Manifold-learning-spike-sorting-master
所属分类：图形图像处理
开发工具：matlab
文件大小：615KB
下载次数：7
上传日期：2018-10-24 21:51:36
上 传 者：
Luffy111
说明： 很好的流形学习，处理高维数据，降维操作，里面含有LLE，PCA，Dijstra,Iosmap等等
(Good manifold learning, processing high dimensional data, dimensionality reduction operations, including LLE, PCA, Dijstra, Iosmap, etc.)
文件列表:
src (0, 2016-01-29)
src\feat_extr-ISOMAP_memo.m (1858, 2016-01-29)
src\spikes.mat (634940, 2016-01-29)
# manifold-learning-spike-sorting
## Source code for the tutorial on how to use low-dimensional embedding (i.e. ISOMAP algorithm) for feature extraction in spike sorting
**A short Intro**
Background noise and spike overlap pose problems in contemporary spike-sorting strategies. The (non-linear) isometric feature mapping (ISOMAP) technique reveals the intrinsic data structure and helps identifying active neurons.
[Isomap is a Nonlinear dimensionality reduction method](https://en.wikipedia.org/wiki/Isomap), used for computing a quasi-isometric, low-dimensional embedding of a set of high-dimensional data points. The algorithm provides a simple method for estimating the intrinsic geometry of a data manifold based on a rough estimate of each data point’s neighbors on the manifold. Isomap is highly efficient and generally applicable to a broad range of data sources and dimensionalities.
[The tutorial is available here](http://neurobot.bio.auth.gr/2013/using-isomap-algorithm-for-feature-extraction-in-spike-sorting). Therein, simulated spikes from 3 neurons, one being a sparsely-firing one, are used.
To reproduce this tutorial in MATLAB you will need :
1. [The ISOMAP source code](http://neurobot.bio.auth.gr/src/IsomapR1.zip) for MATLAB that was used in this tutorial. (for more information and updated version [see here](http://isomap.stanford.edu))
2. Memo script for MATLAB and sample data to reproduce the results shown in the tutorial.
**Notes**
- Changing the ‘k’ value will affect the output of the algorithm, i.e. projecting the data in ISOMAP space.
- Projection coordinates are kept in ‘Y’. ‘R’ denotes residual variance.
For further details and to cite this work, see:
**Adamos DA**, Laskaris NA, Kosmidis EK, Theophilidis G. “[NASS: an empirical approach to spike sorting with overlap resolution based on a hybrid noise-assisted methodology](http://dx.doi.org/10.1016/j.jneumeth.2010.04.018)“. Journal of Neuroscience Methods 2010, vol. 190(1), pp.129-142.
For more information on spike sorting algorithms see: [http://neurobot.bio.auth.gr/spike-sorting](http://neurobot.bio.auth.gr/spike-sorting)
近期下载者：
相关文件：
收藏者：