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Results
Genetic Algorithm Results
To assess the capabilities of a genetic algorithm similar to
one used by Lynch and Houghton (2015), we generated a 2400ms spike train
derived from a DSTRF HR-neuron model with known parameters (table X). For the r(t) parameter, we convolved gaussian random
noise with a filter h(t) presented
below of size 50ms, where τ = 5.
h(t) = t/(τ^2)*exp(-t/τ)
We then created populations of size 50, with each individual
having 7 parameters associated with HR-neuron parameters a, b, c, d, r, x0 and
s. A single parameter (sc) is associated with a scaling constant for the r(t) parameter and 50 parameter
estimates for h(t) for each t. Each population of parameter estimates
was evolved 10 times , with fit(I) being spike distance measured by SPIKy, for the HR-neuron parameters before switching to evolving
filter estimates for 10 times. This back and forth process was repeated 10
times. We ran this same algorithm 10 times in total and took the median values
as our estimates for the DSTRF neuron model.
| Table X | | | | | | | |
| Genetic algorithm estimates of an HR-neuron with known parameters | | | | | | | |
| | | | Parameters |
|
|
|
| a | b | c | d | x0
| r | s |
| Real | 1 | 6 | 4 | 5 | -1.5 | 0.001 | 7 |
| Search Range | 0 - 8 | 0 - 8 | 0 - 6 | 0 - 6 | -2 - 0 | 0 - 0.005 | 0 - 8 |
| Estimate (SD) | .68 (1.30) | 4.92 (1.61) | 3.91 (1.01) | 5.22 (0.54) | -1.62 (0.27) | 0.0013 (0.0002) | 7.14 (0.89) |
MCMC Results
