Re-evaluating Circuit Mechanisms Underlying Pattern Separation
2019; Cell Press; Volume: 101; Issue: 4 Linguagem: Inglês
10.1016/j.neuron.2019.01.044
ISSN1097-4199
AutoresN. Alex Cayco-Gajic, R. Angus Silver,
Tópico(s)Neural Networks and Applications
ResumoWhen animals interact with complex environments, their neural circuits must separate overlapping patterns of activity that represent sensory and motor information. Pattern separation is thought to be a key function of several brain regions, including the cerebellar cortex, insect mushroom body, and dentate gyrus. However, recent findings have questioned long-held ideas on how these circuits perform this fundamental computation. Here, we re-evaluate the functional and structural mechanisms underlying pattern separation. We argue that the dimensionality of the space available for population codes representing sensory and motor information provides a common framework for understanding pattern separation. We then discuss how these three circuits use different strategies to separate activity patterns and facilitate associative learning in the presence of trial-to-trial variability. When animals interact with complex environments, their neural circuits must separate overlapping patterns of activity that represent sensory and motor information. Pattern separation is thought to be a key function of several brain regions, including the cerebellar cortex, insect mushroom body, and dentate gyrus. However, recent findings have questioned long-held ideas on how these circuits perform this fundamental computation. Here, we re-evaluate the functional and structural mechanisms underlying pattern separation. We argue that the dimensionality of the space available for population codes representing sensory and motor information provides a common framework for understanding pattern separation. We then discuss how these three circuits use different strategies to separate activity patterns and facilitate associative learning in the presence of trial-to-trial variability. Imagine you are about to run an experiment in a laboratory when the phone rings. You leave the lab and chat on the phone for a few minutes with your colleague. Upon re-entering the lab, you quickly realize that something has changed (spot the difference, Figure 1A). Although the change in sensory inputs is small relative to the total sensory information contained in each scene, the difference between the two conditions is immediately apparent. This example illustrates the inherent ability of the brain to distinguish between subtle but important differences in the detail of the environment. Sensory, proprioceptive, and motor information is represented by the spatiotemporal firing patterns of populations of neurons. To identify subtle changes in the external world, the brain must distinguish between similar patterns of neuronal activity. This can be facilitated by “pattern separation,” a process in which neural circuits transform similar input activity patterns into more distinct output patterns. Pattern separation in neural circuits was first formulated by David Marr (Marr, 1969Marr D. A theory of cerebellar cortex.J. Physiol. 1969; 202: 437-470Crossref PubMed Scopus (2181) Google Scholar), who was inspired by two generic features of the circuitry of the cerebellar input layer: the extensive divergence from a smaller number of mossy fiber inputs to a much larger number of granule cells and widespread feedback inhibition that regulates granule cell excitability. Based on these features, Marr hypothesized that the cerebellar input layer projects mossy fiber activity patterns onto a much larger population of sparsely active granule cells, thereby reducing the overlap between activated neurons (Figure 1B). James Albus independently developed a similar theory based on analogies between the cerebellar cortex and supervised learning algorithms from early artificial intelligence research (Albus, 1971Albus J.S. A theory of cerebellar function.Math. Biosci. 