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Wednesday, August 13, 2008. Separating fast and slow spikers in exp3. In the last post we talked about various methods for optimizing functions that are very expensive to compute. In this post I'll talk about applying some of those techniques to separating a few neuron model regions. The Fast/Slow Spiker boundary. Is a link to the scheme code used to run this optimization. And the hyperplane coefficients (H) on the left side of the inequality have been normalized to have norm 1. Our next step will be to ...

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Wednesday, August 13, 2008. Separating fast and slow spikers in exp3. In the last post we talked about various methods for optimizing functions that are very expensive to compute. In this post I'll talk about applying some of those techniques to separating a few neuron model regions. The Fast/Slow Spiker boundary. Is a link to the scheme code used to run this optimization. And the hyperplane coefficients (H) on the left side of the inequality have been normalized to have norm 1. Our next step will be to ...
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Wednesday, August 13, 2008. Separating fast and slow spikers in exp3. In the last post we talked about various methods for optimizing functions that are very expensive to compute. In this post I'll talk about applying some of those techniques to separating a few neuron model regions. The Fast/Slow Spiker boundary. Is a link to the scheme code used to run this optimization. And the hyperplane coefficients (H) on the left side of the inequality have been normalized to have norm 1. Our next step will be to ...

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NDVis: Separating fast and slow spikers in exp3

http://ndvis.blogspot.com/2008/08/separating-fast-and-slow-spikers-in.html

Wednesday, August 13, 2008. Separating fast and slow spikers in exp3. In the last post we talked about various methods for optimizing functions that are very expensive to compute. In this post I'll talk about applying some of those techniques to separating a few neuron model regions. The Fast/Slow Spiker boundary. Is a link to the scheme code used to run this optimization. And the hyperplane coefficients (H) on the left side of the inequality have been normalized to have norm 1. Our next step will be to ...

2

NDVis: Multidimensional Optimization to find separating hyperplanes

http://ndvis.blogspot.com/2008/08/multidimensional-optimization-to-find.html

Tuesday, August 12, 2008. Multidimensional Optimization to find separating hyperplanes. The problem we are considering is how to find simple numerical constraints that can be used to determine the type of a neuron in terms of the maximal conductance values. For example, if we let S1 be the set of fast spiking neuron models (with period. And S2 the slow spikers (with period 0.1s and. F H(X) = (H*X = C). That is, if we define. R1(H) = {x: F(x) = true}. R2(H) = {x: F(x) = false}. X(j 1) = xj b*(pj-xj).

3

NDVis: Viewing Single Spike neuron models by period

http://ndvis.blogspot.com/2008/08/viewing-single-spike-neuron-models-by.html

Friday, August 1, 2008. Viewing Single Spike neuron models by period. Given that understanding, we can plot the periods of all single spike neurons as shown below and we see that there are several peaks. And we can use these peaks to define several domains of single spike neuron models, classified by their periods:. D1: period in 0.05-0.10 sec with peak at 0.016 sec. D2: period in 0.10-0.38 sec with peak at 0.30 sec. D3: period in 0.38-0.78 sec with peak at 0.50 sec. The D3 and D4 regions satisfy. Below ...

4

NDVis: Exploring the use of QHull and NDVis

http://ndvis.blogspot.com/2008/08/exploring-use-of-qhull-and-ndvis.html

Sunday, August 10, 2008. Exploring the use of QHull and NDVis. In this post I will document an attempt to study the boundary of a region in the PBM database using the convex hull approach. The region we consider is the set of slow spiking neuron models (i.e. whose period is between 0.1 and 0.38 seconds) which have low values of KCa and CaT (both in the range [0,0.5]). Our first step is to use the database exp3 described above and pull out those neuron models using the following query:. 1884274u 47.01...

5

NDVis: July 2008

http://ndvis.blogspot.com/2008_07_01_archive.html

Tuesday, July 29, 2008. The Silent/Active Boundary for the PBM neuron model. Lets now take a look at the boundary between the silent and active neurons in the PBM model using the two data sets discussed earlier: exp1 with a straight grid sampling method and exp3 with a randomly perturbed grid sampling method. First the plot from the fixed grid experiment exp1 is below:. Next lets look at the distribution obtained from the random perturbation of parameters around the grid points:. The exp3 distribution re...

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Wednesday, August 13, 2008. Separating fast and slow spikers in exp3. In the last post we talked about various methods for optimizing functions that are very expensive to compute. In this post I'll talk about applying some of those techniques to separating a few neuron model regions. The Fast/Slow Spiker boundary. Is a link to the scheme code used to run this optimization. And the hyperplane coefficients (H) on the left side of the inequality have been normalized to have norm 1. Our next step will be to ...

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