The ability to image RNA identity and location with nanoscale precision in intact tissues is of great interest for defining cell types and states in normal and pathological biological settings. Here, we present a strategy for expansion microscopy (ExM) of RNA. We developed a small molecule linker that enables RNA to be covalently attached to a swellable polyelectrolyte gel synthesized throughout a biological specimen. Then, post-expansion, fluorescent in situ hybridization (FISH) imaging of RNA can be performed with high yield and specificity, with single molecule precision, in both cultured cells and intact brain tissue. Expansion FISH (ExFISH) de-crowds RNAs and supports amplification of single molecule signals (i.e., via hybridization chain reaction (HCR)) as well as multiplexed RNA FISH readout. ExFISH thus enables super-resolution imaging of RNA structure and location with diffraction-limited microscopes in thick specimens, such as intact brain tissue and other tissues of importance to biology and medicine.

Neuroscience has focused on the detailed implementation of computation, studying neural codes, dynamics and circuits. In machine learning, however, artificial neural networks tend to eschew precisely designed codes, dynamics or circuits in favor of brute force optimization of a cost function, often using simple and relatively uniform initial architectures. Two recent developments have emerged within machine learning that create an opportunity to connect these seemingly divergent perspectives. First, structured architectures are used, including dedicated systems for attention, recursion and various forms of short- and long-term memory storage. Second, cost functions and training procedures have become more complex and are varied across layers and over time. Here we think about the brain in terms of these ideas. We hypothesize that (1) the brain optimizes cost functions, (2) the cost functions are diverse and differ across brain locations and over development, and (3) optimization operates within a pre-structured architecture matched to the computational problems posed by behavior. In support of these hypotheses, we argue that a range of implementations of credit assignment through multiple layers of neurons are compatible with our current knowledge of neural circuitry, and that the brain's specialized systems can be interpreted as enabling efficient optimization for specific problem classes. Such a heterogeneously optimized system, enabled by a series of interacting cost functions, serves to make learning data-efficient and precisely targeted to the needs of the organism. We suggest directions by which neuroscience could seek to refine and test these hypotheses.

A man with a spinal-cord injury (right) prepares for a virtual cycle race in which competitors steer avatars using brain signals. COMMENT © 2 0 1 7 M a c m i l l a n P u b l i s h e r s L i m i t e d , p a r t o f S p r i n g e r N a t u r e . A l l r i g h t s r e s e r v e d .example. Moreover, researchers can already interpret a person's neural activity from functional magnetic resonance imaging scans at a rudimentary level 1 -that the individual is thinking of a person, say, rather than a car.It might take years or even decades until BCI and other neurotechnologies are part of our daily lives. But technological developments mean that we are on a path to a world in which it will be possible to decode people's mental processes and directly manipulate the brain mechanisms underlying their intentions, emotions and decisions; where individuals could communicate with others simply by thinking; and where powerful computational systems linked directly to people's brains aid their interactions with the world such that their mental and physical abilities are greatly enhanced.Such advances could revolutionize the treatment of many conditions, from brain injury and paralysis to epilepsy and schizophrenia, and transform human experience for the better. But the technology could also exacerbate social inequalities and offer corporations, hackers, governments or anyone else new ways to exploit and manipulate people. And it could profoundly alter some core human characteristics: private mental life, individual agency and an understanding of individuals as entities bound by their bodies.It is crucial to consider the possible ramifications now.The Morningside Group comprises neuroscientists, neurotechnologists, clinicians, ethicists and machine-intelligence engineers. It includes representatives from Google and Kernel (a neurotechnology start-up in Los Angeles, California); from international brain projects; and from academic and research institutions in the United States, Canada, Europe, Israel, China, Japan and Australia. We gathered at a workshop sponsored by the US National Science Foundation at Columbia University, New York, in May 2017 to discuss the ethics of neurotechnologies and machine intelligence.We believe that existing ethics guidelines are insufficient for this realm 2 . These include the Declaration of Helsinki, a statement of ethical principles first established in 1964 for medical research involving human subjects (go.nature.com/2z262ag); the Belmont Report, a 1979 statement crafted by the US National Commission for the Protection of Human Subjects of Biomedical and Behavioural Research (go.nature.com/2hrezmb); and the Asilomar artificial intelligence (AI) statement of cautionary principles, published early this year and signed by business leaders and AI researchers, among others (go.nature.com/2ihnqac).To begin to address this deficit, here we lay out recommendations relating to four areas of concern: privacy and consent; agency and identity; augmentation; and bias. Different nations and people of varying re...

Methods for highly multiplexed RNA imaging are limited in spatial resolution and thus in their ability to localize transcripts to nanoscale and subcellular compartments. We adapt expansion microscopy, which physically expands biological specimens, for long-read untargeted and targeted in situ RNA sequencing. We applied untargeted expansion sequencing (ExSeq) to the mouse brain, which yielded the readout of thousands of genes, including splice variants. Targeted ExSeq yielded nanoscale-resolution maps of RNAs throughout dendrites and spines in the neurons of the mouse hippocampus, revealing patterns across multiple cell types, layer-specific cell types across the mouse visual cortex, and the organization and position-dependent states of tumor and immune cells in a human metastatic breast cancer biopsy. Thus, ExSeq enables highly multiplexed mapping of RNAs from nanoscale to system scale.

Lithographic nanofabrication is often limited to successive fabrication of two-dimensional layers. We present a strategy for the direct assembly of three-dimensional nanomaterials consisting of metals, semiconductors, and biomolecules arranged in virtually any three-dimensional geometry. We use hydrogels as scaffolds for volumetric deposition of materials at defined points in space. We then optically pattern these scaffolds in three dimensions, attach one or more functional materials, and then shrink and dehydrate them in a controlled way to achieve nanoscale feature sizes in a solid substrate. We demonstrate this process, Implosion Fabrication (ImpFab), by directly writing highly conductive, 3D silver nanostructures within an acrylic scaffold using a volumetric silver deposition process, achieving resolutions in the tens of nanometers and complex, non-self-supporting 3D geometries of interest for optical metamaterials.