1971; 10: 25-61Crossref Scopus (1425) Google Scholar). In this framework, each activity pattern can be considered as a point in activity space, where each dimension corresponds to the activity of a different neuron. Albus argued that the divergent architecture of the cerebellar input layer recodes input patterns in an expanded activity space, thereby increasing their linear separability (i.e., the ability to separate different groups of input patterns in activity space via a hyperplane; Figure 1C). This enables a downstream decoder, such as a perceptron (Rosenblatt, 1958Rosenblatt F. The perceptron: a probabilistic model for information storage and organization in the brain.Psychol. Rev. 1958; 65: 386-408Crossref PubMed Scopus (3055) Google Scholar) or support vector machine (Cortes and Vapnik, 1995Cortes C. Vapnik V. Support vector networks.Mach. Learn. 1995; 20: 273-297Crossref Scopus (0) Google Scholar), to better classify input patterns into arbitrary associations using supervised learning. In the cerebellar cortex, such supervised learning is thought to occur largely in Purkinje cells (Brunel et al., 2004Brunel N. Hakim V. Isope P. Nadal J.P. Barbour B. Optimal information storage and the distribution of synaptic weights: perceptron versus Purkinje cell.Neuron. 2004; 43: 745-757Abstract Full Text Full Text PDF PubMed Scopus (162) Google Scholar, Ito, 2006Ito M. Cerebellar circuitry as a neuronal machine.Prog. Neurobiol. 2006; 78: 272-303Crossref PubMed Scopus (460) Google Scholar, Gao et al., 2012Gao Z. van Beugen B.J. De Zeeuw C.I. Distributed synergistic plasticity and cerebellar learning.Nat. Rev. Neurosci. 2012; 13: 619-635Crossref PubMed Scopus (240) Google Scholar, Ohmae and Medina, 2015Ohmae S. Medina J.F. Climbing fibers encode a temporal-difference prediction error during cerebellar learning in mice.Nat. Neurosci. 2015; 18: 1798-1803Crossref PubMed Scopus (115) Google Scholar, Herzfeld et al., 2018Herzfeld D.J. Kojima Y. Soetedjo R. Shadmehr R. Encoding of error and learning to correct that error by the Purkinje cells of the cerebellum.Nat. Neurosci. 2018; 21: 736-743Crossref PubMed Scopus (1) Google Scholar, Raymond and Medina, 2018Raymond J.L. Medina J.F. Computational principles of supervised learning in the cerebellum.Annu. Rev. Neurosci. 2018; 41: 233-253Crossref PubMed Scopus (3) Google Scholar), where precise sensorimotor associations are formed by learning rules with narrow temporal windows (Suvrathan et al., 2016Suvrathan A. Payne H.L. Raymond J.L. Timing rules for synaptic plasticity matched to behavioural function.Neuron. 2016; 92: 959-967Abstract Full Text Full Text PDF PubMed Scopus (0) Google Scholar). However, neural activity exhibits significant trial-to-trial variability due to a variety of factors, including noise from inherently stochastic processes (e.g., neurotransmitter release at synapses), fluctuations in the external stimulus, and changes in the internal state of the animal (e.g., attention). Therefore, a key question for pattern separation is how neural circuits separate overlapping representations in the presence of unwanted variability (Laurent, 2002Laurent G. Olfactory network dynamics and the coding of multidimensional signals.Nat. Rev. Neurosci. 2002; 3: 884-895Crossref PubMed Scopus (474) Google Scholar). Despite the differing conceptual details between their theories, Marr and Albus both predicted that the divergent feedforward excitation present in the cerebellar input layer implements pattern separation (Albus, 1971Albus J.S. A theory of cerebellar function.Math. Biosci. 