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The following is a list of my published papers and 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.


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).


ix. N. Carlson, V.L. Ming, and M.R. DeWeese. Probe stimuli affect receptive field estimation of model auditory neurons. (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).


Using our recently developed sparse coding model of speech, we study how choice of probe stimuli by electrophysiologists affects the measurement of neural receptive fields. We show that a lack of relevant stimulus features, rather than encoding nonlinearities, is the most significant factor contributing to errors, even if the stimulus autocorrelation is accounted for properly.
*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).


viii. S. Marzen, J. Zylberberg, and M.R. DeWeese. The effect of natural scene statistics and oculomotor strategy on binocular disparity and ocular dominance maps. (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).


Once the gaze direction for a given eyeball is determined, the eye could still rotate about the line of sight. In order to predict how the eye should be rotated when viewing objects in different locations in space, we have developed a theory of optimal oculomotor strategy that for the first time incorporates 1) a semi-realistic model of object sizes and locations in the world, 2) the neural mapping from retina to the primary visual cortex (V1), 3) a “minimal wiring” principle in the cortex that posits that the two eyes should be rotated so as to shorten the distance between the pair of projections to V1 of the same points in space, and 4) an energetic cost associated with the required muscular contractions to achieve any given rotation of the eyes. We find that disparity statistics in V1 are far more sensitive to the typical distances of objects in the world than they are to other properties of the objects themselves, and we find that the optimal oculomotor strategy can be reconciled with empirically observed human eye tracking data for reasonable choices of model parameters.
*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).


vii. 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).
We propose a novel optimal framework for task switching that, unlike previous models, generalizes naturally to more than 2 tasks and predicts several counterintuitive observations, such as the fact that people make more errors and hesitations when they are instructed to switch from a hard task to an easy task compared to the reverse situation.


vi. C. Rodgers, M. Dastjerdi, and M.R. DeWeese. Task-dependent anticipatory activity in both prefrontal cortex and auditory cortex during a purely auditory selective attention task. (in preparation).
*43. D. Mandal, K. Klymko, and M.R. DeWeese. Entropy Production and Fluctuation Theorems for Active Matter. Physical Review Letters 119, 258001. (2017).


We have developed a novel, purely auditory selective attention paradigm that rats can typically learn in six weeks — subjects perform alternating blocks of ~100 behavioral trials toggling between pitch discrimination and sound localization utilizing the same set of four acoustic stimuli, allowing us to compare responses to identical stimuli during different tasks. Consistent with some reports of activity in prefrontal cortex (PFC) in macaque monkeys performing visual selective attention tasks, we find that the activity of neurons in rat PFC during the delay period between the time the rat initiates a trial and the time the acoustic stimulus is presented is often modulated depending on which of two alternate tasks the rat is asked to perform in response to the upcoming stimulus. Surprisingly, we also find this anticipatory effect for many neurons in the primary auditory cortex (A1). A similar effect was reported for intrinsic imaging signals from cat visual cortex in one previous study, but that turned out to not reflect any actual changes in the spiking activity of neurons; to our knowledge such a phenomenon has not been reported in any primary sensory area in any previous mammalian attention study.  
*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).


v.     T. Hromádka, A.M. Zador, and M.R. DeWeese. Up-states are rare in awake auditory cortex. (submitted to the Journal of Neurophysiology).
*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).


Using in vivo whole-cell patch clamp methods in awake, head-fixed rats, we find that membrane potential dynamics of individual neurons in primary auditory cortex (A1) look “bumpy.” This is consistent with highly concerted activity among the presynaptic population rather than the random input models commonly used to characterize cortical neurons. These results are consistent with our previous findings for anesthetized A1. Many of these recordings were performed in Dr. Zador’s laboratory.
*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).


iv.   B. Albanna, C. Hillar, J. Sohl-Dickstein, and M.R. DeWeese. Minimum and maximum entropy solutions for binary systems with known means and pairwise correlations. (submitted to Physical Review E).
*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).


