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Advancements in novel neurotechnologies, such as brain computer interfaces (BCI) and neuromodulatory devices such as deep brain stimulators (DBS), will have profound implications for society and human rights. While these technologies are improving the diagnosis and treatment of mental and neurological diseases, they can also alter individual agency and estrange those using neurotechnologies from their sense of self, challenging basic notions of what it means to be human. As an international coalition of interdisciplinary scholars and practitioners, we examine these challenges and make recommendations to mitigate negative consequences that could arise from the unregulated development or application of novel neurotechnologies. We explore potential ethical challenges in four key areas: identity and agency, privacy, bias, and enhancement. To address them, we propose (1) democratic and inclusive summits to establish globally-coordinated ethical and societal guidelines for neurotechnology development and application, (2) new measures, including "Neurorights," for data privacy, security, and consent to empower neurotechnology users' control over their data, (3) new methods of identifying and preventing bias, and (4) the adoption of public guidelines for safe and equitable distribution of neurotechnological devices.

The classical concepts of reinforcement learning in the mammalian brain focus on dopamine release in the basal ganglia as the neural substrate of reward prediction errors, which drive plasticity in striatal and cortico-striatal synapses to maximize the expected aggregate future reward. This temporal difference framework, however, even when augmented with deep credit assignment, does not fully capture higher-order processes such as the influence of goal representations, planning based on learned internal models, and hierarchical decision-making implemented by diverse neocortical areas. Candidate functions for such neocortical contributions to reinforcement learning are increasingly being considered in artificial intelligence algorithms. Here, we review recent experimental neurophysiological findings focusing on the orbitofrontal cortex, a key higher-order association cortex, and highlight emerging concepts that emphasize the role of the neocortex in reward-driven computation, in addition to its role as an input to striatal structures. In this framework, reward drives plasticity in various neocortical regions, implementing multiple distinct reinforcement learning algorithms.

Advances in sensing technology raise the possibility of creating neural interfaces that can more effectively restore or repair neural function and reveal fundamental properties of neural information processing. To realize the potential of these bioelectronic devices, it is necessary to understand the capabilities of emerging technologies and identify the best strategies to translate these technologies into products and therapies that will improve the lives of patients with neurological and other disorders. Here, we discuss emerging technologies for sensing brain activity, anticipated challenges for translation, and perspectives for how to best transition these technologies from academic research labs to useful products for neuroscience researchers and human patients.

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 mouse brain, yielding readout of thousands of genes, including splice variants and novel transcripts. Targeted ExSeq yielded nanoscale-resolution maps of RNAs throughout dendrites and spines in neurons of the mouse hippocampus, revealing patterns across multiple cell types; layer-specific cell types across 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.One Sentence SummaryIn situ sequencing of physically expanded specimens enables multiplexed mapping of RNAs at nanoscale, subcellular resolution.

Lipids are fundamental building blocks of cells and their organelles, yet nanoscale resolution imaging of lipids has been largely limited to electron microscopy techniques. We introduce and validate a chemical tag that enables lipid membranes to be imaged optically at nanoscale resolution via a lipid-optimized form of expansion microscopy, which we call membrane expansion microscopy (mExM). mExM, via a novel post-expansion antibody labeling protocol, enables protein-lipid relationships to be imaged in organelles such as mitochondria, the endoplasmic reticulum, the nuclear membrane, and the Golgi apparatus. mExM may be of use in a variety of biological contexts, including the study of cell-cell interactions, intracellular transport, and neural connectomics.

Artificial intelligence and brain–computer interfaces must respect and preserve people's privacy, identity, agency and equality, say Rafael Yuste, Sara Goering and colleagues.

How can engineers help invent tools for neuroscience? We here explore a model for how engineers can choose problems and map out possible technologies that help address them. We also discuss design principles of tools and the role of failure.

