##Synopsis
At the top of the file there should be a short introductioCox method is based on the theory of modulated renewal processes and it estimates a vector of influence strengths from multiple spike trains (called reference trains) to the selected (target) spike train. Selecting another target spike train and repeating the calculation of the influence strengths from the reference spike trains enables researchers to find all functional connections among multiple spike trains. In order to study functional connectivity an “influence function” is identified. This function recognises the specificity of neuronal interactions and reflects the dynamics of postsynaptic potential. In comparison to existing techniques, the Cox method has the following advantages:
- It does not use bins (binless method)
- It is applicable to cases where the sample size is small
- It is sufficiently sensitive such that it estimates weak influences
- It supports the simultaneous analysis of multiple influences
- It is able to identify a correct connectivity scheme in difficult cases of “common source” or “indirect” connectivity.
The Cox method has been thoroughly tested using multiple sets of data generated by the neural network model of the leaky integrate and fire neurons with a prescribed architecture of connections (Masud & Borisyuk, 2011).
##Motivation
The interest of proposing GPU implementations is in gaining computation time, but another important interest is that such implementation requires rethinking the algorithm in different ways as the sequential implementation. This rethinking in itself brings new optimization possibilities. Utilizing this accelerated implementation, the Cox method is then capable to be applied on an experimental dataset, e.g. CRCNS, in a personal computer. This should facilitate observations of biological neural network organizations that can provide new insights to improve understanding of memory, learning and intelligence.
##API Reference
The API documentation is available in ./Sphinx/output/documentation.html. Downloading the "./Sphinx/output" is sufficient for access.
##Citation
If you use this library for your published research, we suggest that you cite our paper:
Andalibi et al. 2016
##License
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The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
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