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From the neuron doctrine to neural networks

Nature Reviews Neuroscience volume 16, pages 487–497 (2015)Cite this article

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Abstract

For over a century, the neuron doctrine — which states that the neuron is the structural and functional unit of the nervous system — has provided a conceptual foundation for neuroscience. This viewpoint reflects its origins in a time when the use of single-neuron anatomical and physiological techniques was prominent. However, newer multineuronal recording methods have revealed that ensembles of neurons, rather than individual cells, can form physiological units and generate emergent functional properties and states. As a new paradigm for neuroscience, neural network models have the potential to incorporate knowledge acquired with single-neuron approaches to help us understand how emergent functional states generate behaviour, cognition and mental disease.

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Figure 1: Historical evolution of the neuron doctrine and neural network models.
Figure 2: Anatomical and physiological examples of the neuron doctrine.
Figure 3: Spontaneous cortical activity.
Figure 4: Neural networks.
Figure 5: Emergent functional states in multineuronal dynamics during virtual navigation.

References

  1. Shepherd, G. M. Foundations of the Neuron Doctrine (Oxford Univ. Press, 1991).

    Google Scholar 

  2. Ramón y Cajal, S. Estructura de los centros nerviosos de las aves. Rev. Trim. Histol. Norm. Pat. 1, 1–10 (1888) (in Spanish).

    Google Scholar 

  3. Sherrington, C. S. Observations on the scratch-reflex in the spinal dog. J. Physiol. 34, 1–50 (1906).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Golgi, C. Sulla struttura della sostanza grigia del cervello. Gazz. Med. Ital. (Lombardia) 33, 244–246 (1873) (in Italian).

    Google Scholar 

  5. Hubel, D. H. Tungsten microelectrode for recording from single units. Science 125, 549–550 (1957).

    CAS  PubMed  Google Scholar 

  6. Ramón y Cajal, S. La Textura del Sistema Nerviosa del Hombre y los Vertebrados 1st edn (Moya, 1899).

    Google Scholar 

  7. Hubel, D. H. & Wiesel, T. N. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex. J. Physiol. 160, 106–154 (1962).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Churchland, P. S. & Sejnowski, T. The Computational Brain (MIT Press, 1992).

    Google Scholar 

  9. Yuste, R. Dendritic spines and distributed circuits. Neuron 71, 772–781 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. McCulloch, W. S. & Pitts, W. A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5, 115–133 (1943).

    Google Scholar 

  11. Hebb, D. O. The Organization Of Behaviour (Wiley, 1949).

    Google Scholar 

  12. Boyden, E. S., Zhang, F., Bamberg, E., Nagel, G. & Deisseroth, K. Millisecond-timescale, genetically targeted optical control of neural activity. Nat. Neurosci. 8, 1263–1268 (2005).

    CAS  PubMed  Google Scholar 

  13. Yuste, R. (ed) Imaging: A Laboratory Manual (Cold Spring Harbor Press, 2011).

    Google Scholar 

  14. Berenyi, A. et al. Large-scale, high-density (up to 512 channels) recording of local circuits in behaving animals. J. Neurophysiol. 111, 1132–1149 (2014).

    PubMed  Google Scholar 

  15. Dayan, P. & Abbott, L. F. Theoretical Neuroscience (MIT Press, 2001).

    Google Scholar 

  16. Bullock, T. H. et al. Neuroscience. The neuron doctrine, redux. Science 310, 791–793 (2005).

    CAS  PubMed  Google Scholar 

  17. Kandel, E. R., Schwartz, J. H., Jessell, T. M., Siegelbaum, S. A. & Hudspeth, A. J. Principles of Neural Science 5th edn (McGraw-Hill, 2013).

    Google Scholar 

  18. Porter, K. R., Claude, A. & Fullam, E. F. A study of tissue culture cells by electron microscopy: methods and preliminary observations. J. Exp. Med. 81, 233–246 (1945).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. DeRobertis, E. D. P. & Bennett, H. S. Some features of the submicroscopic morphology of synapses in frog and earthworm. J. Biophys. Biochem. Cytol. 1, 47–58 (1955).

    CAS  Google Scholar 

  20. Palay, S. L. Synapses in the central nervous system. J. Biophysiol. Biochem. Cytol. 2, 193–201 (1956).

    CAS  Google Scholar 

  21. Magner, L. N. A History of the Life Sciences (Marcel Dekker, 1979).

    Google Scholar 

  22. von Waldeyer-Hartz, H. W. G. Ueber Einige Neuere Forschungen Gebiete Anatomie Centralnervensystems. Dtsch Med. Wochenschr. 17, 1213–1356 (1891) (in German).

    Google Scholar 

  23. Adrian, E. D. The Basis of Sensation (W. W. Norton & Co., 1928).

    Google Scholar 

  24. Hartline, H. K. The response of single optic nerve fibres of the vertebrate eye to illumination of the retina. Am. J. Physiol. 121, 400–415 (1938).

    Google Scholar 

  25. Maturana, H. R., Lettvin, J. Y., McCulloch, W. S. & Pitts, W. H. Anatomy and physiology of vision in the frog (Rana pipiens). J. Gen. Physiol. 43, 129–175 (1960).

    PubMed  PubMed Central  Google Scholar 

  26. Mountcastle, V. B. Modality and topographic properties of single neurons of cat's somatosensory cortex. J. Neurophysiol. 20, 408–443 (1957).

