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.
Your institute does not have access to this article
Open Access articles citing this article.
Nature Communications Open Access 09 June 2022
Common network effect-patterns after monoamine reuptake inhibition in dissociated hippocampus cultures
Journal of Neural Transmission Open Access 24 February 2022
Molecular Psychiatry Open Access 01 January 2022
Subscribe to Journal
Get full journal access for 1 year
only $4.92 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Get time limited or full article access on ReadCube.
All prices are NET prices.
Shepherd, G. M. Foundations of the Neuron Doctrine (Oxford Univ. Press, 1991).
Ramón y Cajal, S. Estructura de los centros nerviosos de las aves. Rev. Trim. Histol. Norm. Pat. 1, 1–10 (1888) (in Spanish).
Sherrington, C. S. Observations on the scratch-reflex in the spinal dog. J. Physiol. 34, 1–50 (1906).
Golgi, C. Sulla struttura della sostanza grigia del cervello. Gazz. Med. Ital. (Lombardia) 33, 244–246 (1873) (in Italian).
Hubel, D. H. Tungsten microelectrode for recording from single units. Science 125, 549–550 (1957).
Ramón y Cajal, S. La Textura del Sistema Nerviosa del Hombre y los Vertebrados 1st edn (Moya, 1899).
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).
Churchland, P. S. & Sejnowski, T. The Computational Brain (MIT Press, 1992).
Yuste, R. Dendritic spines and distributed circuits. Neuron 71, 772–781 (2011).
McCulloch, W. S. & Pitts, W. A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5, 115–133 (1943).
Hebb, D. O. The Organization Of Behaviour (Wiley, 1949).
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).
Yuste, R. (ed) Imaging: A Laboratory Manual (Cold Spring Harbor Press, 2011).
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).
Dayan, P. & Abbott, L. F. Theoretical Neuroscience (MIT Press, 2001).
Bullock, T. H. et al. Neuroscience. The neuron doctrine, redux. Science 310, 791–793 (2005).
Kandel, E. R., Schwartz, J. H., Jessell, T. M., Siegelbaum, S. A. & Hudspeth, A. J. Principles of Neural Science 5th edn (McGraw-Hill, 2013).
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).
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).
Palay, S. L. Synapses in the central nervous system. J. Biophysiol. Biochem. Cytol. 2, 193–201 (1956).
Magner, L. N. A History of the Life Sciences (Marcel Dekker, 1979).
von Waldeyer-Hartz, H. W. G. Ueber Einige Neuere Forschungen Gebiete Anatomie Centralnervensystems. Dtsch Med. Wochenschr. 17, 1213–1356 (1891) (in German).
Adrian, E. D. The Basis of Sensation (W. W. Norton & Co., 1928).
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).
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).
Mountcastle, V. B. Modality and topographic properties of single neurons of cat's somatosensory cortex. J. Neurophysiol. 20, 408–443 (1957).
Hubel, D. H. Eye, Brain and Vision (Scientific American Library, 1988).
Barlow, H. B. Single units and sensation: a neuron doctrine for perceptual psychology? Perception 1, 371–394 (1972).
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).
Tanaka, K. Inferotemporal cortex and object vision. Annu. Rev. Neurosci. 19, 109–139 (1996).
Kreiman, G., Koch, C. & Fried, I. Category-specific visual responses of single neurons in the human medial temporal lobe. Nat. Neurosci. 946–953 (2000).
Salzman, C. D. & Newsome, W. T. Neural mechanisms for forming a perceptual decision. Science 264, 231–237 (1994).
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).
Houweling, A. R. & Brecht, M. Behavioural report of single neuron stimulation in somatosensory cortex. Nature 451, 65–68 (2008).
Dyson, F. J. History of science. Is science mostly driven by ideas or by tools? Science 338, 1426–1427 (2012).
Kuhn, T. S. The Structure of Scientific Revolutions (Univ. of Chicago Press, 1963).
