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Neuroscience is going through a revolution thanks to new technologies like optogenetics, new microscopy techniques, and multielectrode recordings that allow studying the brain at finer scales than ever before.
However, the brain remains highly complex due to its immense scale (billions of neurons and neuro types), lack of clear organizing principles, and challenges modeling uniquely human behaviors like language.
Large-scale brain mapping initiatives aim to generate unprecedented amounts of neural data but analyzing and extracting insights from these massive datasets will be a major challenge.
Computation is seen as key to building a bridge from raw data to theoretical understanding, as the brain is thought to process information in some computational way, taking inputs and transforming them into models and motor outputs.
While new technologies promise a more detailed view of the brain, they will also raise new questions. Developing analytical and theoretical approaches will be critical to make sense of the glut of neural data on the horizon.
Brain mapping, or creating atlases of the brain, has a long history dating back to ancient Greek physicians like Galen. Major advances were made by early anatomists like Vesalius and neuroanatomists like Brodmann.
Brodmann mapped out 43 distinct regions of the human cerebral cortex based on histological analysis. His work still serves as a guide today.
Modern digital brain atlases integrate data from neuroimaging techniques like MRI, fMRI, DTI, MEG, EEG, PET. They provide high-resolution 3D maps of the brain.
Atlases are used in neuroscience research to characterize neuronal structures and brain organization. They also guide neurosurgery by providing anatomical context and references.
The ultimate goal of brain mapping is to understand the full wiring diagram and map of neuronal connections and activity across the entire brain. New technologies are helping achieve this by providing more detailed views of neural circuits.

Here is a summary of the key points about how brain atlases relate to understanding brain function from behavior to consciousness:

Brain atlases provide a common coordinate framework for organizing and accessing data on brain structure and function. This allows researchers to integrate different types of data and compare findings across individuals and studies.
Gene expression mapping through techniques like in situ hybridization has revealed distinct molecular signatures for different brain regions and cell types. Atlases of gene expression data relate genetic information to brain anatomy and structure.
Pioneering projects like the Allen Brain Atlas mapped gene expression across the mouse brain, providing a foundational resource. Similar projects aim to map gene expression and cell types in the human and other brains.
By standardizing brain data to a common coordinate system, atlases allow identification of average and unique structural and functional features. This helps relate anatomical organization to behavior and cognition.
Integrating data on brain architecture, gene expression, imaging, and connectivity aims to advance understanding of how the brain gives rise to complex functions from behavior to consciousness. Common brain atlasing frameworks are crucial for this combined analysis across levels from genes to cognition.
Neuropsychiatric disorders are now understood to result from pathologies at the system/circuit level, with both genetic and environmental factors impacting neural circuitry. Understanding interactions within neuronal systems/circuits is important for developing therapies.
Mapping the complete neural connections within the brain (the “connectome”) has become an important area of research, inspired by efforts to map the human genome. Important early connectome mapping efforts included mapping the neural circuit of C. elegans and mapping brain areas and connections in the macaque and cat.
Several projects are pursuing large-scale connectome mapping of the mouse brain at different scales, including the Allen Mouse Brain Connectivity Atlas which aims to generate a three-dimensional mesoscale connectivity map.
The Human Connectome Project is working to map macroscale pathways in healthy human adults using noninvasive neuroimaging. More accurate whole-brain parcellation may enable better macroscale human connectomes.
Microscale mammalian connectome mapping faces challenges of data collection, annotation tools, and connectivity analysis algorithms. Advances in imaging techniques offer potential for deeper brain mapping in the future.
Researchers developed a new optical imaging technique called CLARITY that renders brain tissue optically transparent while leaving it intact and permeable to macromolecules. This allows whole-brain imaging of structures like long-range projections, local circuits, cells, and subcellular features in mouse brains using techniques like fluorescence labeling.
The technique involves clearing the tissue using hydrogel embedding and electrophoresis, then staining and re-staining the intact non-sectioned tissue to visualize gene expression or protein binding throughout the brain.
Large-scale digital brain atlases providing molecular, cellular, functional and connectivity data have transformed neuroscience. Near complete 3D atlases of human brain microstructure and connectivity may soon be possible.
New whole-brain imaging techniques are being developed to study the brain in its natural holistic context with the body and environment, using virtual reality and whole-brain neuron recording approaches. This could provide new insights into neural functions involving sensory-motor loops.
Scientists have developed techniques like calcium imaging and optogenetics to visualize and manipulate neural activity at the cellular level in small, transparent organisms like larval zebrafish.
Light-sheet microscopy allows fast, high-resolution 3D imaging of whole brains. The authors developed a light-sheet microscope that could image neural activity across almost all ~80,000 neurons in the larval zebrafish brain multiple times per second.
This enables investigating questions about how neuron groups interact across the entire brain during behaviors and sensory processing. Initial observations found complex, evolving patterns of activity across the brain.
Analysis identified two populations of tightly correlated neurons with distinct anatomical structures, showing structure can be discerned within complex activity patterns.
Whole-brain techniques are needed to study functions involving distributed brain circuits, like sensorimotor transformations and learning.
Analyzing whole-brain data presents major challenges in extracting meaningful insights. Computational approaches and modeling will be important for interpreting datasets and generating testable hypotheses.
Further improvements in microscopy, activity sensors, and perturbation tools are needed to enable insights at finer spatiotemporal scales and causally probe circuit function. Integrating molecular to systems levels will be key to a holistic understanding of brain function.
Project MindScope aims to map the detailed circuitry and record/visualize neuronal activity in the visual cortex and cortico-thalamic system of mice during visuomotor behaviors. It takes an integrated, high-throughput approach across neuroanatomy, electrophysiology, modeling, and theory.
Mice are used as a model organism as their brains are more accessible than humans for cellular-level recording and manipulation techniques. Despite size differences, mouse and human brains share similar genomic and architectural features.
Genetically engineered mice with specific neuronal cell types marked are created using Cre driver lines. This allows targeted labeling and manipulation of defined cell populations.
The goals are to catalog and characterize different neuronal cell types, their connectivity patterns (connectomes), and dynamics at the population level during behavior.
Understanding will be achieved by tightly integrating results across disciplines to map how sensory information is coded, transformed, and used to drive behavior at timescales of 1-2 seconds.
Project MindScope is part of a large, 10-year, $300M initiative launched by the Allen Institute in 2012 to map the mouse corticothalamic visual system and modeling its computations underlying vision and visuomotor behaviors.
The project aims to comprehensively map the visual cortico-thalamic circuit in mice using genetic, molecular, anatomical, and physiological techniques. This will provide insight into visual processing and computation in the cortex.
Over 40 Cre driver lines have been developed to target different excitatory and inhibitory cell types. Molecular tools like GCaMP6 allow visualization and manipulation of neural activity.
A large-scale connectivity mapping effort called the Allen Mouse Brain Connectivity Atlas uses genetic tracing and high-throughput microscopy to image axonal projections throughout the entire mouse brain. Over 2,000 datasets have been generated so far.
The project plans to characterize the physiological, anatomical, transcriptional and synaptic properties of cell types in the visual cortico-thalamic system at single cell resolution. This multi-pronged approach will provide a comprehensive cell type taxonomy.
Higher-order connectivity rules between groups of neurons will be studied using serial-section electron microscopy and rabies virus tracing.
The goal is to understand visual computations by recording large ensembles of neurons in visual cortex and associated areas during visuomotor behaviors using silicon probes and two-photon microscopy.
Ultimately, a functional “projectome” map of physiological signals between visual areas and identified cell types will be delineated.

