Despite over half a century of intense investigation, we still do not understand the role of layers in the complicated microcircuitry of the cortex (Adesnik and Naka, 2018). Over the last decade optogenetic manipulation – either activation or inhibition – of specific cells (Nagel et al., 2003; Boyden et al., 2005) has been established as a powerful method to examine the causal relationship between the specific cell type and the network activity, cognitive functions or animal behavior (Deisseroth, 2015). Multichannel electrical recording is the most widely used approach to measure the neural responses to optogenetic stimulation. This approach is preferred over the optical approaches available (e.g., two-photon imaging) because of the relative ease of use and lower costs. The problem with this approach, however, is that extracellular signals represent the summation of the activity of all structures (dendrites, axons, and cell bodies) from all cell types surrounding each electrode; even worse, extracellular activity is usually very complex in vivo, particularly in the awake brain (Buzsáki, 2006). It is extremely difficult to explain why and how every single pattern of extracellular activities is generated. Polysynaptic activations can also occur through local and long-range connections. It is therefore necessary to consider feedback projections with various delays, which makes it further difficult to disentangle distinct contributions of different neuronal structures to extracellular signals.

One approach to overcome this problem is to use two-photon microscopy and all-optical approach with two light sources – one for optogenetic stimulation and the other for imaging – and visualize how stimulation of specific cell-type evokes responses in surrounding neurons (Packer et al., 2015). Though most approaches are either limited in range (i.e., the total depth) or speed, recently developed methods permit optogenetic stimulation or calcium imaging of ~50 neurons that are arbitrarily distributed in three-dimensional space (Pégard et al., 2017; Geiller et al., 2020) as well as fast volumetric cortex-wide two-photon imaging at cellular resolution (Demas et al., 2021; Prevedel et al., 2016). Imaging across different cortical depths (z-axis) is more difficult to implement than the more conventional x–y scanning.

More invasive approaches using a large (1 mm) prism covering all cortical layers would allow simultaneous imaging to neurons across layers (Chia and Levene, 2009; Andermann et al., 2013); however, the significant damage caused by 1 mm prism insertion into the cortical tissue must be considered. Substantial bleeding during surgery is unavoidable and a significant number of cells are killed or damaged by the procedure (Andermann et al., 2013), suggesting the possibility that repairing and rewiring processes take place, even if the animal survives the injury. Since the repairing process may significantly alter the local and long-range connections between survived neurons, the imaged neuronal network may no longer function in a way the normal healthy network did before the operation. Moreover, this approach typically requires chronic surgery involving a substantial delay between the surgery and recordings. Less invasive approaches use a single tapered fiber that has multiple windows (Pisanello et al., 2014) or micro-LED probes (Cao et al., 2013; McAlinden et al., 2015; Kim et al., 2020); however, they are only capable of optogenetic stimulation and do not permit calcium fluorescence imaging.

A relatively standard approach is the insertion of an endoscope that with the aid of a right-angled microprism can deliver light to and receive light from a single cortical layer simultaneously (Suzuki and Larkum, 2017; Suzuki and Larkum, 2020). To take advantage of the ease and flexibility of the single endoscope approach while allowing the probing of multiple layers, we developed a double-μPeriscope that is capable of optogenetic stimulation, fluorescence imaging or both of two separate cortical layers (Figure 1a, b). Each μPeriscope consists of a 0.10 mm × 0.10 mm or 0.18 mm × 0.18 mm micro right-angled prism, a custom-designed gradient-index (GRIN) lens, and a multimode optical fiber. To examine the focal light delivery from each μPeriscope, we stimulated two compartments of cortical layer 5 (L5) pyramidal neurons by inserting the double-μPeriscope with the lower one in cortical L5 and the upper one in layer 1 (L1) (Figure 1c). We expressed light-sensitive ion channels, channelrhodopsin 2 (ChR2) (Nagel et al., 2003; Boyden et al., 2005) and yellow fluorescent protein (YFP) in L5 pyramidal neurons (Figure 1d). A double-μPeriscope allowed us to address a wide range of neurobiological problems, three of which are described below.