1971; 10: 25-61Crossref Scopus (1425) Google Scholar, Marr, 1969Marr D. A theory of cerebellar cortex.J. Physiol. 1969; 202: 437-470Crossref PubMed Scopus (2181) Google Scholar). These concepts (often combined into “Marr-Albus theory”) have been extended to other divergent circuits that are upstream of areas involved in associative learning. However, 50 years later, it still remains unclear how different neural circuits separate noisy activity patterns. Indeed, several mechanisms believed to be involved in this fundamental computation have recently been called into question by new experimental and theoretical studies in three brain regions proposed to perform pattern separation: the cerebellar cortex, insect mushroom body, and dentate gyrus. In this Review, we re-evaluate classical concepts of Marr-Albus theory of pattern separation in light of these new findings and discuss recent challenges to how they may be implemented in these circuits. The cerebellum is thought to use associative learning to coordinate movements and predict the sensory consequences of active movement (Wolpert et al., 1998Wolpert D.M. Miall R.C. Kawato M. Internal models in the cerebellum.Trends Cogn. Sci. 1998; 2: 338-347Abstract Full Text Full Text PDF PubMed Scopus (1201) Google Scholar, Kennedy et al., 2014Kennedy A. Wayne G. Kaifosh P. Alviña K. Abbott L.F. Sawtell N.B. A temporal basis for predicting the sensory consequences of motor commands in an electric fish.Nat. Neurosci. 2014; 17: 416-422Crossref PubMed Scopus (63) Google Scholar, Brooks et al., 2015Brooks J.X. Carriot J. Cullen K.E. Learning to expect the unexpected: rapid updating in primate cerebellum during voluntary self-motion.Nat. Neurosci. 2015; 18: 1310-1317Crossref PubMed Scopus (54) Google Scholar, Singla et al., 2017Singla S. Dempsey C. Warren R. Enikolopov A.G. Sawtell N.B. A cerebellum-like circuit in the auditory system cancels responses to self-generated sounds.Nat. Neurosci. 2017; 20: 943-950Crossref PubMed Scopus (17) Google Scholar). The evolutionarily conserved, highly regular structure of the cerebellar cortex has encouraged much speculation as to how the computations required for these functions are achieved (Figure 2A) (Braitenberg, 1961Braitenberg V. Functional interpretation of cerebellar histology.Nature. 1961; 190: 539-540Crossref Scopus (58) Google Scholar, Eccles et al., 1967Eccles J.C. Ito M. Szentagothai J. The Cerebellum as a Neuronal Machine. Springer-Verlag, 1967Crossref Google Scholar, Marr, 1969Marr D. A theory of cerebellar cortex.J. Physiol. 1969; 202: 437-470Crossref PubMed Scopus (2181) Google Scholar, Albus, 1971Albus J.S. A theory of cerebellar function.Math. Biosci. 1971; 10: 25-61Crossref Scopus (1425) Google Scholar, Fujita, 1982Fujita M. Adaptive filter model of the cerebellum.Biol. Cybern. 1982; 45: 195-206Crossref PubMed Google Scholar, Pellionisz and Llinás, 1982Pellionisz A. Llinás R. Space-time representation in the brain. the cerebellum as a predictive space-time metric tensor.Neuroscience. 1982; 7: 2949-2970Crossref PubMed Scopus (0) Google Scholar, Kanerva, 1988Kanerva P. Sparse Distributed Memory. MIT Press, 1988Google Scholar, Tyrrell and Willshaw, 1992Tyrrell T. Willshaw D. Cerebellar cortex: its simulation and the relevance of Marr’s theory.Philos. Trans. R. Soc. Lond. B Biol. Sci. 1992; 336: 239-257Crossref PubMed Google Scholar, Miall et al., 1993Miall R.C. Weir D.J. Wolpert D.M. Stein J.F. Is the cerebellum a smith predictor?.J. Mot. Behav. 1993; 25: 203-216Crossref PubMed Google Scholar, Ito, 2006Ito M. Cerebellar circuitry as a neuronal machine.Prog. Neurobiol. 