@article {65364, title = {Recurrent interactions in local cortical circuits.}, journal = {Nature}, volume = {579}, year = {2020}, month = {2020 Mar 04}, pages = {256-59}, abstract = {

Most cortical synapses are local and excitatory. Local recurrent circuits could implement amplification, allowing pattern completion and other computations. Cortical circuits contain subnetworks that consist of neurons with similar receptive fields and increased connectivity relative to the network average. Cortical neurons that encode different types of information are spatially intermingled and distributed over large brain volumes, and this complexity has hindered attempts to probe the function of these subnetworks by perturbing them individually. Here we use computational modelling, optical recordings and manipulations to probe the function of recurrent coupling in layer 2/3 of the mouse vibrissal somatosensory cortex during active tactile discrimination. A neural circuit model of layer 2/3 revealed that recurrent excitation enhances sensory signals by amplification, but only for subnetworks with increased connectivity. Model networks with high amplification were sensitive to damage: loss of a few members of the subnetwork degraded stimulus encoding. We tested this prediction by mapping neuronal selectivity and photoablating neurons with specific selectivity. Ablation of a small proportion of layer 2/3 neurons (10-20, less than 5\% of the total) representing touch markedly reduced responses in the spared touch representation, but not in other representations. Ablations most strongly affected neurons with stimulus responses that were similar to those of the ablated population, which is also consistent with network models. Recurrence among cortical neurons with similar selectivity therefore drives input-specific amplification during behaviour.

}, issn = {1476-4687}, doi = {10.1038/s41586-020-2062-x}, author = {Peron, Simon and Pancholi, Ravi and Voelcker, Bettina and Wittenbach, Jason D and {\'O}lafsd{\'o}ttir, H Freyja and Freeman, Jeremy and Svoboda, Karel} } @article {49535, title = {Brain-wide circuit interrogation at the cellular level guided by online analysis of neuronal function.}, journal = {Nature Methods}, volume = {15}, year = {2018}, month = {2018 Nov 30}, pages = {1117-1125}, abstract = {

Whole-brain imaging allows for comprehensive functional mapping of distributed neural pathways, but neuronal perturbation experiments are usually limited to targeting predefined regions or genetically identifiable cell types. To complement whole-brain measures of activity with brain-wide manipulations for testing causal interactions, we introduce a system that uses measuredactivity patterns to guide optical perturbations of any subset of neurons in the same fictively behaving larval zebrafish. First, a light-sheet microscope collects whole-brain data that are rapidly analyzed by a distributed computing system to generate functional brain maps. On the basis of these maps, the experimenter can then optically ablate neurons and image activity changes across the brain. We applied this method to characterize contributions of behaviorally tuned populations to the optomotor response. We extended the system to optogenetically stimulate arbitrary subsets of neurons during whole-brain imaging. These open-source methods enable delineating the contributions of neurons to brain-wide circuit dynamics and behavior in individual animals.

}, doi = {10.1038/s41592-018-0221-x}, author = {Vladimirov, Nikita and Wang, Chen and H{\"o}ckendorf, Burkhard and Pujala, Avinash and Tanimoto, Masashi and Mu, Yu and Yang, Chao-Tsung and Wittenbach, Jason and Freeman, Jeremy and Preibisch, Stephan and Koyama, Minoru and Keller, Philipp J and Ahrens, Misha B} } @article {67219, title = {Community-based benchmarking improves spike rate inference from two-photon calcium imaging data}, journal = {PLOS Computational Biology}, volume = {14}, year = {2018}, month = {Sep-05-2019}, pages = {e1006157}, abstract = {

In recent years, two-photon calcium imaging has become a standard tool to probe the function of neural circuits and to study computations in neuronal populations. However, the acquired signal is only an indirect measurement of neural activity due to the comparatively slow dynamics of fluorescent calcium indicators. Different algorithms for estimating spike rates from noisy calcium measurements have been proposed in the past, but it is an open question how far performance can be improved. Here, we report the results of the\ spikefinder\ challenge, launched to catalyze the development of new spike rate inference algorithms through crowd-sourcing. We present ten of the submitted algorithms which show improved performance compared to previously evaluated methods. Interestingly, the top-performing algorithms are based on a wide range of principles from deep neural networks to generative models, yet provide highly correlated estimates of the neural activity. The competition shows that benchmark challenges can drive algorithmic developments in neuroscience.