Figure 1

Download asset Open asset

First, a previous in vitro study showed that combined stimulation of cell body and distal apical dendrites within a close time window induces a large, regenerative, long-lasting calcium plateau potential that evokes a burst somatic action potentials (APs) (Larkum et al., 1999) – a phenomenon referred to as ‘backpropagation-activated calcium spike firing’ or BAC firing. However, due to technical difficulties, it remains unknown whether and under what conditions BAC firing occurs in vivo. The double-μPeriscope, in combination with electrophysiological recordings from L5 allowed us to directly examine this question in vivo for the first time. To express ChR2 in L5 pyramidal neurons, a transgenic mouse line Sim1-KJ18-Cre × Ai32 was used. In agreement with the previous in vitro data, somatic L5 stimulation preceding dendritic L1 stimulation in vivo under anesthesia evoked more prominent deflections of the local field potential (Figure 1e) and more APs (Figure 1f), compared to the stimulation of either somatic or distal dendritic compartment alone. This result suggests that the backpropagating AP-induced Ca²⁺ spike firing does occur also in vivo. This finding highlights the benefits of the double-μPeriscope.

The same double-μPeriscope is capable of studying the interaction between cell classes in different layers. As an example, we examined the effect of optogenetic stimulation of cortical layer 2/3 (L2/3) on L5 (Figure 2a) by expressing ChR2 and YFP in L2/3 pyramidal cells in the primary motor cortex (Figure 2b) through combined use of a transgenic mouse line Rasgrf2-2A-dCre with Cre-dependent adeno-associated virus vector with CaMKIIα promotor. The upper μPeriscope stimulated ChR2-expressing L2/3 pyramidal cells while the lower μPeriscope measured the calcium fluorescence in L5 where a cell-permeable synthetic calcium indicator (Cal-590 AM) was preinjected. In agreement with the recent study in primary somatosensory area using a different approach (electrophysiological recordings) (Pluta et al., 2019), the net effect of L2/3 stimulation onto L5 turned out to be inhibitory in the motor cortex (Figure 2c, d). This result suggests that the L2/3-to-L5 inhibition might be a general principle across the cortex in contrast to the prevailing textbook assumption that L2/3-to-L5 projections are excitatory (Douglas and Martin, 2004). The previous study suggested that somatostatin-positive interneurons are likely to be responsible for the translaminar inhibition in the somatosensory cortex (Pluta et al., 2019); whether the same mechanism mediates the translaminar inhibition in the primary motor cortex needs more investigations that are beyond the scope of this technical report.

Figure 2

Download asset Open asset

Lastly, we demonstrate that a double-μPeriscope can be applied for simultaneous fluorescence imaging of axonal terminals distributed at two depths (Figure 2e). Compared to cell bodies and dendrites, two-photon imaging of axonal activities at deep layers is still challenging (Broussard et al., 2018), even at a single focal plane, let alone fast sequential imaging at multiple depths. The posteromedial thalamic nucleus (POm) sends axons to cortical layers 1 and 5a in the primary somatosensory area (Figure 2f; Wimmer et al., 2010); however, it is unknown whether the activities of these two axonal pathways are same or different. This is important for ascertaining if the POm delivers the same or different information to these different layers which in turn is important for describing the functional role of higher-order thalamic input to the cortex. Using the double-μPeriscope we measured the axonal activities in cortical layers 1 and 5a simultaneously (Figure 2e) in the awake head-fixed mice undergoing whisker stimulation. A genetic calcium indicator GCaMP6s (Chen et al., 2013) was expressed in POm using a transgenic mouse line Gpr26-cre (Gong et al., 2007) and a Cre-dependent adeno-associated virus vector encoding GCaMP6s. We found that the axonal activities in layers 1 and 5a are surprisingly variable; they are clearly uncorrelated in some trials but highly correlated in others (Figure 2g, h). The cross-correlation analysis confirmed this variability, although the mean coefficient was lower than 0.3 in all four mice (Figure 2h), suggesting the possibility that POm neurons deliver different types of information to layers 1 and 5a. Though further studies are necessary to explain the large variability of axonal activity in layers 1 and 5a, this new tool will contribute to our understanding of these important thalamocortical pathways and their role in neural computations and cognition (Mease et al., 2016; Audette et al., 2018; Aru et al., 2019; Aru et al., 2020). These three sets of preliminary experiments demonstrate that the new tool provides us with a new approach for addressing previously intractable important neurobiological problems.

