Michael DeWeese: Difference between revisions

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The following is a list of my published papers and some preprints.  Many 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).


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)
*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).


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).
*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).


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).
*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).


Submitted manuscripts:
*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).


v.   T. Hromádka, A.M. Zador, and M.R. DeWeese. Up-states are rare in awake auditory cortex. (submitted to the Journal of Neurophysiology).
*43. D. Mandal, K. Klymko, and M.R. DeWeese. Entropy Production and Fluctuation Theorems for Active Matter. Physical Review Letters 119, 258001. (2017).


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).
*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).


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).
*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).


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).
*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).


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


Publications:
*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).


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).
*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).


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).
*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).


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).
*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).


2011-2012 Publication:
*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). 


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).
*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])


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).
*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).


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).
*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).


2010-2011 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).


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).
*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).


2009-2010 Publications:
*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).


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


2008-2009 Publications:
*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).


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).
*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).


2007-2008 Publications:
*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).


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).
*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).


2006-2007 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).


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).
*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).


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).
*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).


Pre July 1, 2006 Publications:
*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).


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


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


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


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


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).
*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).  


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


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


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


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).
*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).


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


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


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).
*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).


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


2.    M. DeWeese and W. Bialek.  Information flow in sensory neurons.  Nuovo Cimento A17, 733 (1995).
*5.    M.R. DeWeese.  Optimization principles for the neural code.  Network 7, 325-331 (1996).


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).
*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.  [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).


Test text for Mike's new page.
*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).
Test link to a picture (uploaded file):
 
https://redwood.berkeley.edu/wiki/File:Evans_Hall.jpg
*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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