    CAS  PubMed  Google Scholar 

  27. Hubel, D. H. Eye, Brain and Vision (Scientific American Library, 1988).

    Google Scholar 

  28. Barlow, H. B. Single units and sensation: a neuron doctrine for perceptual psychology? Perception 1, 371–394 (1972).

    CAS  PubMed  Google Scholar 

  29. Desimone, R., Albright, T. D., Gross, C. G. & Bruce, C. Stimulus-selective properties of inferior temporal neurons in the macaque. J. Neurosci. 4, 2051–2062 (1984).

    CAS  PubMed  Google Scholar 

  30. Tanaka, K. Inferotemporal cortex and object vision. Annu. Rev. Neurosci. 19, 109–139 (1996).

    CAS  PubMed  Google Scholar 

  31. Kreiman, G., Koch, C. & Fried, I. Category-specific visual responses of single neurons in the human medial temporal lobe. Nat. Neurosci. 946–953 (2000).

  32. Salzman, C. D. & Newsome, W. T. Neural mechanisms for forming a perceptual decision. Science 264, 231–237 (1994).

    CAS  PubMed  Google Scholar 

  33. Brecht, M., Schneider, M., Sakmann, B. & Margrie, T. W. Whisker movements evoked by stimulation of single pyramidal cells in rat motor cortex. Nature 427, 704–710 (2004).

    CAS  PubMed  Google Scholar 

  34. Houweling, A. R. & Brecht, M. Behavioural report of single neuron stimulation in somatosensory cortex. Nature 451, 65–68 (2008).

    CAS  PubMed  Google Scholar 

  35. Dyson, F. J. History of science. Is science mostly driven by ideas or by tools? Science 338, 1426–1427 (2012).

    CAS  PubMed  Google Scholar 

  36. Kuhn, T. S. The Structure of Scientific Revolutions (Univ. of Chicago Press, 1963).

    Google Scholar 

  37. Fairhall, A. The receptive field is dead. Long live the receptive field? Curr. Opin. Neurobiol. 25, ix–xii (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Gallant, J. L., Connor, C. E. & Van Essen, D. C. Responses of visual cortical neurons in a monkey freely viewing natural scenes. Soc. Neurosci. Abstr. 20, 838 (1994).

    Google Scholar 

  39. Ko, H. et al. Functional specificity of local synaptic connections in neocortical networks. Nature 473, 87–91 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Steriade, M., Gloor, P., Llinás, R. R., Lopes da Silva, F. & Mesulam, M. M. Basic mechanisms of cerebral rhythmic activities. Electroencephalogr. Clin. Neurophysiol. 30, 481–508 (1990).

    Google Scholar 

  41. Kenet, T., Bibitchkov, D., Tsodyks, M., Grinvald, A. & Arieli, A. Spontaneously emerging cortical representations of visual attributes. Nature 425, 954–956 (2003).

    CAS  PubMed  Google Scholar 

  42. Ahrens, M. B., Orger, M. B., Robson, D. N., Li, J. M. & Keller, P. J. Whole-brain functional imaging at cellular resolution using light-sheet microscopy. Nat. Methods 10, 413–420 (2013).

    CAS  PubMed  Google Scholar 

  43. Miller, J. E., Ayzenshtat, I., Carrillo-Reid, L. & Yuste, R. Visual stimuli recruit intrinsically generated cortical ensembles. Proc. Natl Acad. Sci. USA 111, E4053–E4061 (2014).

    CAS  PubMed  Google Scholar 

  44. Berger, H. Über das elektrenkephalogramm des menschen. Arch. Psychiatr. Nervenkr. 87, 527–570 (1929) (in German).

    Google Scholar 

  45. Fox, M. D. & Raichle, M. E. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat. Rev. Neurosci. 8, 700–711 (2007).

    CAS  PubMed  Google Scholar 

  46. O'Keefe, J. & Dostrovsky, J. The hippocampus as a spatial map. Preliminary evidence from unit activity in the freely-moving rat. Brain Res. 34, 171–175 (1971).

    CAS  PubMed  Google Scholar 

  47. Buzsaki, G. Neural syntax: cell assemblies, synapsembles, and readers. Neuron 68, 362–385 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Braitenberg, V. & Schüz, A. Anatomy of the Cortex (Springer, 1998).

    Google Scholar 

  49. Shepherd, G. M. The Synaptic Organization of the Brain (Oxford Univ. Press, 1990).

    Google Scholar 

  50. Abeles, M. Corticonics (Cambrdige Univ. Press, 1991).

    Google Scholar 

  51. Peters, A. & Jones, E. G. (eds) Cerebral Cortex (Plenum, 1984).

    Google Scholar 

  52. Llinás, R. & Sotelo, C. (eds). The Cerebellum Revisited (Springer, 1992).

    Google Scholar 

  53. Pfeffer, C. K., Xue, M., He, M., Huang, Z. J. & Scanziani, M. Inhibition of inhibition in visual cortex: the logic of connections between molecularly distinct interneurons. Nat. Neurosci. 16, 1068–1076 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Fino, E., Packer, A. M. & Yuste, R. The logic of inhibitory connectivity in the neocortex. Neuroscientist 19, 228–237 (2013).

    PubMed  Google Scholar 

  55. Avermann, M., Tomm, C., Mateo, C., Gerstner, W. & Petersen, C. C. H. Microcircuits of excitatory and inhibitory neurons in layer 2/3 of mouse barrel cortex. J. Neurophysiol. 107, 3116–3134 (2012).