Multiple bare optical fibers with a flat end have been used to image multiple brain regions (Kim et al., 2016; Sych et al., 2019). However, this approach is suitable if the target brain areas are sufficiently separate (>1 mm), but as shown in the three sets of experiments, our method has significant advantages to investigate more close (<1 mm) structures such as cortical layers in the rodent cortex. Optical fibers with a flat end would need higher pressure and therefore damage the cortical tissue upon insertion. In contrast, the sharp edge of microprism allows us to smoothly insert the probe with significantly less damage. Moreover, layer-specific activation/imaging with a downward oriented light beam from a bare optical fiber is problematic; in principle it is possible to calibrate the light power to optimize the signal from a single layer versus the layers below, but in practice this is always an approximation and it is impossible to be sure in any given preparation exactly how much signal is derived from the desired layer versus the others. Furthermore, it involves reducing the power of the excitation light considerably which leads to signal-to-noise issues.

Previous studies used an electric lens combined with two-photon microscopy (Grewe et al., 2011; Beaulieu-Laroche et al., 2019; Francioni et al., 2019) to measure the cellular activities at two depths. The limitations of this approach are (1) that imaging is not simultaneous because the electric shift of focal plane takes time for shifting and stabilization; and (2) that the maximum distance of shift is 500 μm; therefore, the distance between two structures under study must be within this range. A related issue is that imaging deeper structures needs more laser power. Therefore, even if the lens is capable of shifting the focal plane by a larger distance (than 500 μm), the excitation light must be rapidly switched between high and low power in perfect synchronization with the electrical shift of focal plane, which is technically and financially challenging in most laboratories. Besides, deep subcortical structures cannot be imaged with this approach unless a substantial amount of overlaying cortical tissue is surgically removed (Dombeck et al., 2010). The double-μPeriscope we show here does not have these limitations because the distance between two μPeriscopes can be freely adjusted, and imaging is simultaneous no matter how distant the two μPeriscopes are. Deep subcortical structures can also be imaged or optogenetically stimulated with the double-μPeriscope without substantial damage of cortical tissue.

Importantly, the cost to setup the imaging system with a double-μPeriscope is an order of magnitude less expensive. The reason for the significantly lower cost is that it requires only tiny prisms, GRIN lenses and a high sensitivity scientific camera as the main components. Given ever-improving scientific cameras and lowering costs of micro-optics that can now be 3D printed (Gissibl et al., 2016), approaches using micro-optics such as presented here are likely to be more appealing for many laboratories. We demonstrated its versatility through three sets of experiments where a double-μPeriscope can be used for either optogenetic stimulation, calcium fluorescence imaging, or both of two closely separate cortical layers. The present version of double-μPeriscope uses single-core multimode fiber; therefore, it is impossible to visualize subcellular structures in the imaged layer. However, a commercially available multicore optical fiber bundle would allow this if spatial resolution is required in other applications.

Source data for Figures 1 & 2 are deposited in Dryad under DOI:https://doi.org/10.5061/dryad.crjdfn34z.

