The goal of the workshop is to bring together researchers interested in developing executable models of neural computation/processing of the brain of model organisms. Of interest are models of computation that consist of elementary units of processing using brain circuits and memory elements. Elementary units of computation/processing include population encoding/decoding circuits with biophysically-grounded neuron models, non-linear dendritic processors for motion detection/direction selectivity, spike processing and pattern recognition neural circuits, movement control and decision-making circuits, etc. Memory units include models of spatio-temporal memory circuits, circuit models for memory access and storage, etc. A major aim of the workshop is to explore the integration of various sensory and control circuits in higher brain centers.
A Fruit Fly Brain Hackathon is being conducted in conjunction with the workshop. Workshop participants are welcome to attend the hackathon.
Aurel A. Lazar, Columbia University
Registration is free but all participants have to register. Thank you!
Please follow this link for lodging details and directions to the hotel and venue.
The 2019 Columbia Workshop on Brain Circuits, Memory and Computation is supported by the
Department of Electrical Engineering, Columbia University
Center for Computing Systems for Data-Driven Science, Data Science Institute, Columbia University
School of Engineering and Applied Science, Columbia University
9:00 - 9:45 AM
The Somnostat: Mechanisms for Balancing Sleep Need and SleepGero A. Miesenboeck, Centre for Neural Circuits and Behaviour, University of Oxford.
Sleep is vital and universal, but its biological function remains unknown. We seek to understand why we need to sleep by studying how the brain responds to sleep loss. Our studies in Drosophila have pinpointed neurons whose sleep-inducing activity switches on as sleep deficits accrue, revealed how this activity switch works, and furnished a molecular interpretation of sleep pressure, its accumulation, and its discharge.
9:45 - 10:30 AM
Circuits that Encode and Predict Alcohol Associated PreferenceKarla Kaun, Department of Neuroscience, Brown University.
The ability to associate a rewarding stimulus with a sensory cue from the environment is critical for an animal’s ability to find food and mates. Mapping the circuits in which these associations are formed, then initiate an output response provides a scaffold for understanding how experiences can influence decisions. Drugs of abuse such as alcohol can induce lasting changes in these circuits to induce maladaptive behavioral decisions. Here we describe a how memories for the intoxicating properties of alcohol are acquired and expressed through different mushroom body circuits. Acquisition of odor-alcohol memories induce lasting molecular changes which affect plasticity within circuits important for memory expression. Expression of these memories requires a remarkably complex multi-level circuit whereby dopamine directly, and indirectly via the mushroom body, modulates the activity of glutamatergic and cholinergic output neurons. Moreover, trans-synaptic tracing the outputs of these neurons suggests convergent and divergent networks, providing an elaborate framework for integrating external content and internal state to coordinate an appropriate output response. Together this work provides a snapshot of how alcohol can affect the dynamic molecular and circuit mechanisms required for behavioral decisions.
Joint work with Kristin M. Scaplen1, Mustafa Talay2, Emily Petruccelli3, Nicolas Ledru4, Gilad Barnea1, 1Department of Neuroscience, Brown University, Providence, RI. 2Department of Molecular and Cell Biology, Harvard University, Cambridge, MA. 3Department of Biology, Southern Illinois University, Edwardsville, IL. 4MSTP Program, Washington University, St Louis, MO.
Funding support: NIAAA R01AA024434, NIGMS P20GM103645, and the Smith Family Award for Excellence in Biomedical Research.
11:00 - 11:45 AM
Thermosensing in the Fly: from Genes to Cells to BehaviorPaul A. Garrity, Department of Biology, Brandeis University.
Temperature is a universal physical variable that affects all aspects of physiology. Among other vital functions, animals rely on their thermosensory systems to maintain appropriate body temperatures and avoid thermal extremes. Thermosensing in Drosophila melanogaster depends on multiple classes of thermosensors, which rely on diverse classes of molecular thermoreceptors, exhibit distinct thermal sensitivities and have distinct behavioral functions. How these thermosensory neurons encode temperature information and how the information they provide supports behavior remain largely open questions. I will present recent work in which we have identified a critical role for phasic thermosensors (sensory neurons which primarily respond to heating and cooling rather than hot or cold temperatures) in innocuous thermosensation in Drosophila. At the molecular level, we have identified the molecular receptors that confer thermosensitivity upon these neurons and found that these molecules are critically important for the morphogenesis of the elaborate sensory compartment involved in sensing temperature changes. At the behavioral level, we find that these phasic thermosensors are essential for mediating behavioral responses across a wide range of temperatures, consistent with their ability to respond robustly to small temperature changes at very different ambient temperatures. Together these findings begin to provide a consistent picture of thermosensory processing in the fly brain and raise questions concerning how phasic inputs might combine with other sensory inputs to drive behavior.