We compute tight upper and lower bounds on the entropy of the space of all models consistent with the empirically measured 1st and 2nd order statistics of populations of spiking neurons. Surprisingly, we find that the minimum entropy grows only logarithmically with the number of neurons, demonstrating a large space of feasible models spanning the previously unknown low entropy solutions and the maximum entropy solutions that have received much more attention. Our results have particular relevance to systems neuroscience given the rapid increase in the number of simultaneously recorded neurons in recent experiments and they are relevant to computer science.
*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).


iii. P. King, J. Zylberberg, and M.R. DeWeese. Inhibitory interneurons decorrelate excitatory cells to drive sparse code formation in a spiking model of V1. (submitted to the Journal of Neuroscience).
*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).


We present a model of learning and sensory representation in the primary visual cortex (V1) that incorporates separate populations of excitatory and inhibitory neurons and that can self-organize using synaptically-local learning rules to produce a sparse representation of natural scenes. The model learns receptive fields that are in good agreement with measured receptive fields from V1 and it predicts several known, but poorly understood, features of V1, such as the higher firing rates and lower cell count of the inhibitory population relative to the excitatory population. The success of our model suggests a specific computational role for a class of cortical inhibitory neurons (fast-spiking interneurons) — decorrelating the excitatory neuron responses.
*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).


ii.   J. Zylberberg and M.R. DeWeese. A model of primary visual cortex can exhibit decreasing sparseness while learning a sparse code for natural images. (submitted to Public Library of Science Computational Biology).
*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).


We prove that a realizable network using only synaptically-local plasticity rules can not only achieve a global objective of learning a sparse code, but it can do so while the empirically measured sparseness of the network’s representation of visual input decreases with time. Recent reports that sparseness decreases during development in young ferret V1 prompted some to claim that sparse coding could not be a principle underlying cortical development, but we show that this is not necessarily the case despite the fact that no previous sparse coding model had been reconciled with the ferret data.
*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])


i.   A.j. Apicella and M.R. DeWeese.  Circuit mechanisms that contribute to spatial tuning in primary auditory cortex. (submitted to Journal of Neuroscience).
*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).


Using in vivo whole-cell patch clamp techniques in the anesthetized rat, we demonstrate that excitatory and inhibitory synaptic drive to individual neurons in the primary auditory cortex (A1) are balanced in response to sounds originating from either side of the subject’s head, but the magnitudes of both excitatory and inhibitory inputs are greater for contralateral sounds than for ipsilateral sounds. We are the first to measure the synaptic mechanisms underlying the higher spiking responsiveness of A1 neurons to contralateral sounds.
*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).


Publications:
*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.  Dead leaves and the dirty ground: Low-level image statistics in transmissive and occlusive imaging environments. Physical Review E. (in press; Accepted Dec 4, 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. The geometry of thermodynamic control. Physical Review E. 86(4 Pt 1):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. 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).


2011-2012 Publication:
*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).


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).
*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).


*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).
*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).


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).
*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).


2010-2011 Publications:
*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).


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).
*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).


2009-2010 Publications:
*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).


*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.  [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).


2008-2009 Publications:
*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).


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).
*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).


2007-2008 Publications:
*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).


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).
*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).


2006-2007 Publications:
*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).


*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).
*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).


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).
*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).


Pre July 1, 2006 Publications:
*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).


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


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


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


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


*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).
*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).


10.   M.R. DeWeese, M. Wehr, and A.M. Zador.  Binary spiking in auditory cortex.  J. Neurosci. 23, 7940-7949 (2003).
*5.   M.R. DeWeese.  Optimization principles for the neural code.  Network 7, 325-331 (1996).


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


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


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).
*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).


6.    M.R. DeWeese and A. Zador.  Asymmetric dynamics in optimal variance adaptation.  Neural Computation 10, 1179-1202 (1998).
*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).
 
*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.  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).
 
\* Denotes non-refereed article and/or conference proceedings.
 
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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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