Fairhall, A. The receptive field is dead. Long live the receptive field? Curr. Opin. Neurobiol. 25, ix–xii (2014).
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).
Ko, H. et al. Functional specificity of local synaptic connections in neocortical networks. Nature 473, 87–91 (2011).
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).
Kenet, T., Bibitchkov, D., Tsodyks, M., Grinvald, A. & Arieli, A. Spontaneously emerging cortical representations of visual attributes. Nature 425, 954–956 (2003).
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).
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).
Berger, H. Über das elektrenkephalogramm des menschen. Arch. Psychiatr. Nervenkr. 87, 527–570 (1929) (in German).
Fox, M. D. & Raichle, M. E. Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat. Rev. Neurosci. 8, 700–711 (2007).
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).
Buzsaki, G. Neural syntax: cell assemblies, synapsembles, and readers. Neuron 68, 362–385 (2010).
Braitenberg, V. & Schüz, A. Anatomy of the Cortex (Springer, 1998).
Shepherd, G. M. The Synaptic Organization of the Brain (Oxford Univ. Press, 1990).
Abeles, M. Corticonics (Cambrdige Univ. Press, 1991).
Peters, A. & Jones, E. G. (eds) Cerebral Cortex (Plenum, 1984).
Llinás, R. & Sotelo, C. (eds). The Cerebellum Revisited (Springer, 1992).
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).
Fino, E., Packer, A. M. & Yuste, R. The logic of inhibitory connectivity in the neocortex. Neuroscientist 19, 228–237 (2013).
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).
Karnani, M. M., Agetsuma, M. & Yuste, R. A blanket of inhibition: functional inferences from dense inhibitory connectivity. Curr. Opin. Neurobiol. 26, 96–102 (2014).
Gibson, J. R., Beierlein, M. & Connors, B. W. Two networks of electrically coupled inhibitory neurons in neocortex. Nature 402, 75–79 (1999).
Galarreta, M. & Hestrin, S. A network of fast-spiking cells in the neocortex connected by electrical synapses. Nature 402, 72–75 (1999).
Monyer, H. & Markram, H. Interneuron diversity series: molecular and genetic tools to study GABAergic interneuron diversity and function. Trends Neurosci. 27, 90–97 (2004).
Olah, S. et al. Regulation of cortical microcircuits by unitary GABA-mediated volume transmission. Nature 461, 1278–1281 (2009).
Gray, E. G. Axo-somatic and axo-dendritic synapses of the cerebral cortex: an electron microscopic study. J. Anat. 83, 420–433 (1959).
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).
Chklovskii, D. B., Schikorski, T. & Stevens, C. F. Wiring optimization in cortical circuits. Neuron 34, 341–347 (2002).
Yuste, R. Dendritic Spines (MIT Press, 2010).
Anderson, P. W. More is different. Science 177, 393–396 (1972).
Lorente de Nó, R. Studies on the structure of the cerebral cortex. I. The area entorhinalis. J. Psychol. Neurol. 45, 381–438 (1933).
Lorente de Nó, R. in Physiology of the Nervous System (ed. Fulton, J. F.) 228–330 (Oxford Univ. Press, 1949).
Sejnowski, T. J. The book of Hebb. Neuron 24, 773–776 (1999).
Selverston, A. General principles of rhythmic motor pattern generation derived from invertebrate CPGs. Prog. Brain Res. 123, 247–257 (1999).
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).
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).
Sherrington, C. S. Inhibition as a coordinative factor. Nobelprize.org [online], (1932).
Llinás, R. R. in Induced Rhythms in the Brain (eds Basar, E. & Bullock, T. H.) 269–283 (Birkhauser, 1992).
Buzsaki, G., Horvath, Z., Urioste, R., Hetke, J. & Wise, K. High-frequency network oscillations in the hippocampus. Science 256, 1025–1027 (1992).
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).
Llinas, R. R. The intrinsic electrophysiological properties of mammalian neurons: insights into central nervous system function. Science 242, 1654–1664 (1988).
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).
