ABOUT THE RESEARCHER

OVERVIEW

The intricacy of the neuronal circuitry makes the brain the most complex and fascinating system ever studied by Science. The Kreiman lab is interested in understanding how biological networks encode, process and transmit information. There are two main lines of research in the lab: (i) how circuits of neurons represent visual information and (ii) how gene expression is orchestrated, with a particular emphasis on gene expression in the nervous system. The lab uses a combination of mathematical, computational and experimental tools.

Please visit the Kreiman lab webpage for further information, publications, ongoing projects and job opportunities.

The Kreiman Lab combines high-resolution neurophysiology of the human brain and computational models to understand the processing of visual information--from perception to cognition.

BACKGROUND

Gabriel Kreiman received his MSc and PhD degree from the California Institute of Technology (Caltech) and subsequently worked as a research fellow at the Massachusetts Institute of Technology (MIT). The Kreiman lab is interested in the neuronal circuits and algorithms responsible for visual object recognition and memory formation. Visual object recognition is crucial for most everyday tasks including face identification, reading and navigation. The Kreiman lab combines neurophysiology, psychophysics and theoretical/computational modeling to understand the neuronal circuits, algorithms and computations performed by the visual system and to develop biophysically-inspired computational approaches to machine vision and memory formation.

Selected Publications

  1. Fried I, Mukamel R, Kreiman G. (2011). Internally Generated Preactivation of Single Neurons in Human Medial Frontal Cortex Predicts Volition. Neuron. 69: 548-562.
  2. Agam Y, Liu H, Pappanastassiou A, Buia C, Golby AJ, Madsen JR, Kreiman, G. (2010). Robust selectivity to two-object images in human visual cortex. Current Biology. 20: 872-879.
  3. Kim TK*, Hemberg M*, Gray JM*, Costa A, Bear DM, Wu J, Harmin DA, Laptewicz, M, Barbara-Haley K, Kuersten S, Markenscoff-Papadimitriou E, Kuhl D, Bito H, Worley PF, Kreiman G, Greenberg ME. Widespread transcription at thousands of enhancers during activity-dependent gene expression in neurons. Nature 2010 May 13; 465(7295):182-7. (* = equal contribution)
  4. Liu H, Agam Y, Madsen JR, Kreiman G. Timing, timing, timing: Fast decoding of object information from intracranial field potentials in human visual cortex. Neuron 2009 Apr 30; 62(2):281-90.
  5. Serre T, Kreiman G, Kouh M, Cadieu C, Knoblich U, Poggio T. A quantitative theory of immediate visual recognition. Prog Brain Res 2007; 165:33-56.
  6. Kreiman G. Single neuron approaches to human vision and memories. Curr Opin Neurobiol 2007 Aug; 17(4):471-5.
  7. Quiroga RQ, Reddy L, Kreiman G, Koch C, Fried I. Invariant visual representation by single neurons in the human brain. Nature 2005 Jun 23; 435(7045):1102-7.
  8. Hung CP, Kreiman G, Poggio T, DiCarlo JJ. Fast read-out of object identity from macaque inferior temporal cortex. Science 2005 Nov 4; 310(5749):863-6.

