Cracking the Function of Layers in the Sensory Cortex
2018; Cell Press; Volume: 100; Issue: 5 Linguagem: Inglês
10.1016/j.neuron.2018.10.032
ISSN1097-4199
AutoresHillel Adesnik, Alexander Naka,
Tópico(s)Neuroscience and Neural Engineering
ResumoUnderstanding how cortical activity generates sensory perceptions requires a detailed dissection of the function of cortical layers. Despite our relatively extensive knowledge of their anatomy and wiring, we have a limited grasp of what each layer contributes to cortical computation. We need to develop a theory of cortical function that is rooted solidly in each layer's component cell types and fine circuit architecture and produces predictions that can be validated by specific perturbations. Here we briefly review the progress toward such a theory and suggest an experimental road map toward this goal. We discuss new methods for the all-optical interrogation of cortical layers, for correlating in vivo function with precise identification of transcriptional cell type, and for mapping local and long-range activity in vivo with synaptic resolution. The new technologies that can crack the function of cortical layers are finally on the immediate horizon. Understanding how cortical activity generates sensory perceptions requires a detailed dissection of the function of cortical layers. Despite our relatively extensive knowledge of their anatomy and wiring, we have a limited grasp of what each layer contributes to cortical computation. We need to develop a theory of cortical function that is rooted solidly in each layer's component cell types and fine circuit architecture and produces predictions that can be validated by specific perturbations. Here we briefly review the progress toward such a theory and suggest an experimental road map toward this goal. We discuss new methods for the all-optical interrogation of cortical layers, for correlating in vivo function with precise identification of transcriptional cell type, and for mapping local and long-range activity in vivo with synaptic resolution. The new technologies that can crack the function of cortical layers are finally on the immediate horizon. "At present we have no direct evidence on how the cortex transforms the incoming visual information. Ideally, one should determine the properties of a cortical cell, and then examine one by one the receptive fields of all the afferents projecting upon that cell."– Hubel and Wiesel, 1962, Journal of Physiology A primary goal of cortical physiology is to explain how the cortex transforms incoming information to generate perceptions. More than half a century has passed since the above statement was made, but a detailed understanding of the mechanisms that mediate cortical transformations across the cortical layers remains remarkably incomplete. However, recent technological advances finally allow execution of the experiment that Hubel and Wiesel prescribed, as well as many other sophisticated assays that can overcome this conceptual challenge. First, we briefly review how existing data have motivated the available theories regarding the function of cortical layers, primarily with respect to sensory transformations. Next, we highlight the key data we lack that could confirm or invalidate these models or motivate new ones. Finally, we propose the new technologies and experiments that are needed to obtain the data that will allow us to arrive at a much more mechanistic, circuit-driven theory for the unique contributions of layer-specific circuits in sensory perception. The cortical generation of sensory percepts can be thought of as a synthetic, hierarchical process or as one based largely in statistical inference. In the hierarchical model, neurons integrate their inputs to filter the sensory data and transform it into an output spike train that encodes features of the stimulus. A simple feedforward architecture composed of many neurons filtering their input in this manner should ultimately enable complex computations to mediate object identification and scene analysis (Hubel and Wiesel, 1962Hubel D.H. Wiesel T.N. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex.J. Physiol. 