Steriade, M. & McCarley, R. W. Brainstem Control of Wakefulness and Sleep (Plenum, 1990).
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).
Engel, A. K., Fries, P. & Singer, W. Dynamic predictions: oscillations and synchrony in top-down processing. Nat. Rev. Neurosci. 2, 704–716 (2001).
Crick, F. & Koch, C. Some reflections on visual awareness. Cold Spring Harb. Symp. Quant. Biol. 55, 953–962 (1990).
Jeanmonod, D. et al. Neuropsychiatric thalamocortical dysrhythmia: surgical implications. Neurosurg. Clin. N. Am. 14, 251–265 (2003).
Buzsaki, G. Rhythms of the Brain (Oxford Univ. Press, 2011).
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).
Uhlhaas, P. J. & Singer, W. Abnormal neural oscillations and synchrony in schizophrenia. Nat. Rev. Neurosci. 11, 100–113 (2010).
Llinas, R. & Ribary, U. Coherent 40-Hz oscillation characterizes dream state in humans. Proc. Natl Acad. Sci. USA 90, 2078–2081 (1993).
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).
Llinás, R. I of the Vortex: From Neurons to Self (MIT Press, 2001).
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).
Marr, D. A theory of cerebellar cortex. J. Physiol. 202, 437–470 (1969).
Turing, A. M. On computable numbers, with an application to the Entscheidungsproblem. Proc. Lond. Math. Soc. 42, 230–265 (1936).
Rosenblatt, F. The perceptron: a probabilistic model for information storage and organization in the brain. Psychol. Rev. 65, 386–408 (1958).
Papert, S. & Minsky, M. L. Perceptrons: An Introduction to Computational Geometry (MIT Press, 1988).
Hinton, G. E., Osindero, S. & Teh, Y. W. A fast learning algorithm for deep belief nets. Neural Comput. 18, 1527–1554 (2006).
Farabet, C., Couprie, C., Najman, L. & Lecun, Y. Learning hierarchical features for scene labeling. IEEE Trans. Pattern Anal. Mach. Intell. 35, 1915–1929 (2013).
Hopfield, J. J. Neural networks and physical systems with emergent collective computational abilities. Proc. Natl Acad. Sci. USA 79, 2554–2558 (1982).
Ising, E. Contribution to the theory of ferromagnetism. Z. Phys. 31, 253–258 (1925).
Amit, D. J., Gutfreund, H. & Sompolinsky, H. Spin-glass models of neural networks. Phys. Rev. A 32, 1007–1018 (1985).
Hopfield, J. J. & Tank, D. W. “Neural” computation of decisions in optimization problems. Biol. Cybern. 52, 141–152 (1985).
Hopfield, J. J. & Tank, D. W. Computing with neural circuits: a model. Science 233, 625–633 (1986).
Pfluger, H. J. & Menzel, R. Neuroethology, its roots and future. J. Comp. Physiol. A 185, 389–392 (1999).
Zupanc, G. K. H. Behavioral Neurobiology: An Integrative Approach (Oxford Univ. Press, 2010).
Ratliff, F., Knight, B. W., Toyoda, J. & Hartline, H. K. Enhancement of flicker by lateral inhibition. Science 158, 392–393 (1967).
von der Malsburg, C. Self-organization of orientation sensitive cells in the striate cortex. Kybernetik 14, 85–100 (1973).
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).
Kohonen, T. Self-Organizing Maps (Springer, 1995).
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).
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).
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).
Sussillo, D. & Abbott, L. F. Generating coherent patterns of activity from chaotic neural networks. Neuron 63, 544–557 (2009).
Wang, X. J. Probabilistic decision making by slow reverberation in cortical circuits. Neuron 36, 955–968 (2002).
Buonomano, D. V. & Maass, W. State-dependent computations: spatiotemporal processing in cortical networks. Nat. Rev. Neurosci. 10, 113–125 (2009).
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).