Here is a summary of the key points about ce from the passage:

The Allen Institute takes a highly standardized, industrialized approach to generating large biological datasets through interconnected multi-module pipelines.
Standard operating procedures, quality control, milestones and project management are used to efficiently generate and process data across modules like mouse line breeding, surgery, imaging, etc.
Data generated goes through multiple processing and QC stages as part of a robust informatics pipeline before being released as online products.
A major example project is the Allen Mouse Brain Connectivity Atlas, which used scaled up transgenic mouse generation, stereotaxic injections, and serial two-photon tomography to generate petabytes of neuroimaging data.
The goal is to apply similar industrialized project management techniques to other data products mapping the structure and function of mouse brain circuits under various conditions.

So in summary, the key aspects are taking a standardized, high-throughput pipeline approach to efficiently link together experimental modules and data processing in order to generate large-scale neuroscience datasets for online distribution.

The goal of constructing neural models and mapping them to behavior is to determine which components and details contribute to observable outcomes. Sensitivity analysis of hybrid models at different levels can help predict the effects of optogenetic interventions.
While recording every spike and simulating brains at a massive scale would generate huge amounts of data, it still may not explain the deep principles underlying brain processing. Theoretical work is needed to answer questions about representation, manipulation of representations, predictive coding, feedback pathways, etc.
Theoretical considerations should complement modeling efforts. Bringing together modelers, theorists, anatomists and physiologists working on the same systems can create a virtuous loop where theory guides experiments and modeling, which then advance theory.
Understanding the brain requires obtaining the wiring diagram or “connectome” at single neuron resolution. While progress is rapid, we are still far from being able to build machines with human-level intelligence. Obtaining the connectome may help provide missing information to close this gap.
The author argues that more biologically realistic neural networks or better learning algorithms alone will not be sufficient to solve biological intelligence. Biological organisms have evolved highly specialized “bags of tricks” - finely optimized algorithms and special cases to solve problems effectively in the real world.
These tricks may be difficult for artificial systems to discover because they have access to much smaller training data than biological evolution over hundreds of millions of years.
The neocortex, with its modular columnar structure, may provide clues about basic cortical algorithms, while specialized cortices contain unique tricks for processing different types of information.
Understanding cortical modules and their interconnectivity could help decode the bag of tricks encoded in neural circuits. Studying the mouse cortex using techniques like calcium imaging and future connectome mapping may help achieve this.
Reconstructing full connectomes from electron microscopy data is extremely challenging and labor-intensive. Automating this process through improved technologies is a major barrier to understanding neural circuits.
The brain is massively complex with many levels of organization. At the microscopic level, neurons are densely packed with up to 100,000 neurons and 900 million synaptic connections per cubic millimeter of brain tissue.
Neurons come in hundreds of distinct cell types with unique structures and molecular identities. Synaptic connections can be excitatory or inhibitory and use over 100 neurotransmitters.
Connections change strength over time, breaking and reforming in response to experience. Communication may also occur beyond chemical and electrical synapses via gaseous messengers and long-range electrical interactions.
Glial cells, once thought to be merely supportive, are now believed to play important roles in information processing by making synapses with neurons and releasing neurotransmitters that modulate neuronal activity.
Within each cell is a network of intracellular signaling pathways and gene regulatory networks that determine the cell’s functional properties. Untangling this multilayered complexity is a major challenge for understanding the brain.
The goal is to map and model the brain at multiple interconnected biological levels (cell types, connections, activity patterns, molecular changes, developmental lineages) to better understand its complexity and function.
This requires simultaneously observing and relating information across all these levels within a single brain, analogous to how the Rosetta Stone presented the same information in different languages.
Conceptually, much of the necessary data comes down to operations of labeling and counting - giving each neuron, synapse, molecule a unique identifier or “barcode” and tracking how these change and interact over time.
Connectomes, developmental lineages, cell types, gene expression, synaptic proteins can all be represented as forms of discrete counting and labeling operations on these identifiers.
Electrical activity histories, when combined with anatomical connectivity data, could allow inference of functional connections and synaptic strengths by tracking spike propagation between neurons.
The goal is to generate and read enormous numbers of these identifiers or “barcodes” to represent all the data needed for a comprehensive “Rosetta Brain” dataset that relates structure to function at multiple levels.

Here is a summary of the key points about applying DNA sequencing techniques at the subcellular level:

