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Thomson EE, Kristan WB (2005) Quantifying stimulus discriminability: a comparison of information theory and ideal observer analysis. Neural Comput 17:741-78 [PubMed]

References and models cited by this paper

References and models that cite this paper

Ash RB (1965) Information theory

Bialek W, Nemenman I, Tishby N (2001) Predictability, complexity, and learning. Neural Comput 13:2409-63

Boyd S, Vandenberghe L (2004) Convex optimization

Brenner N, Strong SP, Koberle R, Bialek W, de Ruyter van Steveninck RR (2000) Synergy in a neural code. Neural Comput 12:1531-52 [PubMed]

Britten KH, Shadlen MN, Newsome WT, Movshon JA (1992) The analysis of visual motion: a comparison of neuronal and psychophysical performance. J Neurosci 12:4745-65 [PubMed]

Buracas GT, Albright TD (1999) Gauging sensory representations in the brain. Trends Neurosci 22:303-9 [PubMed]

Cover TJ, Thomas J (2005) Elements of information theory (2nd ed)

Cover TM, Thomas JA (1991) Elements of Information Theory

de_boor C (1978) A Practical Guide to Splines

DeWeese MR, Meister M (1999) How to measure the information gained from one symbol. Network 10:325-40 [PubMed]

Duda RO, Hart PE, Stork DG (2000) Pattern Classification (2nd edition)

Feder M, Merhav M (1994) Relations between entropy and error probability IEEE Transactions On Information Theory 40:259-266

Geisler WS (1989) Ideal observer theory in psychophysics and physiology Physica Scripta 39:153-160

Geisler WS (2003) Ideal observer analysis Visual neurosciences, Chalupa LM:Werner JS, ed.

Golic J (1987) On the relationship between the information measures and the Bayes probability of error IEEE Transactions On Information Theory 33:681-693

Green DM, Swets JA (1966) Signal Detection Theory and Psychophysics.

Knill DC, Kersten D (1991) Ideal perceptual observers for computation, psychophysics, and neural networks Pattern recognition by man and machine, Watt RJ, ed. pp.83

Kovalevsky VA (1968) Character readers and pattern recognition

Lawson JL, Uhlenbeck GE (1950) Threshold signals

Lettvin JY, Maturana HR, Mcculloch WS, Pitts WH (1959) What the frog's eye tells the frog's brain Proceedings Of The Institute Of Radio Engineers 47:1940-1951

Mackay D, McCulloch W (1952) The limiting information capacity of a neuronal link Bull Math Biophy 14:127-135

Paninski L (2004) Estimating entropy on m bins given fewer than m samples IEEE Transactions On Information Theory 50:2200-2203

Perkel D, Bullock G (1968) Neuronal coding. Neurosci Res Prog Bull 6:221-348

Pola G, Thiele A, Hoffmann KP, Panzeri S (2003) An exact method to quantify the information transmitted by different mechanisms of correlational coding. Network 14:35-60 [PubMed]

Rieke F, Warland D, de Ruyter van Steveninck, R, Bialek B (1997) Spikes: Exploring The Neural Code

Schneidman E, Bialek W, Berry MJ (2003) Synergy, redundancy, and independence in population codes. J Neurosci 23:11539-53 [PubMed]

Shannon C, Weaver W (1948) A Mathematical Theory of Communication.

Strong SP, Koberle R, de Ruyter van Steveninck R, Bialek W (1997) Entropy and information in neuronal spike trains. Phys Rev Lett 80:197-201

Tebbe DL, Dwyer SJ (1968) Uncertainty and the probability of error IEEE Transactions On Information Theory 14:516-518

Victor JD (1999) Temporal aspects of neural coding in the retina and lateral geniculate. Network 10:R1-66 [PubMed]

Wagner TJ (1965) Some remarks concerning uncertainty and the probability of error IEEE Transactions On Information Theory 11:144-145

Yates RD, Goodman DJ (1999) Probability and stochastic processes: A friendly introduction for electrical and computer engineers

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