Lithographic nanofabrication is often limited to successive fabrication of two-dimensional (2D) layers. We present a strategy for the direct assembly of 3D nanomaterials consisting of metals, semiconductors, and biomolecules arranged in virtually any 3D geometry. We used hydrogels as scaffolds for volumetric deposition of materials at defined points in space. We then optically patterned these scaffolds in three dimensions, attached one or more functional materials, and then shrank and dehydrated them in a controlled way to achieve nanoscale feature sizes in a solid substrate. We demonstrate that our process, Implosion Fabrication (ImpFab), can directly write highly conductive, 3D silver nanostructures within an acrylic scaffold via volumetric silver deposition. Using ImpFab, we achieve resolutions in the tens of nanometers and complex, non–self-supporting 3D geometries of interest for optical metamaterials. ©2017, ONR (no. N00014-17-1-2977), NIH (no. 1R01EB024261), NIH (no. 1U01MH106011), NIH Director’s Pioneer Award (no. 1DP1NS087724), NIH (no. 1RM1HG008525), NIH (no. 1R24MH106075), National Science Foundation Graduate Research Fellowship Program (award no. 1122374)

DNA polymerase (pol) processivity, i.e., the bases a polymerase extends before falling off the DNA, and activity are important for copying difficult DNA sequences, including simple repeats. Y-family pols would be appealing for copying difficult DNA and incorporating non-natural dNTPs, due to their low fidelity and loose active site, but are limited by poor processivity and activity. In this study, the binding between Dbh and DNA was investigated to better understand how to rationally design enhanced processivity in a Y-family pol. Guided by structural simulation, a fused pol Sdbh with non-specific dsDNA binding protein Sso7d in the N-terminus was designed. This modification increased in vitro processivity 4-fold as compared to the wild-type Dbh. Additionally, bioinformatics was used to identify amino acid mutations that would increase stabilization of Dbh bound to DNA. The variant SdbhM76I further improved the processivity of Dbh by 10 fold. The variant SdbhKSKIP241–245RVRKS showed higher activity than Dbh on the incorporation of dCTP (correct) and dATP (incorrect) opposite the G (normal) or 8-oxoG(damaged) template base. These results demonstrate the capability to rationally design increases in pol processivity and catalytic efficiency through computational DNA binding predictions and the addition of non-specific DNA binding domains.

Lake et al. suggest that current AI systems lack the inductive biases that enable human learning. However, Lake et al.'s proposed biases may not directly map onto mechanisms in the developing brain. A convergence of fields may soon create a correspondence between biological neural circuits and optimization in structured architectures, allowing us to systematically dissect how brains learn.

Transcripts Visualized in Situ Despite advances, current methods for single-cell sequencing are unable to resolve transcript location within the cell, so Lee et al. (p. 1360 , published online 27 February) developed a method of fluorescent in situ RNA sequencing (FISSEQ) that works in vivo to show messenger RNA localization within cells. The method amplifies complementary DNA targets by rolling circle amplification, and then in situ cross-linking locks amplicons to produce ample, highly localized templates for three-dimensional sequencing. The technique was tested in fibroblasts to reveal the differences between individual cells during wound repair.

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., National Institutes of Health (U.S.) (Grant R01MH103910)

We introduce the design and theoretical analysis of a fiber-optic architecture for neural recording without contrast agents, which transduces neural electrical signals into a multiplexed optical readout. Our sensor design is inspired by electro-optic modulators, which modulate the refractive index of a waveguide by applying a voltage across an electro-optic core material. We estimate that this design would allow recording of the activities of individual neurons located at points along a 10-cm length of optical fiber with 40-μm axial resolution and sensitivity down to 100 μV using commercially available optical reflectometers as readout devices. Neural recording sites detect a potential difference against a reference and apply this potential to a capacitor. The waveguide serves as one of the plates of the capacitor, so charge accumulation across the capacitor results in an optical effect. A key concept of the design is that the sensitivity can be improved by increasing the capacitance. To maximize the capacitance, we utilize a microscopic layer of material with high relative permittivity. If suitable materials can be found—possessing high capacitance per unit area as well as favorable properties with respect to toxicity, optical attenuation, ohmic junctions, and surface capacitance—then such sensing fibers could, in principle, be scaled down to few-micron cross-sections for minimally invasive neural interfacing. We study these material requirements and propose potential material choices. Custom-designed multimaterial optical fibers, probed using a reflectometric readout, may, therefore, provide a powerful platform for neural sensing.