DNA can store any sequence of nucleotides (A,T,C,G) and thus has potential as a molecule for storing vast amounts of information at the molecular scale.
Advancing DNA sequencing technologies are putting DNA sequencing on a trajectory that exceeds Moore’s Law for computational power. This enables easy reading and writing of information into DNA.
A technique called fluorescent in situ sequencing (FISSEQ) can be used to sequence DNA inside intact slices of tissue, rather than just freely floating DNA molecules in a test tube.
FISSEQ works by using fluorescently labeled nucleotides and a microscope to record the changing colors as DNA is copied and sequenced within the tissue slice.
This approach could be applied to sequence unique DNA barcodes inserted into each neuron, allowing unique labeling of cells. It could also sequence mRNA to determine cell types.
Combining FISSEQ with labels for synapses would allow mapping of connectivity by identifying which barcodes are paired across synapses. Seqeuncing barcodes that change with cell division could also enable determination of cell lineage.
Even with a complete map of all neurons and connections in the brain (the connectome), we will only partially understand the brain, as the connectome alone does not reveal the computations performed.
May-Britt and Edvard Moser have dissected the neural circuitry underlying spatial navigation in a specific brain region, providing a link between anatomy, physiology, and the computation performed. This is a model for understanding other brain regions.
Understanding individual neurons is not sufficient - we need approaches to understand how thousands or millions interact as large-scale networks perform computations.
Krishna Shenoy describes analyzing large neural datasets to see patterns and interactions across many neurons, rather than analyzing each one separately.
Olaf Sporns argues large-scale neural networks are key to the brain’s operations, and understanding these networks is important for interpreting the connectome and what computations the brain performs.
The challenge is to reverse engineer the brain’s “operating system” - to understand the neural computations, protocols for communication between regions, and how software and hardware work together, all from studying the brain.
Jeremy Freeman describes how new technologies are generating a massive onslaught of neuroscience data, but truly understanding the brain will require more than just data analysis.
Understanding the mammalian cerebral cortex is a major goal of neuroscience, as it is involved in higher-order cognitive functions. Though progress has been made in understanding early sensory areas of the cortex, the “higher-order” integrative areas deeper in the cortex remain poorly understood.
A breakthrough was the discovery of grid cells and place cells in the hippocampus and entorhinal cortex by O’Keefe, Moser, and others. These cells have firing patterns tightly correlated with an animal’s spatial location, providing a window into the function of higher cortical areas.
Grid cells in the entorhinal cortex fire in repeating geometric triangular patterns that map an animal’s environment. Their discovery helped reveal the entorhinal cortex plays a key role in spatial representation and navigation, upstream of the hippocampus.
While new data and technologies are emerging, truly comprehending cognition will require integrating data with theory to understand cortex function at multiple levels, from sensory processing to higher-order integration.
Grid cells were initially observed in rats and have since been found in other mammals like mice, bats, monkeys and humans. They appear to be widespread across mammals.
Grid cells maintain a persistent firing pattern regardless of the animal’s speed or direction of movement.
When two grid cells are recorded simultaneously, their grid firing patterns tend to maintain the same relationship across different environments.
In contrast, place cells in the hippocampus can switch to completely unrelated firing patterns between environments.
Recent multi-electrode recordings revealed grid cells are organized into 4-5 discrete grid maps or modules in the entorhinal cortex.
Modules differ in parameters like grid spacing and orientation.
Modules can respond independently to changes in the environment, like compression of a square box.
This modular organization may allow the hippocampus to generate a huge number of distinct representations for different places and memories by combining inputs from grid modules.
The regular hexagonal grid firing pattern likely emerges through self-organization in the entorhinal cortex network due to inhibitory connections between cells.
In addition to grid cells, the entorhinal cortex also contains head direction cells and border cells with similar persistence across environments.
it is now possible to measure neural activity from hundreds to thousands of individual neurons simultaneously using technologies like multi-electrode arrays and calcium imaging
this provides more detailed data than looking at single neurons or bulk activity, but also presents new challenges in making sense of large and complex datasets
analyzing population-level dynamics from many neurons recorded simultaneously can provide insights into how neural activity is transformed and representations emerge across brain areas
however, there are still barriers to converting raw neuronal measurement data into meaningful scientific understanding, such as determining the appropriate “levels of abstraction” to apply when analyzing high-dimensional neural population data
continued technological advances in neural recording and new analysis methods will hopefully help scientists better understand complex neuronal circuit computation by measuring activity from large ensembles of neurons
The passage discusses new technologies that allow recording simultaneously from hundreds to thousands of individual neurons. This generates a large amount of neural data.
Two key challenges in making sense of this massive neural data are: 1) Figuring out how to analyze and interpret such large datasets. 2) Knowing what aspects of the data to pay attention to at different “levels of abstraction”.
One approach discussed is the “dynamical systems approach”, which involves dimensionality reduction to visualize lower-dimensional population dynamics, temporal smoothing of data, and identifying equations of motion. This relates population neural trajectories to behaviors.
Levels of abstraction is seen as important, analogous to how electronic systems are understood at various scales from atoms to transistors to wiring to software. For the brain, this could involve understanding neurons, then populations, and finally higher-level functions/representations.
Abstraction is needed to simplify and generalize representations at each level, otherwise complexity grows too quickly. But determining the right level of abstraction for neuroscience is still an open question. The concept of levels of abstraction may help leverage massive neural data into scientific insights.
Understanding the brain and how it gives rise to cognition is one of the most important scientific pursuits. Progress will come from accumulating detailed insights about neural mechanisms, but neuroscience also needs to embrace the brain as a complex networked system.
Connectivity is a core theme in many fields today. For humans, connectivity to the internet is seen as empowering and a basic right. Scientists study connectivity to understand complex systems across many disciplines.
Biological researchers recognize that interactions among molecules and cells form complex networks that are crucial to cellular functioning and building organisms. Cells depend on internal gene regulatory, signaling, and metabolic networks. Interactions among cells are also important.
The brain can be viewed as a highly interconnected network with many levels of organization, from cells to cognition. These levels are interconnected and individually irreducible. Understanding the relationships between levels may provide new insights into brain function and mental processes.

So in summary, it argues that understanding the brain requires viewing it as a complex networked system with interconnected levels of organization, from cells to cognition, rather than focusing only on detailed individual mechanisms. Connectivity is a core theme across many fields and for understanding complex biological systems.

Connectivity and brain networks are increasingly recognized as vital for brain function and computation. Disturbances of connectivity are associated with many brain diseases.
Understanding the brain has been challenging due to the gap between single neuron activity and whole-brain function. However, neurons act as part of networks bound by connections that allow interaction and coordination.
Thinking in terms of brain circuits is limited, while network science concepts like emergence and global states help explain phenomena like neural synchronization that transcend localized interactions.
Collective behavior in brain networks, like criticality, depends on many weak local interactions orchestrated by network architecture. Relating network structure to dynamics is an important area of research.
Neuroscience is undergoing a “big data” revolution, with large-scale brain mapping projects producing unprecedented data volumes. This will transform neuroscience similarly to other data-intensive fields like physics and astronomy.
Neuroscience is collecting massive amounts of brain data through new “brain observatories” like particle colliders and telescopes in other fields. But neuroscience lacks overarching theories to make sense of all this data.
Network science provides a framework for analyzing and interpreting large brain data sets. It has revealed important network structures in the brain like resting-state networks.
Network approaches are helping identify network disturbances underlying brain disorders like Alzheimer’s and schizophrenia. Recovery may involve restoring disrupted networks.
Sophisticated computational models that simulate brain networks are providing new insights. They can mimic patterns of resting brain activity and effects of lesions or disease progression.
These “virtual brain” models will become increasingly important platforms for exploring effects on the global brain network, designing personalized therapies, and integrating with new recording technologies. They bring together neuroscience with other networked systems.
As neuroscience experiments now record activity from thousands of neurons simultaneously during complex behaviors, it poses challenges to analyze and understand the vast amounts of data.
Initial steps involve extracting neural signals from noise using algorithms to isolate cell body responses or interrogate all pixels. There is no single correct approach and scientific judgment is needed.
Higher-level analysis aims to find patterns in neural responses related to sensory inputs, behaviors, behavioral states, or a combination. But it is unclear the “right” analysis as many parameters could be relevant.
Functional modeling relates neural responses to observable features to understand what the brain and circuits are doing. Dimensionality reduction techniques can uncover simpler structures in high-dimensional activity patterns.
To analyze more complex representations, examining individual neurons or averages is insufficient and theories or hypotheses may need to guide exploratory data analysis.
Analyzing massive datasets now collected requires scaling up analysis methods while balancing top-down theories with bottom-up data exploration to gain new insights into brain function from a network perspective.
Large-scale neuroscience experiments such as whole-brain calcium imaging in zebrafish are generating terabytes (TBs) of data per experiment, comparable to what social media platforms collect daily.
Analyzing such massive datasets can take hours or days even for simple analyses, and more complex analyses may not be feasible. This presents a major bottleneck for progress.
New distributed computing frameworks like Apache Spark enable analyzing large datasets across computer clusters, making previously unthinkable analyses possible.
However, analysis alone is not sufficient - targeted experiment design in tandem with analysis is needed. As an example from the author’s past work, insights into visual area V2 came not from large datasets but from computationally-driven hypothesis testing with customized stimuli.
Going forward, a strategy of simply collecting massive datasets first before analyzing may not be realistic, as the desired data and optimal experimental conditions are often unclear at the outset. Continuous interplay between analysis and experiment is crucial for understanding brain function from large-scale neural data.
The European Human Brain Project aims to simulate the entire human brain within 10 years through a collaborative effort that brings together neuroscience data, integrates it in unified brain models, runs simulations, and analyzes/visualizes the results to test hypotheses.
Building a model or simulation of the brain can help us understand it, even if we don’t fully understand it yet. The process of organizing data, identifying gaps, and evaluating what we know helps further our understanding.
Simulations act as a proving ground for higher-level theories of brain function, which must be consistent with biological data to be valid.
A bottom-up approach models neurons, synapses, and circuits based on biological details, while a top-down approach develops abstract theories of brain function. An integrated approach is needed to link gene expression to cognition.
A massive amount of neuroscience data exists but is fragmented across many studies and difficult to integrate due to different methods, descriptions, and data formats used. Efforts are needed to standardize data and enable large-scale integration and analysis to gain new insights.

