Michael DeWeese: Difference between revisions

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Here is my [https://redwood.berkeley.edu/w/images/7/76/DeWeese_cv_short.pdf short CV] and below is my publication list including some preprints.  Most papers are available here as PDFs.
Here is my [http://redwood.berkeley.edu/w/images/7/76/DeWeese_cv_short.pdf short CV] and below is my publication list.  Most papers are available here as PDFs.


'''Selected manuscripts in preparation:'''
'''Publications:'''
*49. P.S. Sachdeva, J.A. Livezey, M.R. DeWeese. Heterogeneous synaptic weighting improves neural coding in the presence of common noise. Neural Computation, in press. (2020).


*j. V. Carels and M.R. DeWeese. A comparison of multi- and single-unit spectrotemporal receptive fields in the primary auditory cortex. (in preparation)
*48. M.Y.S. Fang, S. Manipatruni, C. Wierzynski, A. Khosrowshahi, M.R. DeWeese. Design of optical neural networks with component imprecisions. Optics express 27, 14009-14029. (2019).


*i. P.R. Zulkowski and M.R. DeWeese. Optimal entropy production. (in preparation)
*47. M.N. Insanally, I. Carcea, R.E. Field, C.C. Rodgers, B. DePasquale, K. Rajan, M.R. DeWeese, B.F. Albanna, R.C. Froemke. Spike-timing-dependent ensemble encoding by non-classically responsive cortical neurons. eLife 8, e42409. (2019).


*h. N. Carlson, J. Livezey, V.L. Ming, and M.R. DeWeese. Probe stimuli affect receptive field estimation of model auditory neurons optimized to represent speech efficiently. (in preparation)
*46. E.M.V. Dodds, M.R. DeWeese. On the sparse structure of natural sounds and natural images: similarities, differences, and implications for neural coding. Frontiers in computational neuroscience 13, 39. (2019).


*g. S. Marzen, J. Zylberberg, and M.R. DeWeese. [https://redwood.berkeley.edu/w/images/c/ca/Marzen_Zylberberg_DeWeese_BinocularDisparity_R7a_1-28-2013-2_preprint.pdf The effect of natural scene statistics and oculomotor strategy on binocular disparity and ocular dominance maps.] (in preparation)
*45. L. Kang, M.R. DeWeese. Replay as wavefronts and theta sequences as bump oscillations in a grid cell attractor network. eLife 8, e46351. (2019).


*f. S. Corinaldi and M.R. DeWeese. A network model of task switching optimized to minimize errors predicts several counterintuitive features of human behavioral data. (in preparation)
*44. D. Mandal, K. Klymko, and M.R. DeWeese. Reply to Comment on ``Entropy Production and Fluctuation Theorems for Active Matter". Physical review letters 121, 139802. (2018).


*e. J. Sohl-Dickstein, M. Mudigonda, M.R. DeWeese.[https://redwood.berkeley.edu/w/images/2/2b/SohlDickstein_Mudigonda_DeWeese_Sampling_Without_Detailed_Ballance_preprint.pdf Hamiltonian Monte Carlo Without Detailed Balance.] (in preparation; this is an elaboration of [32])
*43. D. Mandal, K. Klymko, and M.R. DeWeese. Entropy Production and Fluctuation Theorems for Active Matter. Physical Review Letters 119, 258001. (2017).


*d. M. Leonard and M.R. DeWeese. [https://redwood.berkeley.edu/w/images/5/59/Leonard_and_DeWeese_Past_Present_and_Future_preprint.pdf Past, present, and future: memory and choice encoding in prefrontal cortex of rats performing a double alternation task.] (in preparation)  
*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).


'''Submitted manuscripts:'''
*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).


*c. J. Sohl-Dickstein, S. Teng, C. Rodgers, M.R. DeWeese, and N. Harper. [https://redwood.berkeley.edu/w/images/3/3e/Sohl-Dickstein_Teng_Gaub_Rodgers_Li_DeWeese_Harper_sonic_eye_preprint.pdf A device for human ultrasonic echolocation.] (submitted)
*40. D. Mandal and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/1/1e/Mandal_%26_DeWeese_PRE_JE_generalized_to_nonHamiltonian_dynamics_2016.pdf Nonequilibrium work energy relation for non-Hamiltonian dynamics.] Physical Review E. 93(4):042129. (2016).