1. 1. Suzuki M
   2. Aru J
   3. Larkum ME

   (2021) Dryad Digital Repository

   Double-Periscope: a tool for multi-layer optical recordings, optogenetic stimulations or both.

   https://doi.org/10.5061/dryad.crjdfn34z

1. 1. Adesnik H
   2. Naka A

   (2018) Cracking the Function of Layers in the Sensory Cortex

   Neuron 100:1028–1043.

   https://doi.org/10.1016/j.neuron.2018.10.032

   • PubMed
   • Google Scholar
2. 1. Andermann ML
   2. Gilfoy NB
   3. Goldey GJ
   4. Sachdev RNS
   5. Wölfel M
   6. McCormick DA
   7. Reid RC
   8. Levene MJ

   (2013) Chronic cellular imaging of entire cortical columns in awake mice using microprisms

   Neuron 80:900–913.

   https://doi.org/10.1016/j.neuron.2013.07.052

   • PubMed
   • Google Scholar
3. 1. Aru J
   2. Suzuki M
   3. Rutiku R
   4. Larkum ME
   5. Bachmann T

   (2019) Coupling the State and Contents of Consciousness

   Frontiers in Systems Neuroscience 13:43.

   https://doi.org/10.3389/fnsys.2019.00043

   • PubMed
   • Google Scholar
4. 1. Aru J
   2. Suzuki M
   3. Larkum ME

   (2020) Cellular Mechanisms of Conscious Processing

   Trends in Cognitive Sciences 24:814–825.

   https://doi.org/10.1016/j.tics.2020.07.006

   • PubMed
   • Google Scholar
5. 1. Audette NJ
   2. Urban-Ciecko J
   3. Matsushita M
   4. Barth AL

   (2018) POm Thalamocortical Input Drives Layer-Specific Microcircuits in Somatosensory Cortex

   Cerebral Cortex 28:1312–1328.

   https://doi.org/10.1093/cercor/bhx044

   • PubMed
   • Google Scholar
6. 1. Beaulieu-Laroche L
   2. Toloza EHS
   3. Brown NJ
   4. Harnett MT

   (2019) Widespread and Highly Correlated Somato-dendritic Activity in Cortical Layer 5 Neurons

   Neuron 103:235–241.

   https://doi.org/10.1016/j.neuron.2019.05.014

   • PubMed
   • Google Scholar
7. 1. Boyden ES
   2. Zhang F
   3. Bamberg E
   4. Nagel G
   5. Deisseroth K

   (2005) Millisecond-timescale, genetically targeted optical control of neural activity

   Nature Neuroscience 8:1263–1268.

   https://doi.org/10.1038/nn1525

   • PubMed
   • Google Scholar
8. 1. Broussard GJ
   2. Liang Y
   3. Fridman M
   4. Unger EK
   5. Meng G
   6. Xiao X
   7. Ji N
   8. Petreanu L
   9. Tian L

   (2018) In vivo measurement of afferent activity with axon-specific calcium imaging

   Nature Neuroscience 21:1272–1280.

   https://doi.org/10.1038/s41593-018-0211-4

   • PubMed
   • Google Scholar
9. Book

   1. Buzsáki G

   (2006) Rhythms of the Brain

   Oxford University Press.

   https://doi.org/10.1093/acprof:oso/9780195301069.001.0001

   • Google Scholar
10. 1. Cao H
    2. Gu L
    3. Mohanty SK
    4. Chiao JC

    (2013) An integrated μLED optrode for optogenetic stimulation and electrical recording

    IEEE Transactions on Bio-Medical Engineering 60:225–229.

    https://doi.org/10.1109/TBME.2012.2217395

    • PubMed
    • Google Scholar
11. 1. Chen T-W
    2. Wardill TJ
    3. Sun Y
    4. Pulver SR
    5. Renninger SL
    6. Baohan A
    7. Schreiter ER
    8. Kerr RA
    9. Orger MB
    10. Jayaraman V
    11. Looger LL
    12. Svoboda K
    13. Kim DS

    (2013) Ultrasensitive fluorescent proteins for imaging neuronal activity

    Nature 499:295–300.

