So far I've presented three different models for simulating neural spiking dynamics. A key assumption that was made but never stated in each of these examples was that the neuron we were modeling had no defined morphology. In other words, we were looking at models that assumed the neuron was a dimensionless sphere or point. These point neurons can be very effective for studying the behavior of large-scale spiking neural networks (e.g. Izhikevich), but are impractical if you want to investigate how anatomical features of a neuron contribute to signal propagation. For this we return to the Hodgkin-Huxley model and cable theory. Read the rest of this entry »
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Neural Modeling with Python (Part 4)
| February 11, 2011Comments: Leave a commentCategories: Computing, Neurobiology -
Neural Modeling with Python (Part 2)
| January 26, 2011
In my last post, I demonstrated how to simulate and plot a simple leaky integrate-and-fire (LIF) neuron using Python. The LIF neuron provides a simple representation of a spiking neuron, but lacks biological plausibility especially when it comes to the actual spike generation. A neural model that does have a solid foundation in physiology is that originally proposed by Alan Hodgkin and Andrew Huxley in 1952. Read the rest of this entry »Comments: Leave a commentCategories: Computing, Neurobiology -
Biologically Realistic Neural Models on GPU
| October 27, 2010
By Anatoli Gorchetchnikov, Heather Ames, Massimiliano Versace.The last post on GPU made me think of a project Anatoli Gorchetchnikov, Heather Ames and myself embarked on in 2006 when we got really interested in general purpose computing on graphic processing cards. At the time, there was no CUDA or OpenGL available: programming GPUs was really tough. But we tried, with some very good results, to port some of the models we used on GPUs. Here is how we did it. Read the rest of this entry »
Comments: Leave a commentCategories: Computing, Neurobiology