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[likelihood] small fixes

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Jan Benda 2021-01-11 16:55:54 +01:00
parent 9c0bdba5e6
commit f111d20976
1 changed files with 6 additions and 6 deletions
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likelihood/lecture/likelihood.tex
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@ -476,12 +476,12 @@ The angle $\phi_{mle}$ that maximizes this likelihood is an estimate
of the true orientation of the bar (\figref{mlecodingfig}). of the true orientation of the bar (\figref{mlecodingfig}).
The noisiness of the neuron's responses as quantified by $\sigma_i$ The noisiness of the neuron's responses as quantified by $\sigma_i$
usually is a function of the neuron's mean firing rate $r_i$, usually is a function of the neuron's mean firing rate $r_i$:
\eqnref{bartuningcurve}: $\sigma_i = \sigma_i(r_i)$. This dependence $\sigma_i = \sigma_i(r_i)$. This dependence has a major impact on the
has a major impact of the maximum likelihood estimation. Usually, the maximum likelihood estimation. Usually, the stronger the response of a
stronger the response of a neuron, the higher its firing rate, the neuron, the higher its firing rate, the lower the noise. In this case,
lower the noise. In this case, strong responses will have a stronger strong responses have a stronger influence on the position of the
influence on the position of the maximum of the log-likelihood. maximum of the log-likelihood.
Whether neural systems really implement maximum likelihood estimators Whether neural systems really implement maximum likelihood estimators
is another question. There are many ways how a stimulus property can is another question. There are many ways how a stimulus property can