1962; 160: 106-154Crossref PubMed Scopus (6788) Google Scholar, Marr, 2010Marr D. Vision: A Computational Investigation Into The Human Representation And Processing of Visual Information. MIT Press, 2010Crossref Google Scholar). The apparent feedforward architecture of the primate visual system might help explain why object recognition is fast (Thorpe et al., 1996Thorpe S. Fize D. Marlot C. Speed of processing in the human visual system.Nature. 1996; 381: 520-522Crossref PubMed Scopus (2064) Google Scholar). In the framework of statistical inference, cortical circuits encode a generative model of the sensory environment, and recurrent interactions between cortical processing stages compare the expectations of the internally generated model with incoming data from the sensory apparatus (Bastos et al., 2012Bastos A.M. Usrey W.M. Adams R.A. Mangun G.R. Fries P. Friston K.J. Canonical microcircuits for predictive coding.Neuron. 2012; 76: 695-711Abstract Full Text Full Text PDF PubMed Scopus (549) Google Scholar). Two of the most compelling examples of the synthetic process are the encoding of edge orientation in the primary visual cortex (Hubel and Wiesel, 1959Hubel D.H. Wiesel T.N. Receptive fields of single neurones in the cat's striate cortex.J. Physiol. 1959; 148: 574-591Crossref PubMed Scopus (2700) Google Scholar, Hubel and Wiesel, 1962Hubel D.H. Wiesel T.N. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex.J. Physiol. 1962; 160: 106-154Crossref PubMed Scopus (6788) Google Scholar) and that of object or face selectivity in the inferotemporal cortex (Bruce et al., 1981Bruce C. Desimone R. Gross C.G. Visual properties of neurons in a polysensory area in superior temporal sulcus of the macaque.J. Neurophysiol. 1981; 46: 369-384Crossref PubMed Google Scholar, Gross et al., 1972Gross C.G. Rocha-Miranda C.E. Bender D.B. Visual properties of neurons in inferotemporal cortex of the Macaque.J. Neurophysiol. 1972; 35: 96-111Crossref PubMed Google Scholar). The emergence of orientation tuning stands as one of the few concrete examples of a de novo transformation that occurs in a layer of the primary visual cortex (V1) and can be well explained by a simple feedforward model involving integration over a specific set of center-surround thalamic relay neurons (Hubel and Wiesel, 1962Hubel D.H. Wiesel T.N. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex.J. Physiol. 1962; 160: 106-154Crossref PubMed Scopus (6788) Google Scholar). Although the mechanistic details of an analogous feedforward circuit for the generation of face selectivity are lacking, one can conceptualize a similar process where neurons exhibiting increasingly sophisticated feature tuning are built by summating over neurons with more elementary filtering properties (e.g., edges to contours and contours to faces) (Chang and Tsao, 2017Chang L. Tsao D.Y. The code for facial identity in the primate brain.Cell. 2017; 169: 1013-1028.e14Abstract Full Text Full Text PDF PubMed Scopus (56) Google Scholar, Liu et al., 2016Liu L. She L. Chen M. Liu T. Lu H.D.D. Dan Y. Poo M.M. Spatial structure of neuronal receptive field in awake monkey secondary visual cortex (V2).Proc. Natl. Acad. Sci. USA. 2016; 113: 1913-1918Crossref PubMed Scopus (8) Google Scholar). Based on this framework, one might expect that further de novo transformations would occur as sensory signals propagate through the layers of the cortex (e.g., from layer 4 to layer 2/3). However, although ample data collected across cortical areas are consistent with the synthetic model, remarkably few, if any, compelling examples of such transformations have been observed between cortical layers of a single sensory area such as V1. Orientation tuning, direction selectivity, and ocular dominance are all observable within layer 4 neurons (Hubel and Wiesel, 1962Hubel D.H. Wiesel T.N. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex.J. Physiol. 1962; 160: 106-154Crossref PubMed Scopus (6788) Google Scholar, Sun et al., 2016Sun W. Tan Z. Mensh B.D. Ji N. Thalamus provides layer 4 of primary visual cortex with orientation- and direction-tuned inputs.Nat. Neurosci. 2016; 19: 308-315Crossref PubMed Scopus (59) Google Scholar). In other cortical areas, such as the somatosensory cortex, we have arguably even less insight into the synthesis of new response properties (Brecht, 2017Brecht M. The body model theory of somatosensory cortex.Neuron. 