Michael DeWeese

Here is my short CV and below is my publication list. Most papers are available here as PDFs.

Publications:

• 43. D. Mandal, K. Klymko, and M.R. DeWeese. Entropy Production and Fluctuation Theorems for Active Matter. Physical Review Letters 119, 258001. (2017).

• 42. B. Albanna, C. Hillar, J. Sohl-Dickstein and M.R. DeWeese. Minimum and Maximum Entropy Distributions for Binary Systems with Known Means and Pairwise Correlations. Entropy 19, 427 (2017).

• 41. G. Dunn, K. Shen, J.N. Belling, T.N.H. Nguyen, E. Barkovich, K. Chism, M.M. Maharbiz, M.R. DeWeese and A. Zettl. Selective Insulation of Carbon Nanotubes. Physica Status Solidi B. 00, 1700202 (2017).

• 40. D. Mandal and M.R. DeWeese. Nonequilibrium work energy relation for non-Hamiltonian dynamics. Physical Review E. 93(4):042129. (2016).

• 39. P.R. Zulkowski and M.R. DeWeese. Optimal control of overdamped systems. Physical Review E. 92(3):032117. (2015).

• 38. P.R. Zulkowski and M.R. DeWeese. Optimal protocols for slowly driven quantum systems. Physical Review E. 92(3):032113. (2015).

• 37. S.E. Marzen, M.R. DeWeese, and J.P. Crutchfield, Time Resolution Dependence of Information Measures for Spiking Neurons: Scaling and Universality. Frontiers in Computational Neuroscience. 9:105. (2015).

• 36. V.M. Carels and M.R. DeWeese. Rats Exert Executive Control. Neuron 86, pp. 1324-1326 (2015).

• 35. J. Sohl-Dickstein, S. Teng, B. Gaub, C. Rodgers, C. Li, M. DeWeese, and N. Harper. A device for human ultrasonic echolocation. IEEE Transactions in Biomedical Engineering. 62(6):1526-1534 (2015).

• 34. P.R. Zulkowski and M.R. DeWeese. Optimal finite-time erasure of a classical bit. Physical Review E. 89(5):052140 (2014).

• 33. C.C. Rodgers and M.R. DeWeese. Neural correlates of task switching in prefrontal cortex and primary auditory cortex in a novel stimulus selection task for rodents. Neuron, 82(5), p1157–1170. (2014). (Neuron Preview of this paper by Odoemene and Churchland)

• 32. J. Sohl-Dickstein, M. Mudigonda, and M.R. DeWeese. Hamiltonian Monte Carlo Without Detailed Balance. Proceedings of the 31st International Conference on Machine Learning (Beijing) (2014).

• 31. P.R. Zulkowski, D.A. Sivak, and M.R. DeWeese. Optimal control of transitions between nonequilibrium steady states. Public Library of Science ONE. 8(12):e82754 (2013).

• 30. J. Zylberberg and M.R. DeWeese. Sparse coding models can exhibit decreasing sparseness while learning sparse codes for natural images. Public Library of Science Computational Biology. 9(8):e1003182 (2013).

• 29. T. Hromádka, A.M. Zador, and M.R. DeWeese. Up-states are rare in awake auditory cortex. Journal of Neurophysiology. 109(8):1989-95. (2013).

• 28. P. King, J. Zylberberg, and M.R. DeWeese. Inhibitory interneurons decorrelate excitatory cells to drive sparse code formation in a spiking model of V1. Journal of Neuroscience 33(13):5475–85 (2013).

• 27. J. Zylberberg, D. Pfau, and M.R. DeWeese. Dead leaves and the dirty ground: Low-level image statistics in transmissive and occlusive imaging environments. Physical Review E. 86(6):066112 (2012).

• 26. P.R. Zulkowski, D.A. Sivak, G.E. Crooks, and M.R. DeWeese. The geometry of thermodynamic control. Physical Review E. 86(4):041148 (2012).

• 25. N. Carlson, V.L. Ming, and M.R. DeWeese. Sparse codes for speech predict spectrotemporal receptive fields in the inferior colliculus. Public Library of Science Computational Biology. 7(10):e1002250 (2012).