*b. B. Albanna, C. Hillar, J. Sohl-Dickstein, and M.R. DeWeese. [https://redwood.berkeley.edu/w/images/8/8a/Albanna_Hillar_SohlDickstein_DeWeese_min_entropy_preprint.pdf Minimum and maximum entropy solutions for binary systems with known means and pairwise correlations.] (submitted).
*39. P.R. Zulkowski and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/f/f0/Zulkowski_DeWeese_Overdamped_Systems_PRE_2015.pdf Optimal control of overdamped systems.] Physical Review E. 92(3):032117. (2015).


*a.   A.j. Apicella, M. Dastjerdi, and M.R. DeWeese. [https://redwood.berkeley.edu/w/images/3/37/Apicella_Dastjerdi_DeWeese_synaptic_mechanisms_spatial_hearing_A1_doublespaced_preprint.pdf Synaptic mechanisms that contribute to spatial tuning in primary auditory cortex.] (submitted).
*38. P.R. Zulkowski and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/3/32/Zulkowski_DeWeese_Driven_Quantum_Systems_PRE_2015.pdf Optimal protocols for slowly driven quantum systems.] Physical Review E. 92(3):032113. (2015).


'''All publications:'''
*37. S.E. Marzen, M.R. DeWeese, and J.P. Crutchfield, [http://redwood.berkeley.edu/w/images/9/93/Marzen_DeWeese_Crutchfield_time_rez_dep_info_spiking_neurons_2015.pdf Time Resolution Dependence of Information Measures for Spiking Neurons: Scaling and Universality.] Frontiers in Computational Neuroscience. 9:105. (2015).


*34. P.R. Zulkowski and M.R. DeWeese. [https://redwood.berkeley.edu/w/images/b/b3/Zulkowski_DeWeese_optimal_erasure_1_bit_preprint.pdf Optimal finite-time erasure of a classical bit.] Physical Review E. 89(5):052140 (2014).
*36. V.M. Carels and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/b/b2/Carels_DeWeese_Neuron_Preview_Duan_et_al_2015_reprint.pdf Rats Exert Executive Control.] Neuron 86, pp. 1324-1326 (2015).


*33. C.C. Rodgers and M.R. DeWeese.   [https://redwood.berkeley.edu/w/images/c/c5/Rodgers_and_DeWeese_rodent_auditory_stim_selection_A1_PFC_Neuron_2014_w_Sup_Info.pdf 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). ([https://redwood.berkeley.edu/w/images/6/6e/Odoemene_and_Churchland_Neuron_Preview_of_Rodgers_and_DeWeese_2014.pdf Neuron Preview of this paper by Odoemene and Churchland])
*35. J. Sohl-Dickstein, S. Teng, B. Gaub, C. Rodgers, C. Li, M. DeWeese, and N. Harper. [http://redwood.berkeley.edu/w/images/b/bb/Sohl-Dickstein_Teng_Gaub_Rodgers_Li_DeWeese_Harper_sonic_eye_no_marquee_preprint.pdf A device for human ultrasonic echolocation.] IEEE Transactions in Biomedical Engineering. 62(6):1526-1534 (2015).


*32. J. Sohl-Dickstein, M. Mudigonda, M.R. DeWeese. [https://redwood.berkeley.edu/w/images/2/2b/SohlDickstein_Mudigonda_DeWeese_Sampling_Without_Detailed_Ballance_preprint.pdf Hamiltonian Monte Carlo Without Detailed Balance.] Proceedings of the 31st International Conference on Machine Learning (Beijing) (2014).
*34. P.R. Zulkowski and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/b/b3/Zulkowski_DeWeese_optimal_erasure_1_bit_preprint.pdf Optimal finite-time erasure of a classical bit.] Physical Review E. 89(5):052140 (2014).