2006; 78: 272-303Crossref PubMed Scopus (460) Google Scholar, Yamazaki and Tanaka, 2007Yamazaki T. Tanaka S. The cerebellum as a liquid state machine.Neural Netw. 2007; 20: 290-297Crossref PubMed Scopus (84) Google Scholar, Dean et al., 2010Dean P. Porrill J. Ekerot C.F. Jörntell H. The cerebellar microcircuit as an adaptive filter: experimental and computational evidence.Nat. Rev. Neurosci. 2010; 11: 30-43Crossref PubMed Scopus (230) Google Scholar). Mossy fibers carry sensory and motor information (van Kan et al., 1993van Kan P.L. Gibson A.R. Houk J.C. Movement-related inputs to intermediate cerebellum of the monkey.J. Neurophysiol. 1993; 69: 74-94Crossref PubMed Google Scholar, Arenz et al., 2008Arenz A. Silver R.A. Schaefer A.T. Margrie T.W. The contribution of single synapses to sensory representation in vivo.Science. 2008; 321: 977-980Crossref PubMed Scopus (121) Google Scholar, Huang et al., 2013Huang C.C. Sugino K. Shima Y. Guo C. Bai S. Mensh B.D. Nelson S.B. Hantman A.W. Convergence of pontine and proprioceptive streams onto multimodal cerebellar granule cells.eLife. 2013; 2: e00400Crossref PubMed Scopus (93) Google Scholar, Proville et al., 2014Proville R.D. Spolidoro M. Guyon N. Dugué G.P. Selimi F. Isope P. Popa D. Léna C. Cerebellum involvement in cortical sensorimotor circuits for the control of voluntary movements.Nat. Neurosci. 2014; 17: 1233-1239Crossref PubMed Scopus (66) Google Scholar, Powell et al., 2015Powell K. Mathy A. Duguid I. Häusser M. Synaptic representation of locomotion in single cerebellar granule cells.eLife. 2015; 4: e07290Crossref Scopus (41) Google Scholar) to the cerebellar input layer (or granule cell layer), where they form multiple “en passant” presynaptic boutons. Each large bouton makes glutamatergic synapses onto the short dendrites of multiple granule cells (Eccles et al., 1967Eccles J.C. Ito M. Szentagothai J. The Cerebellum as a Neuronal Machine. Springer-Verlag, 1967Crossref Google Scholar, Silver et al., 1992Silver R.A. Traynelis S.F. Cull-Candy S.G. Rapid-time-course miniature and evoked excitatory currents at cerebellar synapses in situ.Nature. 1992; 355: 163-166Crossref PubMed Scopus (270) Google Scholar). Granule cell axons ascend into the molecular layer, where they bifurcate to form long “parallel fibers” that synapse onto numerous Purkinje cells and interneurons. Granule cells also receive feedforward and feedback inhibition from Golgi cells (Vos et al., 1999Vos B.P. Volny-Luraghi A. De Schutter E. Cerebellar Golgi cells in the rat: receptive fields and timing of responses to facial stimulation.Eur. J. Neurosci. 1999; 11: 2621-2634Crossref PubMed Scopus (100) Google Scholar, Duguid et al., 2015Duguid I. Branco T. Chadderton P. Arlt C. Powell K. Häusser M. Control of cerebellar granule cell output by sensory-evoked Golgi cell inhibition.Proc. Natl. Acad. Sci. USA. 2015; 112: 13099-13104Crossref PubMed Scopus (18) Google Scholar), which are driven by excitatory inputs from mossy fibers through their basal dendrites (Kanichay and Silver, 2008Kanichay R.T. Silver R.A. Synaptic and cellular properties of the feedforward inhibitory circuit within the input layer of the cerebellar cortex.J. Neurosci. 2008; 28: 8955-8967Crossref PubMed Scopus (71) Google Scholar) and ascending granule cell axons and parallel fibers through their apical dendrites (Dieudonne, 1998Dieudonne S. Submillisecond kinetics and low efficacy of parallel fibre-Golgi cell synaptic currents in the rat cerebellum.J. Physiol. 1998; 510: 845-866Crossref PubMed Google Scholar, Cesana et al., 2013Cesana E. Pietrajtis K. Bidoret C. Isope P. D’Angelo E. Dieudonné S. Forti L. Granule cell ascending axon excitatory synapses onto Golgi cells implement a potent feedback circuit in the cerebellar granular layer.J. Neurosci. 