2017; 94: 985-992Abstract Full Text Full Text PDF PubMed Scopus (0) Google Scholar). Receptive fields in the rodent barrel cortex and in the cat visual cortex tend to grow or change shape across layers (Brecht et al., 2003Brecht M. Roth A. Sakmann B. Dynamic receptive fields of reconstructed pyramidal cells in layers 3 and 2 of rat somatosensory barrel cortex.J. Physiol. 2003; 553: 243-265Crossref PubMed Scopus (196) Google Scholar, Martinez et al., 2005Martinez L.M. Wang Q. Reid R.C. Pillai C. Alonso J.M. Sommer F.T. Hirsch J.A. Receptive field structure varies with layer in the primary visual cortex.Nat. Neurosci. 2005; 8: 372-379Crossref PubMed Scopus (131) Google Scholar), and some evidence supports the de novo generation of complex cells and contextual properties such as end-stopping between layer (L)4 and L2/3 in cats (Alonso and Martinez, 1998Alonso J.M. Martinez L.M. Functional connectivity between simple cells and complex cells in cat striate cortex.Nat. Neurosci. 1998; 1: 395-403Crossref PubMed Google Scholar, Hubel and Wiesel, 1962Hubel D.H. Wiesel T.N. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex.J. Physiol. 1962; 160: 106-154Crossref PubMed Scopus (6788) Google Scholar, Martinez and Alonso, 2001Martinez L.M. Alonso J.M. Construction of complex receptive fields in cat primary visual cortex.Neuron. 2001; 32: 515-525Abstract Full Text Full Text PDF PubMed Scopus (61) Google Scholar). However, the striking lack of concrete examples akin to orientation tuning, at least outside of the monkey V1, implies that the laminar circuity in a single cortical area is not set up to generate new types of feature selectivity. Furthermore, this hierarchical framework fails to account for a wide range of context-dependent phenomena observed in cortical activity, nor does it provide a compelling explanation for the profuse amount of feedback connections from higher cortical areas to lower ones (Felleman and Van Essen, 1991Felleman D.J. Van Essen D.C. Distributed hierarchical processing in the primate cerebral cortex.Cereb. Cortex. 1991; 1: 1-47Crossref PubMed Google Scholar). In contrast, the alternative framework that sees sensory processing as probabilistic inference can explain these "top-down" phenomena. In this scheme, neurons in different cortical layers play unique roles in computing the conditional probabilities that a given pattern of afferent neural input represents a specific sensory stimulus (Bastos et al., 2012Bastos A.M. Usrey W.M. Adams R.A. Mangun G.R. Fries P. Friston K.J. Canonical microcircuits for predictive coding.Neuron. 2012; 76: 695-711Abstract Full Text Full Text PDF PubMed Scopus (549) Google Scholar, Rao and Ballard, 1999Rao R.P.N. Ballard D.H. Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects.Nat. Neurosci. 1999; 2: 79-87Crossref PubMed Scopus (1527) Google Scholar). The core notion is that cortical neurons, moment to moment, compare afferent input from each preceding stage with an internal generative model of the sensory environment conveyed by top-down projections, a model based on both the recent past and accumulated experience. Predictions of this model are passed from higher to lower stages (both across layers in individual areas and between areas) through feedback connections, and neurons in earlier stages compare these predictions with errors indicated by deviation from the afferent, "bottom-up" sensory data. In one version of this theory, principal cells in superficial cortical layers of primary sensory areas encode prediction errors, whereas those in deeper layers encode conditional expectations from which predictions are made (Bastos et al., 2012Bastos A.M. Usrey W.M. Adams R.A. Mangun G.R. Fries P. Friston K.J. Canonical microcircuits for predictive coding.Neuron. 2012; 76: 695-711Abstract Full Text Full Text PDF PubMed Scopus (549) Google Scholar). Inhibitory interneurons within each cortical layer might be critical for canceling errors (i.e., incompatible predictions) when the predictive model matches the sensory data. This conceptual framework is attractive because one can assign specific functions to different layers and cell types that should be experimentally testable. However, at present, data supporting the inferential model is limited (see, for example, Homann et al., 2017Homann J. Koay S.A. Glidden A.M. Tank D.W. Berry M.J. Predictive coding of novel versus familiar stimuli in the primary visual cortex.bioRxiv. 