11:45 - 12:30 PM
Olfactory Evolution in Drosophilids: Receptors, Neurons and BehavioursRichard Benton, Center for Integrative Genomics, University of Lausanne.
My group is interested in understanding the structure, function and evolution of neural circuits. We exploit the olfactory system of Drosophila melanogaster as a model, which is well-described, experimentally accessible and dynamically evolving. Furthermore, genomic and growing genetic access to closely related, but ecologically diverse, drosophilids provides an unparalleled foundation for comparative studies of their olfactory circuits. I will present our recent work on the evolution of the olfactory pathways of Drosophila sechellia, an island endemic that displays extreme specialisation for the Morinda citrifolia “noni” fruit as a food source and breeding site. We have started to define the molecular basis by which D. sechellia’s olfactory receptors are re-tuned to the odors of its host and – through development of novel neurogenetic tools in this species – how these different sensory pathways contribute to host-seeking behaviors.
2:00 - 2:45 PM
Better Connectome Reconstruction from Large Electron and Light Microscopy Volumes of the Drosophila BrainStephan Saalfeld, HHMI Janelia Research Campus, Ashburn, VA.
At the resolution necessary to unambiguously extract the synaptic connectome, the brain of the fruit fly Drosophila melanogaster has a size of about 100 trillion voxels. Today, 3D electron microscopy (3D- EM) is the only available imaging method that provides this resolution.
Automatic reconstruction of neurons and synaptic connections has seen significant improvement over the last few years, in particular thanks to notable advances in machine learning. Our contribution to this improvement is threefold:
(1) We have generated the largest existing training data set for neuron reconstruction and synaptic partner detection from non-isotropic 3D-EM of the Drosophila brain. With secret test data of equal size, we have organized an ongoing public challenge for connectivity reconstruction in the Drosophila brain (http://cremi.org). (2) Together with collaborators, we have developed the leading neuron segmentation method for this dataset. Further evaluations and improvements are currently underway. (3) We developed the leading method for synaptic cleft detection and reconstructed all synaptic clefts in 3D-EM of the complete Drosophila brain. We generated a new, high-resolution statistical atlas of the Drosophila brain from resolution limited confocal microscopy images visualizing all synapses in the neuropils of the fly, and we automatically registered the synapse cloud of the EM volume with the light microscopy atlas such that neurons reconstructed in EM can be matched with sparsely labeled light microscopy images.
I will give a brief overview of the state of automatic connectome reconstruction in the fly brain and will discuss our ongoing efforts to improve upon the current state.
2:45 - 3:30 PM
Data-Driven Modeling of the Cortex Based on a Systematic Experimental PlatformAnton Arkhipov, Allen Institute of Brain Science.
The Mindscope project at the Allen Institute for Brain Science aims to elucidate mechanisms underlying cortical function in the mouse, focusing on the visual system. This involves concerted efforts of multiple teams characterizing cell types, connectivity, and neuronal activity in behaving animals. An integral part of these efforts is the construction of models of the cortical tissue and cortical computations. We will discuss our current progress on this front. First, we built a computational pipeline to produce models of individual neurons based on slice electrophysiology and morphology reconstructions. Second, we used these models as building blocks for large simulations of cortical activity in response to visual stimuli. A highly realistic 230,000-neuron model of the mouse cortical area V1, receiving thalamocortical visual inputs, has been constructed at two levels of resolution: one using the biophysically detailed, compartmental neuron models and the other using point-neuron models. Third, we perform systematic comparisons of simulated responses to in vivo experiments and investigate the structure-function relationships in the models to make mechanistic predictions for experimental testing. To enable this work, we developed the software suite called Brain Modeling ToolKit (BMTK) and a modeling file format called SONATA. These tools, the models, and simulation results are all being made freely available to the community via the Allen Institute Modeling Portal.