Modern analytical and machine learning methods are being used to find patterns and insights in massive amounts of neuroscience data. International organizations like INCF are coordinating standards and infrastructure to facilitate data sharing across labs and projects.

Next-generation brain atlases that integrate different types of data like gene expression, cellular characteristics, and connectivity are being developed. This allows relationships between regions and cell types to be discovered. Precise annotation and coordination is needed.

Predictive neuroscience uses available data and principles to predict missing information and guide further experiments. For example, synapse distribution patterns can be inferred from neuron shapes.

Brain modeling platforms will guide scientists in building computational models at different scales - from proteins to circuits to whole brains. Parameters are populated from data but can be overridden. Standardized cell type catalogs are used to compose circuits. Future work aims to predict cell morphologies from gene expression.

Overall, modern data science methods are being applied to analyze vast neuroscience datasets and build increasingly detailed and predictive models of the brain. Standards and predictive approaches help overcome limitations and knowledge gaps.

Types defines an additional cell type in the cortical microcircuit called “morphoelectric” cells. There are 207 of these types that have both distinctive morphological and electrical properties. Developing models that predict these properties from gene expression will be important.
Connectivity is determined by algorithms that consider physical proximity plus additional factors like axonal density and dendritic spines. New discoveries about connectivity rules can be incorporated. Synaptic plasticity rules will further refine connections based on activity patterns.
Microcircuits are patterned to build brain areas, and long-range connectivity connects them to form whole brain circuits. Models are built and refined iteratively based on new data.
The Brain Simulation Platform allows neuroscientists to build their own models using data from the Neuroinformatics Platform. Models at different scales can explore how data impacts systems-level function.
Models are continually validated against experimental data. Discrepancies guide acquiring new data to improve models. Simulated instruments like virtual electrodes provide direct comparisons.
The goal is continually improved “unifying brain models” that best account for all available data by reproducing experimental findings. Simplification helps identify core principles.
A Neurorobotics Platform will provide simulated bodies and senses for closed-loop modeling of behavior and cognition. Virtual mouse models in mazes can provide insight.
The Medical Informatics Platform analyzes clinical data to build signatures of brain disorders and diseases to parameterize whole-brain disease models to identify treatment targets.
Ensuring ethics guides work on potentially sensitive simulations like consciousness or large brains. Open discussion between scientists and society is needed to establish responsible policies.

Here is a summary of the key points about bal collaboration in neuroscience from the passage:

The Human Brain Project (HBP) aims to serve as a global resource for neuroscience research, similar to how CERN functions as an international resource for physics experiments.
The HBP will create an online portal that allows researchers worldwide to access neuroscience data, build models, and run brain circuitry simulations. This will enable unprecedented collaboration in the neuroscience community.
The portal will integrate scientific social networking to facilitate sharing of data, analyses, models, simulations, and publications. Contributors will be fully attributed, incentivizing data sharing and collaboration.
Impact scores can take into account how widely used or rated different datasets, analyses or models are. Social network analysis can provide recommendations to researchers.
The portal will support dynamic team building, bringing together experts to tackle specific challenges in understanding the brain.
The goal is for vast numbers of scientists worldwide to work together attacking major problems in neuroscience and brain disorders, with individual contributions being recognized. This global collaborative approach is needed as initiatives emerge around the world.
Spaun is a computational model of the brain designed to perform various cognitive tasks through the coordinated functioning of different brain areas and neural populations.
It processes information in a hierarchical manner, with visual and motor areas at the bottom and working memory/cognitive control areas at the top.
For the serial working memory task, it recognizes digits, compresses them into semantic representations, stores them sequentially in working memory, then retrieves and draws them when prompted.
Spaun’s neural activity and behavior matches empirical data from rats and monkeys on reinforcement learning and working memory tasks. Its error patterns and reaction times also match human data.
The same underlying neural mechanisms and parameters are used across different tasks, reducing concerns about overfitting. Spaun makes novel, testable predictions about working memory encoding.
Its flexible coordination of brain areas allows it to deploy different information processing approaches depending on the task, similar to animal cognition. This sets it apart from more limited artificial intelligences.
By quantitatively matching both neural data and behavior across many studies, Spaun provides insights into brain function and organization that more typical neural simulations lack.

The passage discusses the goal of the Spaun project to develop an understanding of how the brain flexibly coordinates different tasks. It notes that this ability is important for humans and animals to operate in dynamic environments.

The Spaun model incorporates distinct midbrain and cortical regions. The midbrain, dominated by the basal ganglia, acts as an “action selector” that monitors the cortex and directs information flow to accomplish goals. However, the basal ganglia does not perform actions itself - it organizes the cortex so its massive processing power can be applied appropriately.

This allows Spaun to perform 8 different tasks in any order while remaining robust. It determines the task based on its input. The model exhibits nascent ability to learn new behaviors while retaining old skills.

Reverse engineering the brain can provide insights into conditions like addiction, OCD, and Parkinson’s. The Spaun model has helped explain cognitive decline in aging by showing neuron loss can uniformly decrease performance.

Understanding brain mechanisms may also inform building more intelligent artificial systems that can flexibly coordinate skills like humans. Efforts are underway to develop brain-inspired “neuromorphic” hardware that could eventually outperform digital computers through low power use and robustness. Challenges include programming such noisy, variable hardware, but methods developed for models like Spaun are addressing this.

Understanding language requires bridging between the vocabulary of linguistic elements (words, syntax) and neural elements (neurons, brain regions). Current approaches like brain imaging provide correlations but not explanatory mechanisms.
Localizing functions to brain regions through imaging is an important start but does not fully explain how the brain implements those functions. We need to decompose language tasks into more basic computational/cognitive operations that can be linked to brain architecture and function.
The challenges are both practical (limitations of current imaging techniques) and principled (aligning the categories of linguistics and neurobiology). Future work needs linking hypotheses at the right level of granularity to meaningfully connect linguistic and neural descriptions.
Progress will come from decomposing complex functions into hypothesized primitive operations, which can then be studied at the neural level using techniques like high-resolution recording. This could provide satisfying accounts of how brain regions underpin language.
Linguistics and the neurosciences define units of analysis like words, phrases, neurons, etc. but it’s unclear how these relate to each other. Bridging language and neurobiology requires explicitly linking these levels of analysis.
David Marr’s computational, algorithmic, and implementational levels of analysis provide a framework for linking high-level linguistic concepts to low-level neural mechanisms.
Minimalist syntactic theory proposes basic operations like combining/concatenating elements that could map to fundamental neural computations. Recent research aims to identify the neural circuits underlying operations like these.
Speech perception involves parsing acoustic signals into meaningful units at different timescales. Cortical oscillations at deltas, theta, and gamma frequencies may provide a mechanism for parsing speech into chunks via phase resetting.
Speech production involves hierarchical planning from features to phrases. Psycholinguistics and motor control can be integrated via hierarchical feedback control models linking different levels of representation.
Models of language processing typically implicate broad brain regions like the “sentence processing network” but lack computational details about how these regions work together.
Recent research aims to bridge this gap by systematically varying properties of linguistic composition and investigating the temporal dynamics and roles of different brain regions.
The emerging picture suggests composition is achieved by a network of regions with varying computational specificity, rather than a single localized region.
The left anterior temporal lobe operates early (~200-300ms) and appears specialized for combining predicates, while other regions like the angular gyrus are more general and later (~400ms).
Connecting linguistics models of representation to neurobiology can help decompose the various computations underlying the brain’s combinatory capacity for language.
This provides an intermediate step toward more explanatory, mechanistic links between neural circuits and hypothesized linguistic functions.
The goal is a “computational neurobiology of language” that fully explains how the brain performs linguistic composition and derivation of meaning from smaller parts.
Genetic influences on human speech and language have strong evidence from family/twin studies showing high heritability.
Direct genomic evidence came with identifying FOXP2 as the first gene linked to a speech/language disorder in 2001. Mutations in FOXP2 cause problems with coordinating speech movements and processing grammar.
Though FOXP2 mutations are rare, it demonstrated gene discovery is possible. Many other unexplained speech/language disorders likely involve other risk genes.
Exome sequencing (of protein-coding genes) provided an initial approach to find causal genes, but interpreting results is challenging due to large number of variants found in each person.
Functional studies using cell/animal models help validate potential causal genes/variants, examining effects at molecular, neuronal and behavioral levels. Experiments in mice have been especially informative.
For FOXP2 specifically, further studies examined how its role as a transcriptional regulator is disrupted by mutations, providing insights into the biological mechanisms involved.