*31. P.R. Zulkowski, D.A. Sivak, and M.R. DeWeese. [https://redwood.berkeley.edu/w/images/a/a4/Zulkowski_Sivak_DeWeese_Optimal_Transitions_Nonequil_Steady_States_PLOS1_2013_accepted.pdf Optimal control of transitions between nonequilibrium steady states.] Public Library of Science ONE. 8(12):e82754 (2013).
*33. C.C. Rodgers and M.R. DeWeese.   [http://redwood.berkeley.edu/w/images/c/c5/Rodgers_and_DeWeese_rodent_auditory_stim_selection_A1_PFC_Neuron_2014_w_Sup_Info.pdf 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).  ([http://redwood.berkeley.edu/w/images/6/6e/Odoemene_and_Churchland_Neuron_Preview_of_Rodgers_and_DeWeese_2014.pdf Neuron Preview of this paper by Odoemene and Churchland])


*30.   J. Zylberberg and M.R. DeWeese.  [https://redwood.berkeley.edu/w/images/6/62/Zylberberg_DeWeese_Decreasing_Sparseness_During_Development_PLoS_CB_2013_reprint.pdf Sparse coding models can exhibit decreasing sparseness while learning sparse codes for natural images.] Public Library of Science Computational Biology. 9(8):e1003182 (2013).
*32. J. Sohl-Dickstein, M. Mudigonda, and M.R. DeWeese.  [http://redwood.berkeley.edu/w/images/a/a3/Sohl-Dickstein_Mudigonda_DeWeese_LAHMC_icml2014_final_reprint.pdf Hamiltonian Monte Carlo Without Detailed Balance.] Proceedings of the 31st International Conference on Machine Learning (Beijing) (2014).


*29.   T. Hromádka, A.M. Zador, and M.R. DeWeese. [https://redwood.berkeley.edu/w/images/3/32/Hromadka_Zador_DeWeese_Up_states_are_rare_in_A1_J_Neurophysiol_2013.pdf Up-states are rare in awake auditory cortex.] Journal of Neurophysiology. 109(8):1989-95. (2013).
*31. P.R. Zulkowski, D.A. Sivak, and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/a/a4/Zulkowski_Sivak_DeWeese_Optimal_Transitions_Nonequil_Steady_States_PLOS1_2013_accepted.pdf Optimal control of transitions between nonequilibrium steady states.] Public Library of Science ONE. 8(12):e82754 (2013).


*28.  P. King, J. Zylberberg, and M.R. DeWeese.  [https://redwood.berkeley.edu/w/images/2/29/King_Zylberberg_DeWeese_E_I_Net_Model_of_V1_JNeurosci_2013.pdf Inhibitory interneurons decorrelate excitatory cells to drive sparse code formation in a spiking model of V1.] Journal of Neuroscience 33(13):5475–85 (2013).
*30.  J. Zylberberg and M.R. DeWeese.  [http://redwood.berkeley.edu/w/images/6/62/Zylberberg_DeWeese_Decreasing_Sparseness_During_Development_PLoS_CB_2013_reprint.pdf Sparse coding models can exhibit decreasing sparseness while learning sparse codes for natural images.] Public Library of Science Computational Biology. 9(8):e1003182 (2013).


*27.  J. Zylberberg, D. Pfau, and M.R. DeWeese.  [https://redwood.berkeley.edu/w/images/e/ee/Zylberberg_Pfau_DeWeese_PRE_2012_reprint.pdf Dead leaves and the dirty ground: Low-level image statistics in transmissive and occlusive imaging environments.] Physical Review E. 86(6):066112 (2012).
*29.  T. Hromádka, A.M. Zador, and M.R. DeWeese.  [http://redwood.berkeley.edu/w/images/3/32/Hromadka_Zador_DeWeese_Up_states_are_rare_in_A1_J_Neurophysiol_2013.pdf Up-states are rare in awake auditory cortex.] Journal of Neurophysiology. 109(8):1989-95. (2013).


*26.  P.R. Zulkowski, D.A. Sivak, G.E. Crooks, and M.R. DeWeese. [https://redwood.berkeley.edu/w/images/c/cb/Zulkowski_Sivak_Crooks_DeWeese_PRE_2012_reprint.pdf The geometry of thermodynamic control.] Physical Review E. 86(4):041148 (2012).
*28.  P. King, J. Zylberberg, and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/2/29/King_Zylberberg_DeWeese_E_I_Net_Model_of_V1_JNeurosci_2013.pdf Inhibitory interneurons decorrelate excitatory cells to drive sparse code formation in a spiking model of V1.] Journal of Neuroscience 33(13):5475–85 (2013).


