- Delbeke (2003) Vision Res. 43(9)
- Verarart (2004) Artif Organs 27(11)
- Brelen (2006) J Neurosurg. 104(4)
Following that, we went back to Computational Neuroscience, looking at Numerical Integration methods for neural modeling. Specifically, we looked at Hansel (1998), which is a really interesting paper that we've replicated in our lab. The paper puts together a population of interconnected integrate and fire neurons with variable synaptic strength and network 'cohesion' factor. Then they simulate the network using (a) exact integration and (b) numerical methods. By comparing the exact results against the numerical methods, it is possible to deduce which numerical methods produce acceptable results. Having run this simulation in the lab, I was able to show the students the code and discuss specific results. We found that switching from floating point to double precision floats didn't improve precision at all, but the value of "dt" made a huge difference.
Hopefully, at least a few students will elect to replicate Hansel for their next assignment (due 4/21). Its a real challenge but its cool once you get it to work...