So in summary, while genetics provides evidence for speech/language heritability, directly identifying causal genes requires integrated genomic and functional experimental approaches.

The researchers observed mutations associated with speech and language disorders and studied their effects using human neuron-like cells grown in the lab. For example, they studied the FOXP2 gene mutation found in the KE family which causes speech/language disorders.
In the lab, they showed the mutant KE protein could not regulate target genes normally. They also inserted the mutation into mice to study its effects on brain development and functions. This revealed early disruptive effects on neurite outgrowth in neurons.
The mouse research also found the mutation reduces neural circuit plasticity and ability to modulate responses to stimuli, affecting circuits important for learning sequences of movements like in speech.
Work with targeted mutations in other species like mice and studies of FOXP2 in various animals shows this gene’s important role in neural functions related to learning vocalizations/songs across vertebrates.
New techniques like neuroimaging genomics couple high-throughput DNA screening with brain imaging to study correlations between genetic variants and human brain features. This could provide insights but requires large sample sizes due to complex datasets and small effect sizes.
Future generations of neuroscientists have much opportunity to better understand language and other traits by taking advantage of new genomics tools to connect genes to neurons, circuits, brain and cognition. But more conceptual frameworks may still be needed.

Here is a summary of the key points about cortical regions involved in visual perception from the passage:

Four distinct cortical visual areas are projected onto the Allen Mouse Brain Connectivity Atlas: visual area 1 (V1), visual area 2 (V2), anterolateral area (AL), posteromedial area (PM).
These cortical visual areas are highly interconnected with each other. They are also linked with the thalamus and midbrain.
The connectivity map shows the hierarchical organization of visual areas in the cortex, with sensory cortices like the retinal ganglion cells and lateral geniculate nucleus at the bottom. Areas higher up in the cortical hierarchy like the entorhinal cortex and hippocampus are connected indirectly to lower areas via multiple synapses.
The goal is to understand how higher cortical areas like the entorhinal cortex and hippocampus work based on their connectivity and relationship to lower visual and sensory areas. They are thought to integrate and bind representations across widespread regions of the cortex.

So in summary, it outlines four key visual cortical regions in the mouse brain and their interconnectivity, showing how they are organized hierarchically with feedback connections. The higher areas like the entorhinal cortex and hippocampus integrate information from lower visual and sensory areas.

Neuroscientists are trying to study the neural basis of consciousness using the “contrastive method” - comparing brain activity during conscious perception vs. unconscious perception. However, this method has a major problem called the “measurement problem”.

The problem is that we can only tell whether a perception is conscious or unconscious based on the subject’s report or response. So when comparing conscious vs. unconscious perception, we end up conflating the neural basis of conscious perception with the neural basis of cognitive access/ability to report on the perception.

Some argue this makes the problem intractable, as we can’t separate consciousness from the non-conscious processes that allow us to report on it. Others propose methods like looking for activation in known perception areas even when subjects report not perceiving, but these have their own issues.

There is also debate around whether cognitive access/reporting is actually constitutive of consciousness itself. Cognitive theories say yes, while other views argue conscious perception can exist without actual cognitive access or reporting in that moment.

Experimental evidence from Lamme’s lab suggests subjects can maintain conscious representation of stimuli even when only able to report/access half of them cognitively, challenging cognitive theories of consciousness. This issue remains heavily debated in the field.

In summary, the “measurement problem” poses a major challenge for neuroscientists seeking to study the neural basis of consciousness directly using current methods. There is no consensus on how to definitively separate conscious representation from non-conscious processes.

Dehaene argues that the idea of qualia as pure mental experiences detached from any information processing role will be seen as an outdated prescientific view. Lamme, Zeki, and Block don’t think phenomenal consciousness has no information processing role - they think it “greases the wheels” of cognitive access but can occur without it.
The measurement problem is how we can determine if a perception is conscious or not, given that our ability to find out depends on cognitive processes, making the evidence circular.
Koch and Tsuchiya propose using optogenetics to study consciousness in transgenic mice. By turning off top-down feedback using light, they hypothesize attention and global broadcasting will be disrupted, preventing conscious perception.
They propose using post-decision wagering, where mice express confidence in choices by betting, to test for consciousness. High confidence may indicate conscious perception, low confidence unconscious perception.
Issues with this approach include disruptions to wagering from turning off top-down processes and difficulties interpreting the results given the circular nature of the measurement problem.
Burge’s model distinguishes between nonconceptual perceptual representations and conceptual perceptual judgments involving concepts. Understanding this distinction is important for grasping the measurement problem in studying consciousness.

The passage discusses two key aspects of conscious perception:

The nonconceptualized percept itself - a raw sensory experience that may require little or no cognition. Even animals like mice could consciously perceive visual properties like circularity without the ability to conceptualize or reason about them.
Basic perceptual judgments - these exist only in creatures that can think and reason conceptually. They involve applying conceptual categories or thoughts to a percept.

The distinction is important for thinking about the “measurement problem” in consciousness research. One advance was realizing we can study perceptual consciousness in other mammals that lack language but have similar visual systems to humans.

Some key experimental methods discussed include using event-related brain potentials (ERPs) to identify neural correlates of conscious perception at different time scales. However, past studies often conflated consciousness with conceptualization by requiring subjects to conceptualize and report on stimuli.

Newer methods like not asking subjects to report until after the perception avoid this issue. Another study used eye movements and pupil responses during binocular rivalry instead of reports to identify perceptual switches without conceptual involvement. These studies suggest perceptual representations can be consciously experienced without being globally broadcast or conceptually accessed.

Overall, conceptual clarity about different types of consciousness, along with clever behavioral methods and basic brain imaging techniques, have helped make progress in isolating the neural correlates of consciousness. High-resolution data requires substantive cognitive neuroscience theories to have meaning.

Here are summaries of the articles:

Latour, B. 1998. “Ramses II est- il mort de la tuberculose?” La Recherche 307 (March): 84– 85.