    CAS  PubMed  Google Scholar 

  56. Karnani, M. M., Agetsuma, M. & Yuste, R. A blanket of inhibition: functional inferences from dense inhibitory connectivity. Curr. Opin. Neurobiol. 26, 96–102 (2014).

    CAS  PubMed  Google Scholar 

  57. Gibson, J. R., Beierlein, M. & Connors, B. W. Two networks of electrically coupled inhibitory neurons in neocortex. Nature 402, 75–79 (1999).

    CAS  PubMed  Google Scholar 

  58. Galarreta, M. & Hestrin, S. A network of fast-spiking cells in the neocortex connected by electrical synapses. Nature 402, 72–75 (1999).

    CAS  PubMed  Google Scholar 

  59. Monyer, H. & Markram, H. Interneuron diversity series: molecular and genetic tools to study GABAergic interneuron diversity and function. Trends Neurosci. 27, 90–97 (2004).

    CAS  PubMed  Google Scholar 

  60. Olah, S. et al. Regulation of cortical microcircuits by unitary GABA-mediated volume transmission. Nature 461, 1278–1281 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Gray, E. G. Axo-somatic and axo-dendritic synapses of the cerebral cortex: an electron microscopic study. J. Anat. 83, 420–433 (1959).

    Google Scholar 

  62. Harris, K. M. & Kater, S. B. Dendritic spines: cellular specializations imparting both stability and flexibility to synaptic function. Annu. Rev. Neurosci. 17, 341–371 (1994).

    CAS  PubMed  Google Scholar 

  63. Chklovskii, D. B., Schikorski, T. & Stevens, C. F. Wiring optimization in cortical circuits. Neuron 34, 341–347 (2002).

    CAS  PubMed  Google Scholar 

  64. Yuste, R. Dendritic Spines (MIT Press, 2010).

    Google Scholar 

  65. Anderson, P. W. More is different. Science 177, 393–396 (1972).

    CAS  PubMed  Google Scholar 

  66. Lorente de Nó, R. Studies on the structure of the cerebral cortex. I. The area entorhinalis. J. Psychol. Neurol. 45, 381–438 (1933).

    Google Scholar 

  67. Lorente de Nó, R. in Physiology of the Nervous System (ed. Fulton, J. F.) 228–330 (Oxford Univ. Press, 1949).

    Google Scholar 

  68. Sejnowski, T. J. The book of Hebb. Neuron 24, 773–776 (1999).

    CAS  PubMed  Google Scholar 

  69. Selverston, A. General principles of rhythmic motor pattern generation derived from invertebrate CPGs. Prog. Brain Res. 123, 247–257 (1999).

    CAS  PubMed  Google Scholar 

  70. Brown, T. G. On the nature of the fundamental activity of the nervous centres; together with an analysis of the conditioning of rhythmic activity in progression, and a theory of the evolution of function in the nervous system. J. Physiol. 48, 18–46 (1914).

    CAS  PubMed  PubMed Central  Google Scholar 

  71. Brown, T. G. On the activities of the central nervous system of the unborn foetus of the cat, with a discussion of the question whether progression (walking, etc.) is a 'learnt' complex. J. Physiol. 49, 208–215 (1915).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. Sherrington, C. S. Inhibition as a coordinative factor. Nobelprize.org [online], (1932).

  73. Llinás, R. R. in Induced Rhythms in the Brain (eds Basar, E. & Bullock, T. H.) 269–283 (Birkhauser, 1992).

    Google Scholar 

  74. Buzsaki, G., Horvath, Z., Urioste, R., Hetke, J. & Wise, K. High-frequency network oscillations in the hippocampus. Science 256, 1025–1027 (1992).

    CAS  PubMed  Google Scholar 

  75. Llinás, R. & Yarom, Y. Properties and distribution of ionic conductances generating electroresponsiveness of mammalian inferior olivary neurones in vitro. J. Physiol. 315, 569–584 (1981).

    PubMed  PubMed Central  Google Scholar 

  76. Llinas, R. R. The intrinsic electrophysiological properties of mammalian neurons: insights into central nervous system function. Science 242, 1654–1664 (1988).

    CAS  PubMed  Google Scholar 

  77. Steriade, M. Cholinergic blockage of network- and intrinsically generated slow oscillations promotes waking and REM sleep activity patterns in thalamic and cortical neurons. Prog. Brain Res. 98, 345–355 (1993).

    CAS  PubMed  Google Scholar 

  78. Steriade, M. & McCarley, R. W. Brainstem Control of Wakefulness and Sleep (Plenum, 1990).

    Google Scholar 

  79. Gray, C. M., Konig, P., Engel, A. K. & Singer, W. Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties. Nature 338, 334–337 (1989).

    CAS  PubMed  Google Scholar 

  80. Engel, A. K., Fries, P. & Singer, W. Dynamic predictions: oscillations and synchrony in top-down processing. Nat. Rev. Neurosci. 2, 704–716 (2001).

    CAS  PubMed  Google Scholar 

  81. Crick, F. & Koch, C. Some reflections on visual awareness. Cold Spring Harb. Symp. Quant. Biol. 55, 953–962 (1990).

    CAS  PubMed  Google Scholar 

  82. Jeanmonod, D. et al. Neuropsychiatric thalamocortical dysrhythmia: surgical implications. Neurosurg. Clin. N. Am. 14, 251–265 (2003).