Using Machine Vision and Learning to Discover How the Brain Generates BehaviorKristin Branson, HHMI Janelia Research Campus, Ashburn, VA.
In this talk, I will overview our lab's work using machine vision and learning to analyze animal behavior from video in large-scale neuroscience experiments, including pose tracking, supervised behavior classification, and unsupervised behavior analysis.
The Neural Circuit Basis of Behavioral IndividualityBenjamin L. de Bivort, Department of Organismic and Evolutionary Biology, Harvard University.
Individuals animals vary in their behaviors even when their genetics and environment are held constant. The mechanisms underlying this variation is still largely uncharacterized, though we have made some progress in understanding genetic and circuit variants that lead a population of animals to exhibit high or low variability in behavior. These are key insights, but fall short of predicting the specific behavioral biases of individual animals. We term the causal biological features that determine individual behavioral biases "loci of individuality," and we have begun to search for them in the circuits that mediate sensory-evoked and spontaneous behaviors. We have found neural circuit elements, whose morphological properties predict behavioral biases. Specifically, the volume of axonal output arbors of central complex neurons that project to the Lateral Accessory Lobe correlates with changes in locomotor behavior in specific sensory contexts. We hypothesize that individual wiring variation in these neurons has a large effect on behavior because they lie at a bottleneck in the sensorimotor circuit, where stochastic fluctuations have an outsized effect on circuit outputs. Thus, we have found that individual variation in the structure of small numbers of neurons, in topologically critical circuit positions, predict individual behavioral biases in a sensory-context specific fashion.
Neural Circuits Underlying Sex-Specific BehavioursStephen F. Goodwin, Centre for Neural Circuits and Behaviour, University of Oxford.
All animals must continuously sequence and coordinate behaviours appropriate to both their environment and internal state if they are to survive and reproduce. Dissecting the neural substrates that initiate, organize, and terminate these behavioural sequences is critical to understanding behaviour. Courtship behaviour in Drosophila has proven to be an outstanding model for understanding how a compact circuit coordinates these critical action sequences. Our group is interested in understanding how neural circuits are functionally assembled, and how they integrate information to generate appropriate sex-specific responses.
In the fly, sex-specific behaviours are hardwired into the nervous system via the actions of two sex determination transcription factors (TFs), Doublesex (Dsx) and Fruitless (Fru). We have focused our attention on neurons that express these TFs to find anatomical or molecular sex differences in neuronal populations in order to gain an entry point into the neural circuits underlying gender-typical behaviours and identify the neuronal nodes that control component behaviours and behavioural sequencing. We have focused on the role of neurons that express dsx. These dsx+ neurons control male courtship behaviour and aspects of female receptivity. Yet little is known about the specific role of dsx+ neurons in the brain, which are believed to control mating decisions. We identified a group of sexually-dimorphic dsx neurons that are critical to sensory integration in males and females. Importantly, the dimorphism we uncovered can alter connectivity and information flow between males and females.
Towards a Brain Architecture for Visual Behavior SelectionGwyneth Card, HHMI Janelia Research Campus, Ashburn, VA.
Selecting the right behavior at the right time is critical for animal survival. Animals rely on their senses to deliver information about the environment to sensory processing areas in the brain that extract relevant features and form the perceptual representations that guide behavior. We aim to uncover the organization of this feature space and the neural mechanisms by which these cues are translated into dynamic motor activity.
Our current focus is visually-driven behaviors of the fly. In particular, those driven by visual looming cues produced by an approaching predator or an imminent collision. The same looming stimulus can evoke a wide range of different behaviors, including a rapid escape jump, a slower, more stable takeoff sequence, or a landing response. We use whole-cell patch clamp physiology in behaving flies, calcium imaging, high-throughput/high-resolution behavioral assays, and genetic tools to examine the transformation of information from sensory to motor. I will discuss our recent work investigating the representation of ethologically-relevant visual features in the fly optic glomeruli and the mechanisms by which descending neurons read out this feature information to produce an appropriate behavioral choice.