2013; 33: 12430-12446Crossref PubMed Scopus (0) Google Scholar). Golgi cells are sparsely interconnected via chemical synapses (Hull and Regehr, 2012Hull C. Regehr W.G. Identification of an inhibitory circuit that regulates cerebellar Golgi cell activity.Neuron. 2012; 73: 149-158Abstract Full Text Full Text PDF PubMed Scopus (59) Google Scholar) and densely connected via electrical synapses (Dugué et al., 2009Dugué G.P. Brunel N. Hakim V. Schwartz E. Chat M. Lévesque M. Courtemanche R. Léna C. Dieudonné S. Electrical coupling mediates tunable low-frequency oscillations and resonance in the cerebellar Golgi cell network.Neuron. 2009; 61: 126-139Abstract Full Text Full Text PDF PubMed Scopus (121) Google Scholar, Szoboszlay et al., 2016Szoboszlay M. Lőrincz A. Lanore F. Vervaeke K. Silver R.A. Nusser Z. Functional properties of dendritic gap junctions in cerebellar Golgi cells.Neuron. 2016; 90: 1043-1056Abstract Full Text Full Text PDF PubMed Google Scholar), which enables them to respond to excitatory input in a concerted (Vervaeke et al., 2012Vervaeke K. Lorincz A. Nusser Z. Silver R.A. Gap junctions compensate for sublinear dendritic integration in an inhibitory network.Science. 2012; 335: 1624-1628Crossref PubMed Scopus (58) Google Scholar) or desynchronized manner (Vervaeke et al., 2010Vervaeke K. Lorincz A. Gleeson P. Farinella M. Nusser Z. Silver R.A. Rapid desynchronization of an electrically coupled interneuron network with sparse excitatory synaptic input.Neuron. 2010; 67: 435-451Abstract Full Text Full Text PDF PubMed Scopus (125) Google Scholar). Two converging lines of thought have led to the hypothesis that the cerebellar input layer separates overlapping input patterns. The first stems from the cerebellum’s role in associative learning. Pattern separation is a useful pre-processing step for associative learning, so any circuit that is involved in associative learning is also a key candidate for pattern separation. A classic example is eyeblink conditioning, in which animals are trained to associate a neutral sensory cue (e.g., auditory or visual) with a delayed presentation of an unconditional stimulus (air puff or electric shock) (Attwell et al., 2002Attwell P.J. Ivarsson M. Millar L. Yeo C.H. Cerebellar mechanisms in eyeblink conditioning.Ann. N Y Acad. Sci. 2002; 978: 79-92Crossref PubMed Scopus (0) Google Scholar). Pattern separation could facilitate this form of associative learning by making neural representations of different sensory inputs more distinct, ensuring that the unconditional stimulus is not mistakenly associated with similar, but not identical, cues. This hypothesis is consistent with functional evidence of the cerebellum’s involvement in sensory discrimination (Gao et al., 1996Gao J.H. Parsons L.M. Bower J.M. Xiong J. Li J. Fox P.T. Cerebellum implicated in sensory acquisition and discrimination rather than motor control.Science. 1996; 272: 545-547Crossref PubMed Google Scholar, Parsons et al., 1997Parsons L.M. Bower J.M. Gao J.H. Xiong J. Li J. Fox P.T. Lateral cerebellar hemispheres actively support sensory acquisition and discrimination rather than motor control.Learn. Mem. 1997; 4: 49-62Crossref PubMed Google Scholar, Parsons et al., 2009Parsons L.M. Petacchi A. Schmahmann J.D. Bower J.M. Pitch discrimination in cerebellar patients: evidence for a sensory deficit.Brain Res. 2009; 1303: 84-96Crossref PubMed Scopus (0) Google Scholar), but most eyeblink conditioning studies have used a single sensory cue. A recent study found that lesioning cerebellar nuclei affected the ability of mice to discriminate between two tones in a delay eyeblink conditioning task (Sakamoto and Endo, 2013Sakamoto T. Endo S. Deep cerebellar nuclei play an important role in two-tone discrimination on delay eyeblink conditioning in C57BL/6 mice.PLoS ONE. 2013; 8: e59880Crossref PubMed Scopus (1) Google Scholar). However, lesioned animals were still able to learn a simple eyeblink conditioning task, suggesting that the task was not fully dependent on the cerebellum. Moreover, a patient with cerebellar cortical atrophy was unable to learn to associate two tones with different delays (Fortier et al., 2000Fortier C.B. Disterhoft J.F. McGlinchey-Berroth R. Cerebellar cortical degeneration disrupts discrimination learning but not delay or trace classical eyeblink conditioning.Neuropsychology. 