2017; https://doi.org/10.1101/197608Crossref Scopus (0) Google Scholar). An intriguing variant on this theme is a "body model" for the somatosensory cortex in which the goal of S1 is to generate mental simulations of planned body actions. This model also assigns specific functions to each layer: body simulation to L4, sensory memory storage to L2/3, motor memory storage in L5, and relay of the top-down drive from M1 through L6 and back to L4 (Brecht, 2017Brecht M. The body model theory of somatosensory cortex.Neuron. 2017; 94: 985-992Abstract Full Text Full Text PDF PubMed Scopus (0) Google Scholar). More recently, the canonical circuit has been conceptualized less in terms of layers and more in terms of cell types that occupy specific layers and are connected by cell type-specific pathways (Harris and Shepherd, 2015Harris K.D. Shepherd G.M. The neocortical circuit: themes and variations.Nat. Neurosci. 2015; 18: 170-181Crossref PubMed Scopus (188) Google Scholar). Although layer and cell type are closely intertwined, this is an important distinction. A layer-centric view implicitly assumes that at least some basic cortical computations can be understood mechanistically by analyzing the activity of neurons in just one layer; the cell type-centered view assumes that we cannot achieve a satisfactory understanding of any computation without taking into account coordinated activity across multiple layers. Although we retain layer as an organizing concept for the purposes of this perspective, we note that the experimental approaches we outline below apply equally well to cracking the function of cortical sublaminae or cortical cell types. Nevertheless, we argue that the cortical literature regarding layers supports the notion that, in a specific set of contexts, ensembles of neurons located in just one layer are sufficient to mediate key cortical computations, such as fast sensorimotor transformations. However, under most conditions, such as those involved in generating conscious sensory percepts, the basic unit of cortical computation is a neuronal ensemble spread across multiple layers or spread across multiple layers and cortical areas. Our empirical knowledge of cortical layers from which we can build theories of their function comes from four types of exploration: anatomy of single cells and their projections, connectivity of pairs of cells according to their laminar location and cell type, physiological responses to sensory stimuli, and activation or suppression of neurons in discrete layers. Anatomy and connectivity are the most absolute in that they do not depend on brain state or type of sensory stimulation. They constrain the types of computations layers can perform and the dynamics they can exhibit but, on their own, provide limited insight into function. Conversely, physiological perturbations are much less absolute in that the resulting data will depend on the brain state and the context in which they were obtained. However, they should provide the most direct insight into the different functions of cortical layers. The often-repeated (although just as often maligned) notion of the "canonical cortical microcircuit" is largely based on studies of anatomy and connectivity in rodents, primates, and cats (Gilbert, 1983Gilbert C.D. Microcircuitry of the visual cortex.Annu. Rev. Neurosci. 1983; 6: 217-247Crossref PubMed Google Scholar). These data have converged on a core model where thalamic input drives activity in a feedforward and sequential fashion from L4 to L2/3 to L5 and out to other cortical and subcortical regions (Armstrong-James et al., 1992Armstrong-James M. Fox K. Das-Gupta A. Flow of excitation within rat barrel cortex on striking a single vibrissa.J. Neurophysiol. 1992; 68: 1345-1358Crossref PubMed Scopus (329) Google Scholar, Binzegger et al., 2004Binzegger T. Douglas R.J. Martin K.A. A quantitative map of the circuit of cat primary visual cortex.J. Neurosci. 2004; 24: 8441-8453Crossref PubMed Scopus (482) Google Scholar, Douglas and Martin, 1991Douglas R.J. Martin K.A. A functional microcircuit for cat visual cortex.J. Physiol. 