Decoding and Demixing SmellsVenkatesh N. Murthy, Department of Molecular and Cellular Biology, Harvard University.
Fluctuating mixtures of odorants, often transported in fluid environments, are detected by an array of chemical sensors and parsed by neural circuits to recognize odor objects that can inform behavioral decisions. Whether and how mice segment odor mixtures into individual components (“demix”) remains unclear. We have found that mice can be trained to recognize individual odorants embedded in unpredictable and variable background mixtures with high degree of success . Despite nonlinear interactions and variability in the representations of odor mixtures by odorant receptors , a simple linear feedforward decoding is sufficient to explain the performance of mice in this task . Current experiments are aimed at understanding how the mouse brain represents information about odor mixtures to aid odor object identification and categorization.
Recurrent Circuitry Stabilizes Cortical Odor Representations Despite Degraded Sensory InputsKevin M. Franks, Department of Neurobiology, Duke University.
Animals must recognize familiar objects even when incoming sensory input may be noisy, degraded or incomplete. Theoretical studies have shown that this process, often called “pattern completion”, can be implemented by recurrent cortical circuits within an autoassociative network. However, direct experimental evidence for this process has been lacking. Here, we recorded odor-evoked activity from simultaneous populations of neurons in mouse olfactory bulb and olfactory cortex before and after inducing anesthesia. We found that cortical odor representations remained stable across brain states even though upstream representations, in olfactory bulb, were markedly degraded under anesthesia. Odor representations were more robust in cortical pyramidal cells, which receive recurrent connections, than in semilunar cells, which do not. Furthermore, cortical odor representations became as state-dependent as those in olfactory bulb after blocking recurrent connections between pyramidal cells. Thus, we provide direct evidence for the crucial role of recurrent cortical circuitry in stabilizing sensory representations driven by degraded sensory input.
Building the Functional Map of the Fruit Fly BrainAurel A. Lazar, Columbia University, New York, NY.
I will describe the open interactive computing platform, called FlyBrainLab (FBL), designed for studying the function of executable circuits constructed from fly brain data. FBL provides easy access to biological data and brain circuit models of the Fruit Fly Brain Observatory (FFBO). FFBO lists a (i) hub for storing and integrating fruit fly brain data from multiple data sources worldwide, (ii) unified repository of tools and methods to build, emulate and compare brain models in health and disease, and (iii) an open framework for brain data processing and model execution. Furthermore, FFBO provides access to application tools for visualizing, configuring, simulating and analyzing computational models of brain circuits of the cell-type map, connectome, synaptome, and activity map using intuitive queries in plain English. FBL code and installation instructions are available on Github.
Connecting the Structure and Function of Neural CircuitsSrinivas C. Turaga, HHMI Janelia Research Campus, Ashburn, VA.
In this talk, I will describe how we developed deep learning based computational tools to solve two problems in neuroscience: inferring the activity of a neural network from measurements of its structural connectivity, and inferring the connectivity of a network of neurons from measurements and perturbation of neural activity.
Microcircuitry of the Cortex: Connectivity, Strength, and Short-Term Plasticity
Tim Jarsky, Allen Institute of Brain Science.
We seek to characterize the microcircuitry of the cortex in mouse and human using multipatch electrophysiology supplemented with 2-photon optogenetic circuit mapping. Using the approaches mentioned above, we quantify connection probability as a function of distance, synaptic strength, and short-term synaptic dynamics among the major classes of cortical neurons. We rely on the dimensionality reduction that modeling can provide to quantify and compare differences in synaptic dynamics. We intend to share the data so that accurate, integrative computer models of cortex can be made. This presentation will summarize our results to date and share how our data may be accessed.
Completing the Fly Model?Louis Scheffer, HHMI Janelia Research Campus, Ashburn, VA.
We are on the verge of having a full connectome of all neurons and their chemical synapses in Drosophila. But while necessary, this is not sufficient to model the fly's nervous system. Additional information includes gap junction varieties and locations, identities of neurotransmitters, receptor types and locations, neuromodulators and hormones (with sources and receptors), the role of glial cells, time evolution rules for synapses, and more. This talk will outline these missing pieces, look at methods by which the data might be obtained, and summarize existing efforts (where they exist) to get this needed information.