1
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Broggini T, Duckworth J, Ji X, Liu R, Xia X, Mächler P, Shaked I, Munting LP, Iyengar S, Kotlikoff M, van Veluw SJ, Vergassola M, Mishne G, Kleinfeld D. Long-wavelength traveling waves of vasomotion modulate the perfusion of cortex. Neuron 2024; 112:2349-2367.e8. [PMID: 38781972 PMCID: PMC11257831 DOI: 10.1016/j.neuron.2024.04.034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 03/28/2024] [Accepted: 04/30/2024] [Indexed: 05/25/2024]
Abstract
Brain arterioles are active, multicellular complexes whose diameters oscillate at ∼ 0.1 Hz. We assess the physiological impact and spatiotemporal dynamics of vaso-oscillations in the awake mouse. First, vaso-oscillations in penetrating arterioles, which source blood from pial arterioles to the capillary bed, profoundly impact perfusion throughout neocortex. The modulation in flux during resting-state activity exceeds that of stimulus-induced activity. Second, the change in perfusion through arterioles relative to the change in their diameter is weak. This implies that the capillary bed dominates the hydrodynamic resistance of brain vasculature. Lastly, the phase of vaso-oscillations evolves slowly along arterioles, with a wavelength that exceeds the span of the cortical mantle and sufficient variability to establish functional cortical areas as parcels of uniform phase. The phase-gradient supports traveling waves in either direction along both pial and penetrating arterioles. This implies that waves along penetrating arterioles can mix, but not directionally transport, interstitial fluids.
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Affiliation(s)
- Thomas Broggini
- Department of Physics, University of California, San Diego, La Jolla, CA 92093, USA; Goethe University Frankfurt, Department of Neurosurgery, 60528 Frankfurt am Main, Germany; Frankfurt Cancer Institute, Goethe University Frankfurt, 60528 Frankfurt am Main, Germany
| | - Jacob Duckworth
- Department of Physics, University of California, San Diego, La Jolla, CA 92093, USA
| | - Xiang Ji
- Department of Physics, University of California, San Diego, La Jolla, CA 92093, USA
| | - Rui Liu
- Department of Physics, University of California, San Diego, La Jolla, CA 92093, USA
| | - Xinyue Xia
- Halıcıoğlu Data Science Institute, University of California, San Diego, La Jolla, CA 92093, USA
| | - Philipp Mächler
- Department of Physics, University of California, San Diego, La Jolla, CA 92093, USA
| | - Iftach Shaked
- Department of Physics, University of California, San Diego, La Jolla, CA 92093, USA
| | - Leon Paul Munting
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Satish Iyengar
- Department of Statistics, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Michael Kotlikoff
- College of Veterinary Medicine, Cornell University, Ithaca, NY 14853, USA
| | - Susanne J van Veluw
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
| | | | - Gal Mishne
- Halıcıoğlu Data Science Institute, University of California, San Diego, La Jolla, CA 92093, USA
| | - David Kleinfeld
- Department of Physics, University of California, San Diego, La Jolla, CA 92093, USA; Department of Neurobiology, University of California, San Diego, La Jolla, CA 92093, USA.
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2
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Hiramoto M, Cline HT. Visual neurons recognize complex image transformations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.10.598314. [PMID: 38915552 PMCID: PMC11195111 DOI: 10.1101/2024.06.10.598314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
Natural visual scenes are dominated by sequences of transforming images. Spatial visual information is thought to be processed by detection of elemental stimulus features which are recomposed into scenes. How image information is integrated over time is unclear. We explored visual information encoding in the optic tectum. Unbiased stimulus presentation shows that the majority of tectal neurons recognize image sequences. This is achieved by temporally dynamic response properties, which encode complex image transitions over several hundred milliseconds. Calcium imaging reveals that neurons that encode spatiotemporal image sequences fire in spike sequences that predict a logical diagram of spatiotemporal information processing. Furthermore, the temporal scale of visual information is tuned by experience. This study indicates how neurons recognize dynamic visual scenes that transform over time.
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Affiliation(s)
- Masaki Hiramoto
- Department of Neuroscience, Dorris Neuroscience Center, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Hollis T Cline
- Department of Neuroscience, Dorris Neuroscience Center, The Scripps Research Institute, La Jolla, CA 92037, USA
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3
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Uhrhan MJ, Bomphrey RJ, Lin HT. Flow sensing on dragonfly wings. Ann N Y Acad Sci 2024; 1536:107-121. [PMID: 38837424 DOI: 10.1111/nyas.15152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/07/2024]
Abstract
One feature of animal wings is their embedded mechanosensory system that can support flight control. Insect wings are particularly interesting as they are highly deformable yet the actuation is limited to the wing base. It is established that strain sensors on insect wings can directly mediate reflexive control; however, little is known about airflow sensing by insect wings. What information can flow sensors capture and how can flow sensing benefit flight control? Here, we use the dragonfly (Sympetrum striolatum) as a model to explore the function of wing sensory bristles in the context of flight control. Combining our detailed anatomical reconstructions of both the sensor microstructures and wing architecture, we used computational fluid dynamics simulations to ask the following questions. (1) Are there strategic locations on wings that sample flow for estimating aerodynamically relevant parameters such as the local effective angle of attack? (2) Is the sensory bristle distribution on dragonfly wings optimal for flow sensing? (3) What is the aerodynamic effect of microstructures found near the sensory bristles on dragonfly wings? We discuss the benefits of flow sensing for flexible wings and how the evolved sensor placement affects information encoding.