DNA polymerase fidelity is affected by both intrinsic properties and environmental conditions. Current strategies for measuring DNA polymerase error rate in vitro are constrained by low error subtype sensitivity, poor scalability, and lack of flexibility in types of sequence contexts that can be tested. We have developed the Magnification via Nucleotide Imbalance Fidelity (MagNIFi) assay, a scalable next-generation sequencing assay that uses a biased deoxynucleotide pool to quantitatively shift error rates into a range where errors are frequent and hence measurement is robust, while still allowing for accurate mapping to error rates under typical conditions. This assay is compatible with a wide range of fidelity-modulating conditions, and enables high-throughput analysis of sequence context effects on base substitution and single nucleotide deletion fidelity using a built-in template library. We validate this assay by comparing to previously established fidelity metrics, and use it to investigate neighboring sequence-mediated effects on fidelity for several DNA polymerases. Through these demonstrations, we establish the MagNIFi assay for robust, high-throughput analysis of DNA polymerase fidelity.

DNA nanotechnology exploits the programmable specificity afforded by base-pairing to produce self-assembling macromolecular objects of custom shape. For building megadalton-scale DNA nanostructures, a long 'scaffold' strand can be employed to template the assembly of hundreds of oligonucleotide 'staple' strands into a planar antiparallel array of cross-linked helices. We recently adapted this 'scaffolded DNA origami' method to producing 3D shapes formed as pleated layers of double helices constrained to a honeycomb lattice. However, completing the required design steps can be cumbersome and time-consuming. Here we present caDNAno, an open-source software package with a graphical user interface that aids in the design of DNA sequences for folding 3D honeycomb-pleated shapes A series of rectangular-block motifs were designed, assembled, and analyzed to identify a well-behaved motif that could serve as a building block for future studies. The use of caDNAno significantly reduces the effort required to design 3D DNA-origami structures. The software is available at http://cadnano.org/, along with example designs and video tutorials demonstrating their construction. The source code is released under the MIT license.

Based on a survey of the literature, we attempt to answer Frequently Asked Questions on issues of cortical uniformity vs. non-uniformity, the neural mechanisms of symbolic variable binding, and other issues highlighted in (Marcus, Marblestone and Dean. "The Atoms of Neural Computation". Science. 31 October 2014. Vol 346. Issue 6209).

De novo synthesis of long double-stranded DNA constructs has a myriad of applications in biology and biological engineering. However, its widespread adoption has been hindered by high costs. Cost can be significantly reduced by using oligonucleotides synthesized on high-density DNA chips. However, most methods for using off-chip DNA for gene synthesis have failed to scale due to the high error rates, low yields, and high chemical complexity of the chip-synthesized oligonucleotides. We have recently demonstrated that some commercial DNA chip manufacturers have improved error rates, and that the issues of chemical complexity and low yields can be solved by using barcoded primers to accurately and efficiently amplify subpools of oligonucleotides. This article includes protocols for computationally designing the DNA chip, amplifying the oligonucleotide subpools, and assembling 500-800 basepair (bp) constructs.

We analyze the scaling and cost-performance characteristics of current and projected connectomics approaches, with reference to the potential implications of recent advances in diverse contributing fields. Three generalized strategies for dense connectivity mapping at the scale of whole mammalian brains are considered: electron microscopic axon tracing, optical imaging of combinatorial molecular markers at synapses, and bulk DNA sequencing of trans-synaptically exchanged nucleic acid barcode pairs. Due to advances in parallel-beam instrumentation, whole mouse brain electron microscopic image acquisition could cost less than $100 million, with total costs presently limited by image analysis to trace axons through large image stacks. Optical microscopy at 50 to 100 nm isotropic resolution could potentially read combinatorially multiplexed molecular information from individual synapses, which could indicate the identifies of the pre-synaptic and post-synaptic cells without relying on axon tracing. An optical approach to whole mouse brain connectomics may be achievable for less than $10 million and could be enabled by emerging technologies to sequence nucleic acids in-situ in fixed tissue via fluorescent microscopy. Novel strategies relying on bulk DNA sequencing, which would extract the connectome without direct imaging of the tissue, could produce a whole mouse brain connectome for $100k to $1 million or a mouse cortical connectome for $10k to $100k. Anticipated further reductions in the cost of DNA sequencing could lead to a $1000 mouse cortical connectome.