*25.  N. Carlson, V.L. Ming, and M.R. DeWeese. [https://redwood.berkeley.edu/w/images/4/4b/Carlson_Ming_DeWeese_Sparse_speech_ICC_PLoS_CB_2012_reprint.pdf Sparse codes for speech predict spectrotemporal receptive fields in the inferior colliculus.] Public Library of Science Computational Biology. 7(10):e1002250 (2012).
*27.  J. Zylberberg, D. Pfau, and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/e/ee/Zylberberg_Pfau_DeWeese_PRE_2012_reprint.pdf Dead leaves and the dirty ground: Low-level image statistics in transmissive and occlusive imaging environments.] Physical Review E. 86(6):066112 (2012).


*24.  J. Sohl-Dickstein, P. Battaglino, and M.R. DeWeese. [https://redwood.berkeley.edu/w/images/f/fd/SohlDickstein_Battaglino_DeWeese_MinProbFlow_PRL_2011_reprint.pdf New method for parameter estimation in probabilistic models: Minimum probability flow.] Physical Review Letters. 107(22):220601 (2011).
*26.  P.R. Zulkowski, D.A. Sivak, G.E. Crooks, and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/c/cb/Zulkowski_Sivak_Crooks_DeWeese_PRE_2012_reprint.pdf The geometry of thermodynamic control.] Physical Review E. 86(4):041148 (2012).


*23.  J. Sohl-Dickstein, P. Battaglino, and M.R. DeWeese. [https://redwood.berkeley.edu/w/images/e/eb/SohlDickstein_Battaglino_DeWeese_MPF_ICML_2011_with_SupMat.pdf Minimum Probability Flow Learning.] Proceedings of the 28th International Conference on Machine Learning (Bellevue, WA) (2011).
*25.  N. Carlson, V.L. Ming, and M.R. DeWeese. [http://redwood.berkeley.edu/w/images/4/4b/Carlson_Ming_DeWeese_Sparse_speech_ICC_PLoS_CB_2012_reprint.pdf Sparse codes for speech predict spectrotemporal receptive fields in the inferior colliculus.] Public Library of Science Computational Biology. 7(10):e1002250 (2012).


*22.  J. Zylberberg, J.T. Murphy, and M.R. DeWeese.  [https://redwood.berkeley.edu/w/images/5/57/Zylberberg_DeWeese_SAILnet_PLoS_CB_2011_reprint.pdf 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).
*24.  J. Sohl-Dickstein, P. Battaglino, and M.R. DeWeese.  [http://redwood.berkeley.edu/w/images/f/fd/SohlDickstein_Battaglino_DeWeese_MinProbFlow_PRL_2011_reprint.pdf New method for parameter estimation in probabilistic models: Minimum probability flow.] Physical Review Letters. 107(22):220601 (2011).


*21.  J. Zylberberg, and M.R. DeWeese.  [https://redwood.berkeley.edu/w/images/7/7d/Zylberberg_DeWeese_prey_escape_Frontiers_2011_reprint.pdf How should prey animals respond to uncertain threats?] Frontiers in Computational Neuroscience 5:20. doi: 10.3389/fncom.2011.00020 (2011).
*23.  J. Sohl-Dickstein, P. Battaglino, and M.R. DeWeese.  [http://redwood.berkeley.edu/w/images/e/eb/SohlDickstein_Battaglino_DeWeese_MPF_ICML_2011_with_SupMat.pdf Minimum Probability Flow Learning.] Proceedings of the 28th International Conference on Machine Learning (Bellevue, WA) (2011).


*20.  M.A. Olshausen and M.R. DeWeese.  [https://redwood.berkeley.edu/w/images/0/00/Olshausen_DeWeese_Statistics_of_Style_Nature_2010.pdf Applied mathematics: The statistics of style.] Nature 463(7284), 1027-1028 (2010).
*22.  J. Zylberberg, J.T. Murphy, and M.R. DeWeese.  [http://redwood.berkeley.edu/w/images/5/57/Zylberberg_DeWeese_SAILnet_PLoS_CB_2011_reprint.pdf 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).