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Affiliation(s)
- Myriam J Uhrhan
- Department of Bioengineering, Imperial College London, London, UK
| | - Richard J Bomphrey
- Department of Comparative Biomedical Sciences, Royal Veterinary College, London, UK
| | - Huai-Ti Lin
- Department of Bioengineering, Imperial College London, London, UK
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4
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Parker JE, Aristieta A, Gittis A, Rubin JE. Introducing the STReaC (Spike Train Response Classification) toolbox. J Neurosci Methods 2024; 401:110000. [PMID: 38486714 PMCID: PMC10936710 DOI: 10.1016/j.jneumeth.2023.110000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2024]
Abstract
Background This work presents a toolbox that implements methodology for automated classification of diverse neural responses to optogenetic stimulation or other changes in conditions, based on spike train recordings. New Method The toolbox implements what we call the Spike Train Response Classification algorithm (STReaC), which compares measurements of activity during a baseline period with analogous measurements during a subsequent period to identify various responses that might result from an event such as introduction of a sustained stimulus. The analyzed response types span a variety of patterns involving distinct time courses of increased firing, or excitation, decreased firing, or inhibition, or combinations of these. Excitation (inhibition) is identified from a comparative analysis of the spike density function (interspike interval function) for the baseline period relative to the corresponding function for the response period. Results The STReaC algorithm as implemented in this toolbox provides a user-friendly, tunable, objective methodology that can detect a variety of neuronal response types and associated subtleties. We demonstrate this with single-unit neural recordings of rodent substantia nigra pars reticulata (SNr) during optogenetic stimulation of the globus pallidus externa (GPe). Comparison with existing methods In several examples, we illustrate how the toolbox classifies responses in situations in which traditional methods (spike counting and visual inspection) either fail to detect a response or provide a false positive. Conclusions The STReaC toolbox provides a simple, efficient approach for classifying spike trains into a variety of response types defined relative to a period of baseline spiking.
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Affiliation(s)
- John E. Parker
- Department of Mathematics, University of Pittsburgh, Pittsburgh, PA, U.S.A
- Center for the Neural Basis of Cognition, Pittsburgh, PA, U.S.A
| | - Asier Aristieta
- Center for the Neural Basis of Cognition, Pittsburgh, PA, U.S.A
- Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, U.S.A
| | - Aryn Gittis
- Center for the Neural Basis of Cognition, Pittsburgh, PA, U.S.A
- Department of Biological Sciences, Carnegie Mellon University, Pittsburgh, PA, U.S.A
| | - Jonathan E. Rubin
- Department of Mathematics, University of Pittsburgh, Pittsburgh, PA, U.S.A
- Center for the Neural Basis of Cognition, Pittsburgh, PA, U.S.A
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5
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Matteucci G, Bellacosa Marotti R, Zattera B, Zoccolan D. Truly pattern: Nonlinear integration of motion signals is required to account for the responses of pattern cells in rat visual cortex. SCIENCE ADVANCES 2023; 9:eadh4690. [PMID: 37939191 PMCID: PMC10631736 DOI: 10.1126/sciadv.adh4690] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 10/06/2023] [Indexed: 11/10/2023]
Abstract
A key feature of advanced motion processing in the primate dorsal stream is the existence of pattern cells-specialized cortical neurons that integrate local motion signals into pattern-invariant representations of global direction. Pattern cells have also been reported in rodent visual cortex, but it is unknown whether the tuning of these neurons results from truly integrative, nonlinear mechanisms or trivially arises from linear receptive fields (RFs) with a peculiar geometry. Here, we show that pattern cells in rat primary (V1) and lateromedial (LM) visual cortex process motion direction in a way that cannot be explained by the linear spatiotemporal structure of their RFs. Instead, their tuning properties are consistent with and well explained by those of units in a state-of-the-art neural network model of the dorsal stream. This suggests that similar cortical processes underlay motion representation in primates and rodents. The latter could thus serve as powerful model systems to unravel the underlying circuit-level mechanisms.
