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SummaryTargeting genes to specific neuronal or glial cell types is valuable both for understanding and for repairing brain circuits. Adeno-associated viral vectors (AAVs) are frequently used for gene delivery, but targeting expression to specific cell types is a challenge. We created a library of 230 AAVs, each with a different synthetic promoter designed using four independent strategies. We show that ~11% of these AAVs specifically target expression to neuronal and glial cell types in the mouse retina, mouse brain, non-human primate retinain vivo, and in the human retinain vitro. We demonstrate applications for recording, stimulation, and molecular characterization, as well as the intersectional and combinatorial labeling of cell types. These resources and approaches allow economic, fast, and efficient cell-type targeting in a variety of species, both for fundamental science and for gene therapy.

Neurons must faithfully encode signals that can vary over many orders of magnitude despite having only limited dynamic ranges. For a correlated signal, this dynamic range constraint can be relieved by subtracting away components of the signal that can be predicted from the past, a strategy known as predictive coding, that relies on learning the input statistics. However, the statistics of input natural signals can also vary over very short time scales e.g., following saccades across a visual scene. To maintain a reduced transmission cost to signals with rapidly varying statistics, neuronal circuits implementing predictive coding must also rapidly adapt their properties. Experimentally, in different sensory modalities, sensory neurons have shown such adaptations within 100 ms of an input change. Here, we show first that linear neurons connected in a feedback inhibitory circuit can implement predictive coding. We then show that adding a rectification nonlinearity to such a feedback inhibitory circuit allows it to automatically adapt and approximate the performance of an optimal linear predictive coding network, over a wide range of inputs, while keeping its underlying temporal and synaptic properties unchanged. We demonstrate that the resulting changes to the linearized temporal filters of this nonlinear network match the fast adaptations observed experimentally in different sensory modalities, in different vertebrate species. Therefore, the nonlinear feedback inhibitory network can provide automatic adaptation to fast varying signals, maintaining the dynamic range necessary for accurate neuronal transmission of natural inputs., Author Summary An animal exploring a natural scene receives sensory inputs that vary, rapidly, over many orders of magnitude. Neurons must transmit these inputs faithfully despite both their limited dynamic range and relatively slow adaptation time scales. One well-accepted strategy for transmitting signals through limited dynamic range channels–predictive coding–transmits only components of the signal that cannot be predicted from the past. Predictive coding algorithms respond maximally to unexpected inputs, making them appealing in describing sensory transmission. However, recent experimental evidence has shown that neuronal circuits adapt quickly, to respond optimally following rapid input changes. Here, we reconcile the predictive coding algorithm with this automatic adaptation, by introducing a fixed nonlinearity into a predictive coding circuit. The resulting network automatically “adapts” its linearized response to different inputs. Indeed, it approximates the performance of an optimal linear circuit implementing predictive coding, without having to vary its internal parameters. Further, adding this nonlinearity to the predictive coding circuit still allows the input to be compressed losslessly, allowing for additional downstream manipulations. Finally, we demonstrate that the nonlinear circuit dynamics match responses in both auditory and visual neurons. Therefore, we believe that this nonlinear circuit may be a general circuit motif that can be applied in different neural circuits, whenever it is necessary to provide an automatic improvement in the quality of the transmitted signal, for a fast varying input distribution.

International audience; Schwann cells elaborate myelin sheaths around axons by spirally wrapping and compacting their plasma membranes. Although actin remodeling plays a crucial role in this process, the effectors that modulate the Schwann cell cytoskeleton are poorly defined. Here, we show that the actin cytoskeletal regulator, neural Wiskott-Aldrich syndrome protein (N-WASp), is upregulated in myelinating Schwann cells coincident with myelin elaboration. When N-WASp is conditionally deleted in Schwann cells at the onset of myelination, the cells continue to ensheath axons but fail to extend processes circumferentially to elaborate myelin. Myelin-related gene expression is also severely reduced in the N-WASp-deficient cells and in vitro process and lamellipodia formation are disrupted. Although affected mice demonstrate obvious motor deficits these do not appear to progress, the mutant animals achieving normal body weights and living to advanced age. Our observations demonstrate that N-WASp plays an essential role in Schwann cell maturation and myelin formation.