}, doi = {10.1371/journal.pcbi.1006157}, url = {https://journals.plos.org/ploscompbiol/article?rev=2\&id=10.1371/journal.pcbi.1006157}, author = {Berens, Philipp and Freeman, Jeremy and Deneux, Thomas and Chenkov, Nikolay and McColgan, Thomas and Speiser, Artur and Macke, Jakob H. and Turaga, Srinivas C. and Mineault, Patrick and Rupprecht, Peter and Gerhard, Stephan and Friedrich, Rainer W. and Friedrich, Johannes and Paninski, Liam and Pachitariu, Marius and Harris, Kenneth D. and Bolte, Ben and Machado, Timothy A. and Ringach, Dario and Stone, Jasmine and Rogerson, Luke E. and Sofroniew, Nicolas J. and Reimer, Jacob and Froudarakis, Emmanouil and Euler, Thomas and Rom{\'a}n Ros{\'o}n, Miroslav and Theis, Lucas and Tolias, Andreas S. and Bethge, Matthias}, editor = {Bush, Daniel} } @article {48836, title = {Community-based benchmarking improves spike inference from two-photon calcium imaging data.}, journal = {PLoS Computational Biology}, volume = {14}, year = {2017}, month = {2018 May 21}, pages = {e1006157}, abstract = {

In recent years, two-photon calcium imaging has become a standard tool to probe the function of neural circuits and to study computations in neuronal populations. However, the acquired signal is only an indirect measurement of neural activity due to the comparatively slow dynamics of fluorescent calcium indicators. Different algorithms for estimating spike rates from noisy calcium measurements have been proposed in the past, but it is an open question how far performance can be improved. Here, we report the results of the spikefinder challenge, launched to catalyze the development of new spike rate inference algorithms through crowd-sourcing. We present ten of the submitted algorithms which show improved performance compared to previously evaluated methods. Interestingly, the top-performing algorithms are based on a wide range of principles from deep neural networks to generative models, yet provide highly correlated estimates of the neural activity. The competition shows that benchmark challenges can drive algorithmic developments in neuroscience.

}, doi = {10.1371/journal.pcbi.1006157}, author = {Berens, Philipp and Freeman, Jeremy and Deneux, Thomas and Chenkov, Nicolay and McColgan, Thomas and Speiser, Artur and Macke, Jakob H and Turaga, Srinivas C and Mineault, Patrick and Rupprecht, Peter and Gerhard, Stephan and Friedrich, Rainer W and Friedrich, Johannes and Paninski, Liam and Pachitariu, Marius and Harris, Kenneth D and Bolte, Ben and Machado, Timothy A and Ringach, Dario and etal} } @article {47380, title = {Brain-wide mapping of neural activity controlling zebrafish exploratory locomotion.}, journal = {eLife}, year = {2016}, month = {2016 Mar 22}, abstract = {

In the absence of salient sensory cues to guide behavior, animals must still execute sequences of motor actions in order to forage and explore. How such successive motor actions are coordinated to form global locomotion trajectories is unknown. We mapped the structure of larval zebrafish swim trajectories in homogeneous environments and found that trajectories were characterized by alternating sequences of repeated turns to the left and to the right. Using whole-brain light-sheet imaging, we identified activity relating to the behavior in specific neural populations that we termed the anterior rhombencephalic turning region (ARTR). ARTR perturbations biased swim direction and reduced the dependence of turn direction on turn history, indicating that the ARTR is part of a network generating the temporal correlations in turn direction. We also find suggestive evidence for ARTR mutual inhibition and ARTR projections to premotor neurons. Finally, simulations suggest the observed turn sequences may underlie efficient exploration of local environments.