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6
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Weiss O, Bounds HA, Adesnik H, Coen-Cagli R. Modeling the diverse effects of divisive normalization on noise correlations. PLoS Comput Biol 2023; 19:e1011667. [PMID: 38033166 PMCID: PMC10715670 DOI: 10.1371/journal.pcbi.1011667] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 12/12/2023] [Accepted: 11/07/2023] [Indexed: 12/02/2023] Open
Abstract
Divisive normalization, a prominent descriptive model of neural activity, is employed by theories of neural coding across many different brain areas. Yet, the relationship between normalization and the statistics of neural responses beyond single neurons remains largely unexplored. Here we focus on noise correlations, a widely studied pairwise statistic, because its stimulus and state dependence plays a central role in neural coding. Existing models of covariability typically ignore normalization despite empirical evidence suggesting it affects correlation structure in neural populations. We therefore propose a pairwise stochastic divisive normalization model that accounts for the effects of normalization and other factors on covariability. We first show that normalization modulates noise correlations in qualitatively different ways depending on whether normalization is shared between neurons, and we discuss how to infer when normalization signals are shared. We then apply our model to calcium imaging data from mouse primary visual cortex (V1), and find that it accurately fits the data, often outperforming a popular alternative model of correlations. Our analysis indicates that normalization signals are often shared between V1 neurons in this dataset. Our model will enable quantifying the relation between normalization and covariability in a broad range of neural systems, which could provide new constraints on circuit mechanisms of normalization and their role in information transmission and representation.
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Affiliation(s)
- Oren Weiss
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, New York, United States of America
| | - Hayley A. Bounds
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, California, United States of America
| | - Hillel Adesnik
- Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, California, United States of America
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, California, United States of America
| | - Ruben Coen-Cagli
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, New York, United States of America
- Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, New York, United States of America
- Department of Ophthalmology and Visual Sciences, Albert Einstein College of Medicine, Bronx, New York, United States of America
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7
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Callado Pérez A, Demers M, Fassihi A, Moore JD, Kleinfeld D, Deschênes M. A brainstem circuit for the expression of defensive facial reactions in rat. Curr Biol 2023; 33:4030-4035.e3. [PMID: 37703878 PMCID: PMC11034846 DOI: 10.1016/j.cub.2023.08.041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 08/08/2023] [Accepted: 08/15/2023] [Indexed: 09/15/2023]
Abstract
The brainstem houses neuronal circuits that control homeostasis of vital functions. These include the depth and rate of breathing1,2 and, critically, apnea, a transient cessation of breathing that prevents noxious vapors from entering further into the respiratory tract. Current thinking is that this reflex is mediated by two sensory pathways. One known pathway involves vagal and glossopharyngeal afferents that project to the nucleus of the solitary tract.3,4,5 Yet, apnea induced by electrical stimulation of the nasal epithelium or delivery of ammonia vapors to the nose persists after brainstem transection at the pontomedullary junction, indicating that the circuitry that mediates this reflex is intrinsic to the medulla.6 A second potential pathway, consistent with this observation, involves trigeminal afferents from the nasal cavity that project to the muralis subnucleus of the spinal trigeminal complex.7,8 Notably, the apneic reflex is not dependent on olfaction as it can be initiated even after disruption of olfactory pathways.9 We investigated how subnucleus muralis cells mediate apnea in rat. By means of electrophysiological recordings and lesions in anesthetized rats, we identified a pathway from chemosensors in the nostrils through the muralis subnucleus and onto both the preBötzinger and facial motor nuclei. We then monitored breathing and orofacial reactions upon ammonia delivery near the nostril of alert, head-restrained rats. The apneic reaction was associated with a grimace, characterized by vibrissa protraction, wrinkling of the nose, and squinting of the eyes. Our results show that a brainstem circuit can control facial expressions for nocifensive and potentially pain-inducing stimuli.
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Affiliation(s)
- Amalia Callado Pérez
- Cervo Research Center, Université Laval, Québec City, Québec G1J 2R3, Canada; Department of Physics, University of California, San Diego, La Jolla, CA 92093, USA
| | - Maxime Demers
- Cervo Research Center, Université Laval, Québec City, Québec G1J 2R3, Canada
| | - Arash Fassihi
- Department of Physics, University of California, San Diego, La Jolla, CA 92093, USA
| | - Jeffrey D Moore
- Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA
| | - David Kleinfeld
- Department of Physics, University of California, San Diego, La Jolla, CA 92093, USA; Department of Neurobiology, University of California, San Diego, La Jolla, CA 92093, USA.
| | - Martin Deschênes
- Cervo Research Center, Université Laval, Québec City, Québec G1J 2R3, Canada.
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8
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Liao SM, Kleinfeld D. A change in behavioral state switches the pattern of motor output that underlies rhythmic head and orofacial movements. Curr Biol 2023; 33:1951-1966.e6. [PMID: 37105167 PMCID: PMC10225163 DOI: 10.1016/j.cub.2023.04.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 03/27/2023] [Accepted: 04/06/2023] [Indexed: 04/29/2023]
Abstract
The breathing rhythm serves as a reference that paces orofacial motor actions and orchestrates active sensing. Past work has reported that pacing occurs solely at a fixed phase relative to sniffing. We re-evaluated this constraint as a function of exploratory behavior. Allocentric and egocentric rotations of the head and the electromyogenic activity of the motoneurons for head and orofacial movements were recorded in free-ranging rats as they searched for food. We found that a change in state from foraging to rearing is accompanied by a large phase shift in muscular activation relative to sniffing, and a concurrent change in the frequency of sniffing, so that pacing now occurs at one of the two phases. Further, head turning is biased such that an animal gathers a novel sample of its environment upon inhalation. In total, the coordination of active sensing has a previously unrealized computational complexity. This can emerge from hindbrain circuits with fixed architecture and credible synaptic time delays.