    CAS  PubMed  Google Scholar 

  83. Buzsaki, G. Rhythms of the Brain (Oxford Univ. Press, 2011).

    Google Scholar 

  84. Llinás, R. R., Ribary, U., Jeanmonod, D., Kronberg, E. & Mitra, P. P. Thalamocortical dysrhythmia: a neurological and neuropsychiatric syndrome characterized by magnetoencephalography. Proc. Natl Acad. Sci. USA 96, 15222–15227 (1999).

    PubMed  Google Scholar 

  85. Uhlhaas, P. J. & Singer, W. Abnormal neural oscillations and synchrony in schizophrenia. Nat. Rev. Neurosci. 11, 100–113 (2010).

    CAS  PubMed  Google Scholar 

  86. Llinas, R. & Ribary, U. Coherent 40-Hz oscillation characterizes dream state in humans. Proc. Natl Acad. Sci. USA 90, 2078–2081 (1993).

    CAS  PubMed  Google Scholar 

  87. Fries, P., Roelfsema, P. R., Engel, A. K., König, P. & Singer, W. Synchronization of oscillatory responses in visual cortex correlates with perception in interocular rivalry. Proc. Natl Acad. Sci. USA 94, 12699–12704 (1997).

    CAS  PubMed  Google Scholar 

  88. Llinás, R. I of the Vortex: From Neurons to Self (MIT Press, 2001).

    Google Scholar 

  89. Seung, H. S. & Yuste, R. in Principles of Neural Science 5th edn (eds Kandel, E. R., Schwartz, J. H., Jessel, T. M., Siegelbaum, S. A. & Hudspeth, A. J.) 1581–1600 (Mc Graw-Hill, 2013).

    Google Scholar 

  90. Marr, D. A theory of cerebellar cortex. J. Physiol. 202, 437–470 (1969).

    CAS  PubMed  PubMed Central  Google Scholar 

  91. Turing, A. M. On computable numbers, with an application to the Entscheidungsproblem. Proc. Lond. Math. Soc. 42, 230–265 (1936).

    Google Scholar 

  92. Rosenblatt, F. The perceptron: a probabilistic model for information storage and organization in the brain. Psychol. Rev. 65, 386–408 (1958).

    CAS  PubMed  Google Scholar 

  93. Papert, S. & Minsky, M. L. Perceptrons: An Introduction to Computational Geometry (MIT Press, 1988).

    Google Scholar 

  94. Hinton, G. E., Osindero, S. & Teh, Y. W. A fast learning algorithm for deep belief nets. Neural Comput. 18, 1527–1554 (2006).

    PubMed  Google Scholar 

  95. Farabet, C., Couprie, C., Najman, L. & Lecun, Y. Learning hierarchical features for scene labeling. IEEE Trans. Pattern Anal. Mach. Intell. 35, 1915–1929 (2013).

    PubMed  Google Scholar 

  96. Hopfield, J. J. Neural networks and physical systems with emergent collective computational abilities. Proc. Natl Acad. Sci. USA 79, 2554–2558 (1982).

    CAS  PubMed  Google Scholar 

  97. Ising, E. Contribution to the theory of ferromagnetism. Z. Phys. 31, 253–258 (1925).

    CAS  Google Scholar 

  98. Amit, D. J., Gutfreund, H. & Sompolinsky, H. Spin-glass models of neural networks. Phys. Rev. A 32, 1007–1018 (1985).

    CAS  Google Scholar 

  99. Hopfield, J. J. & Tank, D. W. “Neural” computation of decisions in optimization problems. Biol. Cybern. 52, 141–152 (1985).

    CAS  PubMed  Google Scholar 

  100. Hopfield, J. J. & Tank, D. W. Computing with neural circuits: a model. Science 233, 625–633 (1986).

    CAS  PubMed  Google Scholar 

  101. Pfluger, H. J. & Menzel, R. Neuroethology, its roots and future. J. Comp. Physiol. A 185, 389–392 (1999).

    CAS  PubMed  Google Scholar 

  102. Zupanc, G. K. H. Behavioral Neurobiology: An Integrative Approach (Oxford Univ. Press, 2010).

    Google Scholar 

  103. Ratliff, F., Knight, B. W., Toyoda, J. & Hartline, H. K. Enhancement of flicker by lateral inhibition. Science 158, 392–393 (1967).

    CAS  PubMed  Google Scholar 

  104. von der Malsburg, C. Self-organization of orientation sensitive cells in the striate cortex. Kybernetik 14, 85–100 (1973).

    CAS  PubMed  Google Scholar 

  105. Kohonen, T. & Oja, E. Fast adaptive formation of orthogonalizing filters and associative memory in recurrent networks of neuron-like elements. Biol. Cybern. 21, 85–95 (1976).

    CAS  PubMed  Google Scholar 

  106. Kohonen, T. Self-Organizing Maps (Springer, 1995).

    Google Scholar 

  107. Seung, H. S., Lee, D. D., Reis, B. Y. & Tank, D. W. Stability of the memory of eye position in a recurrent network of conductance-based model neurons. Neuron 26, 259–271 (2000).

    CAS  PubMed  Google Scholar 

  108. Ben-Yishai, R., Lev Bar-Or, R. & Sompolinsky, H. Orientation tuning by recurrent neural networks in visual cortex. Proc. Natl Acad. Sci. USA 92, 3844–3848 (1995).