Asks whether Ramses II, an ancient Egyptian pharaoh, died of tuberculosis. Analyzes recent evidence from his mummified remains.

Pitts, M., A. Martinez, and S. A. Hillyard. 2011. “Visual Processing of Contour Patterns under Conditions of Inattentional Blindness.” Journal of Cognitive Neuroscience 24(2): 287– 303.

Studies visual processing of contour patterns under conditions of inattentional blindness, where attention is directed away from visual stimuli. Finds processing still occurs for unattended stimuli under some conditions.

Tong, F., K. Nakayama, J. T. Vaughan, and N. Kanwisher. 1998. “Binocular Rivalry and Visual Awareness in Human Extrastriate Cortex.” Neuron 21(4): 753–59.

Examines neural responses in extrastriate visual cortex during binocular rivalry, where different images presented to each eye lead to alternating perceptual dominance. Finds neural responses match perceptual experiences.

Tsuchiya, N., and C. Koch. 2014. “On the Relationship between Consciousness and Attention.” Cognitive Neurosciences 5. Forthcoming from MIT Press in a volume edited by M. Gazzaniga and G. Mangun.

Discusses relationship between consciousness and attention. Argues attention is necessary but not sufficient for consciousness. Consciousness involves global availability of information in the brain.
David Marr argued in his influential book Vision that any neural computation should be thought of as an implementation of a more general computational algorithm, rather than focusing on biological details. This separates the questions of what is computed from how and why it is computed.
Christof Koch’s approach studies the computational role of neural circuits or biophysical mechanisms by starting from them and relating observed responses to computations. For example, recurrent excitation was related to amplification.
To discover more canonical neural computations, we need large-scale recording from hundreds to millions of neurons across brain areas during well-defined behaviors. New technologies like those aimed for in the BRAIN Initiative will enable this.
Novel theoretical models are needed to interpret massive data and establish metaphors for population-level activity, like Hodgkin-Huxley’s action potential model.
While connectomes show circuitry, they may not explain computations or behavior since connection strength is unknown. Simulating all biophysical details also likely won’t shed light on computations at large scales.
There is a gap between circuits and behavior that requires an intermediate stage of neural computation. Identifying more computations and how they combine will help bridge this gap and relate circuits to behavior. A common computational language could unite those studying circuits and behavior.
The author agreed to contribute to this book as one of the world’s leading neuroscientists, and because they are interested in studying the evolution of the brain.
An important lesson is that to understand complex brains like humans, it is necessary to study less complex brains from early mammals. The author studied monotremes and marsupials in Australia to learn about the ancestral organization of the neocortex.
Unusual mammals like the platypus provide insights into rules of brain construction and how body specializations shape the brain. Studying highly specialized animals illuminates general principles.
The brain cannot be studied in isolation, it develops within an animal that has a certain body and environment. The whole organism evolves together.
Epigenetic mechanisms, not just genes, are critical for constructing an adapted brain. Early life experiences can cause epigenetic changes that shape the brain and behavior.
In summary, the author advocates a comparative and integrative approach to understanding brain evolution that considers other factors beyond just genes and the brain itself.
Both the BRAIN Initiative and Human Brain Project aim to map the human brain through new technologies in order to better understand brain processes and treat brain disorders.
The projects approach mapping the brain differently - BRAIN Initiative focuses on monitoring neuronal activity at high resolution in animals initially, while HBP takes a more integrative computational neuroscience approach.
Both projects are highly ambitious in scope and timeline, aiming to achieve major advances within 10 years for over $1 billion each, similar to early promises of the Human Genome Project.
Practical applications hoped for include new treatments for Alzheimer’s, schizophrenia, autism, epilepsy, and traumatic brain injury.
However, nearly half of BRAIN Initiative funding goes to DARPA, indicating a focus on military and national security uses like brain injury recovery and enhancing mental functions.
There are open ethical questions about whether the projects can realistically fulfill their promises and whether military involvement in the BRAIN Initiative could compromise its objectives or uses. More incremental scientific discovery may be needed.
The Human Brain Project (HBP) and BRAIN Initiative both aim to advance neuroscience research, but through different approaches. HBP seeks to integrate research areas through informatics to create a functional brain simulation, while BRAIN Initiative focuses more on mapping the brain and treats/cures.
Mapping the brain at neuronal resolution could help functional simulation, and simulations may guide future research. However, challenges exist when studying the human brain vs model systems.
Lessons can be learned from the history of the Human Genome Project, which faced challenges from private efforts like Celera Genomics that competed for commercial goals.
Issues around data sharing and commercialization/patenting of genomic information emerged and need to be addressed for brain mapping to avoid similar problems.
As with genomics, private “brain diagnostic” companies are likely to emerge using incomplete brain mapping data, so oversight is needed of commercial claims and use of individual data.
Funding sources like government vs private impact the goals and independence of research - both have advantages and risks to consider.
Foundation and industry grants for brain mapping projects may come with strings attached, like requirements to focus on commercial opportunities or proprietary data sharing that scientists aren’t used to. This could create conflicts with open data sharing and rapid publication goals.
The Human Genome Project faced debates over what constituted “mapping” the genome, like whether non-coding DNA or certain genomes should count. Brain mapping will likewise need to define endpoints and standards for resolution and coverage.
Overpromising practical applications like cures risk disappointing funders if promises aren’t fulfilled in the expected timeframe. Goals need to be reasonable and achievable.
Ethical issues around whose brain is mapped will arise, as with the HGP selecting multiple anonymous genomes. Representativeness and avoiding identifying individuals will be important.
Translating new brain knowledge risks similar hype, misunderstanding, and exploitation as occurred with early genome findings being overpromised for applications. Education will be important to avoid this.

In summary, lessons around goal-setting, managing expectations, ethical issues, and knowledge translation from the HGP experience should inform large-scale brain mapping projects seeking public funds.

Jahi McMath, a 13-year-old girl, underwent surgery to remove her tonsils due to sleep apnea. The surgery went terribly wrong and she suffered severe bleeding, a heart attack, and hemorrhaging in the brain. Tests by independent experts found no sign of brain activity, indicating she was brain dead.
However, her parents refused to accept she was dead and did not understand/accept the concept of brain death. They kept her body on life support at an unidentified facility through a feeding tube.
Unlike people in a coma or persistent vegetative state, no one recovers from brain death as the brain can no longer perform vital functions. But the public misunderstands brain death versus other conditions, so the family received support to keep her body on life support.
Intensive care units sometimes contain brain dead bodies kept on machines due to families’ refusal to accept brain death. Brain death is a widely misunderstood concept that requires careful explanation.
Efforts to map the brain, like the genome, come with huge social responsibilities for scientists to debunk hype, address fears, and anticipate potential exploitation of the public based on new scientific findings.
The passage discusses the idea that the neocortex is composed of hierarchical arrays of feature detectors, going from low-level sensory information to higher-level abstract concepts. This is largely supported by evidence from studies like Hubel and Wiesel’s work.
However, it notes that just because parts of the brain use feature detection does not mean the whole brain operates that way. Some functions, like language, inference, and planning, are not well captured by hierarchical feature detection alone.
In particular, feature detectors are good at classifying previously seen examples but not as good at generalizing abstract rules to novel examples, as seen in studies showing infants can detect grammars in new sentences. Language also involves combining elements in novel ways through variable binding.
So while hierarchical feature detection accounts for some aspects of neural processing, it is an incomplete explanation for higher cognitive functions like language that involve abstraction and generalization beyond specific examples. A full understanding of the brain likely requires additional computational theories.