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Affiliation(s)
- Song-Mao Liao
- Department of Physics, University of California San Diego, La Jolla, CA 92093, USA
| | - David Kleinfeld
- Department of Physics, University of California San Diego, La Jolla, CA 92093, USA; Department of Neurobiology, University of California San Diego, La Jolla, CA 92093, USA.
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9
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Li J, Fu Y, Dong S, Yu Z, Huang T, Tian Y. Asynchronous Spatiotemporal Spike Metric for Event Cameras. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023; 34:1742-1753. [PMID: 33684047 DOI: 10.1109/tnnls.2021.3061122] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Event cameras as bioinspired vision sensors have shown great advantages in high dynamic range and high temporal resolution in vision tasks. Asynchronous spikes from event cameras can be depicted using the marked spatiotemporal point processes (MSTPPs). However, how to measure the distance between asynchronous spikes in the MSTPPs still remains an open issue. To address this problem, we propose a general asynchronous spatiotemporal spike metric considering both spatiotemporal structural properties and polarity attributes for event cameras. Technically, the conditional probability density function is first introduced to describe the spatiotemporal distribution and polarity prior in the MSTPPs. Besides, a spatiotemporal Gaussian kernel is defined to capture the spatiotemporal structure, which transforms discrete spikes into the continuous function in a reproducing kernel Hilbert space (RKHS). Finally, the distance between asynchronous spikes can be quantified by the inner product in the RKHS. The experimental results demonstrate that the proposed approach outperforms the state-of-the-art methods and achieves significant improvement in computational efficiency. Especially, it is able to better depict the changes involving spatiotemporal structural properties and polarity attributes.
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10
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Freedland J, Rieke F. Systematic reduction of the dimensionality of natural scenes allows accurate predictions of retinal ganglion cell spike outputs. Proc Natl Acad Sci U S A 2022; 119:e2121744119. [PMID: 36343230 PMCID: PMC9674269 DOI: 10.1073/pnas.2121744119] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 09/23/2022] [Indexed: 11/09/2022] Open
Abstract
The mammalian retina engages a broad array of linear and nonlinear circuit mechanisms to convert natural scenes into retinal ganglion cell (RGC) spike outputs. Although many individual integration mechanisms are well understood, we know less about how multiple mechanisms interact to encode the complex spatial features present in natural inputs. Here, we identified key spatial features in natural scenes that shape encoding by primate parasol RGCs. Our approach identified simplifications in the spatial structure of natural scenes that minimally altered RGC spike responses. We observed that reducing natural movies into 16 linearly integrated regions described ∼80% of the structure of parasol RGC spike responses; this performance depended on the number of regions but not their precise spatial locations. We used simplified stimuli to design high-dimensional metamers that recapitulated responses to naturalistic movies. Finally, we modeled the retinal computations that convert flashed natural images into one-dimensional spike counts.
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Affiliation(s)
- Julian Freedland
- Molecular Engineering & Sciences Institute, University of Washington, Seattle, WA 98195
| | - Fred Rieke
- Department of Physiology and Biophysics, University of Washington, Seattle, WA 98195
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11
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Levi A, Spivak L, Sloin HE, Someck S, Stark E. Error correction and improved precision of spike timing in converging cortical networks. Cell Rep 2022; 40:111383. [PMID: 36130516 PMCID: PMC9513803 DOI: 10.1016/j.celrep.2022.111383] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 05/26/2022] [Accepted: 08/28/2022] [Indexed: 11/20/2022] Open
Abstract
The brain propagates neuronal signals accurately and rapidly. Nevertheless, whether and how a pool of cortical neurons transmits an undistorted message to a target remains unclear. We apply optogenetic white noise signals to small assemblies of cortical pyramidal cells (PYRs) in freely moving mice. The directly activated PYRs exhibit a spike timing precision of several milliseconds. Instead of losing precision, interneurons driven via synaptic activation exhibit higher precision with respect to the white noise signal. Compared with directly activated PYRs, postsynaptic interneuron spike trains allow better signal reconstruction, demonstrating error correction. Data-driven modeling shows that nonlinear amplification of coincident spikes can generate error correction and improved precision. Over multiple applications of the same signal, postsynaptic interneuron spiking is most reliable at timescales ten times shorter than those of the presynaptic PYR, exhibiting temporal coding. Similar results are observed in hippocampal region CA1. Coincidence detection of convergent inputs enables messages to be precisely propagated between cortical PYRs and interneurons. PYR-to-interneuron spike transmission exhibits error correction and improved precision Interneuron precision is higher when a larger pool of presynaptic PYRs is recruited Error correction and improved precision are consistent with coincidence detection Interneurons activated by synaptic transmission act as temporal coders
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Affiliation(s)
- Amir Levi
- Sagol School of Neuroscience and Department of Physiology and Pharmacology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Lidor Spivak
- Sagol School of Neuroscience and Department of Physiology and Pharmacology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Hadas E Sloin
- Sagol School of Neuroscience and Department of Physiology and Pharmacology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Shirly Someck
- Sagol School of Neuroscience and Department of Physiology and Pharmacology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Eran Stark
- Sagol School of Neuroscience and Department of Physiology and Pharmacology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv 6997801, Israel.