1991; 440: 735-769Crossref PubMed Google Scholar, Lefort et al., 2009Lefort S. Tomm C. Floyd Sarria J.C. Petersen C.C. The excitatory neuronal network of the C2 barrel column in mouse primary somatosensory cortex.Neuron. 2009; 61: 301-316Abstract Full Text Full Text PDF PubMed Scopus (405) Google Scholar). Although numerous examples of alternate connections exist (e.g., thalamus to other layers, L5 to L2/3, and L4 to L5), the anatomy of these neurons (i.e., their intracortical axons and dendrites) matches well with paired electrophysiological recording and circuit mapping via optical approaches (see full citations below). Because these and other pathways in the cortex have been extensively reviewed elsewhere (Callaway, 1998Callaway E.M. Local circuits in primary visual cortex of the macaque monkey.Annu. Rev. Neurosci. 1998; 21: 47-74Crossref PubMed Scopus (393) Google Scholar, Douglas and Martin, 2004Douglas R.J. Martin K.A. Neuronal circuits of the neocortex.Annu. Rev. Neurosci. 2004; 27: 419-451Crossref PubMed Scopus (890) Google Scholar, Feldmeyer, 2012Feldmeyer D. Excitatory neuronal connectivity in the barrel cortex.Front. Neuroanat. 2012; 6: 24Crossref PubMed Scopus (85) Google Scholar, Gilbert, 1983Gilbert C.D. Microcircuitry of the visual cortex.Annu. Rev. Neurosci. 1983; 6: 217-247Crossref PubMed Google Scholar, Harris and Shepherd, 2015Harris K.D. Shepherd G.M. The neocortical circuit: themes and variations.Nat. Neurosci. 2015; 18: 170-181Crossref PubMed Scopus (188) Google Scholar, Thomson and Lamy, 2007Thomson A.M. Lamy C. Functional maps of neocortical local circuitry.Front. Neurosci. 2007; 1: 19-42Crossref PubMed Google Scholar), we will focus on data revealing the physiology and functional effect of the principal excitatory neurons in different cortical layers (Figure 1). The long-range input/output logic of the canonical microcircuit is organized by layer. L4 neurons are thought to primarily target their local neighbors (Binzegger et al., 2004Binzegger T. Douglas R.J. Martin K.A. A quantitative map of the circuit of cat primary visual cortex.J. Neurosci. 2004; 24: 8441-8453Crossref PubMed Scopus (482) Google Scholar). The principal neurons of L2/3 are intratelencephalic (IT) cells, meaning that their long-range axons project only to targets within the telencephalon, such as other cortical areas and the striatum. L5, sometimes called the primary cortical output layer, harbors IT cells as well as pyramidal tract (PT) cells, which send widely divergent projections to subcortical areas. L6 contains corticothalamic (CT) cells, which provide a major feedback projection to the thalamus, as well as IT cells. Corticocortical pathways (i.e, inter-areal) are often conceptualized as being either feedforward, lateral, or feedback pathways (Felleman and Van Essen, 1991Felleman D.J. Van Essen D.C. Distributed hierarchical processing in the primate cerebral cortex.Cereb. Cortex. 1991; 1: 1-47Crossref PubMed Google Scholar, Gămănuţ et al., 2018Gămănuţ R. Kennedy H. Toroczkai Z. Ercsey-Ravasz M. Van Essen D.C. Knoblauch K. Burkhalter A. The mouse cortical connectome, characterized by an ultra-dense cortical graph, maintains specificity by distinct connectivity profiles.Neuron. 2018; 97: 698-715.e10Abstract Full Text Full Text PDF PubMed Google Scholar). Layer plays a key organizing role in this inter-areal hierarchical scheme (D'Souza and Burkhalter, 2017D'Souza R.D. Burkhalter A. A laminar organization for selective cortico-cortical communication.Front. Neuroanat. 2017; 11: 71Crossref PubMed Scopus (3) Google Scholar). Interestingly, as one moves along the canonical pathway, translaminar connectivity becomes increasingly specific (Figure 1). The primary sensory thalamus, constituting the input stage of the hierarchy, provides highly divergent output impinging on cells in all cortical layers (Cruikshank et al., 2010Cruikshank S.J. Urabe H. Nurmikko A.V. Connors B.W. Pathway-specific feedforward circuits between thalamus and neocortex revealed by selective optical stimulation of axons.Neuron. 2010; 65: 230-245Abstract Full Text Full Text PDF PubMed Scopus (212) Google Scholar, Petreanu et al., 2009Petreanu L. Mao T. Sternson S.M. Svoboda K. The subcellular organization of neocortical excitatory connections.Nature. 