Understanding brain function requires monitoring and interpreting the activity of large networks of neurons during behavior. Advances in recording technology are greatly increasing the size and complexity of neural data. Analyzing such data will pose a fundamental bottleneck for neuroscience. We present a library of analytical tools called Thunder built on the open-source Apache Spark platform for large-scale distributed computing. The library implements a variety of univariate and multivariate analyses with a modular, extendable structure well-suited to interactive exploration and analysis development. We demonstrate how these analyses find structure in large-scale neural data, including whole-brain light-sheet imaging data from fictively behaving larval zebrafish, and two-photon imaging data from behaving mouse. The analyses relate neuronal responses to sensory input and behavior, run in minutes or less and can be used on a private cluster or in the cloud. Our open-source framework thus holds promise for turning brain activity mapping efforts into biological insights.

Empirical estimates of the dimensionality of neural population activity are often much lower than the population size. Similar phenomena are also observed in trained and designed neural network models. These experimental and computational results suggest that mapping low-dimensional dynamics to high-dimensional neural space is a common feature of cortical computation. Despite the ubiquity of this observation, the constraints arising from such mapping are poorly understood. Here we consider a specific example of mapping low-dimensional dynamics to high-dimensional neural activity-the neural engineering framework. We analytically solve the framework for the classic ring model-a neural network encoding a static or dynamic angular variable. Our results provide a complete characterization of the success and failure modes for this model. Based on similarities between this and other frameworks, we speculate that these results could apply to more general scenarios.

Mapping mammalian synaptic connectivity has long been an important goal of neuroscientists since it is considered crucial for explaining human perception and behavior. Yet, despite enormous efforts, the overwhelming complexity of the neural circuitry and the lack of appropriate techniques to unravel it have limited the success of efforts to map connectivity. However, recent technological advances designed to overcome the limitations of conventional methods for connectivity mapping may bring about a turning point. Here, we address the promises and pitfalls of these new mapping technologies.

A tool to map changes in synaptic strength during a defined time window could provide powerful insights into the mechanisms governing learning and memory. We developed a technique, Extracellular Protein Surface Labeling in Neurons (EPSILON), to map α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR) insertion in vivo by pulse-chase labeling of surface AMPARs with membrane-impermeable dyes. This approach allows for single-synapse resolution maps of plasticity in genetically targeted neurons during memory formation. We investigated the relationship between synapse-level and cell-level memory encodings by mapping synaptic plasticity and cFos expression in hippocampal CA1 pyramidal cells upon contextual fear conditioning (CFC). We observed a strong correlation between synaptic plasticity and cFos expression, suggesting a synaptic mechanism for the association of cFos expression with memory engrams. The EPSILON technique is a useful tool for mapping synaptic plasticity and may be extended to investigate trafficking of other transmembrane proteins.

Identifying the neurotransmitters used by specific neurons is a critical step in understanding the function of neural circuits. However, methods for the consistent and efficient detection of neurotransmitter markers remain limited. Fluorescence hybridization (FISH) enables direct labeling of type-specific mRNA in neurons. Recent advances in FISH allow this technique to be carried out in intact tissue samples such as whole-mount brains. Here, we present a FISH platform for high-throughput detection of eight common neurotransmitter phenotypes in brains. We greatly increase FISH throughput by processing samples mounted on coverslips and optimizing fluorophore choice for each probe to facilitate multiplexing. As application examples, we demonstrate cases of neurotransmitter co-expression, reveal neurotransmitter phenotypes of specific cell types and explore the onset of neurotransmitter expression in the developing optic lobe. Beyond neurotransmitter markers, our protocols can in principle be used for large scale FISH detection of any mRNA in whole-mount fly brains.