*19.  Y. Yang, M.R. DeWeese, G. Otazu, and A.M. Zador.  [https://redwood.berkeley.edu/w/images/f/fb/Yang_DeWeese_Otazu_Zador_microstim_timing_NatNeuro_2008_epub.pdf Millisecond-scale differences in neural activity in auditory cortex can drive decisions.] Nature Neuroscience 11, 1262-1263 (2008).
*21.  J. Zylberberg, and M.R. DeWeese.  [http://redwood.berkeley.edu/w/images/7/7d/Zylberberg_DeWeese_prey_escape_Frontiers_2011_reprint.pdf How should prey animals respond to uncertain threats?] Frontiers in Computational Neuroscience 5:20. doi: 10.3389/fncom.2011.00020 (2011).


*18.  T. Hromadka, M.R. DeWeese, and A.M. Zador.  [https://redwood.berkeley.edu/w/images/5/5f/Hromadka_DeWeese_Zador_Sparse_Awake_cellat_PLoS_2008.pdf Sparse representation of sounds in the unanesthetized auditory cortex.] PLoS Biol. 6, 124-137 (2008).
*20.  M.A. Olshausen and M.R. DeWeese.  [http://redwood.berkeley.edu/w/images/0/00/Olshausen_DeWeese_Statistics_of_Style_Nature_2010.pdf Applied mathematics: The statistics of style.] Nature 463(7284), 1027-1028 (2010).


*17.  M.R. DeWeese. [https://redwood.berkeley.edu/w/images/a/aa/DeWeese_CPNS_wholecell_invivo_methods_2007.pdf Whole-Cell Recording In Vivo.] Chapter 6 in Current Protocols in Neuroscience.  John Wiley & Sons, Inc., pp. 6.22.1-15 (2007).
*19.  Y. Yang, M.R. DeWeese, G. Otazu, and A.M. Zador.  [http://redwood.berkeley.edu/w/images/f/fb/Yang_DeWeese_Otazu_Zador_microstim_timing_NatNeuro_2008_epub.pdf Millisecond-scale differences in neural activity in auditory cortex can drive decisions.] Nature Neuroscience 11, 1262-1263 (2008).


*16.  M.R. DeWeese and A.M. Zador.  [https://redwood.berkeley.edu/w/images/f/f9/DeWeese_Zador_Bumps_reprint_JN_2006.pdf Non-Gaussian membrane potential dynamics imply sparse, synchronous activity in auditory cortex.]  J. Neuroscience 26(47), 12206-12218 (2006).
*18.  T. Hromadka, M.R. DeWeese, and A.M. Zador.  [http://redwood.berkeley.edu/w/images/5/5f/Hromadka_DeWeese_Zador_Sparse_Awake_cellat_PLoS_2008.pdf Sparse representation of sounds in the unanesthetized auditory cortex.]  PLoS Biol. 6, 124-137 (2008).


*15.  M.R. DeWeese and A.M. Zador. [https://redwood.berkeley.edu/w/images/3/38/DeWeese_Zador_Nature_N%26V_2006.pdf Neurobiology: Efficiency Measures.]  Nature 439(7079), 920-921 (2006).
*17.  M.R. DeWeese. [http://redwood.berkeley.edu/w/images/a/aa/DeWeese_CPNS_wholecell_invivo_methods_2007.pdf Whole-Cell Recording In Vivo.]  Chapter 6 in Current Protocols in Neuroscience.  John Wiley & Sons, Inc., pp. 6.22.1-15 (2007).


*14.  M.R. DeWeese, T. Hromádka, and A.M. Zador.  [https://redwood.berkeley.edu/w/images/d/d6/Hromadka_DeWeese_Zador_Neuron_AC_Bandwidth_Neuron_2005.pdf Reliability and representational bandwidth in auditory cortex.]  Neuron 48, 479-588 (2005).  
*16.  M.R. DeWeese and A.M. Zador.  [http://redwood.berkeley.edu/w/images/f/f9/DeWeese_Zador_Bumps_reprint_JN_2006.pdf Non-Gaussian membrane potential dynamics imply sparse, synchronous activity in auditory cortex.]  J. Neuroscience 26(47), 12206-12218 (2006).