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12
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Takatoh J, Prevosto V, Thompson PM, Lu J, Chung L, Harrahill A, Li S, Zhao S, He Z, Golomb D, Kleinfeld D, Wang F. The whisking oscillator circuit. Nature 2022; 609:560-568. [PMID: 36045290 PMCID: PMC10038238 DOI: 10.1038/s41586-022-05144-8] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 07/25/2022] [Indexed: 11/09/2022]
Abstract
Central oscillators are primordial neural circuits that generate and control rhythmic movements1,2. Mechanistic understanding of these circuits requires genetic identification of the oscillator neurons and their synaptic connections to enable targeted electrophysiological recording and causal manipulation during behaviours. However, such targeting remains a challenge with mammalian systems. Here we delimit the oscillator circuit that drives rhythmic whisking-a motor action that is central to foraging and active sensing in rodents3,4. We found that the whisking oscillator consists of parvalbumin-expressing inhibitory neurons located in the vibrissa intermediate reticular nucleus (vIRtPV) in the brainstem. vIRtPV neurons receive descending excitatory inputs and form recurrent inhibitory connections among themselves. Silencing vIRtPV neurons eliminated rhythmic whisking and resulted in sustained vibrissae protraction. In vivo recording of opto-tagged vIRtPV neurons in awake mice showed that these cells spike tonically when animals are at rest, and transition to rhythmic bursting at the onset of whisking, suggesting that rhythm generation is probably the result of network dynamics, as opposed to intrinsic cellular properties. Notably, ablating inhibitory synaptic inputs to vIRtPV neurons quenched their rhythmic bursting, impaired the tonic-to-bursting transition and abolished regular whisking. Thus, the whisking oscillator is an all-inhibitory network and recurrent synaptic inhibition has a key role in its rhythmogenesis.
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Affiliation(s)
- Jun Takatoh
- Department of Brain and Cognitive Sciences, McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Department of Neurobiology, Duke University, Durham, NC, USA.
| | - Vincent Prevosto
- Department of Brain and Cognitive Sciences, McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Neurobiology, Duke University, Durham, NC, USA
| | - P M Thompson
- Department of Brain and Cognitive Sciences, McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | - Jinghao Lu
- Department of Brain and Cognitive Sciences, McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Neurobiology, Duke University, Durham, NC, USA
| | - Leeyup Chung
- F.M. Kirby Neurobiology Center, Boston Children's Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
| | - Andrew Harrahill
- Department of Brain and Cognitive Sciences, McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Shun Li
- Department of Neurobiology, Duke University, Durham, NC, USA
| | - Shengli Zhao
- Department of Neurobiology, Duke University, Durham, NC, USA
| | - Zhigang He
- F.M. Kirby Neurobiology Center, Boston Children's Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
| | - David Golomb
- Department of Physiology and Cell Biology, Ben Gurion University, Be'er Sheva, Israel
- Department of Physics, Ben Gurion University, Be'er Sheva, Israel
- Zlotowski Center for Neuroscience, Ben Gurion University, Be'er Sheva, Israel
| | - David Kleinfeld
- Department of Physics, University of California at San Diego, La Jolla, CA, USA
- Department of Neurobiology, University of California at San Diego, La Jolla, CA, USA
| | - Fan Wang
- Department of Brain and Cognitive Sciences, McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Department of Neurobiology, Duke University, Durham, NC, USA.
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13
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Miller CT, Gire D, Hoke K, Huk AC, Kelley D, Leopold DA, Smear MC, Theunissen F, Yartsev M, Niell CM. Natural behavior is the language of the brain. Curr Biol 2022; 32:R482-R493. [PMID: 35609550 PMCID: PMC10082559 DOI: 10.1016/j.cub.2022.03.031] [Citation(s) in RCA: 54] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
The breadth and complexity of natural behaviors inspires awe. Understanding how our perceptions, actions, and internal thoughts arise from evolved circuits in the brain has motivated neuroscientists for generations. Researchers have traditionally approached this question by focusing on stereotyped behaviors, either natural or trained, in a limited number of model species. This approach has allowed for the isolation and systematic study of specific brain operations, which has greatly advanced our understanding of the circuits involved. At the same time, the emphasis on experimental reductionism has left most aspects of the natural behaviors that have shaped the evolution of the brain largely unexplored. However, emerging technologies and analytical tools make it possible to comprehensively link natural behaviors to neural activity across a broad range of ethological contexts and timescales, heralding new modes of neuroscience focused on natural behaviors. Here we describe a three-part roadmap that aims to leverage the wealth of behaviors in their naturally occurring distributions, linking their variance with that of underlying neural processes to understand how the brain is able to successfully navigate the everyday challenges of animals' social and ecological landscapes. To achieve this aim, experimenters must harness one challenge faced by all neurobiological systems, namely variability, in order to gain new insights into the language of the brain.
