Margot edited untitled.html over 8 years ago

Commit id: 1401f11d1b82d29f5fca6b8bff51cb66d9f6fd41

deletions | additions

genetic algorithm to estimate parameters of multiple neuron models. Of particular interest, they propose genetic algorithms as a means of solving STRF models. We applied this method as one means of estimating the parameters of an augmented HR-neuron model that includes a sensory filter (DSTRF).

Twin studies with simulated auditory and visual data

Validation with real data

Results

not sure we have much to go here yet

Bobby: Maybe break this into a emcee and and GA section? All I have ready s stuff from the 1D twin data.

Figures

Let's get down some ideas for the figures we want. Don't worry about the order for now, we can rearrange.

Figure 1:

Figure 2:

Figure 3:

Figure 4:

Figure 5:

Figure 6:

Figure 7:

Figure 8:

class="ltx_title_section">References

Hindmarsh,
class="ltx_title_section">References
1. Foreman-Mackey, D., Hogg, D. W., Lang, D., Goodman, J. (2013). emcee: The MCMC Hammer. PASP 125, 306-312.
2. Hindmarsh, J. L., and Rose, R. M. (1984) A model of neuronal bursting using three coupled first order differential equations. Proc. R. Soc. London, Ser. B 221, 87–102.
3. Hodgkin, A., and Huxley, A. (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. J. Physiol. 117, 500–544.
4. Holland, J. H. (1973). Genetic algorithms and the optimal allocation of trials. SIAMJ. Comput.2, 88–105.
5. Izhikevich, E. M. (2003). Simple model of spiking neurons. IEEE Transactions on Neural Networks 14, 1569-1572.
6. Lynch, E. P., and Houghton, C. J. (2015). Parameter estimation of neuron models using in-vitro and in-vivo electrophysiological data. Frontiers in Neuroinformatics 9, 1-15.
7. van Rossum, M. (2001). A novel spike distance. Neural Comput. 13, 751–763.