2000; 14: 537-550Crossref PubMed Scopus (19) Google Scholar). These studies are consistent with the idea that pattern separation plays a role in cerebellar supervised learning by helping discrimination of sensory cues, but direct experimental evidence of cerebellar pattern separation is lacking. Second, substantial theoretical work based on Marr-Albus theory has shown that the circuitry of the input layer of the cerebellar cortex is well suited for pattern separation (Marr, 1969Marr D. A theory of cerebellar cortex.J. Physiol. 1969; 202: 437-470Crossref PubMed Scopus (2181) Google Scholar, Albus, 1971Albus J.S. A theory of cerebellar function.Math. Biosci. 1971; 10: 25-61Crossref Scopus (1425) Google Scholar, Kanerva, 1988Kanerva P. Sparse Distributed Memory. MIT Press, 1988Google Scholar, Tyrrell and Willshaw, 1992Tyrrell T. Willshaw D. Cerebellar cortex: its simulation and the relevance of Marr’s theory.Philos. Trans. R. Soc. Lond. B Biol. Sci. 1992; 336: 239-257Crossref PubMed Google Scholar, Billings et al., 2014Billings G. Piasini E. Lőrincz A. Nusser Z. Silver R.A. Network structure within the cerebellar input layer enables lossless sparse encoding.Neuron. 2014; 83: 960-974Abstract Full Text Full Text PDF PubMed Scopus (48) Google Scholar, Cayco-Gajic et al., 2017Cayco-Gajic N.A. Clopath C. Silver R.A. Sparse synaptic connectivity is required for decorrelation and pattern separation in feedforward networks.Nat. Commun. 2017; 8: 1116Crossref PubMed Scopus (5) Google Scholar, Litwin-Kumar et al., 2017Litwin-Kumar A. Harris K.D. Axel R. Sompolinsky H. Abbott L.F. Optimal degrees of synaptic connectivity.Neuron. 2017; 93: 1153-1164Abstract Full Text Full Text PDF PubMed Scopus (32) Google Scholar). This relies on three main circuit mechanisms: a large divergence (or “expansion”), sparse synaptic connectivity, and broad feedback inhibition. First, the projection from mossy fibers to granule cells is highly divergent, with granule cells greatly outnumbering mossy fibers. Indeed, cerebellar granule cells are the most abundant of all neurons in the vertebrate brain. Second, broad feedback inhibition is provided by Golgi cells, which form electrically coupled syncytia and have large axonal arbors. Finally, the connectivity structure is sparse, with each granule cell receiving synaptic input from only four mossy fibers on average. These circuit mechanisms are conserved across different cerebellar regions and species (Wittenberg and Wang, 2007Wittenberg G. Wang S. Evolution and scaling of dendrites.in: Stuart G. Spruston N. Hausser M. Dendrites. Oxford University Press, 2007Crossref Scopus (0) Google Scholar), suggesting that the cerebellar cortical circuitry is important for survival. Together, these studies have built an anatomical and theoretical foundation for cerebellar pattern separation based on Marr-Albus theory. However, direct experimental evidence in the cerebellum is still lacking due to the technical difficulty of studying populations of densely packed cells in awake animals with conventional multi-unit electrophysiological recordings. Several groups have recently overcome the technical hurdles of recording granule cell population activity by using two-photon imaging and genetically encoded calcium indicators (Giovannucci et al., 2017Giovannucci A. Badura A. Deverett B. Najafi F. Pereira T.D. Gao Z. Ozden I. Kloth A.D. Pnevmatikakis E. Paninski L. et al.Cerebellar granule cells acquire a widespread predictive feedback signal during motor learning.Nat. Neurosci. 