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Affiliation(s)
- Cory T Miller
- Cortical Systems and Behavior Laboratory, University of California San Diego, 9500 Gilman Drive, La Jolla, CA 92039, USA.
| | - David Gire
- Department of Psychology, University of Washington, Guthrie Hall, Seattle, WA 98105, USA
| | - Kim Hoke
- Department of Biology, Colorado State University, 1878 Campus Delivery, Fort Collins, CO 80523, USA
| | - Alexander C Huk
- Center for Perceptual Systems, Departments of Neuroscience and Psychology, University of Texas at Austin, 116 Inner Campus Drive, Austin, TX 78712, USA
| | - Darcy Kelley
- Department of Biological Sciences, Columbia University, 1212 Amsterdam Avenue, New York, NY 10027, USA
| | - David A Leopold
- Section of Cognitive Neurophysiology and Imaging, National Institute of Mental Health, 49 Convent Drive, Bethesda, MD 20892, USA
| | - Matthew C Smear
- Department of Psychology and Institute of Neuroscience, University of Oregon, 1227 University Street, Eugene, OR 97403, USA
| | - Frederic Theunissen
- Department of Psychology, University of California Berkeley, 2121 Berkeley Way, Berkeley, CA 94720, USA
| | - Michael Yartsev
- Department of Bioengineering, University of California Berkeley, 306 Stanley Hall, Berkeley, CA 94720, USA
| | - Cristopher M Niell
- Department of Biology and Institute of Neuroscience, University of Oregon, 222 Huestis Hall, Eugene, OR 97403, USA.
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14
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Duffy A, Latimer KW, Goldberg JH, Fairhall AL, Gadagkar V. Dopamine neurons evaluate natural fluctuations in performance quality. Cell Rep 2022; 38:110574. [PMID: 35354031 PMCID: PMC9013488 DOI: 10.1016/j.celrep.2022.110574] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Revised: 01/04/2022] [Accepted: 03/04/2022] [Indexed: 11/25/2022] Open
Abstract
Many motor skills are learned by comparing ongoing behavior to internal performance benchmarks. Dopamine neurons encode performance error in behavioral paradigms where error is externally induced, but it remains unknown whether dopamine also signals the quality of natural performance fluctuations. Here, we record dopamine neurons in singing birds and examine how spontaneous dopamine spiking activity correlates with natural fluctuations in ongoing song. Antidromically identified basal ganglia-projecting dopamine neurons correlate with recent, and not future, song variations, consistent with a role in evaluation, not production. Furthermore, maximal dopamine spiking occurs at a single vocal target, consistent with either actively maintaining the existing song or shifting the song to a nearby form. These data show that spontaneous dopamine spiking can evaluate natural behavioral fluctuations unperturbed by experimental events such as cues or rewards. Learning and producing skilled behavior requires an internal measure of performance. Duffy et al. examine dopamine neurons’ relationship to natural song in singing birds. Spontaneous dopamine activity correlates with song fluctuations in a manner consistent with evaluation of natural behavioral variations, independent of external perturbations, cues, or rewards.
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Affiliation(s)
- Alison Duffy
- Department of Physiology and Biophysics, University of Washington, Seattle, WA 98195, USA; Computational Neuroscience Center, University of Washington, Seattle, WA 98195, USA
| | - Kenneth W Latimer
- Department of Physiology and Biophysics, University of Washington, Seattle, WA 98195, USA; Department of Neurobiology, University of Chicago, Chicago, IL 60637, USA
| | - Jesse H Goldberg
- Department of Neurobiology and Behavior, Cornell University, Ithaca, NY 14853, USA
| | - Adrienne L Fairhall
- Department of Physiology and Biophysics, University of Washington, Seattle, WA 98195, USA; Computational Neuroscience Center, University of Washington, Seattle, WA 98195, USA.
| | - Vikram Gadagkar
- Department of Neuroscience, Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA.
