We've seen how Python can be an effective tool for simply and efficiently implementing and simulating four different neural models using Python: leaky Integrate-and-fire, Hodgkin-Huxley, Izhikevich, and active compartments. However, other than some convenience methods and simple tricks, these examples could have been done in MATLAB without too much pain for an experienced user. So where does Python really stand out? In my first post I argued that a major advantage of Python is it's power and flexibility as a general programming language. In this case, its extensibility has allowed it to become the interface of choice for many powerful neural simulator tools. Read the rest of this entry »
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Neural Modeling with Python (Part 5)
| February 22, 2011Comments: Leave a commentCategories: Computing, Neurobiology -
Software tools for Neurdons
| July 23, 2009
"You think you know when you learn, are more sure when you can write, even more when you can teach, but certain when you can program." Alan J. Perlis, Yale UniversityNeurdons cannot agree more. Reading and writing about neuroscience is not nearly as fun as creating a pulsing neural model! Recently, the Technology Lab at the Department of Cognitive and Neural Systems, where Neurdon was founded, has started to post a number of software tools, most of them in MATLAB, ranging from neural simulation software, to simple neural models, to biologically-inspired machine learning and machine vision tools. Read the rest of this entry »
Comments: Leave a commentCategories: ComputingAlso tagged continous firing neurons, MATLAB, rate-based models, spike-based models, spiking neurons