2017; 20: 727-734Crossref PubMed Scopus (22) Google Scholar, Knogler et al., 2017Knogler L.D. Markov D.A. Dragomir E.I. Štih V. Portugues R. Sensorimotor representations in cerebellar granule cells in larval zebrafish are dense, spatially organized, and non-temporally patterned.Curr. Biol. 2017; 27: 1288-1302Abstract Full Text Full Text PDF PubMed Scopus (0) Google Scholar, Wagner et al., 2017Wagner M.J. Kim T.H. Savall J. Schnitzer M.J. Luo L. Cerebellar granule cells encode the expectation of reward.Nature. 2017; 544: 96-100Crossref PubMed Scopus (80) Google Scholar; see also Ozden et al., 2012Ozden I. Dombeck D.A. Hoogland T.M. Tank D.W. Wang S.S. Widespread state-dependent shifts in cerebellar activity in locomoting mice.PLoS ONE. 2012; 7: e42650Crossref PubMed Scopus (56) Google Scholar). These studies have challenged several long-held assumptions about the properties of granule cell activity, suggesting that traditional concepts underlying cerebellar function should be re-evaluated (discussed in detail below). Early lesion and ablation experiments identified the mushroom body as a center for olfactory memory and associative learning in insects (de Belle and Heisenberg, 1994de Belle J.S. Heisenberg M. Associative odor learning in Drosophila abolished by chemical ablation of mushroom bodies.Science. 1994; 263: 692-695Crossref PubMed Google Scholar, Connolly et al., 1996Connolly J.B. Roberts I.J. Armstrong J.D. Kaiser K. Forte M. Tully T. O’Kane C.J. Associative learning disrupted by impaired Gs signaling in Drosophila mushroom bodies.Science. 1996; 274: 2104-2107Crossref PubMed Scopus (370) Google Scholar). The axons of olfactory receptor neurons expressing the same olfactory receptor converge onto specific glomeruli in the antennal lobes, forming a spatial odorant map (Fishilevich and Vosshall, 2005Fishilevich E. Vosshall L.B. Genetic and functional subdivision of the Drosophila antennal lobe.Curr. Biol. 2005; 15: 1548-1553Abstract Full Text Full Text PDF PubMed Scopus (351) Google Scholar). Projection neurons integrate signals from stereotyped sets of glomeruli, which form random synaptic connections onto Kenyon cells in the mushroom body calyx (Figure 2B) (Masuda-Nakagawa et al., 2005Masuda-Nakagawa L.M. Tanaka N.K. O’Kane C.J. Stereotypic and random patterns of connectivity in the larval mushroom body calyx of Drosophila.Proc. Natl. Acad. Sci. USA. 2005; 102: 19027-19032Crossref PubMed Scopus (54) Google Scholar, Murthy et al., 2008Murthy M. Fiete I. Laurent G. Testing odor response stereotypy in the Drosophila mushroom body.Neuron. 2008; 59: 1009-1023Abstract Full Text Full Text PDF PubMed Scopus (83) Google Scholar, Caron et al., 2013Caron S.J.C. Ruta V. Abbott L.F. Axel R. Random convergence of olfactory inputs in the Drosophila mushroom body.Nature. 2013; 497: 113-117Crossref PubMed Scopus (126) Google Scholar; but see Eichler et al., 2017Eichler K. Li F. Litwin-Kumar A. Park Y. Andrade I. Schneider-Mizell C.M. Saumweber T. Huser A. Eschbach C. Gerber B. et al.The complete connectome of a learning and memory centre in an insect brain.Nature. 