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14 Publications

Showing 1-10 of 14 results
Freeman Lab
08/26/16 | Improving data quality in neuronal population recordings.
Harris KD, Quiroga RQ, Freeman J, Smith SL
Nature Neuroscience. 2016 Aug 26;19(9):1165-74. doi: 10.1038/nn.4365

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.

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Ji LabFreeman Lab
08/26/16 | Technologies for imaging neural activity in large volumes.
Ji N, Freeman J, Smith SL
Nature Neuroscience. 2016 Aug 26;19(9):1154-64. doi: 10.1038/nn.4358

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.

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Freeman Lab
04/01/16 | MLlib: Machine learning in Apache Spark.
Meng X, Bradley J, Yavus B, Sparks E, Venkataraman S, Liu D, Freeman J, Tsai DB, Amde M, Owen S, Xin D, Franklin MJ, Zadeh R, Zaharia M, Talwalkar A
Journal of Machine Learning Research. 2016 Apr 01;17:1-7

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’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’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. 

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Freeman LabAhrens Lab
03/22/16 | Brain-wide mapping of neural activity controlling zebrafish exploratory locomotion.
Dunn TW, Mu Y, Narayan S, Randlett O, Naumann EA, Yang C, Schier AF, Freeman J, Engert F, Ahrens MB
eLife. 2016 Mar 22:. doi: 10.7554/eLife.12741

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.

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Svoboda LabFreeman Lab
12/23/15 | Neural coding in barrel cortex during whisker-guided locomotion.
Sofroniew NJ, Vlasov YA, Andrew Hires S, Freeman J, Svoboda K
eLife. 2015 Dec 23;4:. doi: 10.7554/eLife.12559

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.

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Freeman Lab
10/30/15 | Mapping nonlinear receptive field structure in primate retina at single cone resolution.
Freeman J, Field GD, Li PH, Greschner M, Gunning DE, Mathieson K, Sher A, Litke AM, Paninski L, Simoncelli EP, Chichilnisky EJ
eLife. 2015 Oct 30;4:. doi: 10.7554/eLife.05241

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.

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Branson LabFreeman Lab
10/22/15 | Imaging the neural basis of locomotion.
Branson K, Freeman J
Cell. 2015 Oct 22;163(3):541-2. doi: 10.1016/j.cell.2015.10.014

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.

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08/11/15 | Whole-central nervous system functional imaging in larval Drosophila.
Lemon WC, Pulver SR, Höckendorf B, McDole K, Branson KM, Freeman J, Keller PJ
Nature Communications. 2015 Aug 11;6:7924. doi: 10.1038/ncomms8924

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.

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Freeman Lab
06/01/15 | Open source tools for large-scale neuroscience.
Freeman J
Current Opinion in Neurobiology. 2015 Jun;32:156-63. doi: 10.1016/j.conb.2015.04.002

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.

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Freeman LabSvoboda Lab
04/21/15 | A cellular resolution map of barrel cortex activity during tactile behavior.
Peron SP, Freeman J, Iyer V, Guo C, Svoboda K
Neuron. 2015 Apr 21;86(3):783-99. doi: 10.1016/j.neuron.2015.03.027

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.

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