Jeremy Freeman

Mapping brain activity at scale with cluster computing

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.

Whole-central nervous system functional imaging in larval Drosophila

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.

Brain-wide mapping of neural activity controlling zebrafish exploratory locomotion

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: http://dx.doi.org/10.7554/eLife.12741.001

Technologies for imaging neural activity in large volumes.

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.

Light-sheet imaging for systems neuroscience

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.

The future of the brain : essays by the world's leading neuroscientists

List of Contributors ix Preface Gary Marcus and Jeremy Freeman xi MAPPING THE BRAIN Building Atlases of the Brain 3 Mike Hawrylycz with Chinh Dang, Christof Koch, and Hongkui Zeng Whole Brain Neuroimaging and Virtual Reality 17 Misha B. Ahrens Project MindScope 25 Christof Koch with Clay Reid, Hongkui Zeng, Stefan Mihalas, Mike Hawrylycz, John Philips, Chinh Dang, and Allan Jones The Connectome as a DNA Sequencing Problem 40 Anthony Zador Rosetta Brain 50 George Church with Adam Marblestone and Reza Kalhor COMPUTATION Understanding the Cortex through Grid Cells 67 May-Britt Moser and Edvard I. Moser Recording from Many Neurons Simultaneously: From Measurement to Meaning 78 Krishna V. Shenoy Network Neuroscience 90 Olaf Sporns Large-Scale Neuroscience: From Analytics to Insight 100 Jeremy Freeman SIMULATING THE BRAIN Whole Brain Simulation 111 Sean Hill Building a Behaving Brain 125 Chris Eliasmith LANGUAGE The Neurobiology of Language 139 David Poeppel Translating the Genome in Human Neuroscience 149 Simon E. Fisher Color plates follow p. 160 SKEPTICS Consciousness, Big Science, and Conceptual Clarity 161 Ned Block From Circuits to Behavior: A Bridge Too Far? 177 Matteo Carandini Lessons from Evolution 186 Leah Krubitzer Lessons from the Genome 194 Arthur Caplan with Nathan Kunzler The Computational Brain 205 Gary Marcus IMPLICATIONS Neurotechnology 219 John Donoghue The Miswired Brain, Genes, and Mental Illness 234 Kevin J. Mitchell Neural Dust: An Untethered Approach to Chronic Brain-Machine Interfaces 243 Michel M. Maharbiz with Dongjin Seo, Jose M. Carmena, Jan M. Rabaey, and Elad Alon AFTERWORD Neuroscience in 2064: A Look at the Last Century 255 Christof Koch and Gary Marcus Glossary 271 Index 275

Unwrapping the Ventral Stream

It seems immediately obvious that the apple in my hand is the same apple I just picked up from the table. How can my brain recognize an object despite substantial variation in its location, size, and context? In a series of cortical areas known as the ventral stream, the brain performs sophisticated

Representing “stuff” in visual cortex

Despite decades of study, we do not understand the fundamental processes by which our brain encodes and represents incoming visual information and uses it to guide perception and action. A wealth of evidence suggests that visual recognition is mediated by a series of areas in primate cortex known as the ventral stream, including V1 (primary visual cortex), V2, and V4 (Fig. 1A) (1). The earliest stages are to some extent understood; Hubel and Wiesel famously discovered, for example, that neurons in V1 respond selectively to the orientation and direction of a moving edge (2). However, a vast gulf remains between coding for a simple edge and representing the full richness of our visual world. David Hubel himself observed in 2012 that we still “have almost no examples of neural structures in which we know the difference between the information coming in and what is going out—what the structure is for. We have some idea of the answer for the retina, the lateral geniculate body, and the primary visual cortex, but that’s about it” (3). In PNAS, Okazawa et al. (4) make significant headway in this quest by uncovering and characterizing a unique form of neural selectivity in area V4.