    CAS  PubMed  Google Scholar 

  109. Maass, W., Natschlager, T. & Markram, H. Real-time computing without stable states: a new framework for neural computation based on perturbations. Neural Comput. 14, 2531–2560 (2002).

    PubMed  Google Scholar 

  110. Sussillo, D. & Abbott, L. F. Generating coherent patterns of activity from chaotic neural networks. Neuron 63, 544–557 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  111. Wang, X. J. Probabilistic decision making by slow reverberation in cortical circuits. Neuron 36, 955–968 (2002).

    CAS  PubMed  Google Scholar 

  112. Buonomano, D. V. & Maass, W. State-dependent computations: spatiotemporal processing in cortical networks. Nat. Rev. Neurosci. 10, 113–125 (2009).

    CAS  PubMed  Google Scholar 

  113. Abbott, L. F., Fusi, S. & Miller, E. K. in Principles of Neural Science 5th edn (eds Kandel, E. R., Schwartz, J. H., Jessel, T. M., Siegelbaum, S. A. & Hudspeth, A. J.) 1601–1617 (McGraw-Hill, 2013).

    Google Scholar 

  114. Mante, V., Sussillo, D., Shenoy, K. V. & Newsome, W. T. Context-dependent computation by recurrent dynamics in prefrontal cortex. Nature 503, 78–84 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  115. Klampfl, S. & Maass, W. Emergence of dynamic memory traces in cortical microcircuit models through STDP. J. Neurosci. 33, 11515–11529 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  116. Constantinidis, C. & Wang, X. J. A neural circuit basis for spatial working memory. Neuroscientist 10, 553–565 (2004).

    PubMed  Google Scholar 

  117. Wang, X. J. Synaptic basis of cortical persistent activity: the importance of NMDA receptors to working memory. J. Neurosci. 19, 9587–9603 (1999).

    CAS  PubMed  Google Scholar 

  118. Marr, D. Vision (W. H. Freeman, 1982).

    Google Scholar 

  119. Jaeger, H. & Haas, H. Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication. Science 304, 78–80 (2004).

    CAS  PubMed  Google Scholar 

  120. Goel, A. & Buonomano, D. V. Timing as an intrinsic property of neural networks: evidence from in vivo and in vitro experiments. Phil. Trans. R. Soc. B 369, 20120460 (2014).

    PubMed  Google Scholar 

  121. Ganguli, S., Huh, D. & Sompolinsky, H. Memory traces in dynamical systems. Proc. Natl Acad. Sci. USA 105, 18970–18975 (2008).

    CAS  PubMed  Google Scholar 

  122. Pellionisz, A. & Llinas, R. Brain modeling by tensor network theory and computer simulation. The cerebellum: distributed processor for predictive coordination. Neuroscience 4, 323–348 (1979).

    CAS  PubMed  Google Scholar 

  123. Rolls, E. T. & Treves, A. Neural Networks and Brain Function (Oxford Univ. Press, 1998).

    Google Scholar 

  124. Wills, T. J., Lever, C., Cacucci, F., Burgess, N. & O'Keefe, J. Attractor dynamics in the hippocampal representation of the local environment. Science 308, 873–876 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  125. Colgin, L. L. et al. Attractor-map versus autoassociation based attractor dynamics in the hippocampal network. J. Neurophysiol. 104, 35–50 (2010).

    PubMed  PubMed Central  Google Scholar 

  126. Solstad, T., Moser, E. I. & Einevoll, G. T. From grid cells to place cells: a mathematical model. Hippocampus 16, 1026–1031 (2006).

    PubMed  Google Scholar 

  127. Paulsen, O. & Moser, E. I. A model of hippocampal memory encoding and retrieval: GABAergic control of synaptic plasticity. Trends Neurosci. 21, 273–278 (1998).

    CAS  PubMed  Google Scholar 

  128. Fyhn, M., Hafting, T., Treves, A., Moser, M. B. & Moser, E. I. Hippocampal remapping and grid realignment in entorhinal cortex. Nature 446, 190–194 (2007).

    CAS  PubMed  Google Scholar 

  129. Nakashiba, T. et al. Young dentate granule cells mediate pattern separation, whereas old granule cells facilitate pattern completion. Cell 149, 188–201 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  130. Nadasdy, Z., Hirase, H., Czurko, A., Csicsvari, J. & Buzsaki, G. Replay and time compression of recurring spike sequences in the hippocampus. J. Neurosci. 19, 9497–9507 (1999).

    CAS  PubMed  Google Scholar 

  131. Liu, X. et al. Optogenetic stimulation of a hippocampal engram activates fear memory recall. Nature 484, 381–385 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  132. Ramirez, S. et al. Creating a false memory in the hippocampus. Science 341, 387–391 (2013).

    CAS  PubMed  Google Scholar 

  133. Marr, D. A theory for cerebral neocortex. Proc. R. Soc. Lond. B 176, 161–234 (1970).

    CAS  PubMed  Google Scholar 

  134. Grinvald, A., Arieli, A., Tsodyks, M. & Kenet, T. Neuronal assemblies: single cortical neurons are obedient members of a huge orchestra. Biopolymers 68, 422–436 (2003).