The function of a neural circuit is shaped by the computations performed by its interneurons, which in many cases are not easily accessible to experimental investigation. Here, we elucidate the transformation of visual signals flowing from the input to the output of the primate retina, using a combination of large-scale multi-electrode recordings from an identified ganglion cell type, visual stimulation targeted at individual cone photoreceptors, and a hierarchical computational model. The results reveal nonlinear subunits in the circuity of OFF midget ganglion cells, which subserve high-resolution vision. The model explains light responses to a variety of stimuli more accurately than a linear model, including stimuli targeted to cones within and across subunits. The recovered model components are consistent with known anatomical organization of midget bipolar interneurons. These results reveal the spatial structure of linear and nonlinear encoding, at the resolution of single cells and at the scale of complete circuits.

Neurons express various combinations of neurotransmitter receptor (NR) subunits and receive inputs from multiple neuron types expressing different neurotransmitters. Localizing NR subunits to specific synaptic inputs has been challenging. Here, we use epitope-tagged endogenous NR subunits, expansion light-sheet microscopy, and electron microscopy (EM) connectomics to molecularly characterize synapses in Drosophila. We show that in directionally selective motion-sensitive neurons, different multiple NRs elaborated a highly stereotyped molecular topography with NR localized to specific domains receiving cell-type-specific inputs. Developmental studies suggested that NRs or complexes of them with other membrane proteins determine patterns of synaptic inputs. In support of this model, we identify a transmembrane protein selectively associated with a subset of spatially restricted synapses and demonstrate its requirement for synapse formation through genetic analysis. We propose that mechanisms that regulate the precise spatial distribution of NRs provide a molecular cartography specifying the patterns of synaptic connections onto dendrites.

Assigning behavioral functions to neural structures has long been a central goal in neuroscience and is a necessary first step toward a circuit-level understanding of how the brain generates behavior. Here, we map the neural substrates of locomotion and social behaviors for Drosophila melanogaster using automated machine-vision and machine-learning techniques. From videos of 400,000 flies, we quantified the behavioral effects of activating 2,204 genetically targeted populations of neurons. We combined a novel quantification of anatomy with our behavioral analysis to create brain-behavior correlation maps, which are shared as browsable web pages and interactive software. Based on these maps, we generated hypotheses of regions of the brain causally related to sensory processing, locomotor control, courtship, aggression, and sleep. Our maps directly specify genetic tools to target these regions, which we used to identify a small population of neurons with a role in the control of walking.

•We developed machine-vision methods to broadly and precisely quantify fly behavior•We measured effects of activating 2,204 genetically targeted neuronal populations•We created whole-brain maps of neural substrates of locomotor and social behaviors•We created resources for exploring our results and enabling further investigation

Machine-vision analyses of large behavior and neuroanatomy data reveal whole-brain maps of regions associated with numerous complex behaviors.

Understanding the principles governing neuronal diversity is a fundamental goal for neuroscience. Here we provide an anatomical and transcriptomic database of nearly 200 genetically identified cell populations. By separately analyzing the robustness and pattern of expression differences across these cell populations, we identify two gene classes contributing distinctly to neuronal diversity. Short homeobox transcription factors distinguish neuronal populations combinatorially, and exhibit extremely low transcriptional noise, enabling highly robust expression differences. Long neuronal effector genes, such as channels and cell adhesion molecules, contribute disproportionately to neuronal diversity, based on their patterns rather than robustness of expression differences. By linking transcriptional identity to genetic strains and anatomical atlases we provide an extensive resource for further investigation of mouse neuronal cell types.

Messenger RNA localization is important for cell motility by local protein translation. However, while single mRNAs can be imaged and their movements tracked in single cells, it has not yet been possible to determine whether these mRNAs are actively translating. Therefore, we imaged single β-actin mRNAs tagged with MS2 stem loops colocalizing with labeled ribosomes to determine when polysomes formed. A dataset of tracking information consisting of thousands of trajectories per cell demonstrated that mRNAs co-moving with ribosomes have significantly different diffusion properties from non-translating mRNAs that were exposed to translation inhibitors. These data indicate that ribosome load changes mRNA movement and therefore highly translating mRNAs move slower. Importantly, β-actin mRNA near focal adhesions exhibited sub-diffusive corralled movement characteristic of increased translation. This method can identify where ribosomes become engaged for local protein production and how spatial regulation of mRNA-protein interactions mediates cell directionality.