    https://doi.org/10.1038/nature12354

    • PubMed
    • Google Scholar
12. 1. Chia TH
    2. Levene MJ

    (2009) Microprisms for in vivo multilayer cortical imaging

    Journal of Neurophysiology 102:1310–1314.

    https://doi.org/10.1152/jn.91208.2008

    • PubMed
    • Google Scholar
13. 1. Deisseroth K

    (2015) Optogenetics: 10 years of microbial opsins in neuroscience

    Nature Neuroscience 18:1213–1225.

    https://doi.org/10.1038/nn.4091

    • PubMed
    • Google Scholar
14. 1. Demas J
    2. Manley J
    3. Tejera F
    4. Barber K
    5. Kim H
    6. Traub FM
    7. Chen B
    8. Vaziri A

    (2021) High-speed, cortex-wide volumetric recording of neuroactivity at cellular resolution using light beads microscopy

    Nature Methods 18:1103–1111.

    https://doi.org/10.1038/s41592-021-01239-8

    • PubMed
    • Google Scholar
15. 1. Dombeck DA
    2. Harvey CD
    3. Tian L
    4. Looger LL
    5. Tank DW

    (2010) Functional imaging of hippocampal place cells at cellular resolution during virtual navigation

    Nature Neuroscience 13:1433–1440.

    https://doi.org/10.1038/nn.2648

    • PubMed
    • Google Scholar
16. 1. Douglas RJ
    2. Martin KAC

    (2004) Neuronal circuits of the neocortex

    Annual Review of Neuroscience 27:419–451.

    https://doi.org/10.1146/annurev.neuro.27.070203.144152

    • PubMed
    • Google Scholar
17. 1. Francioni V
    2. Padamsey Z
    3. Rochefort NL

    (2019) High and asymmetric somato-dendritic coupling of V1 layer 5 neurons independent of visual stimulation and locomotion

    eLife 8:e49145.

    https://doi.org/10.7554/eLife.49145

    • PubMed
    • Google Scholar
18. 1. Geiller T
    2. Vancura B
    3. Terada S
    4. Troullinou E
    5. Chavlis S
    6. Tsagkatakis G
    7. Tsakalides P
    8. Ócsai K
    9. Poirazi P
    10. Rózsa BJ
    11. Losonczy A

    (2020) Large-Scale 3D Two-Photon Imaging of Molecularly Identified CA1 Interneuron Dynamics in Behaving Mice

    Neuron 108:968–983.

    https://doi.org/10.1016/j.neuron.2020.09.013

    • PubMed
    • Google Scholar
19. 1. Gerfen CR
    2. Paletzki R
    3. Heintz N

    (2013) GENSAT BAC cre-recombinase driver lines to study the functional organization of cerebral cortical and basal ganglia circuits

    Neuron 80:1368–1383.

    https://doi.org/10.1016/j.neuron.2013.10.016

    • PubMed
    • Google Scholar
20. 1. Gissibl T
    2. Thiele S
    3. Herkommer A
    4. Giessen H

    (2016) Two-photon direct laser writing of ultracompact multi-lens objectives

    Nature Photonics 10:554–560.

    https://doi.org/10.1038/nphoton.2016.121

    • Google Scholar
21. 1. Gong S
    2. Doughty M
    3. Harbaugh CR
    4. Cummins A
    5. Hatten ME
    6. Heintz N
    7. Gerfen CR

    (2007) Targeting Cre recombinase to specific neuron populations with bacterial artificial chromosome constructs

    The Journal of Neuroscience 27:9817–9823.

    https://doi.org/10.1523/JNEUROSCI.2707-07.2007

    • PubMed
    • Google Scholar
22. 1. Grewe BF
    2. Voigt FF
    3. van ’t Hoff M
    4. Helmchen F

    (2011) Fast two-layer two-photon imaging of neuronal cell populations using an electrically tunable lens

    Biomedical Optics Express 2:2035–2046.