2017; 548: 175-182Crossref PubMed Scopus (71) Google Scholar, Zheng et al., 2018Zheng Z. Lauritzen J.S. Perlman E. Robinson C.G. Nichols M. Milkie D. Torrens O. Price J. Fisher C.B. Sharifi N. et al.A complete electron microscopy volume of the brain of adult Drosophila melanogaster.Cell. 2018; 174: 730-743.e22Abstract Full Text Full Text PDF PubMed Scopus (0) Google Scholar). Like the cerebellar input layer, the mushroom body circuitry is highly divergent, with the number of Kenyon cells far exceeding the number of projection neurons. In Drosophila, the synaptic connectivity is also sparse: Kenyon cells have an average of seven dendritic claws, each of which is innervated by a single projection neuron (Butcher et al., 2012Butcher N.J. Friedrich A.B. Lu Z. Tanimoto H. Meinertzhagen I.A. Different classes of input and output neurons reveal new features in microglomeruli of the adult Drosophila mushroom body calyx.J. Comp. Neurol. 2012; 520: 2185-2201Crossref PubMed Scopus (43) Google Scholar, Caron et al., 2013Caron S.J.C. Ruta V. Abbott L.F. Axel R. Random convergence of olfactory inputs in the Drosophila mushroom body.Nature. 2013; 497: 113-117Crossref PubMed Scopus (126) Google Scholar, Gruntman and Turner, 2013Gruntman E. Turner G.C. Integration of the olfactory code across dendritic claws of single mushroom body neurons.Nat. Neurosci. 2013; 16: 1821-1829Crossref PubMed Scopus (55) Google Scholar; but see Jortner et al., 2007Jortner R.A. Farivar S.S. Laurent G. A simple connectivity scheme for sparse coding in an olfactory system.J. Neurosci. 2007; 27: 1659-1669Crossref PubMed Scopus (119) Google Scholar for a denser connectivity scheme in locust). Global feedback inhibition is provided by a single GABAergic neuron, called the anterior paired lateral (APL) neuron in Drosophila, which is both presynaptic and postsynaptic to virtually all Kenyon cells (Leitch and Laurent, 1996Leitch B. Laurent G. GABAergic synapses in the antennal lobe and mushroom body of the locust olfactory system.J. Comp. Neurol. 1996; 372: 487-514Crossref PubMed Scopus (118) Google Scholar, Liu and Davis, 2009Liu X. Davis R.L. The GABAergic anterior paired lateral neuron suppresses and is suppressed by olfactory learning.Nat. Neurosci. 2009; 12: 53-59Crossref PubMed Scopus (102) Google Scholar, Papadopoulou et al., 2011Papadopoulou M. Cassenaer S. Nowotny T. Laurent G. Normalization for sparse encoding of odors by a wide-field interneuron.Science. 2011; 332: 721-725Crossref PubMed Scopus (99) Google Scholar), although recent evidence suggests that the APL neuron may also mediate local lateral inhibition (Inada et al., 2017Inada K. Tsuchimoto Y. Kazama H. Origins of cell-type-specific olfactory processing in the Drosophila mushroom body circuit.Neuron. 2017; 95: 357-367.e4Abstract Full Text Full Text PDF PubMed Scopus (5) Google Scholar). Kenyon cell axons project out from the calyx to form the lobes, where they converge onto a smaller number of mushroom body output neurons. The role of the mushroom body in associative olfactory learning and its broad similarities with the divergent circuitry of the cerebellar cortex (Farris, 2011Farris S.M. Are mushroom bodies cerebellum-like structures?.Arthropod Struct. Dev. 2011; 40: 368-379Crossref PubMed Scopus (40) Google Scholar) led to the idea that the mushroom body performs pattern separation (Laurent, 2002Laurent G. Olfactory network dynamics and the coding of multidimensional signals.Nat. Rev. Neurosci. 2002; 3: 884-895Crossref PubMed Scopus (474) Google Scholar). This concept was directly tested by a study that used whole-cell recordings to determine that average odor-evoked Kenyon cell responses were indeed more separated than those of olfactory receptor neurons (Turner et al., 2008Turner G.C. Bazhenov M. Laurent G. Olfactory representations by Drosophila mushroom body neurons.J. Neurophysiol. 2008; 99: 734-746Crossref PubMed Scopus (187) Google Scholar). More recent work has taken advantage of genetically encoded calcium indicators to image populations of Kenyon cells during an olfactory learning task, verifying that the ability of flies to generalize aversive associa
Referência(s)