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An information-theoretic library for the analysis of neural codes

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R. A. A. Ince, C. Bartolozzi, and S. Panzeri

29 June 2009

PyEntropy may prove an essential tool for the evaluation, comparison, and parametric analysis of neural networks.

Authors

R. A. A. Ince
Faculty of Life Sciences, University of Manchester

Robin Ince has masters degrees in mathematics, audio acoustics, and computational neuroscience. He is currently studying for a PhD at the University of Manchester, on the topic of information theoretic analysis of neural data.

C. Bartolozzi
Robotics, Brain, and Cognitive Sciences Department, Italian Institute of Technology

Chiara Bartolozzi is a postdoctoral fellow currently working on the application of neuromorphic engineering approaches to the design of sensors for robotic platforms.

S. Panzeri
Robotics, Brain, and Cognitive Sciences Department, Italian Institute of Technology

Stefano Panzeri is a senior research fellow whose current research focuses on developing quantitative data analysis techniques based on information-theoretic principles and on applying these algorithms to neuronal recordings. His goal is to understand how neuronal populations encode and transmit sensory information.

References

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T. M. Cover and J. A. Thomas, Elements of Information Theory, 2nd Ed., John Wiley & sons, 2006.

S. Panzeri, C. Magri and L. Carraro, Sampling bias, Scholarpedia 3 (9), pp. 4258, 2008.

J. Victor, Approaches to information-theoretic analysis of neural activity, Biological Theory 1, pp. 302-316, 2006.

G. Pola, A. Thiele, K. Hoffmann and S. Panzeri, An exact method to quantify the information transmitted by different mechanisms of correlational coding, Network Comp. in Neural Sys. 14 (1), pp. 35-60, 2003.

G. Indiveri, E. Chicca and R. Douglas, Artificial cognitive systems: From VLSI networks of spiking neurons to neuromorphic cognition, Cognitive Comp., 2009. (In press.)

T. Schreiber, Measuring Information Transfer, Phys. Rev. Lett. 85 (2), pp. 461-464 Jul, 2000.

E. Schneidman, M. Berry II, R. Segev and W. Bialek, Weak pairwise correlations imply strongly correlated network states in a neural population, Nature 440 (7087), pp. 1007-1012, 2006.

R. A. A. Ince, R. S. Petersen, D. C. Swan and S. Panzeri, Python for information theoretic analysis of neural data, Frontiers in Neuroinformatics 3 (4), 2009.

S. Panzeri, R. Senatore, M. Montemurro and R. Petersen, Correcting for the Sampling Bias Problem in Spike Train Information Measures, J. Neurophysiology 98 (3), pp. 1064-1072, 2007.

http://code.google.com/p/pyentropy/

F. Folowosele, R. Vogelstein and R. Etienne-Cummings, Real-time silicon implementation of V1 in hierarchical visual information processing, Biomedical Circuits and Sys. Conf., 2008. BioCAS 2008. IEEE, pp. 181-184 Nov., 2008.

M. Giulioni, P. Camilleri, V. Dante, D. Badoni, G. Indiveri, J. Braun and P. Del Giudice, A VLSI network of spiking neurons with plastic fully configurable ?stop-learning? synapses, IEEE Int'l Conf. Electronics, Circuits and Sys., ICECS 2008, pp. 678-681, 2008.

E. Chicca, G. Indiveri and R. Douglas, Neural Information Processing Systems Foundation, pp. 257-264, MIT Press, Cambridge, MA Dec, 2007.

DOI: 10.2417/1200906.1663

@NeuroEng

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