2009; 457: 1142-1145Crossref PubMed Scopus (456) Google Scholar). L4 neurons exhibit strong recurrent intralaminar connectivity (Binzegger et al., 2004Binzegger T. Douglas R.J. Martin K.A. A quantitative map of the circuit of cat primary visual cortex.J. Neurosci. 2004; 24: 8441-8453Crossref PubMed Scopus (482) Google Scholar) and broadcast their output to all other layers but do not provide feedback to the thalamus. Similarly, L2/3 has minimal feedback connectivity to L4 but connects densely to both types of pyramidal neurons in L5 (Adesnik and Scanziani, 2010Adesnik H. Scanziani M. Lateral competition for cortical space by layer-specific horizontal circuits.Nature. 2010; 464: 1155-1160Crossref PubMed Scopus (189) Google Scholar, Lefort et al., 2009Lefort S. Tomm C. Floyd Sarria J.C. Petersen C.C. The excitatory neuronal network of the C2 barrel column in mouse primary somatosensory cortex.Neuron. 2009; 61: 301-316Abstract Full Text Full Text PDF PubMed Scopus (405) Google Scholar). In turn, L5 IT neurons exhibit dense, asymmetric connectivity onto PT neurons, and, finally, PT neurons appear to connect primarily only to other PT neurons, providing minimal feedback to any of the earlier layers (Yamawaki and Shepherd, 2015Yamawaki N. Shepherd G.M.G. Synaptic circuit organization of motor corticothalamic neurons.J. Neurosci. 2015; 35: 2293-2307Crossref PubMed Scopus (30) Google Scholar). An exception to this pattern of increasing selectivity is an ascending connection from L5 IT cells to L2/3 (Binzegger et al., 2004Binzegger T. Douglas R.J. Martin K.A. A quantitative map of the circuit of cat primary visual cortex.J. Neurosci. 2004; 24: 8441-8453Crossref PubMed Scopus (482) Google Scholar). As for L6, earlier models of the canonical microcircuit based primarily on data from monkeys and cats proposed that L6 receives major input from L5 and from superficial layers and then "closes the loop" by projecting back to L4 (Binzegger et al., 2004Binzegger T. Douglas R.J. Martin K.A. A quantitative map of the circuit of cat primary visual cortex.J. Neurosci. 2004; 24: 8441-8453Crossref PubMed Scopus (482) Google Scholar, Briggs and Callaway, 2001Briggs F. Callaway E.M. Layer-specific input to distinct cell types in layer 6 of monkey primary visual cortex.J. Neurosci. 2001; 21: 3600-3608Crossref PubMed Google Scholar, Douglas and Martin, 2004Douglas R.J. Martin K.A. Neuronal circuits of the neocortex.Annu. Rev. Neurosci. 2004; 27: 419-451Crossref PubMed Scopus (890) Google Scholar, Gilbert, 1983Gilbert C.D. Microcircuitry of the visual cortex.Annu. Rev. Neurosci. 1983; 6: 217-247Crossref PubMed Google Scholar). In rodents, L6 CT neurons project to L5a and, to a lesser extent, L4 (Kim et al., 2014Kim J. Matney C.J. Blankenship A. Hestrin S. Brown S.P. Layer 6 corticothalamic neurons activate a cortical output layer, layer 5a.J. Neurosci. 2014; 34: 9656-9664Crossref PubMed Scopus (41) Google Scholar) and receive strong long-range inputs (Kinnischtzke et al., 2016Kinnischtzke A.K. Fanselow E.E. Simons D.J. Target-specific M1 inputs to infragranular S1 pyramidal neurons.J. Neurophysiol. 2016; 116: 1261-1274Crossref PubMed Scopus (5) Google Scholar). There are putative discrepancies in the basic cortical circuitry between different mammalian species, implying that a unifying "canonical cortical circuit" might not exist across mammals. However, despite their heterogeneity, the existing data still indicate that cortical circuits across brain areas and species share some common functional principles that are key to understanding their function. Understanding any neural circuit requires perturbing it and observing changes in the computations it performs. Examples of putative computations cortical circuits implement are schematized in Figure 2. These include summations that give rise to oriented edge detectors (Figures 2A and 2B), signal amplification through recurrent excitation (Figure 2C), coincidence detection (Figure 2D), generation of a sparse code (Figure 2E), lateral integration that might facilitate contour or boundary detection (Figure 2F), and coding through synchronization (Figure 2G). Although the anatomy and physiology of the neurons in any layer can help us