Protein pathways are dynamic and highly coordinated spatially and temporally, capable of performing a diverse range of complex chemistries and enzymatic reactions with precision and at high efficiency. Biotechnology aims to harvest these natural systems to construct more advanced in vitro reactions, capable of new chemistries and operating at high yield. Here, we present an efficient Multiplex Automated Genome Engineering (MAGE) strategy to simultaneously modify and co-purify large protein complexes and pathways from the model organism Escherichia coli to reconstitute functional synthetic proteomes in vitro. By application of over 110 MAGE cycles, we successfully inserted hexa-histidine sequences into 38 essential genes in vivo that encode for the entire translation machinery. Streamlined co-purification and reconstitution of the translation protein complex enabled protein synthesis in vitro. Our approach can be applied to a growing area of applications in in vitro one-pot multi-enzyme catalysis (MEC) to manipulate or enhance in vitro pathways such as natural product or carbohydrate biosynthesis.

The human cerebral cortex is central to a wide array of cognitive functions, from vision to language, reasoning, decision-making, and motor control. Yet, nearly a century after the neuroanatomical organization of the cortex was first defined, its basic logic remains unknown. One hypothesis is that cortical neurons form a single, massively repeated “canonical” circuit, characterized as a kind of a “nonlinear spatiotemporal filter with adaptive properties” ( 1 ). In this classic view, it was “assumed that these…properties are identical for all neocortical areas.” Nearly four decades later, there is still no consensus about whether such a canonical circuit exists, either in terms of its anatomical basis or its function. Likewise, there is little evidence that such uniform architectures can capture the diversity of cortical function in simple mammals, let alone characteristically human processes such as language and abstract thinking ( 2 ). Analogous software implementations in artificial intelligence (e.g., deep learning networks) have proven effective in certain pattern classification tasks, such as speech and image recognition, but likewise have made little inroads in areas such as reasoning and natural language understanding. Is the search for a single canonical cortical circuit misguided?

We consider classical and entanglement-assisted versions of a distributed computation scheme that computes nonlinear Boolean functions of a set of input bits supplied by separated parties. Communication between the parties is restricted to take place through a specific apparatus which enforces the constraints that all nonlinear, nonlocal classical logic is performed by a single receiver, and that all communication occurs through a limited number of one-bit channels. In the entanglement-assisted version, the number of channels required to compute a Boolean function of fixed nonlinearity can become exponentially smaller than in the classical version. We demonstrate this exponential enhancement for the problem of distributed integer addition., Comment: To appear in Quantum Information Processing

We present "molecular threading", a surface independent tip-based method for stretching and depositing single and double-stranded DNA molecules. DNA is stretched into air at a liquid-air interface, and can be subsequently deposited onto a dry substrate isolated from solution. The design of an apparatus used for molecular threading is presented, and fluorescence and electron microscopies are used to characterize the angular distribution, straightness, and reproducibility of stretched DNA deposited in arrays onto elastomeric surfaces and thin membranes. Molecular threading demonstrates high straightness and uniformity over length scales from nanometers to micrometers, and represents an alternative to existing DNA deposition and linearization methods. These results point towards scalable and high-throughput precision manipulation of single-molecule polymers.

We have developed a general method for solving a key challenge for nanotechnology: programmable self-assembly of complex, three-dimensional nanostructures [Douglas, Dietz, et al. 2009]. Previously, scaffolded DNA origami has been used to build arbitrary flat shapes 100 nm in diameter and almost twice the mass of a ribosome [Rothemund 2006]. Now we have succeeded in building custom three-dimensional structures that can be conceived as stacks of nearly flat layers of DNA. Successful extension from two dimensions to three dimensions in this way depended critically on calibration of folding conditions. A general capability for building complex, three-dimensional nanostructures will be of great interest to biologists, chemists, physicists, engineers, and computer scientists.