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15
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Sarmashghi M, Jadhav SP, Eden U. Efficient spline regression for neural spiking data. PLoS One 2021; 16:e0258321. [PMID: 34644315 PMCID: PMC8513896 DOI: 10.1371/journal.pone.0258321] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Accepted: 09/27/2021] [Indexed: 11/29/2022] Open
Abstract
Point process generalized linear models (GLMs) provide a powerful tool for characterizing the coding properties of neural populations. Spline basis functions are often used in point process GLMs, when the relationship between the spiking and driving signals are nonlinear, but common choices for the structure of these spline bases often lead to loss of statistical power and numerical instability when the signals that influence spiking are bounded above or below. In particular, history dependent spike train models often suffer these issues at times immediately following a previous spike. This can make inferences related to refractoriness and bursting activity more challenging. Here, we propose a modified set of spline basis functions that assumes a flat derivative at the endpoints and show that this limits the uncertainty and numerical issues associated with cardinal splines. We illustrate the application of this modified basis to the problem of simultaneously estimating the place field and history dependent properties of a set of neurons from the CA1 region of rat hippocampus, and compare it with the other commonly used basis functions. We have made code available in MATLAB to implement spike train regression using these modified basis functions.
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Affiliation(s)
- Mehrad Sarmashghi
- Systems Engineering/Systems Engineering/Boston University, Boston, Massachusetts, United States of America
| | - Shantanu P. Jadhav
- Psychology/Neuroscience/Brandeis University, Waltham, Massachusetts, United States of America
| | - Uri Eden
- Mathematics and Statistics/Mathematics and Statistics/Boston University, Boston, Massachusetts, United States of America
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16
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Foo C, Lozada A, Aljadeff J, Li Y, Wang JW, Slesinger PA, Kleinfeld D. Reinforcement learning links spontaneous cortical dopamine impulses to reward. Curr Biol 2021; 31:4111-4119.e4. [PMID: 34302743 DOI: 10.1016/j.cub.2021.06.069] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 05/28/2021] [Accepted: 06/24/2021] [Indexed: 11/15/2022]
Abstract
In their pioneering study on dopamine release, Romo and Schultz speculated "...that the amount of dopamine released by unmodulated spontaneous impulse activity exerts a tonic, permissive influence on neuronal processes more actively engaged in preparation of self-initiated movements...."1 Motivated by the suggestion of "spontaneous impulses," as well as by the "ramp up" of dopaminergic neuronal activity that occurs when rodents navigate to a reward,2-5 we asked two questions. First, are there spontaneous impulses of dopamine that are released in cortex? Using cell-based optical sensors of extrasynaptic dopamine, [DA]ex,6 we found that spontaneous dopamine impulses in cortex of naive mice occur at a rate of ∼0.01 per second. Next, can mice be trained to change the amplitude and/or timing of dopamine events triggered by internal brain dynamics, much as they can change the amplitude and timing of dopamine impulses based on an external cue?7-9 Using a reinforcement learning paradigm based solely on rewards that were gated by feedback from real-time measurements of [DA]ex, we found that mice can volitionally modulate their spontaneous [DA]ex. In particular, by only the second session of daily, hour-long training, mice increased the rate of impulses of [DA]ex, increased the amplitude of the impulses, and increased their tonic level of [DA]ex for a reward. Critically, mice learned to reliably elicit [DA]ex impulses prior to receiving a reward. These effects reversed when the reward was removed. We posit that spontaneous dopamine impulses may serve as a salient cognitive event in behavioral planning.
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Affiliation(s)
- Conrad Foo
- Department of Physics, University of California at San Diego, La Jolla, CA 92093, USA
| | - Adrian Lozada
- Department of Physics, University of California at San Diego, La Jolla, CA 92093, USA
| | - Johnatan Aljadeff
- Section of Neurobiology, University of California at San Diego, La Jolla, CA 92093, USA
| | - Yulong Li
- Peking University, School of Life Sciences, Peking University, Beijing 100871, P.R. China
| | - Jing W Wang
- Section of Neurobiology, University of California at San Diego, La Jolla, CA 92093, USA
| | - Paul A Slesinger
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - David Kleinfeld
- Department of Physics, University of California at San Diego, La Jolla, CA 92093, USA; Section of Neurobiology, University of California at San Diego, La Jolla, CA 92093, USA.
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17
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Grisham W, Abrams M, Babiec WE, Fairhall AL, Kass RE, Wallisch P, Olivo R. Teaching Computation in Neuroscience: Notes on the 2019 Society for Neuroscience Professional Development Workshop on Teaching. JOURNAL OF UNDERGRADUATE NEUROSCIENCE EDUCATION : JUNE : A PUBLICATION OF FUN, FACULTY FOR UNDERGRADUATE NEUROSCIENCE 2021; 19:A185-A191. [PMID: 34552436 PMCID: PMC8437361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 11/08/2020] [Accepted: 01/20/2021] [Indexed: 06/13/2023]
Abstract
The 2019 Society for Neuroscience Professional Development Workshop on Teaching reviewed current tools, approaches, and examples for teaching computation in neuroscience. Robert Kass described the statistical foundations that students need to properly analyze data. Pascal Wallisch compared MATLAB and Python as programming languages for teaching students. Adrienne Fairhall discussed computational methods, training opportunities, and curricular considerations. Walt Babiec provided a view from the trenches on practical aspects of teaching computational neuroscience. Mathew Abrams concluded the session with an overview of resources for teaching and learning computational modeling in neuroscience.