    https://doi.org/10.1364/BOE.2.002035

    • PubMed
    • Google Scholar
23. 1. Harris JA
    2. Hirokawa KE
    3. Sorensen SA
    4. Gu H
    5. Mills M
    6. Ng LL
    7. Bohn P
    8. Mortrud M
    9. Ouellette B
    10. Kidney J
    11. Smith KA
    12. Dang C
    13. Sunkin S
    14. Bernard A
    15. Oh SW
    16. Madisen L
    17. Zeng H

    (2014) Anatomical characterization of Cre driver mice for neural circuit mapping and manipulation

    Frontiers in Neural Circuits 8:76.

    https://doi.org/10.3389/fncir.2014.00076

    • PubMed
    • Google Scholar
24. 1. Kim CK
    2. Yang SJ
    3. Pichamoorthy N
    4. Young NP
    5. Kauvar I
    6. Jennings JH
    7. Lerner TN
    8. Berndt A
    9. Lee SY
    10. Ramakrishnan C
    11. Davidson TJ
    12. Inoue M
    13. Bito H
    14. Deisseroth K

    (2016) Simultaneous fast measurement of circuit dynamics at multiple sites across the mammalian brain

    Nature Methods 13:325–328.

    https://doi.org/10.1038/nmeth.3770

    • PubMed
    • Google Scholar
25. 1. Kim K
    2. Vöröslakos M
    3. Seymour JP
    4. Wise KD
    5. Buzsáki G
    6. Yoon E

    (2020) Artifact-free and high-temporal-resolution in vivo opto-electrophysiology with microLED optoelectrodes

    Nature Communications 11:2063.

    https://doi.org/10.1038/s41467-020-15769-w

    • PubMed
    • Google Scholar
26. 1. Larkum ME
    2. Zhu JJ
    3. Sakmann B

    (1999) A new cellular mechanism for coupling inputs arriving at different cortical layers

    Nature 398:338–341.

    https://doi.org/10.1038/18686

    • PubMed
    • Google Scholar
27. 1. Madisen L
    2. Mao T
    3. Koch H
    4. Zhuo J
    5. Berenyi A
    6. Fujisawa S
    7. Hsu YWA
    8. Garcia AJ
    9. Gu X
    10. Zanella S
    11. Kidney J
    12. Gu H
    13. Mao Y
    14. Hooks BM
    15. Boyden ES
    16. Buzsáki G
    17. Ramirez JM
    18. Jones AR
    19. Svoboda K
    20. Han X
    21. Turner EE
    22. Zeng H

    (2012) A toolbox of Cre-dependent optogenetic transgenic mice for light-induced activation and silencing

    Nature Neuroscience 15:793–802.

    https://doi.org/10.1038/nn.3078

    • PubMed
    • Google Scholar
28. 1. McAlinden N
    2. Gu E
    3. Dawson MD
    4. Sakata S
    5. Mathieson K

    (2015) Optogenetic activation of neocortical neurons in vivo with a sapphire-based micro-scale LED probe

    Frontiers in Neural Circuits 9:25.

    https://doi.org/10.3389/fncir.2015.00025

    • PubMed
    • Google Scholar
29. 1. Mease RA
    2. Metz M
    3. Groh A

    (2016) Cortical Sensory Responses Are Enhanced by the Higher-Order Thalamus

    Cell Reports 14:208–215.

    https://doi.org/10.1016/j.celrep.2015.12.026

    • PubMed
    • Google Scholar
30. 1. Nagel G
    2. Szellas T
    3. Huhn W
    4. Kateriya S
    5. Adeishvili N
    6. Berthold P
    7. Ollig D
    8. Hegemann P
    9. Bamberg E

    (2003) Channelrhodopsin-2, a directly light-gated cation-selective membrane channel

    PNAS 100:13940–13945.

    https://doi.org/10.1073/pnas.1936192100

    • PubMed
    • Google Scholar
31. 1. Packer AM
    2. Russell LE
    3. Dalgleish HWP
    4. Häusser M

    (2015) Simultaneous all-optical manipulation and recording of neural circuit activity with cellular resolution in vivo

