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Bobby edited MethodsThe_Hindmarsh_Rose_ModelThe_Hindmarsh__.html
over 8 years ago
Commit id: 0964171c5ba649f75776e523dab17f10b384c310
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...
replacing the applied current, I, with an estimated response function,
x=y-ax^3+bx^2-z+\hat{r}(t)
\hat{r}(t) = h(t) \star s(t)
where
h(t) is a linear filter, s(t) is the stimulus, and $\star$ is
convolution. This adjustment allows us to fit the HR model to in vivio
neuron recording data.
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Genetic
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Genetic
Algorithm
Mathematical
models such as the HR-neuron provide researchers a framework to understand and
predict qualities of a given system of interest. Researchers have developed
...
the posterior distribution, the ensemble sampler has hundreds of small
chains sampling at once. These features should allow the sampler to
converge quicker than standard MCMC algorithms on good parameter
estimations
Evaluating Fitness
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**Tyler - This is where we'll talk about SPIKy and stuff. I'm working on this part now.**
SYNC = \frac{1}{M}\sum_{k=1}^{M}C_k
\[C_i^{(n,m)} = \left \{\begin{matrix}
1 if min_j(|t_i^m-t_j^m)|) <\tau_{i,j}& \\
0 > otherwise &
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We class="ltx_title_subsection">
Method Validation - Twin Data Analysis
We demonstrate
the effectiveness of both MCMC and genetic algorithms for solving DSTRF HR-neuron
models by means of “twin data” analysis (Toth et al., 2011). We set the
parameters of a HR-neuron model to known fixed values and integrated across
...
the same manner from MCMC and genetic algorithm estimates of the DSTRF model.
When an MCMC or genetic algorithm generated spike train bests matches our original
spike train based on some fitness function, we inspect and compare parameter
estimates.
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