The Fuzzy Logic of Network Connectivity in Mouse Visual Thalamus

Morgan, Josh; Berger, Daniel R.; Wetzel, Arthur W.; Lichtman, Jeff W.

doi:10.1016/j.cell.2016.02.033

Cited by 239 publications

(291 citation statements)

References 44 publications

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“…In this issue of Cell , Morgan et al (2016) address this question in a study that is literally cutting-edge. The team slices a substantial chunk of the murine lateral geniculate nucleus (LGN) of the thalamus into a tissue tape from which they form a connectome—an ultrastructural 3D reconstruction of retinal afferents and the thalamic relay cells they target.…”

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confidence: 99%

“…Morgan, Lichtman, and colleagues (Morgan et al, 2016) analyze tissue from a 32-day-old animal, a stage at which substantial experience-dependent synapse remodeling of retinogeniculate connectivity occurs (Hooks and Chen, 2006). Could some boutons in the connectome be remnants of the maturation process or, perhaps, latent synapses silently waiting to be called to action?…”

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confidence: 99%

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Untangling the Web between Eye and Brain

Chen

Bickford

Hirsch

2016

Cell

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confidence: 99%

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confidence: 99%

Untangling the Web between Eye and Brain

Chen

Bickford

Hirsch

2016

Cell

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“…On this point, we respond with enthusiastic agreement. By continued application of the multipatching approach and the addition of complementary approaches in the intact brain (12)(13)(14)(33)(34)(35)(36), we hope to asymptotically approach the true underlying average connectivity matrix and to identify variations linked to species, ages, and cortical areas. These are exciting times, and we look forward to participating in this common concerted effort.…”

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confidence: 99%

Response to Comment on “Principles of connectivity among morphologically defined cell types in adult neocortex”

Jiang

Shen

Sinz

et al. 2016

Science

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The critique of Barth et al. centers on three points: (i) the completeness of our study is overstated; (ii) the connectivity matrix we describe is biased by technical limitations of our brain-slicing and multipatching methods; and (iii) our cell classification scheme is arbitrary and we have simply renamed previously identified interneuron types. We address these criticisms in our Response. W e will address Barth et al.'s (1) three critiques of our Research Article (2) in turn.(i) The strength of our study is the number of connections tested (>11,000) in one species, cortical area, and age range, using the same experimental protocol. We agree that our study is not exhaustive, because it is focused on synaptic connectivity between interneurons in layers 1, 2/3, and 5, and we point this out in the abstract of the original paper. However, we do believe that no single study to date has presented a more complete wiring diagram of the adult neocortical microcircuit.(ii) Severed connections are a well-known limitation of slice recordings, which we discussed explicitly on p. 3 of the supplementary materials (SM) for (2). However, we do not agree that our study is substantially more susceptible to connection loss than previous patching studies, most of which also used 300-to 350-mm slices [we found fewer than half a dozen multipatching studies using 450-to 500-mm slices (3-5)]. Although cell depth is not reported in many of these studies, the depths at which we recorded (15 to 60 mm) seem typical (6). We patched cells at a wide range of intersomatic distances [see fig. S13A in (2)]; this range reflects the sparsity of labeled interneurons and is not a limitation of octuple patching.Like many previous studies, we used currentclamp mode to measure connectivity, which suffers less distance-dependent voltage attenuation compared with voltage clamp (7). Using potassium internal solution allowed us to characterize spiking properties and test bidirectional connectivity, whereas cesium-based internal solutions would prevent the patched neuron from firing. We discussed potential problems of voltage attenuation [SM for (2), p. 9], such as the underestimation of synaptic event amplitudes and potential nondetection of small synaptic events on the distal dendrites [e.g., connections from Martinotti cells onto layer 5 (L5) pyramidal neurons].To directly address Barth et al.'s concern that the connectivity matrix may be distorted due to slice cutting, we simulated the effect of slicing based on interneuron morphology (8) (Fig. 1A) [see also (6)] for the worst-case scenario where two neurons (pre-and postsynaptic) are both located 15-mm deep from the cutting surface. The resulting correction factor was similar across pairs of cell types (Fig. 1B), more or less scaling the entire connectivity matrix by a factor of 1.36 ± 0.10 (mean ± SD) (Fig. 1, C and D). Given these results and the fact that our connectivity principles mostly rely on the presence or absence of connectivity between specific types regardless of its magnitude, we believe ...

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“…Mouse lateral geniculate nucleus SEM data with fold artifacts We next sought to assess the applicability of our new alignment method to data acquired from different EM imaging methods, using different model organisms, and containing fold artifacts. A small volume (1520 × 2500 × 100) was selected from a mouse lateral geniculate nucleus dataset generously provided by the Lichtman laboratory [8]. This dataset was acquired using SEM with a resolution of 4 × 4 × 30 nm 3 vx −1 , and contains folds caused by cracks in the substrate onto which sections were collected.…”

Section: Resultsmentioning

confidence: 99%

ssEMnet: Serial-Section Electron Microscopy Image Registration Using a Spatial Transformer Network with Learned Features

Yoo

Hildebrand

Tobin

et al. 2017

Lecture Notes in Computer Science

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Abstract. The alignment of serial-section electron microscopy (ssEM) images is critical for efforts in neuroscience that seek to reconstruct neuronal circuits. However, each ssEM plane contains densely packed structures that vary from one section to the next, which makes matching features across images a challenge. Advances in deep learning has resulted in unprecedented performance in similar computer vision problems, but to our knowledge, they have not been successfully applied to ssEM image co-registration. In this paper, we introduce a novel deep network model that combines a spatial transformer for image deformation and a convolutional autoencoder for unsupervised feature learning for robust ssEM image alignment. This results in improved accuracy and robustness while requiring substantially less user intervention than conventional methods. We evaluate our method by comparing registration quality across several datasets.

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The Fuzzy Logic of Network Connectivity in Mouse Visual Thalamus

Cited by 239 publications

References 44 publications

Untangling the Web between Eye and Brain

Untangling the Web between Eye and Brain

Response to Comment on “Principles of connectivity among morphologically defined cell types in adult neocortex”

ssEMnet: Serial-Section Electron Microscopy Image Registration Using a Spatial Transformer Network with Learned Features

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