    Nature Methods 12:140–146.

    https://doi.org/10.1038/nmeth.3217

    • PubMed
    • Google Scholar
32. 1. Pégard NC
    2. Mardinly AR
    3. Oldenburg IA
    4. Sridharan S
    5. Waller L
    6. Adesnik H

    (2017) Three-dimensional scanless holographic optogenetics with temporal focusing (3D-SHOT)

    Nature Communications 8:1228.

    https://doi.org/10.1038/s41467-017-01031-3

    • PubMed
    • Google Scholar
33. 1. Pisanello F
    2. Sileo L
    3. Oldenburg IA
    4. Pisanello M
    5. Martiradonna L
    6. Assad JA
    7. Sabatini BL
    8. De Vittorio M

    (2014) Multipoint-emitting optical fibers for spatially addressable in vivo optogenetics

    Neuron 82:1245–1254.

    https://doi.org/10.1016/j.neuron.2014.04.041

    • PubMed
    • Google Scholar
34. 1. Pluta SR
    2. Telian GI
    3. Naka A
    4. Adesnik H

    (2019) Superficial Layers Suppress the Deep Layers to Fine-tune Cortical Coding

    The Journal of Neuroscience 39:2052–2064.

    https://doi.org/10.1523/JNEUROSCI.1459-18.2018

    • PubMed
    • Google Scholar
35. 1. Prevedel R
    2. Verhoef AJ
    3. Pernía-Andrade AJ
    4. Weisenburger S
    5. Huang BS
    6. Nöbauer T
    7. Fernández A
    8. Delcour JE
    9. Golshani P
    10. Baltuska A
    11. Vaziri A

    (2016) Fast volumetric calcium imaging across multiple cortical layers using sculpted light

    Nature Methods 13:1021–1028.

    https://doi.org/10.1038/nmeth.4040

    • PubMed
    • Google Scholar
36. 1. Suzuki M
    2. Larkum ME

    (2017) Dendritic calcium spikes are clearly detectable at the cortical surface

    Nature Communications 8:276.

    https://doi.org/10.1038/s41467-017-00282-4

    • PubMed
    • Google Scholar
37. 1. Suzuki M
    2. Larkum ME

    (2020) General Anesthesia Decouples Cortical Pyramidal Neurons

    Cell 180:666–676.

    https://doi.org/10.1016/j.cell.2020.01.024

    • Google Scholar
38. 1. Sych Y
    2. Chernysheva M
    3. Sumanovski LT
    4. Helmchen F

    (2019) High-density multi-fiber photometry for studying large-scale brain circuit dynamics

    Nature Methods 16:553–560.

    https://doi.org/10.1038/s41592-019-0400-4

    • PubMed
    • Google Scholar
39. 1. Wimmer VC
    2. Bruno RM
    3. de Kock CPJ
    4. Kuner T
    5. Sakmann B

    (2010) Dimensions of a projection column and architecture of VPM and POm axons in rat vibrissal cortex

    Cerebral Cortex 20:2265–2276.

    https://doi.org/10.1093/cercor/bhq068

    • PubMed
    • Google Scholar

Author details

1. Mototaka Suzuki

   Institute of Biology, Humboldt University of Berlin, Berlin, Germany

   Present address

   Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, Netherlands

   Contribution

   Conceptualization, Formal analysis, Funding acquisition, Visualization, Writing – original draft, Writing – review and editing

   For correspondence

   mototaka@gmail.com

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2151-4882

2. Jaan Aru

   1. Institute of Biology, Humboldt University of Berlin, Berlin, Germany
   2. Institute of Computer Science, University of Tartu, Tartu, Estonia

   Contribution

   Formal analysis, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

3. Matthew E Larkum

   Institute of Biology, Humboldt University of Berlin, Berlin, Germany

   Contribution

   Funding acquisition, Supervision, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-9799-2656

• 1,571

  views

• 179

  downloads

• 0

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.