    CAS  PubMed  Google Scholar 

  135. Abeles, M., Bergman, H., Margalit, E. & Vaadia, E. Spatiotemporal firing patterns in the frontal cortex of behaving monkeys. J. Neurophysiol. 70, 1629–1638 (1993).

    CAS  PubMed  Google Scholar 

  136. Luczak, A., Bartho, P., Marguet, S. L., Buzsaki, G. & Harris, K. D. Sequential structure of neocortical spontaneous activity in vivo. Proc. Natl Acad. Sci. USA 104, 347–352 (2007).

    CAS  PubMed  Google Scholar 

  137. Plenz, D. & Kitai, S. T. Up and down states in striatal medium spiny neurons simultaneously recorded with spontaneous activity in fast-spiking interneurons studied in cortex–striatum–substantia nigra organotypic cultures. J. Neurosci. 18, 266–283 (1998).

    CAS  PubMed  Google Scholar 

  138. Ikegaya, Y. et al. Synfire chains and cortical songs: temporal modules of cortical activity. Science 304, 559–564 (2004).

    CAS  PubMed  Google Scholar 

  139. Tsodyks, M., Kenet, T., Grinvald, A. & Arieli, A. Linking spontaneous activity of single cortical neurons and the underlying functional architecture. Science 286, 1943–1946 (1999).

    CAS  PubMed  Google Scholar 

  140. MacLean, J. N., Watson, B. O., Aaron, G. B. & Yuste, R. Internal dynamics determine the cortical response to thalamic stimulation. Neuron 48, 811–823 (2005).

    CAS  PubMed  Google Scholar 

  141. Luczak, A., Bartho, P. & Harris, K. D. Spontaneous events outline the realm of possible sensory responses in neocortical populations. Neuron 62, 413–425 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  142. Rigotti, M. et al. The importance of mixed selectivity in complex cognitive tasks. Nature 497, 585–590 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  143. Churchland, M. M. et al. Neural population dynamics during reaching. Nature 487, 51–56 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  144. Harvey, C. D., Coen, P. & Tank, D. W. Choice-specific sequences in parietal cortex during a virtual-navigation decision task. Nature 484, 62–68 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  145. Briggman, K. L., Abarbanel, H. D. & Kristan, W. B. Jr. Optical imaging of neuronal populations during decision-making. Science 307, 896–901 (2005).

    CAS  PubMed  Google Scholar 

  146. Dombeck, D. A., Graziano, M. S. & Tank, D. W. Functional clustering of neurons in motor cortex determined by cellular resolution imaging in awake behaving mice. J. Neurosci. 29, 13751–13760 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  147. Miller, E. W. et al. Optically monitoring voltage in neurons by photo-induced electron transfer through molecular wires. Proc. Natl Acad. Sci. USA 109, 2114–2119 (2012).

    CAS  PubMed  Google Scholar 

  148. McNaughton, B. L., O'Keefe, J. & Barnes, C. A. The stereotrode: a new technique for simultaneous isolation of several single units in the central nervous system from multiple unit records. J. Neurosci. Methods 8, 391–397 (1983).

    CAS  PubMed  Google Scholar 

  149. Meister, M., Pine, J. & Baylor, D. A. Multi-neuronal signals from the retina: acquisition and analysis. J. Neurosci. Methods 51, 95–106 (1994).

    CAS  PubMed  Google Scholar 

  150. Lei, N. et al. High-resolution extracellular stimulation of dispersed hippocampal culture with high-density CMOS multielectrode array based on non-Faradaic electrodes. J. Neural Eng. 8, 044003 (2011).

    CAS  PubMed  Google Scholar 

  151. Field, G. D. et al. Functional connectivity in the retina at the resolution of photoreceptors. Nature 467, 673–677 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  152. Grynkiewicz, G., Poenie, M. & Tsien, R. Y. A new generation of Ca2+ indicators with greatly improved fluorescence properties. J. Biol. Chem. 260, 3440–3450 (1985).

    CAS  PubMed  Google Scholar 

  153. Salzberg, B. M., Grinvald, A., Cohen, L. B., Davila, H. V. & Ross, W. N. Optical recording of neuronal activity in an invertebrate central nervous system: simultaneous monitoring of several neurons. J. Neurophys. 40, 1281–1291 (1977).

    CAS  Google Scholar 

  154. Grinvald, A., Salzberg, B. M., Lev-Ram, V. & Hildesheim, R. Optical recording of synaptic potentials from processes of single neurons using intracellular potentiometric dyes. Biophys. J. 51, 643–651 (1987).

    CAS  PubMed  PubMed Central  Google Scholar 

  155. Miyawaki, A. et al. Fluorescent indicators for Ca2+ based on green fluorescent proteins and calmodulin. Nature 388, 882–887 (1997).

    CAS  PubMed  Google Scholar 

  156. Chen, T. W. et al. Ultrasensitive fluorescent proteins for imaging neuronal activity. Nature 499, 295–300 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  157. Jin, L. et al. Single action potentials and subthreshold electrical events imaged in neurons with a fluorescent protein voltage probe. Neuron 75, 779–785 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  158. Kralj, J. M., Hochbaum, D. R., Douglass, A. D. & Cohen, A. E. Electrical spiking in Escherichia coli probed with a fluorescent voltage-indicating protein. Science 333, 345–348 (2011).