PUBLICATIONS

Publications powered by Harvard Catalyst Profiles

  1. Localized Task-Invariant Emotional Valence Encoding Revealed by Intracranial Recordings. Soc Cogn Affect Neurosci. 2021 Dec 23. View abstract
  2. Mesoscopic physiological interactions in the human brain reveal small-world properties. Cell Rep. 2021 Aug 24; 36(8):109585. View abstract
  3. Beauty is in the eye of the machine. Nat Hum Behav. 2021 06; 5(6):675-676. View abstract
  4. Incorporating intrinsic suppression in deep neural networks captures dynamics of adaptation in neurophysiology and perception. Sci Adv. 2020 Oct; 6(42). View abstract
  5. Can Deep Learning Recognize Subtle Human Activities? Conf Comput Vis Pattern Recognit Workshops. 2020 Jun; 2020. View abstract
  6. Putting visual object recognition in context. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. 2020 Jun; 2020:12982-12991. View abstract
  7. XDream: Finding preferred stimuli for visual neurons using generative networks and gradient-free optimization. PLoS Comput Biol. 2020 06; 16(6):e1007973. View abstract
  8. Minimal videos: Trade-off between spatial and temporal information in human and machine vision. Cognition. 2020 08; 201:104263. View abstract
  9. A neural network trained for prediction mimics diverse features of biological neurons and perception. Nat Mach Intell. 2020 Apr; 2(4):210-219. View abstract
  10. Beyond the feedforward sweep: feedback computations in the visual cortex. Ann N Y Acad Sci. 2020 03; 1464(1):222-241. View abstract
  11. Neural Interactions Underlying Visuomotor Associations in the Human Brain. Cereb Cortex. 2019 12 17; 29(11):4551-4567. View abstract
  12. Evolving Images for Visual Neurons Using a Deep Generative Network Reveals Coding Principles and Neuronal Preferences. Cell. 2019 05 02; 177(4):999-1009.e10. View abstract
  13. It's a small dimensional world after all: Comment on "The unreasonable effectiveness of small neural ensembles in high-dimensional brain" by Alexander N. Gorban et al. Phys Life Rev. 2019 07; 29:96-97. View abstract
  14. Minimal memory for details in real life events. Sci Rep. 2018 11 12; 8(1):16701. View abstract
  15. Finding any Waldo with zero-shot invariant and efficient visual search. Nat Commun. 2018 09 13; 9(1):3730. View abstract
  16. Recurrent computations for visual pattern completion. Proc Natl Acad Sci U S A. 2018 08 28; 115(35):8835-8840. View abstract
  17. Automating Interictal Spike Detection: Revisiting A Simple Threshold Rule. Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul; 2018:299-302. View abstract
  18. What is changing when: Decoding visual information in movies from human intracranial recordings. Neuroimage. 2018 10 15; 180(Pt A):147-159. View abstract
  19. Predicting episodic memory formation for movie events. Sci Rep. 2016 Sep 30; 6:30175. View abstract
  20. A null model for cortical representations with grandmothers galore. Lang Cogn Neurosci. 2017; 32(3):274-285. View abstract
  21. Bottom-Up and Top-Down Input Augment the Variability of Cortical Neurons. Neuron. 2016 Aug 03; 91(3):540-547. View abstract
  22. f-divergence cutoff index to simultaneously identify differential expression in the integrated transcriptome and proteome. Nucleic Acids Res. 2016 06 02; 44(10):e97. View abstract
  23. Cascade of neural processing orchestrates cognitive control in human frontal cortex. Elife. 2016 Feb 18; 5. View abstract
  24. There's Waldo! A Normalization Model of Visual Search Predicts Single-Trial Human Fixations in an Object Search Task. Cereb Cortex. 2016 07; 26(7):3064-82. View abstract
  25. Decrease in gamma-band activity tracks sequence learning. Front Syst Neurosci. 2014; 8:222. View abstract
  26. Sensitivity to timing and order in human visual cortex. J Neurophysiol. 2015 Mar 01; 113(5):1656-69. View abstract
  27. Quantitative profiling of peptides from RNAs classified as noncoding. Nat Commun. 2014 Nov 18; 5:5429. View abstract
  28. Spatiotemporal dynamics underlying object completion in human ventral visual cortex. Neuron. 2014 Aug 06; 83(3):736-48. View abstract
  29. Corticocortical feedback increases the spatial extent of normalization. Front Syst Neurosci. 2014; 8:105. View abstract
  30. Short temporal asynchrony disrupts visual object recognition. J Vis. 2014 May 12; 14(5):7. View abstract
  31. Neural dynamics underlying target detection in the human brain. J Neurosci. 2014 Feb 19; 34(8):3042-55. View abstract
  32. Theory on the coupled stochastic dynamics of transcription and splice-site recognition. PLoS Comput Biol. 2012; 8(11):e1002747. View abstract
  33. Temporal stability of visually selective responses in intracranial field potentials recorded from human occipital and temporal lobes. J Neurophysiol. 2012 Dec; 108(11):3073-86. View abstract