Mante, V., Sussillo, D., Shenoy, K. V. & Newsome, W. T. Context-dependent computation by recurrent dynamics in prefrontal cortex. Nature 503, 78–84 (2013).
Klampfl, S. & Maass, W. Emergence of dynamic memory traces in cortical microcircuit models through STDP. J. Neurosci. 33, 11515–11529 (2013).
Constantinidis, C. & Wang, X. J. A neural circuit basis for spatial working memory. Neuroscientist 10, 553–565 (2004).
Wang, X. J. Synaptic basis of cortical persistent activity: the importance of NMDA receptors to working memory. J. Neurosci. 19, 9587–9603 (1999).
Marr, D. Vision (W. H. Freeman, 1982).
Jaeger, H. & Haas, H. Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication. Science 304, 78–80 (2004).
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).
Ganguli, S., Huh, D. & Sompolinsky, H. Memory traces in dynamical systems. Proc. Natl Acad. Sci. USA 105, 18970–18975 (2008).
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).
Rolls, E. T. & Treves, A. Neural Networks and Brain Function (Oxford Univ. Press, 1998).
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).
Colgin, L. L. et al. Attractor-map versus autoassociation based attractor dynamics in the hippocampal network. J. Neurophysiol. 104, 35–50 (2010).
Solstad, T., Moser, E. I. & Einevoll, G. T. From grid cells to place cells: a mathematical model. Hippocampus 16, 1026–1031 (2006).
Paulsen, O. & Moser, E. I. A model of hippocampal memory encoding and retrieval: GABAergic control of synaptic plasticity. Trends Neurosci. 21, 273–278 (1998).
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).
Nakashiba, T. et al. Young dentate granule cells mediate pattern separation, whereas old granule cells facilitate pattern completion. Cell 149, 188–201 (2012).
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).
Liu, X. et al. Optogenetic stimulation of a hippocampal engram activates fear memory recall. Nature 484, 381–385 (2012).
Ramirez, S. et al. Creating a false memory in the hippocampus. Science 341, 387–391 (2013).
Marr, D. A theory for cerebral neocortex. Proc. R. Soc. Lond. B 176, 161–234 (1970).
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).
Abeles, M., Bergman, H., Margalit, E. & Vaadia, E. Spatiotemporal firing patterns in the frontal cortex of behaving monkeys. J. Neurophysiol. 70, 1629–1638 (1993).
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).
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).
Ikegaya, Y. et al. Synfire chains and cortical songs: temporal modules of cortical activity. Science 304, 559–564 (2004).
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).
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).
Luczak, A., Bartho, P. & Harris, K. D. Spontaneous events outline the realm of possible sensory responses in neocortical populations. Neuron 62, 413–425 (2009).
Rigotti, M. et al. The importance of mixed selectivity in complex cognitive tasks. Nature 497, 585–590 (2013).
Churchland, M. M. et al. Neural population dynamics during reaching. Nature 487, 51–56 (2012).
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).
Briggman, K. L., Abarbanel, H. D. & Kristan, W. B. Jr. Optical imaging of neuronal populations during decision-making. Science 307, 896–901 (2005).
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).
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).
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).
Meister, M., Pine, J. & Baylor, D. A. Multi-neuronal signals from the retina: acquisition and analysis. J. Neurosci. Methods 51, 95–106 (1994).
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).
Field, G. D. et al. Functional connectivity in the retina at the resolution of photoreceptors. Nature 467, 673–677 (2010).
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).
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).
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).
Miyawaki, A. et al. Fluorescent indicators for Ca2+ based on green fluorescent proteins and calmodulin. Nature 388, 882–887 (1997).
Chen, T. W. et al. Ultrasensitive fluorescent proteins for imaging neuronal activity. Nature 499, 295–300 (2013).
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).
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).
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).
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).
Denk, W., Strickler, J. H. & Webb, W. W. Two-photon laser scanning fluorescence microscopy. Science 248, 73–76 (1990).
Yuste, R. & Denk, W. Dendritic spines as basic units of synaptic integration. Nature 375, 682–684 (1995).