}, doi = {10.7554/eLife.12741}, url = {http://dx.doi.org/10.7554/eLife.12741}, author = {Dunn, Timothy W. and Mu, Yu and Narayan, Sujatha and Randlett, Owen and Naumann, Eva A. and Yang, Chao-Tsung and Schier, Alexander F. and Freeman, Jeremy and Engert, Florian and Ahrens, Misha B.} } @article {47812, title = {Improving data quality in neuronal population recordings.}, journal = {Nature Neuroscience}, volume = {19}, year = {2016}, month = {2016 Aug 26}, pages = {1165-74}, abstract = {

Understanding how the brain operates requires understanding how large sets of neurons function together. Modern recording technology makes it possible to simultaneously record the activity of hundreds of neurons, and technological developments will soon allow recording of thousands or tens of thousands. As with all experimental techniques, these methods are subject to confounds that complicate the interpretation of such recordings, and could lead to erroneous scientific conclusions. Here we discuss methods for assessing and improving the quality of data from these techniques and outline likely future directions in this field.

}, issn = {1546-1726}, doi = {10.1038/nn.4365}, author = {Harris, Kenneth D and Quiroga, Rodrigo Quian and Freeman, Jeremy and Smith, Spencer L} } @article {47752, title = {MLlib: Machine learning in Apache Spark.}, journal = {Journal of Machine Learning Research}, volume = {17}, year = {2016}, month = {2016 Apr 01}, pages = {1-7}, abstract = {

Apache Spark is a popular open-source platform for large-scale data processing that is well-suited for iterative machine learning tasks. In this paper we present MLlib, Spark{\textquoteright}s open-source distributed machine learning library. MLlib provides efficient functionality for a wide range of learning settings and includes several underlying statistical, optimization, and linear algebra primitives. Shipped with Spark, MLlib supports several languages and provides a high-level API that leverages Spark{\textquoteright}s rich ecosystem to simplify the development of end-to-end machine learning pipelines. MLlib has experienced a rapid growth due to its vibrant open-source community of over 140 contributors, and includes extensive documentation to support further growth and to let users quickly get up to speed.\ 

}, url = {http://www.jmlr.org/papers/volume17/15-237/15-237.pdf}, author = {Meng, Xiangrui and Bradley, Joseph and Yavus, Burak and Sparks, Evan and Venkataraman, Shivaram and Liu, Davies and Freeman, Jeremy and Tsai, D B and Amde, Manish and Owen, Sean and Xin, Doris and Franklin, Michael J and Zadeh, Reza and Zaharia, Matei and Talwalkar, Ameet} } @article {47813, title = {Technologies for imaging neural activity in large volumes.}, journal = {Nature Neuroscience}, volume = {19}, year = {2016}, month = {2016 Aug 26}, pages = {1154-64}, abstract = {

Neural circuitry has evolved to form distributed networks that act dynamically across large volumes. Conventional microscopy collects data from individual planes and cannot sample circuitry across large volumes at the temporal resolution relevant to neural circuit function and behaviors. Here we review emerging technologies for rapid volume imaging of neural circuitry. We focus on two critical challenges: the inertia of optical systems, which limits image speed, and aberrations, which restrict the image volume. Optical sampling time must be long enough to ensure high-fidelity measurements, but optimized sampling strategies and point-spread function engineering can facilitate rapid volume imaging of neural activity within this constraint. We also discuss new computational strategies for processing and analyzing volume imaging data of increasing size and complexity. Together, optical and computational advances are providing a broader view of neural circuit dynamics and helping elucidate how brain regions work in concert to support behavior.