  34. Integrated genome analysis suggests that most conserved non-coding sequences are regulatory factor binding sites. Nucleic Acids Res. 2012 Sep; 40(16):7858-69. View abstract
  35. Depression-biased reverse plasticity rule is required for stable learning at top-down connections. PLoS Comput Biol. 2012; 8(3):e1002393. View abstract
  36. Nine criteria for a measure of scientific output. Front Comput Neurosci. 2011; 5:48. View abstract
  37. Face recognition: vision and emotions beyond the bubble. Curr Biol. 2011 Nov 08; 21(21):R888-90. View abstract
  38. On the minimization of fluctuations in the response times of autoregulatory gene networks. Biophys J. 2011 Sep 21; 101(6):1297-306. View abstract
  39. Conservation of transcription factor binding events predicts gene expression across species. Nucleic Acids Res. 2011 Sep 01; 39(16):7092-102. View abstract
  40. Internally generated preactivation of single neurons in human medial frontal cortex predicts volition. Neuron. 2011 Feb 10; 69(3):548-62. View abstract
  41. Neuroscience: what we cannot model, we do not understand. Curr Biol. 2011 Feb 08; 21(3):R123-5. View abstract
  42. Decoding ensemble activity from neurophysiological recordings in the temporal cortex. Annu Int Conf IEEE Eng Med Biol Soc. 2011; 2011:5904-7. View abstract
  43. How cortical neurons help us see: visual recognition in the human brain. J Clin Invest. 2010 Sep; 120(9):3054-63. View abstract
  44. Robust selectivity to two-object images in human visual cortex. Curr Biol. 2010 May 11; 20(9):872-9. View abstract
  45. Widespread transcription at neuronal activity-regulated enhancers. Nature. 2010 May 13; 465(7295):182-7. View abstract
  46. Measuring sparseness in the brain: comment on Bowers (2009). Psychol Rev. 2010 Jan; 117(1):291-7. View abstract
  47. Toward unmasking the dynamics of visual perception. Neuron. 2009 Nov 25; 64(4):446-7. View abstract
  48. From neurons to circuits: linear estimation of local field potentials. J Neurosci. 2009 Nov 04; 29(44):13785-96. View abstract
  49. Timing, timing, timing: fast decoding of object information from intracranial field potentials in human visual cortex. Neuron. 2009 Apr 30; 62(2):281-90. View abstract
  50. Dynamic population coding of category information in inferior temporal and prefrontal cortex. J Neurophysiol. 2008 Sep; 100(3):1407-19. View abstract
  51. Single unit approaches to human vision and memory. Curr Opin Neurobiol. 2007 Aug; 17(4):471-5. View abstract
  52. Differential gene expression between sensory neocortical areas: potential roles for Ten_m3 and Bcl6 in patterning visual and somatosensory pathways. Cereb Cortex. 2008 Jan; 18(1):53-66. View abstract
  53. A quantitative theory of immediate visual recognition. Prog Brain Res. 2007; 165:33-56. View abstract
  54. Gene expression changes and molecular pathways mediating activity-dependent plasticity in visual cortex. Nat Neurosci. 2006 May; 9(5):660-8. View abstract
  55. Object selectivity of local field potentials and spikes in the macaque inferior temporal cortex. Neuron. 2006 Feb 02; 49(3):433-45. View abstract
  56. Fast readout of object identity from macaque inferior temporal cortex. Science. 2005 Nov 04; 310(5749):863-6. View abstract
  57. Invariant visual representation by single neurons in the human brain. Nature. 2005 Jun 23; 435(7045):1102-7. View abstract
  58. Variation in alternative splicing across human tissues. Genome Biol. 2004; 5(10):R74. View abstract
  59. Consciousness and neurosurgery. Neurosurgery. 2004 Aug; 55(2):273-281; discussion 281-2. View abstract
  60. Identification of sparsely distributed clusters of cis-regulatory elements in sets of co-expressed genes. Nucleic Acids Res. 2004; 32(9):2889-900. View abstract
  61. A gene atlas of the mouse and human protein-encoding transcriptomes. Proc Natl Acad Sci U S A. 2004 Apr 20; 101(16):6062-7. View abstract
  62. Single-neuron correlates of subjective vision in the human medial temporal lobe. Proc Natl Acad Sci U S A. 2002 Jun 11; 99(12):8378-83. View abstract
  63. Neural correlates of consciousness in humans. Nat Rev Neurosci. 2002 Apr; 3(4):261-70. View abstract
  64. Stimulus encoding and feature extraction by multiple sensory neurons. J Neurosci. 2002 Mar 15; 22(6):2374-82. View abstract
  65. Amygdala-enriched genes identified by microarray technology are restricted to specific amygdaloid subnuclei. Proc Natl Acad Sci U S A. 2001 Apr 24; 98(9):5270-5. View abstract
  66. Imagery neurons in the human brain. Nature. 2000 Nov 16; 408(6810):357-61. View abstract
  67. Category-specific visual responses of single neurons in the human medial temporal lobe. Nat Neurosci. 2000 Sep; 3(9):946-53. View abstract
  68. Tetanic stimulation leads to increased accumulation of Ca(2+)/calmodulin-dependent protein kinase II via dendritic protein synthesis in hippocampal neurons. J Neurosci. 1999 Sep 15; 19(18):7823-33. View abstract