Comprehensive Classification of Retinal Bipolar Neurons by Single-Cell Transcriptomics.

Saturated Reconstruction of a Volume of Neocortex

CLARITY enables the chemical transformation of intact biological tissues into a hydrogel–tissue hybrid. The hybrid samples can be interrogated using light and macromolecular labels, whilst retaining fine structure and native biological molecules.

/pdf/advanced-clarity-for-rapid-and-high-resolution-imaging-of-vxi2hwa1he.pdf

Advanced CLARITY for rapid and high-resolution imaging of intact tissues

Systems neuroscience seeks explanations for how the brain implements a wide variety of perceptual, cognitive and motor tasks. Conversely, artificial intelligence attempts to design computational systems based on the tasks they will have to solve. In artificial neural networks, the three components specified by design are the objective functions, the learning rules and the architectures. With the growing success of deep learning, which utilizes brain-inspired architectures, these three designed components have increasingly become central to how we model, engineer and optimize complex artificial learning systems. Here we argue that a greater focus on these components would also benefit systems neuroscience. We give examples of how this optimization-based framework can drive theoretical and experimental progress in neuroscience. We contend that this principled perspective on systems neuroscience will help to generate more rapid progress.

https://research-information.bris.ac.uk/ws/files/218472922/A_deep_learning_framework_for_neuroscience_vFinal_RC.pdf

A deep learning framework for neuroscience

Neurons have diverse molecular, morphological, connectional and functional properties. We believe that the only realistic way to manage this complexity - and thereby pave the way for understanding the structure, function and development of brain circuits - is to group neurons into types, which can then be analysed systematically and reproducibly. However, neuronal classification has been challenging both technically and conceptually. New high-throughput methods have created opportunities to address the technical challenges associated with neuronal classification by collecting comprehensive information about individual cells. Nonetheless, conceptual difficulties persist. Borrowing from the field of species taxonomy, we propose principles to be followed in the cell-type classification effort, including the incorporation of multiple, quantitative features as criteria, the use of discontinuous variation to define types and the creation of a hierarchical system to represent relationships between cells. We review the progress of classifying cell types in the retina and cerebral cortex and propose a staged approach for moving forward with a systematic cell-type classification in the nervous system.

Neuronal cell-type classification: challenges, opportunities and the path forward

How does the mammalian retina detect motion? This classic problem in visual neuroscience has remained unsolved for 50 years. In search of clues, here we reconstruct Off-type starburst amacrine cells (SACs) and bipolar cells (BCs) in serial electron microscopic images with help from EyeWire, an online community of ‘citizen neuroscientists’. On the basis of quantitative analyses of contact area and branch depth in the retina, we find evidence that one BC type prefers to wire with a SAC dendrite near the SAC soma, whereas another BC type prefers to wire far from the soma. The near type is known to lag the far type in time of visual response. A mathematical model shows how such ‘space–time wiring specificity’ could endow SAC dendrites with receptive fields that are oriented in space–time and therefore respond selectively to stimuli that move in the outward direction from the soma. Motion detection by the retina is thought to rely largely on the biophysics of starburst amacrine cell dendrites; here machine learning is used with gamified crowdsourcing to draw the wiring diagram involving amacrine and bipolar cells to identify a plausible circuit mechanism for direction selectivity; the model suggests similarities between mammalian and insect vision. Motion detection by the mammalian retina has been thought to rely largely on the intrinsic biophysics of the dendrites of starburst amacrine cells (SACs). Now Sebastian Seung and colleagues have combined new machine-learning techniques with crowd sourcing via the EyeWire brain-mapping game to redraw the wiring diagram for amacrine cells and bipolar cells. Their results show that direction selectivity is established at the presynaptic level — in the spatiotemporal inputs to the amacrine cells — identifying neural circuits rather than intrinsic properties of SACs as the key to direction selectivity. This new model brings the mouse retina closer in certain respects to the Reichardt motion detector characteristic of insect vision.

/pdf/space-time-wiring-specificity-supports-direction-selectivity-50xlesv4c6.pdf

Matthew Balkam

Papers

Space–time wiring specificity supports direction selectivity in the retina