Metabolically efficient information processing

Neural Computation

Volumn 13, Issue 4, 2001, Pages 799-815

  Balasubramanian, Vijay ^a   Kimber, Don ^b   Berry II, Michael J ^c  

^a HARVARD UNIVERSITY   (United States)

^b FX PALO ALTO LABORATORY   (United States)

^c PRINCETON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACTION POTENTIAL; ALGORITHM; ARTICLE; BIOLOGICAL MODEL; ENERGY METABOLISM; INFORMATION SCIENCE; PHYSIOLOGY; RETINA GANGLION CELL; SYNAPTIC TRANSMISSION;

ACTION POTENTIALS; ALGORITHMS; ENERGY METABOLISM; INFORMATION THEORY; MODELS, NEUROLOGICAL; RETINAL GANGLION CELLS; SYNAPTIC TRANSMISSION;

EID: 0035319236     PISSN: 08997667     EISSN: None     Source Type: Journal    
DOI: 10.1162/089976601300014358     Document Type: Article

References (20)

1. Arimoto, S. (1972). An algorithm for computing the capacity of an arbitrary discrete memoryless channel. IEEE Trans. on Info. Theory, IT-18, 14-20.
2. Berry II, M. J., & Meister, M. (1998). Refractoriness and neural precisions. Journal of Neuroscience, 18, 2200-2211.
3. Berry II, M. J., Warland, D. W., & Meister, M. (1997). The structure and precision of retinal spike trains. Proc. Natl. Acad. Sci. USA, 94, 5411-5416.
4. Berry II, M. J., Warland, D. W., & Meister, M. (2000). Firing events: fundamental symbols in the retinal code. Manuscript in preparation.
5. Blahut, R. E. (1972). Computation of channel capacity and rate distortion functions. IEEE Trans. on Info. Theory, IT-18, 460-473.
6. Blahut, R. E. (1987). Principles and practice of information theory. Reading, MA: Addison-Wesley.
7. Buracas, G. T., Zador, A. M., deWeese, M. R., & Albright, T. D. (1998). Efficient discrimination of temporal patterns by motion-sensitive neurons in the primate visual cortex. Neuron, 20(5), 959-969.
8. Cover, T. M., & Thomas, J. A. (1991). Elements of information theory. New York: Wiley.
9. de Ruyter van Steveninck, R. R., Lewen, G. D., Strong, S. P., Koberle, R., & Bialek, W. (1997). Reproducibility and variability in neural spike trains. Science, 275, 1805-1808.
10. Laughlin, S. B., de Ruyter van Steveninck, R., & Anderson, J. C. (1998). The metabolic cost of neural information. Nature Neuroscience, 1(1), 36-41.
11. Levy, W. B., & Baxter, R. A. (1996). Energy-efficient neural codes. Neural Computation, 8, 531-543.
12. Reinagel, P., & Reid, R. C. (1998). Visual stimulus statistics and the reliability of spike timing in the LGN. Soc. Neurosci. Abstr., 24, 139.
13. Rieke, F., Bodnar, D., & Bialek, W. (1995). Naturalistic stimuli increase the rate and efficiency of information transmission by primary auditory neurons. Proceedings of the Royal Society of London Series B, 262, 259-265.
14. Rieke, F., Warland, D., & Bialek, W. (1993). Coding efficiency and information rates in sensory neurons. Europhys. Lett., 22, 151-156.
15. Rieke, F., Warland, D., & de Ruyter van Steveninck, R. R. (1997). Spikes: Exploring the neural code. Cambridge, MA: MIT Press.
16. Sarpeshkar, R. (1998). Analog versus digital: Extrapolating from electronics to neurobiology. Neural Computation, 10, 1601-1638.
17. Siesjo, B. K. (1978). Brain energy metabolism. New York: Wiley.
18. Strong, S. P., Koberle, R., de Ruyter van Steveninck, R. R., & Bialek, W. (1997). Entropy and information in neural spike trains. Phys. Rev. Lett., 80, 197-200.
19. Warland, D. (1991). Reading between the spikes: Real-time processing in neural systems. Unpublished doctoral dissertation, University of California at Berkeley.
20. Warland, D. K., Reinagel, P., & Meister, M. (1997). Decoding visual information from a population of retinal ganglion cells. J. Neurophysiol., 78(5), 2336-2350.