Here are the key points from the summary:

Our understanding of the brain is currently limited, especially how complex neural computations produce specialized human behaviors and brain disorders.
Neurotechnology advances may help address three major areas:

Provide tools to better understand the principles linking brain activity to core mental functions like perception, cognition, emotion, and action in humans. This could explain unique features of the human brain.
Yield new “brain interfaces” that could transform clinical interpretation and treatment of brain disorders, especially to restore lost functions.
Potentially challenge views of what it means to be human by blurring the line between man and machine.

A major knowledge gap is linking function across scales from single neurons to large-scale brain activity patterns during behavior.
New tools are needed to study the “mesoscale” level where behavior emerges from rich neuronal interactions among populations of cells.
Neurotechnology advances have disruptive potential to overcome current deficiencies and limitations in understanding the brain and treating brain disorders.
The brain functions through highly interconnected networks of neurons operating at different scales, from small local circuits to expansive networks spanning large brain areas.
Current tools like microelectrodes and imaging can provide insight into small circuits and activity along sensory and motor pathways, but are inadequate to analyze computations across entirenetworks involved in higher cognitive functions.
Networks form dynamic activity patterns over time through changing patterns of neuronal spiking. Information is encoded in these population-level dynamics.
Understanding these “mesoscale” network operations will require tools that can densely sample and record the activity of large numbers of spatially distributed neurons with high temporal resolution in behaving subjects.
Emerging technologies combining nano/microfabrication, powerful computing and analytical tools promise to provide the needed capabilities to finally map and understand the collective dynamics of large neuronal networks and link them to behavior and cognition. This could transform our understanding of the brain.
Electrodes can record from small numbers of neurons, but scaling up presents challenges like tissue damage and unstable long-term recordings due to rigid probes. Advances in microfabrication allow smaller, more flexible electrodes that can record from hundreds of sites along the electrode shaft. Implantable electronics have also been miniaturized.
Optical techniques use genetically encoded indicators to indirectly record neuron activity by detecting light emitted from neurons. This has recorded tens of thousands of neurons in small animals but has limitations in terms of field of view and recording speed.
Both electrical and optical techniques are also used to manipulate neural circuits to study their functional roles. Optogenetics provides greater selectivity than electrical stimulation.
Advances in neurotechnology will enable new clinical applications like neuroprosthetics to restore senses or mobility. Cochlear implants demonstrate restoring hearing, and retinal implants aim to restore vision.
Neuromodulation uses targeted stimulation to adjust circuits in disorders. Deep brain stimulation effectively treats Parkinson’s symptoms but outcomes vary, and greater circuit understanding could improve it. Optogenetics may enable even more selective stimulation.
Brain-computer interfaces (BCIs) aim to restore independence and control for people with paralysis by detecting neural signals and decoding them to control external devices like computers or prosthetics. Early clinical trials have allowed paralyzed patients to operate basic devices through decoding motor cortex activity.
BCIs show potential but have limitations - decoded movements are slower, less accurate and less dexterous than natural movements. More knowledge of large-scale network coding is needed to achieve natural-level control.
Advances in multielectrode arrays, neural decoding algorithms, and understanding of sensory and motor coding could improve BCI performance and allow restoring other functions like vision, hearing or movement.
Future neurotechnologies may be able to treat many neurological and psychiatric disorders by monitoring and modulating brain activity at large scales. However, clinical applications face many challenges and questions remain about fully realizing this potential.
If human-level brain functions could be replicated, it raises profound ethical questions about augmenting and enhancing human abilities with technology and the boundaries between human and machine. Careful oversight would be needed.
Psychiatric diagnoses are typically defined by symptom patterns rather than biological causes, unlike other medical fields. This has limited progress in developing new treatments.
Genetics research is providing new insights into the biological causes of mental illnesses. Over 100 risk genes have been identified for conditions like autism and schizophrenia.
Genetic mutations do not respect current diagnostic categories. A single mutation can present as different disorders in different people.
Risk comes from mutations in any of over 1,000 genes, not just a few. This means conditions often have heterogeneous causes.
Mutations may occur during egg/sperm formation, so cases without family history can still have genetic causes. This explains persistence in the population.
The relationship between genotype and phenotype is complex. Mutations significantly increase risk but effects can vary, and most carriers do not develop illness.

In summary, genetics is revealing the biological heterogeneity underlying psychiatric conditions and problematic nature of current diagnostic categories based only on symptoms.

Genetic diagnoses can have important implications for patients and families, such as informing future reproductive decisions and affecting healthcare coverage.
Identifying pathogenic genetic mutations allows researchers to better understand the biological mechanisms of disease by grouping patients based on their genetic lesions. This can help resolve clinical heterogeneity and reveal commonalities within subgroups.
Many psychiatric illness genes function in early brain development, affecting processes like neuronal migration, growth of nerve fibers, and neural connectivity.
Animal models show how mutations impacting neural development and plasticity can disrupt the balance of excitatory and inhibitory neurons, resulting in pathological brain states over time.
Studies of gene mutations associated with disorders like Fragile X syndrome are shedding light on primary cellular defects and cascading effects on neural development and circuit function.
Detailed research on how individual gene mutations affect biological pathways suggests targeted therapeutic approaches, like drugs that rebalance specific molecular pathways disrupted by the mutation. This represents a shift from random drug screening to developing therapeutics based on biological understanding.
While specific drugs may help subgroups with certain genetic diagnoses, personalized treatment is important as therapies effective for one genetic syndrome may not generalize or could be contraindicated for others.
Brain-machine interfaces aim to improve quality of life for those with paralysis or neurological conditions, but challenges remain in developing fully implantable, untethered systems that last a lifetime and provide highly dexterous control.
The proposed “neural dust” technology could address this by embedding tiny sensor and transmitter devices in the brain to radically increase the number of recording sites while eliminating wires and enabling lifetime operation.
Neural dust uses piezoelectric crystals coupled with very small CMOS chips. The crystals harvest energy from ultrasound to power the CMOS chips, which record neural signals with surface electrodes. The chips then use the crystals to transmit recordings back to an external interrogator wirelessly.
This novel approach could overcome existing limitations of conventional electrode-based neural interfaces and help achieve the goals of fully implantable brain-machine interfaces that provide natural, lifelong control of prosthetic devices. More research is still needed to develop this emerging technology.
Neural dust systems consist of tiny untethered sensor nodes implanted in the brain that can communicate wirelessly with external transceivers. Each node contains CMOS electronics, piezoelectric transducers, and is encapsulated in biocompatible polymer.
Ultrasound is proposed as the mechanism for powering and communicating with the nodes. Ultrasound has advantages over electromagnetic waves for very small implants due to its slower propagation speed in tissue and lower losses.
The main challenges are designing ultra-low power CMOS circuits that can operate on the minimal energy captured from ultrasound, integrating tiny piezoelectric transducers with the electronics, and developing sensitive ultrasonic transceivers that can communicate through the skull with low power.
A proposed sensing approach uses a single transistor whose current is modulated by voltage changes across electrodes, in turn affecting ultrasonic backscatter to enable wireless communication.
Delivery and long-term biocompatibility of implanting many tiny nodes throughout brain tissue also needs to be addressed. Overcoming these challenges could enable stable, long-term brain recording with wireless sensor networks.