• 24. J. Sohl-Dickstein, P. Battaglino, and M.R. DeWeese. New method for parameter estimation in probabilistic models: Minimum probability flow. Physical Review Letters. 107(22):220601 (2011).

• 23. J. Sohl-Dickstein, P. Battaglino, and M.R. DeWeese. Minimum Probability Flow Learning. Proceedings of the 28th International Conference on Machine Learning (Bellevue, WA) (2011).

• 22. J. Zylberberg, J.T. Murphy, and M.R. DeWeese. A sparse coding model with synaptically local plasticity and spiking neurons can account for the diverse shapes of V1 simple cell receptive fields. Public Library of Science Computational Biology. 7(10):e1002250 (2011).

• 21. J. Zylberberg, and M.R. DeWeese. How should prey animals respond to uncertain threats? Frontiers in Computational Neuroscience 5:20. doi: 10.3389/fncom.2011.00020 (2011).

• 20. M.A. Olshausen and M.R. DeWeese. Applied mathematics: The statistics of style. Nature 463(7284), 1027-1028 (2010).

• 19. Y. Yang, M.R. DeWeese, G. Otazu, and A.M. Zador. Millisecond-scale differences in neural activity in auditory cortex can drive decisions. Nature Neuroscience 11, 1262-1263 (2008).

• 18. T. Hromadka, M.R. DeWeese, and A.M. Zador. Sparse representation of sounds in the unanesthetized auditory cortex. PLoS Biol. 6, 124-137 (2008).

• 17. M.R. DeWeese. Whole-Cell Recording In Vivo. Chapter 6 in Current Protocols in Neuroscience. John Wiley & Sons, Inc., pp. 6.22.1-15 (2007).

• 16. M.R. DeWeese and A.M. Zador. Non-Gaussian membrane potential dynamics imply sparse, synchronous activity in auditory cortex. J. Neuroscience 26(47), 12206-12218 (2006).

• 15. M.R. DeWeese and A.M. Zador. Neurobiology: Efficiency Measures. Nature 439(7079), 920-921 (2006).

• 14. M.R. DeWeese, T. Hromádka, and A.M. Zador. Reliability and representational bandwidth in auditory cortex. Neuron 48, 479-588 (2005).

• 13. M.R. DeWeese and A.M. Zador. Neural gallops across auditory streams. Neuron 48, 5-7 (2005).

• 12. M.R. DeWeese and A.M. Zador. Shared and private variability in the auditory cortex. J. Neurophysiol. 92, 1840-1855 (2004).

• 11. M.R. DeWeese and A.M. Zador. Binary coding in auditory cortex. In Advances in Neural Information Processing Systems. MIT Press, Cambridge, MA, Vol. 15, 101 (2003).

• 10. M.R. DeWeese, M. Wehr, and A.M. Zador. Binary spiking in auditory cortex. J. Neurosci. 23, 7940-7949 (2003).

• 9. M.R. DeWeese. An optimal preparation for studying optimization. Neuron 26, 546-548 (2000).

• 8. M.R. DeWeese and M. Meister. How to measure the information gained from one symbol. Network 10, 325-340 (1999).

• 7. G. Buracas, A.M. Zador, M.R. DeWeese, and T. Albright. Efficient discrimination of temporal patterns by motion-sensitive neurons in primate visual cortex. Neuron 20, 959-969 (1998).

• 6. M.R. DeWeese and A. Zador. Asymmetric dynamics in optimal variance adaptation. Neural Computation 10, 1179-1202 (1998).

• 5. M.R. DeWeese. Optimization principles for the neural code. Network 7, 325-331 (1996).

• 4. M. DeWeese. Optimization principles for the neural code. In Advances in Neural Information Processing Systems. MIT Press, Cambridge, MA, Vol. 8, p. 281 (1996).

• 3. W. Bialek and M. DeWeese. Random switching and optimal processing in the perception of ambiguous signals. Phys. Rev. Lett. 74, 3077-3080 (1995).

• 2. M. DeWeese and W. Bialek. Information flow in sensory neurons. Il Nuovo Cimento A17, 733 (1995).

• 1. W. Bialek, M. DeWeese, F. Rieke, and D. Warland. Bits and brains: information flow in the nervous system. Physica A 200, 581-593 (1993).