
of the times when spikes occurred. For n spikes, we denote these times
by $ti$ with i = 1, 2,... , n. The trial during which the spikes are recorded
is taken to start at time zero and end at time T, so 0 ≤ $ti$ ≤ T for all i. The
spike sequence can also be represented as a sum of infinitesimally narrow,
idealized spikes in the form of Dirac δ functions . "</p>

<p><a href="https://i.sstatic.net/U01iQ.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/U01iQ.png" alt="enter image description here"></a></p>

<p>then it says we average  ρ(t) s that we get during the trials .</p>

<p>" We use angle brackets, &lt;> , to denote averages over trials that use
the same stimulus, so that  for any quantity z is the sum of the values
of z obtained from many trials involving the same stimulus, divided by
the number of trials. The trial-averaged neural response function is thus
denoted by &lt;ρ(t)> "</p>

<p>how can we average ρ(t) . it consists of many infinity long spikes . so summing them and then dividing them by number of trials . is just like summing them . 
and does not give us the sense of average spikes .</p>

<p>the book further explains :</p>

<p>" In any integral expression, the neural response function generates a contribution whenever a spike occurs. If instead, we use the trial-average response function this generates contributions proportional to the fraction of trials on which a spike occurred. "</p>

<p>the materials I'm referring to are from pages 7 and 8 .</p>

<theoretical-neuroscience><computational-modeling>
what is the "trial-averaged neural response function"?
KFkf
# what is the "trial-averaged neural response function"?

(Asked by: username_0 on 2016-03-16)

i'am currently reading " theoretical neuroscience by dayan abbott" and it says

" If we ignore the brief duration of an action potential (about
1 ms), an action potential sequence can be characterized simply by a list
of the times when spikes occurred. For n spikes, we denote these times
by $ti$ with i = 1, 2,... , n. The trial during which the spikes are recorded
is taken to start at time zero and end at time T, so 0 ≤ $ti$ ≤ T for all i. The
spike sequence can also be represented as a sum of infinitesimally narrow,
idealized spikes in the form of Dirac δ functions . "

[![enter image description here](https://i.sstatic.net/U01iQ.png)](https://i.sstatic.net/U01iQ.png)

then it says we average ρ(t) s that we get during the trials .

" We use angle brackets, <> , to denote averages over trials that use
the same stimulus, so that for any quantity z is the sum of the values
of z obtained from many trials involving the same stimulus, divided by
the number of trials. The trial-averaged neural response function is thus
denoted by <ρ(t)> "

how can we average ρ(t) . it consists of many infinity long spikes . so summing them and then dividing them by number of trials . is just like summing them .
and does not give us the sense of average spikes .

the book further explains :

" In any integral expression, the neural response function generates a contribution whenever a spike occurs. If instead, we use the trial-average response function this generates contributions proportional to the fraction of trials on which a spike occurred. "

the materials I'm referring to are from pages 7 and 8 .

**Tags:** theoretical-neuroscience, computational-modeling


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## 1 answers

### Answer by username_1 on 2016-03-16. Score: 3

ρ(t) in Equation 1.1 cannot be summed and averaged because the idealized spikes are infinitesimally narrow and the chance of two spikes from different trials occurring at the same time t is zero.

In Equation 1.2, the idea of convolving each idealized spike with a "well behaved function" h(t). If you use a rectangular function around zero as h(t), with a reasonable width (say 1ms), then Equation 1.2 will give you a timeseries that can be averaged. This is because the chances of two nonzero values from different trials occurring at the same t will become a more reasonable (nonzero) value.

If you have enough trials, then your trial-averaged neural response function using this more reasonable representation of spikes will give you a good sense of the average spike density.


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