    CAS  PubMed  Google Scholar 

  159. Schrodel, T., Prevedel, R., Aumayr, K., Zimmer, M. & Vaziri, A. Brain-wide 3D imaging of neuronal activity in Caenorhabditis elegans with sculpted light. Nat. Methods 10, 1013–1020 (2013).

    PubMed  Google Scholar 

  160. Connor, J. A. Digital imaging of free calcium changes and of spatial gradients in growing processes in single, mammalian central nervous system cells. Proc. Natl Acad. Sci. USA 83, 6179–6183 (1986).

    CAS  PubMed  Google Scholar 

  161. Denk, W., Strickler, J. H. & Webb, W. W. Two-photon laser scanning fluorescence microscopy. Science 248, 73–76 (1990).

    CAS  PubMed  Google Scholar 

  162. Yuste, R. & Denk, W. Dendritic spines as basic units of synaptic integration. Nature 375, 682–684 (1995).

    CAS  PubMed  Google Scholar 

  163. Svoboda, K., Denk, W., Kleinfeld, D. & Tank, D. W. In vivo dendritic calcium dynamics in neocortical pyramidal neurons. Nature 385, 161–165 (1997).

    CAS  PubMed  Google Scholar 

  164. Quirin, S., Jackson, J., Peterka, D. S. & Yuste, R. Simultaneous imaging of neural activity in three dimensions. Front. Neural Circuits 8, 29 (2014).

    PubMed  PubMed Central  Google Scholar 

  165. Frostig, R. D., Lieke, E. E., Ts'o, D. Y. & Grinvald, A. Cortical functional architecture and local coupling betwen neuronal activity and the microcirculation revealed by in vivo high-resolution optical imaging of intrinsic signals. Proc. Natl Acad. Sci. USA 87, 6082–6086 (1990).

    CAS  PubMed  Google Scholar 

  166. Bonhoeffer, T. & Grinvald, A. Iso-orientation domains in cat visual cortex are arranged in pinwheel-like patterns. Nature 353, 429–431 (1991).

    CAS  PubMed  Google Scholar 

  167. Ogawa, S., Lee, T. M., Kay, A. R. & Tank, D. W. Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proc. Natl Acad. Sci. USA 87, 9868–9872 (1990).

    CAS  PubMed  Google Scholar 

  168. Mather, M., Cacioppo, J. T. & Kanwisher, N. How fMRI can inform cognitive theories. Perspect. Psychol. Sci. 8, 108–113 (2013).

    PubMed  PubMed Central  Google Scholar 

  169. Barttfeld, P. et al. Signature of consciousness in the dynamics of resting-state brain activity. Proc. Natl Acad. Sci. USA 112, 887–892 (2015).

    CAS  PubMed  Google Scholar 

  170. Banghart, M., Borges, K., Isacoff, E., Trauner, D. & Kramer, R. H. Light-activated ion channels for remote control of neuronal firing. Nat. Neurosci. 7, 1381–1386 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  171. Zayat, L., Baraldo, L. & Etchenique, R. Inorganic caged compounds: uncaging with visible light. CSH protoc. 2007, pdb ip39 (2007).

  172. Nikolenko, V., Poskanzer, K. E. & Yuste, R. Two-photon photostimulation and imaging of neural circuits. Nat. Methods 4, 943–950 (2007).

    CAS  PubMed  Google Scholar 

  173. Packer, A. M. et al. Two-photon optogenetics of dendritic spines and neural circuits. Nat. Methods 9, 1202–1205 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  174. Rickgauer, J. P., Deisseroth, K. & Tank, D. W. Simultaneous cellular-resolution optical perturbation and imaging of place cell firing fields. Nat. Neurosci. 17, 1816–1824 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  175. Packer, A. M., Russell, L. E., Dalgleish, H. W. & Hausser, M. Simultaneous all-optical manipulation and recording of neural circuit activity with cellular resolution in vivo. Nat. Methods 12, 140–146 (2015).

    CAS  PubMed  Google Scholar 

  176. Fairhall, A. & Sompolinsky, H. Editorial overview: theoretical and computational neuroscience. Curr. Opin. Neurobiol. 25, v–viii (2014).

    CAS  PubMed  Google Scholar 

  177. Rieke, F., Warland, D., de Ruyter van Steveninck, R. & Bialek, W. Spikes: Exploring the Neural Code (MIT Press, 1997).

    Google Scholar 

  178. Cohen, L. Optical approaches to neuronal function. Annu. Rev. Physiol. 51, 487–582 (1989).

    CAS  PubMed  Google Scholar 

  179. Stosiek, C., Garaschuk, O., Holthoff, K. & Konnerth, A. In vivo two-photon calcium imaging of neuronal networks. Proc. Natl Acad. Sci. USA 100, 7319–7324 (2003).

    CAS  PubMed  Google Scholar 

  180. Dombeck, D. A., Khabbaz, A. N., Collman, F., Adelman, T. L. & Tank, D. W. Imaging large-scale neural activity with cellular resolution in awake, mobile mice. Neuron 56, 43–57 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  181. Vogels, T. P., Rajan, K. & Abbott, L. F. Neural network dynamics. Annu. Rev. Neurosci. 28, 357–376 (2005).

    CAS  PubMed  Google Scholar 

  182. Alivisatos, A. P. et al. The brain activity map project and the challenge of functional connectomics. Neuron 74, 970–974 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  183. Insel, T. R., Landis, S. C. & Collins, F. S. Research priorities. The NIH BRAIN initiative. Science 340, 687–688 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  184. Kant, I. Kritik der reinen Vernunft (Cambridge Univ. Press, 1781).

