SummaryAnimal behavior arises from computations in neuronal circuits, but our understanding of these computations has been frustrated by the lack of detailed synaptic connection maps, or connectomes. For example, despite intensive investigations over half a century, the neuronal implementation of local motion detection in the insect visual system remains elusive. Here, we developed a semi-automated pipeline using electron microscopy to reconstruct a connectome, containing 379 neurons and 8,637 chemical synaptic contacts, within the Drosophila optic medulla. By matching reconstructed neurons to examples from light microscopy, we assigned neurons to cell types and assembled a connectome of the medulla's repeating module. Within this module, we identified cell types constituting a motion detection circuit and showed that the connections onto individual motion-sensitive neurons in this circuit were consistent with their direction selectivity. Our results identify cellular targets for future functional investigations, and demonstrate that connectomes can provide key insights into neuronal computations.

How does the brain compute? Answering this question necessitates neuronal connectomes, annotated graphs of all synaptic connections within defined brain areas. Further, understanding the energetics of the brain’s computations requires vascular graphs. The assembly of a connectome requires sensitive hardware tools to measure neuronal and neurovascular features in all three dimensions, as well as software and machine learning for data analysis and visualization. We present the state-of-the-art on the reconstruction of circuits and vasculature that link brain anatomy and function. Analysis at the scale of tens of nanometers yields connections between identified neurons, while analysis at the micrometer scale yields probabilistic rules of connection between neurons and exact vascular connectivity.

Targeting 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 retina in vivo, and in the human retina in 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.Despite the central importance for both basic and translational research, most current technologies available for cell-type-targeting rely on transgenic animals, which limits their applicability. Either the genetic tool that senses or modulates brain function, or the enzyme, such as Cre recombinase, that allows the genetic tool to be conditionally expressed, is expressed from the animal's genome. The inclusion of a transgenic component in the cell-type-targeting strategy excludes its use in therapy for humans, limits its range of application in pre-clinical, non-human primate research, and complicates its use in model organisms such as mice. The development of transgenic non-human primates and mice is costly and slow, especially since cell-type targeting is often applied in the context of other genetic manipulations, such as double or triple gene knockouts, or when targeting different cell types with different tools.Viral vectors for cell-type-targeting may overcome such limitations. AAVs are the most frequently used vectors in both basic research and gene therapy, as they are safe for use in all tested species, including humans and non-human primates, and their production is simple, cheap, and fast (Planul and Dalkara, 2017). They have three important components: the capsid for cell entry, the promoter that drives transgene expression, and the gene of interest to be expressed in the transduced cells, and they drive expression episomally (Duan et al., 1998; Penaud-Budloo et al., 2008). Futhermore, many genetic tools are small enough to fit into AAVs, different AAVs can be injected together, and synthetic AAV capsids allow brain-wide delivery (Deverman et al., 2016).Cell-type-targeting by AAVs could be achieved by engineering the capsid and/or by using specific promoters. Capsid protein mutations can be used to tune the efficacy of

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.

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Targeting 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 retina in vivo, and in the human retina in 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.Despite the central importance for both basic and translational research, most current technologies available for cell-type-targeting rely on transgenic animals, which limits their applicability. Either the genetic tool that senses or modulates brain function, or the enzyme, such as Cre recombinase, that allows the genetic tool to be conditionally expressed, is expressed from the animal's genome. The inclusion of a transgenic component in the cell-type-targeting strategy excludes its use in therapy for humans, limits its range of application in pre-clinical, non-human primate research, and complicates its use in model organisms such as mice. The development of transgenic non-human primates and mice is costly and slow, especially since cell-type targeting is often applied in the context of other genetic manipulations, such as double or triple gene knockouts, or when targeting different cell types with different tools.

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.

Quantum conditions on the control of dynamics of a system coupled to an environment are obtained. Specifically, consider a system initially in a system subspace H0 of dimensionality M0, which evolves to populate system subspaces H1, H2 of dimensionality M1, M2. Then there always exists an initial state in H0 that does not evolve into H2 if M0 > dM2, where 2 ≤ d ≤ (M0 + M1 + M2)2 is the number of operators in the Kraus representation. Note, significantly, that the maximum d can be far smaller than the dimension of the bath. If this condition is not satisfied then dynamics from H0 that avoids H2 can only be attained physically under stringent conditions. An example from molecular dynamics and spectroscopy, i.e. donor to acceptor energy transfer, is provided.The study of open quantum systems [1,2,3] is of great current interest, a consequence of the desire to understand and manipulate devices that are reliant on quantum effects. Of particular interest is the control of such quantum systems, with coherent control [4,5] being a most promising approach.The essential principle of the coherent control of atomic and molecular processes relies on the creation of multiple interfering pathways to the same final state [4,5]. Manipulation of laboratory parameters that affect these coherent pathways then allows direct control over the associated interference contributions, and hence control over the relative cross sections involved in the process. Numerous theoretical scenarios and experimental results, in a host of applications, have successfully demonstrated such control.Despite the importance and generality of the field, there are remarkably few formal results that provide necessary and sufficient conditions under which complete control of the system dynamics is possible. These include theorems such as that of Huang-Tarn-Clark[6], a theorem by Ramakrisnan et al. on the dimensionality of the Lie Algebra induced by the interaction between the system and the control field [7], and a theorem by Shapiro and Brumer[8], where control was shown to depend on the dimensionality of the controlled subspaces. These theorems all deal with closed systems, i.e. those that are not in contact with an environment. Real quantum systems are, however, not isolated. They often interact with a surrounding environment, which is regarded as an uncontrolled system of arbitrarily large dimension. As a result the system is open to its environment, and proofs regarding control in open systems are becoming the focus of serious attention. For example, Reference [9] provides formal theorems on controllability in open system dynamics treated via the Kraus representation.In this letter we significantly generalize a prior argument [8] for closed systems and obtain quantum control conditions in open systems. We show that the condition for a specific control problem in open systems is stronger than that in closed systems, but that the relative dimensionality of the different subspaces remains a crucial determinant of controllability. In addition, our approach g...