}, issn = {1546-1726}, doi = {10.1038/nn.4358}, author = {Ji, Na and Freeman, Jeremy and Smith, Spencer L} } @article {46664, title = {A cellular resolution map of barrel cortex activity during tactile behavior.}, journal = {Neuron}, volume = {86}, year = {2015}, month = {2015 Apr 21}, pages = {783-99}, abstract = {

Comprehensive measurement of neural activity remains challenging due to the large numbers of neurons in each brain area. We used volumetric two-photon imaging in mice expressing GCaMP6s and nuclear red fluorescent proteins to sample activity in 75\% of superficial barrel cortex neurons across the relevant cortical columns, approximately 12,000 neurons per animal, during performance of a single whisker object localization task. Task-related activity peaked during object palpation. An encoding model\ related activity to behavioral variables. In the column corresponding to the spared whisker, 300 layer (L) 2/3 pyramidal neurons (17\%) each encoded touch and whisker movements. Touch representation declined by half in surrounding columns; whisker movement representation was unchanged. Following the emergence of stereotyped task-related movement, sensory representations showed no measurable plasticity. Touch direction was topographically organized, with distinct organization for passive and active touch. Our work reveals sparse and spatially intermingled representations of multiple tactile features.

}, issn = {1097-4199}, doi = {10.1016/j.neuron.2015.03.027}, author = {Peron, Simon P and Freeman, Jeremy and Iyer, Vijay and Guo, Caiying and Svoboda, Karel} } @article {47127, title = {Imaging the neural basis of locomotion.}, journal = {Cell}, volume = {163}, year = {2015}, month = {2015 Oct 22}, pages = {541-2}, abstract = {

To investigate the fundamental question of how nervous systems encode, organize, and sequence behaviors, Kato et\ al. imaged neural activity with cellular resolution across the brain of the worm Caenorhabditis elegans. Locomotion behavior seems to be continuously represented by cyclical patterns of distributed neural activity that are present even in immobilized animals.

}, issn = {1097-4172}, doi = {10.1016/j.cell.2015.10.014}, author = {Branson, Kristin and Freeman, Jeremy} } @article {47143, title = {Mapping nonlinear receptive field structure in primate retina at single cone resolution.}, journal = {eLife}, volume = {4}, year = {2015}, month = {2015 Oct 30}, abstract = {

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.

}, issn = {2050-084X}, doi = {10.7554/eLife.05241}, author = {Freeman, Jeremy and Field, Greg D and Li, Peter H and Greschner, Martin and Gunning, Deborah E and Mathieson, Keith and Sher, Alexander and Litke, Alan M and Paninski, Liam and Simoncelli, Eero P and Chichilnisky, E J} } @article {47354, title = {Neural coding in barrel cortex during whisker-guided locomotion.}, journal = {eLife}, volume = {4}, year = {2015}, month = {2015 Dec 23}, abstract = {

Animals seek out relevant information by moving through a dynamic world, but sensory systems are usually studied under highly constrained and passive conditions that may not probe important dimensions of the neural code. Here, we explored neural coding in the barrel cortex of head-fixed mice that tracked walls with their whiskers in tactile virtual reality. Optogenetic manipulations revealed that barrel cortex plays a role in wall-tracking. Closed-loop optogenetic control of layer 4 neurons can substitute for whisker-object contact to guide behavior resembling wall tracking. We measured neural activity using two-photon calcium imaging and extracellular recordings. Neurons were tuned to the distance between the animal snout and the contralateral wall, with monotonic, unimodal, and multimodal tuning curves. This rich representation of object location in the barrel cortex could not be predicted based on simple stimulus-response relationships involving individual whiskers and likely emerges within cortical circuits.

}, issn = {2050-084X}, doi = {10.7554/eLife.12559}, author = {Sofroniew, Nicholas James and Vlasov, Yurii A and Andrew Hires, Samuel and Freeman, Jeremy and Svoboda, Karel} } @article {46861, title = {Open source tools for large-scale neuroscience.}, journal = {Current Opinion in Neurobiology}, volume = {32}, year = {2015}, month = {2015 Jun}, pages = {156-63}, abstract = {

New technologies for monitoring and manipulating the nervous system promise exciting biology but pose challenges for analysis and computation. Solutions can be found in the form of modern approaches to distributed computing, machine learning, and interactive visualization. But embracing these new technologies will require a cultural shift: away from independent efforts and proprietary methods and toward an open source and collaborative neuroscience.