*13.  M.R. DeWeese and A.M. Zador. [https://redwood.berkeley.edu/w/images/7/75/DeWeese_Zador_Neuron_Preview_2005.pdf Neural gallops across auditory streams.]  Neuron 48, 5-7 (2005).
*15.  M.R. DeWeese and A.M. Zador. [http://redwood.berkeley.edu/w/images/3/38/DeWeese_Zador_Nature_N%26V_2006.pdf Neurobiology: Efficiency Measures.]  Nature 439(7079), 920-921 (2006).


*12.  M.R. DeWeese and A.M. Zador.  [https://redwood.berkeley.edu/w/images/5/5a/DeWeese_and_Zador_variability_JNeurophys_2004.pdf Shared and private variability in the auditory cortex.]  J. Neurophysiol. 92, 1840-1855 (2004).
*14.  M.R. DeWeese, T. Hromádka, and A.M. Zador.  [http://redwood.berkeley.edu/w/images/d/d6/Hromadka_DeWeese_Zador_Neuron_AC_Bandwidth_Neuron_2005.pdf Reliability and representational bandwidth in auditory cortex.]  Neuron 48, 479-588 (2005).
 
*13.  M.R. DeWeese and A.M. Zador. [http://redwood.berkeley.edu/w/images/7/75/DeWeese_Zador_Neuron_Preview_2005.pdf Neural gallops across auditory streams.]  Neuron 48, 5-7 (2005).
 
*12.  M.R. DeWeese and A.M. Zador.  [http://redwood.berkeley.edu/w/images/5/5a/DeWeese_and_Zador_variability_JNeurophys_2004.pdf 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).
*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.  [https://redwood.berkeley.edu/w/images/5/5d/DeWeese_Wehr_Zador_binary_spiking_A1_2003.pdf Binary spiking in auditory cortex.]  J. Neurosci. 23, 7940-7949 (2003).
*10.  M.R. DeWeese, M. Wehr, and A.M. Zador.  [http://redwood.berkeley.edu/w/images/5/5d/DeWeese_Wehr_Zador_binary_spiking_A1_2003.pdf Binary spiking in auditory cortex.]  J. Neurosci. 23, 7940-7949 (2003).


*9.    M.R. DeWeese.  [https://redwood.berkeley.edu/w/images/0/0c/DeWeese_Optim_Prep_for_Studying_Opt_Neuron_2000.pdf An optimal preparation for studying optimization.]  Neuron 26, 546-548 (2000).
*9.    M.R. DeWeese.  [http://redwood.berkeley.edu/w/images/0/0c/DeWeese_Optim_Prep_for_Studying_Opt_Neuron_2000.pdf An optimal preparation for studying optimization.]  Neuron 26, 546-548 (2000).


*8.    M.R. DeWeese and M. Meister.  [https://redwood.berkeley.edu/w/images/8/80/DeWeese_Meister_Info_per_observation_Network_1999.pdf How to measure the information gained from one symbol.]  Network 10, 325-340 (1999).
*8.    M.R. DeWeese and M. Meister.  [http://redwood.berkeley.edu/w/images/8/80/DeWeese_Meister_Info_per_observation_Network_1999.pdf 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.  [https://redwood.berkeley.edu/w/images/0/00/Buracas_Zador_DeWeese_Albright_Efficient_Discrimination_Neuron_1998.pdf Efficient discrimination of temporal patterns by motion-sensitive neurons in primate visual cortex.]  Neuron 20, 959-969 (1998).
*7.    G. Buracas, A.M. Zador, M.R. DeWeese, and T. Albright.  [http://redwood.berkeley.edu/w/images/0/00/Buracas_Zador_DeWeese_Albright_Efficient_Discrimination_Neuron_1998.pdf 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.  [https://redwood.berkeley.edu/w/images/2/26/DeWeese_Zador_adaptation_to_variance_Neural_Computation_2003.pdf Asymmetric dynamics in optimal variance adaptation.]  Neural Computation 10, 1179-1202 (1998).
*6.    M.R. DeWeese and A. Zador.  [http://redwood.berkeley.edu/w/images/2/26/DeWeese_Zador_adaptation_to_variance_Neural_Computation_2003.pdf 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).
*5.    M.R. DeWeese.  Optimization principles for the neural code.  Network 7, 325-331 (1996).


