I would like a review of the state of the art of the main supervised techniques used to train spiking neural networks: advantages and disadvantages. Do these techniques affect the spiking neuron behavior? In which way? Please, if it is possible, provide me biobliographic references. Thanks!
dear friend
there are some issues with the supervised learning.....
as You have asked for some references, you can see some of them given below:
Mandal, I., Sairam, N. New machine-learning algorithms for prediction of Parkinson's disease (2014) International Journal of Systems Science, 45 (3), pp. 647-666. DOI: 10.1080/00207721.2012.724114
Mandal, I., Sairam, N. Accurate telemonitoring of Parkinson's disease diagnosis using robust inference system (2013) International Journal of Medical Informatics, 82 (5), pp. 359-377. DOI: 10.1016/j.ijmedinf.2012.10.006
Mandal, I., Sairam, N. Accurate prediction of coronary artery disease using reliable diagnosis system (2012) Journal of Medical Systems, 36 (5), pp. 3353-3373. DOI: 10.1007/s10916-012-9828-0
Mandal, I., Sairam, N. Enhanced classification performance using computational intelligence (2011) Communications in Computer and Information Science, 204 CCIS, pp. 384-391. DOI: 10.1007/978-3-642-24043-0_39
Mandal, I. Software reliability assessment using artificial neural network (2010) ICWET 2010 - International Conference and Workshop on Emerging Trends in Technology 2010, Conference Proceedings, pp. 698-699. DOI: 10.1145/1741906.1742067
Mandal, I. A low-power content-addressable memory (CAM) using pipelined search scheme (2010) ICWET 2010 - International Conference and Workshop on Emerging Trends in Technology 2010, Conference Proceedings, pp. 853-858. DOI: 10.1145/1741906.1742103
Indrajit Mandal, Developing New Machine Learning Ensembles for Quality Spine Diagnosis, Knowledge-Based Systems, Available online 19 October 2014, ISSN 0950-7051, http://dx.doi.org/10.1016/j.knosys.2014.10.012.
http://www.sciencedirect.com/science/article/pii/S0950705114003797
Mandal, I. A novel approach for accurate identification of splice junctions based on hybrid algorithms (2014) Journal of Biomolecular Structure and Dynamics
pp. 1-10 | DOI: 10.1080/07391102.2014.944218 PMID: 25203504
http://bit.ly/1vQZsei
I hope this will help you.
Best regards
Dr.Indrajit Mandal
Unfortunately I'm looking for an overview of the issues and benefits of supervised learning techniques used to train Spiking Neural Networks. These kind of networks are quite different from Bayesian Network, SVM or Artificial Neural Networks studied in your works. However, thanks for the attention.
There are many issues with spiking learning algorithms, gradient-based methods are problematic because of the discrete nature of spikes. STDP and other Hebbian approaches whilst biologically plausible do not necessarily promote selectivity of neurons during learning. For a good overview you should look at Kasinski and Ponulak's excellent comparison between the majority of the known techniques:
Kasiński, Andrzej, and Filip Ponulak. "Comparison of supervised learning methods for spike time coding in spiking neural networks." International Journal of Applied Mathematics and Computer Science 16 (2006): 101-113.
I recommend the recent review by Gütig:
Gütig, R. (2014). To spike, or when to spike? Current Opinion in Neurobiology, 25, 134–139. doi:10.1016/j.conb.2014.01.004
- Badges
- Science topic