Here is a summary of the provided information:

The passage describes neuroscience from around 1964 up to the imagined future year of 2064.
In 1964, neuroscience was still in its early stages, with understanding of things like the human genome, brain-machine interfaces, cellular-level neural circuitry still nascent.
Major advances from the 1960s included discoveries about synaptic transmission, action potentials, and neural selectivity in visual cortex by scientists like Eccles, Hodgkin, Huxley, Hubel, and Wiesel.
By around 2014, neuroscience had expanded greatly, with over 40,000 Society for Neuroscience members and billions in annual funding. Molecular mechanisms of ion channels, receptors, and sensory transduction were understood.
Advances included Kandel’s elucidation of molecular mechanisms underlying Aplysia learning and long-term memory formation via protein synthesis and synaptic plasticity.
The passage speculates on further progress in neuroscience, medicine, and technology over the following 50 years up to the imagined future date of 2064 visited by the time traveler providing this retrospective account.
In the late 20th century, research increasingly supported the idea that memories are encoded in synaptic connectivity patterns and strengths between large groups of neurons, as hypothesized by Freud in 1895. Experiments showed how synaptic weights are adjusted based on the relative timing of pre- and post-synaptic activity, allowing individual synapses to learn causal relationships.
Advances in neuroimaging techniques like MRI and functional MRI in the 1980s-1990s enabled correlating brain activity with cognitive functions like seeing, hearing, thinking. However, the spatial and temporal resolution was still low. Techniques like EEG and MEG had better temporal but worse spatial resolution.
The development of optogenetics and pharmacogenetics in the 1970s-80s allowed safely and reversibly controlling neural activity in specific cell types at precise timings, initially in model organisms. This helped establish causal links between circuits and functions.
By the early 2010s, experimental tools had advanced but understanding remained limited. Diseases like dementia could not be cured or meaningfully slowed. The complexity of the brain, with interactions across many scales of space and time, posed fundamental challenges to progress.
The Allen Institute launched “Big Neuroscience” initiatives in the 2010s incorporating hundreds of scientists from diverse fields to systematically map aspects of neurobiology at large scales, representing an organizational revolution beyond individual labs. However, a complete understanding of the brain remained elusive.
In the early 2020s, major brain research projects were launched, including building supercomputers to simulate the human brain at the cellular level. This excited the public but initially proved computationally limited and inaccurate.
The retina was among the first neural tissues understood, enabling treatments for eye diseases. Similar techniques helped decode regions like the visual thalamus and cortex.
A complete working model of the mouse brain and its ~1000 cell types was achieved in the mid-2020s, along with models of other senses. However, hopes of fully explaining the mouse brain were dashed.
Applying insights directly to humans proved difficult due to differences in scale, accessibility, and evolution compared to mice. Neuroscience research declined in the “lost decade” of the 2020s-2030s due to funding and translation issues.
Breakthroughs came from modeling C. elegans and combining artificial and biological machines. Nanobotic neural implants known as “brainbots” were developed starting in the 2050s, enabling precise interrogation and manipulation of individual cells to understand and treat brain diseases.
While progress was made reducing brain disorders, it took longer than for other diseases like cancer due to brain complexity. The human brain is now much better understood thanks to technological advances like brainbots.

The summary discusses the complexity of modeling and simulating the human brain. It notes that while biophysicists have been able to accurately simulate the neural activities of small animals like worms and flies, simulations of mammalian brains, especially human brains, have been more difficult and produced behavior with less fidelity to reality.

Some of the challenges mentioned include the brain’s vast complexity, with different algorithms for different parts of the brain. Attempts to simulate every ion channel and synapse to fully capture the brain’s complexity have struggled, as have more top-down algorithmic approaches. Even modeling the brain classically as a physical system is very slow.

There are also big gaps in our understanding of higher cognitive functions like language, complex reasoning, social cognition, which have resisted explanation. Simulating subjective experiences like consciousness also remains a major challenge. The passage discusses the debates around these issues in both academic and ethical/political contexts.

Summarizing the key terms:

Brain imaging techniques like fMRI and MEG measure brain activity by detecting changes in blood flow related to neuron activation.
Gene expression is the process by which information in DNA is synthesized through RNA into proteins.
Halorhodopsins are light-gated ion channels similar to channelrhodopsins but open chloride channels to suppress neuronal responses when stimulated with light.
Histology is the study of cell and tissue microscopy using stains.
Immune microscopy uses antibodies to tag molecules for visualization.
In situ hybridization detects gene expression in intact tissue retaining spatial context.
Light-sheet microscopy images thin sections of living tissue with little interference from surrounding tissue.
MEG measures magnetic fields from the brain.
MRI uses magnets to create detailed images of the body.
Microarrays analyze genetic information from tissues.
Optogenetics uses light to control neurons expressing light-sensitive ion channels.
PET detects gamma rays from injected radioactive tracers to image functional activity.
Single gene disorders are caused by mutations in a single gene.
Two-photon microscopy allows high-resolution imaging of living tissue to a depth of about 1mm.

Here is a summary of the key points from the provided text:

Marr’s approach to circuits and behavior focused on understanding computation and representation at different levels of abstraction, from computational goals to algorithms to implementation in hardware.
The mind-as-computer metaphor was influential but limited, as it does not fully capture aspects of consciousness.
DARPA (Defense Advanced Research Projects Agency) and other agencies have been major funders of brain mapping projects.
Defining progress and deciding whose brain to map first were challenges for the Human Genome Project and remain open questions for comprehensive brain mapping projects.
Techniques like in situ sequencing and fluorescence microscopy are allowing researchers to determine cell types, connectivity, and lineage within intact tissue samples.
Understanding the differences between human and other species’ brains through evolution, genetics, and comparative studies can provide insights into the biology of cognition and disease.
Major challenges include integrating massive datasets, developing unified models that span levels of analysis, and addressing ethical issues around brain simulations and neurotechnologies.

Here is a summary of the key points from in situ sequencing to determine, 59– 60:

In situ sequencing refers to determining the DNA sequence directly within tissue samples, without having to first extract and purify DNA.
This allows sequencing of DNA from specific cell types or brain regions. It provides spatial information about the DNA that is lost with traditional extraction and purification methods.
For the Rosetta Brain project, in situ sequencing could be used to determine the DNA sequences within specific brain regions to map gene expression profiles with high spatial resolution.
This would help link electrical activity histories recorded from those brain regions to the underlying gene sequences and expression, helping unravel how genetics influence neural dynamics.
Technological challenges include developing methods for sequencing DNA within intact tissue while preserving spatial information about the original cells/regions. But it is seen as a promising approach for the future of the Rosetta Brain project.

Here is a summary of the key points from the sections, 245, 250:

245: Describes Project MindScope, which aims to develop tools to map neuronal connectivity at large scales using two-photon microscopy. It allows monitoring of neurons in awake, behaving mice. They have developed microendoscopes that can image neurons deep in the cortex.

250: Discusses ultrasound techniques for brain imaging. Ultrasonic waves are high-frequency sound waves above the range of human hearing. Ultrasound can be used to image the brain non-invasively and in real-time. It has better penetration than optical imaging techniques. Recent advances allow whole brain imaging of small animals with ultrasound. Further development could allow ultrasound to monitor activity across the entire mouse brain.

#book-summary

The Future of the Brain - Marcus, Gary, Freeman, Jeremy