build theories and propose hypotheses for how layers contribute to each of these computations, only manipulating the activity of specific layers or subsets of layers can test and validate these theoretical hypotheses. Prior to the advent of cell type-specific perturbations (via opto- or chemogenetics), the primary tools for perturbation were chemical lesions (reversible or irreversible), cortical cooling, and electrical microstimulation. A common weakness of all of these tools is that precisely calibrating the spatial extent of the perturbation is extremely challenging, and they cannot be absolutely layer-specific because cortical neurons' dendrites and axons often stretch across laminar boundaries. This last fact even muddies the concept of what a layer is in the cortex because some deep-layer pyramidal neurons derive much of their synaptic input from their dendrites, which occupy different layers than the one in which their cell body resides (Larkum et al., 2018Larkum M.E. Petro L.S. Sachdev R.N.S. Muckli L. A perspective on cortical layering and layer-spanning neuronal elements.Front. Neuroanat. 2018; 12: 56Crossref PubMed Scopus (1) Google Scholar). Layer-specific optogenetic manipulation overcomes this last problem, but the results of both chemical and optogenetic perturbations of cortical layers have often challenged the canonical model of information flow across the cortical layers. For instance, reversibly blocking L4 activity (by chemically silencing specific layers of the visual thalamus) does not block much of the sensory evoked responses in L2/3 of the visual cortex of anesthetized cats (Malpeli, 1983Malpeli J.G. Activity of cells in area 17 of the cat in absence of input from layer a of lateral geniculate nucleus.J. Neurophysiol. 1983; 49: 595-610Crossref PubMed Scopus (59) Google Scholar; but see Martinez and Alonso, 2001Martinez L.M. Alonso J.M. Construction of complex receptive fields in cat primary visual cortex.Neuron. 2001; 32: 515-525Abstract Full Text Full Text PDF PubMed Scopus (61) Google Scholar) but simultaneously suppressing higher visual cortex areas does (Mignard and Malpeli, 1991Mignard M. Malpeli J.G. Paths of information flow through visual cortex.Science. 1991; 251: 1249-1251Crossref PubMed Google Scholar). In a similar vein, direct application of the action potential blocker lidocaine to the superficial layers of the somatosensory cortex has essentially no effect on whisker-evoked activity in L5 pyramidal cells (PCs) in sedated and paralyzed rats (Constantinople and Bruno, 2013Constantinople C.M. Bruno R.M. Deep cortical layers are activated directly by thalamus.Science. 2013; 340: 1591-1594Crossref PubMed Scopus (158) Google Scholar; but see Wright and Fox, 2010Wright N. Fox K. Origins of cortical layer V surround receptive fields in the rat barrel cortex.J. Neurophysiol. 2010; 103: 709-724Crossref PubMed Scopus (0) Google Scholar). Direct optogenetic suppression of L4 in awake, locomoting mice leads to a modest reduction in sensory evoked activity in L2/3 of V1 or S1. However, it simultaneously leads to a potent disinhibition of activity in L5 because of a disynaptic translaminar inhibitory circuit between L4 and L5 (Pluta et al., 2015Pluta S. Naka A. Veit J. Telian G. Yao L. Hakim R. Taylor D. Adesnik H. A direct translaminar inhibitory circuit tunes cortical output.Nat. Neurosci. 2015; 18: 1631-1640Crossref PubMed Scopus (26) Google Scholar). Optogenetic suppression of L6 in awake mice also has a largely disinhibitory effect across most layers of the cortex, an effect attributed to the deactivation of a broadly inhibiting translaminar inhibitory neuron (Bortone et al., 2014Bortone D.S. Olsen S.R. Scanziani M. Translaminar inhibitory cells recruited by layer 6 corticothalamic neurons suppress visual cortex.Neuron. 2014; 82: 474-485Abstract Full Text Full Text PDF PubMed Scopus (84) Google Scholar, Olsen et al., 2012Olsen S.R. Bortone D.S. Adesnik H. Scanziani M. Gain control by layer six in cortical circuits of vision.Nature. 2012; 483: 47-52Crossref PubMed Scopus (209) Google Scholar). In one study, optogenetic activation of L4, L2/3, or L5 in brain slices from the mouse primary visual cortex revealed that activation of each of these layers suppressed a
Referência(s)