    Google Scholar 

  185. Bargmann, C. I. & Marder, E. From the connectome to brain function. Nat. Methods 10, 483–490 (2013).

    CAS  PubMed  Google Scholar 

  186. Bargmann, C. I. Beyond the connectome: how neuromodulators shape neural circuits. Bioessays 34, 458–465 (2012).

    CAS  PubMed  Google Scholar 

  187. Stuart, G., Spruston, N. & Hausser, M. (eds). Dendrites (Oxford Univ. Press, 1999).

    Google Scholar 

  188. Lorente de No, R. Analysis of the activity of the chains of internuncial neurons. J. Neurophysiol. 1, 207–244 (1938).

    Google Scholar 

  189. Lettvin, J. Y., Maturana, H. R., McCulloch, W. S. & Pitts, W. H. What the frog's eye tells the frog's brain. Proc. Inst. Radio Engr. 47, 1940–1951 (1959).

    Google Scholar 

  190. Yuste, R. & Katz, L. C. Control of postsynaptic Ca2+ influx in developing neocortex by excitatory and inhibitory neurotransmitters. Neuron 6, 333–344 (1991).

    CAS  PubMed  Google Scholar 

  191. Ben-Yishai, R., Bar-Or, R. L. & Sompolinsky, H. Theory of orientation tuning in visual cortex. Proc. Natl Acad. Sci. USA 92, 3844–3848 (1995).

    CAS  PubMed  Google Scholar 

  192. Ramon y Cajal, S. The structure and connexions of neurons. Nobelprize.org [online], (1906).

  193. Rolls, E. T. Memory, Attention and Decision-Making: A Unifying Computational Neuroscience Approach (Oxford Univ. Press, 2008).

    Google Scholar 

  194. Eliasmith, C. Attractor network. Scholarpedia 2, 1380 (2007).

    Google Scholar 

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Acknowledgements

The author thanks L. Abbott, J. Cunningham, A. Fairhall and members of the laboratory for their comments, and G.M. Shepherd for long lasting inspiration. Supported by DP1EY024503 and DARPA contract N66001-15-C-4032. This material is based on work fully or partly supported by the US Army Research Laboratory and the US Army Research Office under contract number W911NF-12-1-0594 (MURI).

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  1. Departments of Biological Sciences and Neuroscience, Neurotechnology Center and Kavli Institute of Brain Sciences, Columbia University, New York, 10027, New York, USA

    Rafael Yuste

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Glossary

Attractors

Stable or semi-stable states in the temporal dynamics of the activity of a neuronal population. They arise naturally in neural networks that have a recurrent (feedback) architecture with symmetric connections.

Boolean logic

A form of algebra in which all values are reduced to either true or false. Boolean logic is especially important for computer science because it fits nicely with its binary numbering system. Boolean logic depends on the use of three logical operators: AND, OR and NOT.

BRAIN initiative

The Brain Research through Advancing Innovative Neurotechnologies (BRAIN) initiative is a decade-long large-scale scientific project, sponsored by the White House, to accelerate the development and application of innovative neurotechnologies to revolutionize the understanding of the brain.

Activity map

In a neural network context, the activity map is a three-dimensional representation of all the activity states of the network, where the depth dimension corresponds to the energy function of the activity, which captures the propensity of the network activity to change. This topological representation provides an intuition of how the activity of the circuit evolves in time, as it progresses through this energy landscape to find its lower-energy (attractor) points.

Ensembles

A group of neurons that show spatiotemporal co-activation. Ensembles provide an example of an emergent state of the circuit.

Gap junctions

Cellular specializations that allow the non-selective passage of small molecules between the cytoplasm of adjacent cells. They are formed by channels termed connexons, which are multimeric complexes of proteins known as connexins. Gap junctions are structural elements of electrical synapses.

Golgi stain

A staining technique introduced by Camillo Golgi in 1873 that involves impregnating the tissue with silver nitrate. This labels a random subset of neurons, allowing the entire cell and its processes to be visualized.

Grid cells

Neurons in the rodent entorhinal cortex that fire when the animal is at one of several specific locations in an environment; these locations are organized in a grid-like manner.

Learning rule

The alteration of the strength of a synaptic connection in a neural network, as a consequence of the pattern of activity experienced by that synapse (or the network).

Neuronal assemblies

Originally proposed by Hebb; groups of neurons that become bound together owing to synaptic plasticity, and whose coordinated activity progresses through the circuits, often in a closed loop.

Pattern completion

A process by which a stored neural representation is reactivated by a cue that consists of a subset of that representation.

Pattern separation

A process by which overlapping neural representations are separated to keep episodes independent of each other in memory.

Perceptrons

Multilayer feedforward artificial neural networks in which activity flows unidirectonally from one layer to the next. Multilayer perceptrons are often used to implement classification problems.

Place cells

Hippocampal neurons that specifically respond to stimuli in certain spatial locations. Their firing rate increases when an animal or subject approaches the respective location.

Recurrent connectivity

The concept that neurons within a class connect with one another, implying feedback communication within the network.

Replay

Recapitulation of experience-dependent patterns of neural activity previously observed during awake periods.

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Yuste, R. From the neuron doctrine to neural networks. Nat Rev Neurosci 16, 487–497 (2015). https://doi.org/10.1038/nrn3962

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