*4.    M. DeWeese.  [https://redwood.berkeley.edu/w/images/1/14/DeWeese_NIPS_1996.pdf Optimization principles for the neural code.]  In Advances in Neural Information Processing Systems.  MIT Press, Cambridge, MA, Vol. 8, p. 281 (1996).
*4.    M. DeWeese.  [http://redwood.berkeley.edu/w/images/1/14/DeWeese_NIPS_1996.pdf 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.  [https://redwood.berkeley.edu/w/images/5/5a/Bialek_DeWeese_Random_Switching_Optimal_PRL_1995.pdf Random switching and optimal processing in the perception of ambiguous signals.]  Phys. Rev. Lett. 74, 3077-3080 (1995).
*3.    W. Bialek and M. DeWeese.  [http://redwood.berkeley.edu/w/images/5/5a/Bialek_DeWeese_Random_Switching_Optimal_PRL_1995.pdf Random switching and optimal processing in the perception of ambiguous signals.]  Phys. Rev. Lett. 74, 3077-3080 (1995).


*2.    M. DeWeese and W. Bialek.  [https://redwood.berkeley.edu/w/images/8/87/DeWeese_Bialek_Info_Flow_in_Sensory_Neurons_Il_Nuovo_Cimento_1995.pdf Information flow in sensory neurons.] Il Nuovo Cimento A17, 733 (1995).
*2.    M. DeWeese and W. Bialek.  [http://redwood.berkeley.edu/w/images/8/87/DeWeese_Bialek_Info_Flow_in_Sensory_Neurons_Il_Nuovo_Cimento_1995.pdf Information flow in sensory neurons.] Il Nuovo Cimento A17, 733 (1995).


*1.    W. Bialek, M. DeWeese, F. Rieke, and D. Warland.  [https://redwood.berkeley.edu/w/images/3/32/Bialek_et_al_Bits_and_Brains_1993.PDF Bits and brains:  information flow in the nervous system.]  Physica A 200, 581-593 (1993).
*1.    W. Bialek, M. DeWeese, F. Rieke, and D. Warland.  [http://redwood.berkeley.edu/w/images/3/32/Bialek_et_al_Bits_and_Brains_1993.PDF Bits and brains:  information flow in the nervous system.]  Physica A 200, 581-593 (1993).

Latest revision as of 09:22, 4 March 2020

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

Publications:

  • 49. P.S. Sachdeva, J.A. Livezey, M.R. DeWeese. Heterogeneous synaptic weighting improves neural coding in the presence of common noise. Neural Computation, in press. (2020).
  • 48. M.Y.S. Fang, S. Manipatruni, C. Wierzynski, A. Khosrowshahi, M.R. DeWeese. Design of optical neural networks with component imprecisions. Optics express 27, 14009-14029. (2019).
  • 47. M.N. Insanally, I. Carcea, R.E. Field, C.C. Rodgers, B. DePasquale, K. Rajan, M.R. DeWeese, B.F. Albanna, R.C. Froemke. Spike-timing-dependent ensemble encoding by non-classically responsive cortical neurons. eLife 8, e42409. (2019).
  • 46. E.M.V. Dodds, M.R. DeWeese. On the sparse structure of natural sounds and natural images: similarities, differences, and implications for neural coding. Frontiers in computational neuroscience 13, 39. (2019).
  • 45. L. Kang, M.R. DeWeese. Replay as wavefronts and theta sequences as bump oscillations in a grid cell attractor network. eLife 8, e46351. (2019).
  • 44. D. Mandal, K. Klymko, and M.R. DeWeese. Reply to Comment on ``Entropy Production and Fluctuation Theorems for Active Matter". Physical review letters 121, 139802. (2018).
  • 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).
  • 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).
  • 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).
  • 5. M.R. DeWeese. Optimization principles for the neural code. Network 7, 325-331 (1996).
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