Svoboda, K., Denk, W., Kleinfeld, D. & Tank, D. W. In vivo dendritic calcium dynamics in neocortical pyramidal neurons. Nature 385, 161–165 (1997).
Quirin, S., Jackson, J., Peterka, D. S. & Yuste, R. Simultaneous imaging of neural activity in three dimensions. Front. Neural Circuits 8, 29 (2014).
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).
Bonhoeffer, T. & Grinvald, A. Iso-orientation domains in cat visual cortex are arranged in pinwheel-like patterns. Nature 353, 429–431 (1991).
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).
Mather, M., Cacioppo, J. T. & Kanwisher, N. How fMRI can inform cognitive theories. Perspect. Psychol. Sci. 8, 108–113 (2013).
Barttfeld, P. et al. Signature of consciousness in the dynamics of resting-state brain activity. Proc. Natl Acad. Sci. USA 112, 887–892 (2015).
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).
Zayat, L., Baraldo, L. & Etchenique, R. Inorganic caged compounds: uncaging with visible light. CSH protoc. 2007, pdb ip39 (2007).
Nikolenko, V., Poskanzer, K. E. & Yuste, R. Two-photon photostimulation and imaging of neural circuits. Nat. Methods 4, 943–950 (2007).
Packer, A. M. et al. Two-photon optogenetics of dendritic spines and neural circuits. Nat. Methods 9, 1202–1205 (2012).
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).
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).
Fairhall, A. & Sompolinsky, H. Editorial overview: theoretical and computational neuroscience. Curr. Opin. Neurobiol. 25, v–viii (2014).
Rieke, F., Warland, D., de Ruyter van Steveninck, R. & Bialek, W. Spikes: Exploring the Neural Code (MIT Press, 1997).
Cohen, L. Optical approaches to neuronal function. Annu. Rev. Physiol. 51, 487–582 (1989).
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).
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).
Vogels, T. P., Rajan, K. & Abbott, L. F. Neural network dynamics. Annu. Rev. Neurosci. 28, 357–376 (2005).
Alivisatos, A. P. et al. The brain activity map project and the challenge of functional connectomics. Neuron 74, 970–974 (2012).
Insel, T. R., Landis, S. C. & Collins, F. S. Research priorities. The NIH BRAIN initiative. Science 340, 687–688 (2013).
Kant, I. Kritik der reinen Vernunft (Cambridge Univ. Press, 1781).
Bargmann, C. I. & Marder, E. From the connectome to brain function. Nat. Methods 10, 483–490 (2013).
Bargmann, C. I. Beyond the connectome: how neuromodulators shape neural circuits. Bioessays 34, 458–465 (2012).
Stuart, G., Spruston, N. & Hausser, M. (eds). Dendrites (Oxford Univ. Press, 1999).
Lorente de No, R. Analysis of the activity of the chains of internuncial neurons. J. Neurophysiol. 1, 207–244 (1938).
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).
Yuste, R. & Katz, L. C. Control of postsynaptic Ca2+ influx in developing neocortex by excitatory and inhibitory neurotransmitters. Neuron 6, 333–344 (1991).
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).
Ramon y Cajal, S. The structure and connexions of neurons. Nobelprize.org [online], (1906).
Rolls, E. T. Memory, Attention and Decision-Making: A Unifying Computational Neuroscience Approach (Oxford Univ. Press, 2008).
Eliasmith, C. Attractor network. Scholarpedia 2, 1380 (2007).
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).
The author declares no competing financial interests.
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.
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.
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.
Recapitulation of experience-dependent patterns of neural activity previously observed during awake periods.
About this article
Cite this article
Yuste, R. From the neuron doctrine to neural networks. Nat Rev Neurosci 16, 487–497 (2015). https://doi.org/10.1038/nrn3962
Nature Reviews Neuroscience (2022)
Nature Communications (2022)
Nature Neuroscience (2022)
Molecular Psychiatry (2022)