}, issn = {1873-6882}, doi = {10.1016/j.conb.2015.04.002}, author = {Freeman, Jeremy} } @article {46344, title = {Representing "stuff" in visual cortex.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {112}, year = {2015}, month = {2015 Jan 27}, pages = {942-3}, issn = {1091-6490}, doi = {10.1073/pnas.1423496112}, author = {Ziemba, Corey M and Freeman, Jeremy} } @article {46984, title = {Whole-central nervous system functional imaging in larval Drosophila.}, journal = {Nature Communications}, volume = {6}, year = {2015}, month = {2015 Aug 11}, pages = {7924}, abstract = {

Understanding how the brain works in tight concert with the rest of the central nervous system (CNS) hinges upon knowledge of coordinated activity patterns across the whole CNS. We present a method for measuring activity in an entire, non-transparent CNS with high spatiotemporal resolution. We combine a light-sheet microscope capable of simultaneous multi-view imaging at volumetric speeds 25-fold faster than the state-of-the-art, a whole-CNS imaging assay for the isolated Drosophila larval CNS and a computational framework for analysing multi-view, whole-CNS calcium imaging data. We image both brain and ventral nerve cord, covering the entire CNS at 2 or 5 Hz with two- or one-photon excitation, respectively. By mapping network activity during fictive behaviours and quantitatively comparing high-resolution whole-CNS activity maps across individuals, we predict functional connections between CNS regions and reveal neurons in the brain that identify type and temporal state of motor programs executed in the ventral nerve cord.

}, doi = {10.1038/ncomms8924}, author = {Lemon, William C and Pulver, Stefan R and H{\"o}ckendorf, Burkhard and McDole, Katie and Branson, Kristin M and Freeman, Jeremy and Keller, Philipp J} } @article {45177, title = {Light-sheet functional imaging in fictively behaving zebrafish.}, journal = {Nature Methods}, volume = {11}, year = {2014}, month = {2014 Jul 27}, pages = {883-4}, abstract = {

The processing of sensory input and the generation of behavior involves large networks of neurons, which necessitates new technology for recording from many neurons in behaving animals. In the larval zebrafish, light-sheet microscopy can be used to record the activity of almost all neurons in the brain simultaneously at single-cell resolution. Existing implementations, however, cannot be combined with visually driven behavior because the light sheet scans over the eye, interfering with presentation of controlled visual stimuli. Here we describe a system that overcomes the confounding eye stimulation through the use of two light sheets and combines whole-brain light-sheet imaging with virtual reality for fictively behaving larval zebrafish.

}, issn = {1548-7105}, doi = {10.1038/nmeth.3040}, author = {Vladimirov, Nikita and Mu, Yu and Kawashima, Takashi and Bennett, Davis V and Yang, Chao-Tsung and Looger, Loren L and Keller, Philipp J and Freeman, Jeremy and Ahrens, Misha B} } @article {45868, title = {Light-sheet imaging for systems neuroscience.}, journal = {Nature Methods}, volume = {12}, year = {2014}, month = {2014 Dec 30}, pages = {27-9}, abstract = {

Developments in electrical and optical recording technology are scaling up the size of neuronal populations that can be monitored simultaneously. Light-sheet imaging is rapidly gaining traction as a method for optically interrogating activity in large networks and presents both opportunities and challenges for understanding circuit function.

}, issn = {1548-7105}, doi = {10.1038/nmeth.3214}, author = {Keller, Philipp J and Ahrens, Misha B and Freeman, Jeremy} } @article {45065, title = {Mapping brain activity at scale with cluster computing.}, journal = {Nature Methods}, volume = {11}, year = {2014}, month = {2014 Jul 27}, pages = {941-950}, abstract = {

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.

}, issn = {1548-7091}, doi = {10.1038/nmeth.3041}, author = {Freeman, Jeremy and Vladimirov, Nikita and Kawashima, Takashi and Mu, Yu and Sofroniew, Nicholas J. and Bennett, Davis V. and Rosen, Joshua and Yang, Chao-Tsung and Looger, Loren L. and Ahrens, Misha B.} }