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Affiliation(s)
| | - Mathew Abrams
- International Neuroinformatics Coordinating Facility, Karolinska Institutet. Nobels väg 15A, Stockholm. Sweden SE-171 77
| | - Walt E. Babiec
- Neuroscience Interdepartmental Program/Physiology, UCLA, Los Angeles, CA, 90095-1761
| | - Adrienne L. Fairhall
- Department of Physiology and Biophysics and Computational Neuroscience Center, University of Washington, Seattle WA 98195
| | - Robert E. Kass
- Department of Statistics & Data Science, Machine Learning Department, and Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Pascal Wallisch
- Department of Psychology, New York University, New York, NY 10003
| | - Richard Olivo
- Department of Biological Sciences, Smith College, Northampton, MA 01063
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18
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Neuronal Activity in the Posterior Cingulate Cortex Signals Environmental Information and Predicts Behavioral Variability during Trapline Foraging. J Neurosci 2021; 41:2703-2712. [PMID: 33536199 DOI: 10.1523/jneurosci.0305-20.2020] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Revised: 11/02/2020] [Accepted: 12/03/2020] [Indexed: 11/21/2022] Open
Abstract
Animals engage in routine behavior to efficiently navigate their environments. This routine behavior may be influenced by the state of the environment, such as the location and size of rewards. The neural circuits tracking environmental information and how that information impacts decisions to deviate from routines remain unexplored. To investigate the representation of environmental information during routine foraging, we recorded the activity of single neurons in posterior cingulate cortex (PCC) in 2 male monkeys searching through an array of targets in which the location of rewards was unknown. Outside the laboratory, people and animals solve such traveling salesman problems by following routine traplines that connect nearest-neighbor locations. In our task, monkeys also deployed traplining routines; but as the environment became better known, they deviate from them despite the reduction in foraging efficiency. While foraging, PCC neurons tracked environmental information but not reward and predicted variability in the pattern of choices. Together, these findings suggest that PCC may mediate the influence of information on variability in choice behavior.SIGNIFICANCE STATEMENT Many animals seek information to better guide their decisions and update behavioral routines. In our study, subjects visually searched through a set of targets on every trial to gather two rewards. Greater amounts of information about the distribution of rewards predicted less variability in choice patterns, whereas smaller amounts predicted greater variability. We recorded from the posterior cingulate cortex, an area implicated in the coding of reward and uncertainty, and discovered that these neurons signaled the expected information about the distribution of rewards instead of signaling expected rewards. The activity in these cells also predicted the amount of variability in choice behavior. These findings suggest that the posterior cingulate helps direct the search for information to augment routines.
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19
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Remapping of Adult-Born Neuron Activity during Fear Memory Consolidation in Mice. Int J Mol Sci 2021; 22:ijms22062874. [PMID: 33808976 PMCID: PMC7999719 DOI: 10.3390/ijms22062874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 03/03/2021] [Accepted: 03/10/2021] [Indexed: 11/17/2022] Open
Abstract
The mammalian hippocampal dentate gyrus is a unique memory circuit in which a subset of neurons is continuously generated throughout the lifespan. Previous studies have shown that the dentate gyrus neuronal population can hold fear memory traces (i.e., engrams) and that adult-born neurons (ABNs) support this process. However, it is unclear whether ABNs themselves hold fear memory traces. Therefore, we analyzed ABN activity at a population level across a fear conditioning paradigm. We found that fear learning did not recruit a distinct ABN population. In sharp contrast, a completely different ABN population was recruited during fear memory retrieval. We further provide evidence that ABN population activity remaps over time during the consolidation period. These results suggest that ABNs support the establishment of a fear memory trace in a different manner to directly holding the memory. Moreover, this activity remapping process in ABNs may support the segregation of memories formed at different times. These results provide new insight into the role of adult neurogenesis in the mammalian memory system.
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20
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Sex Differences in Biophysical Signatures across Molecularly Defined Medial Amygdala Neuronal Subpopulations. eNeuro 2020; 7:ENEURO.0035-20.2020. [PMID: 32493755 PMCID: PMC7333980 DOI: 10.1523/eneuro.0035-20.2020] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Accepted: 04/20/2020] [Indexed: 12/29/2022] Open
Abstract
The medial amygdala (MeA) is essential for processing innate social and non-social behaviors, such as territorial aggression and mating, which display in a sex-specific manner. While sex differences in cell numbers and neuronal morphology in the MeA are well established, if and how these differences extend to the biophysical level remain unknown. Our previous studies revealed that expression of the transcription factors, Dbx1 and Foxp2, during embryogenesis defines separate progenitor pools destined to generate different subclasses of MEA inhibitory output neurons. We have also previously shown that Dbx1-lineage and Foxp2-lineage neurons display different responses to innate olfactory cues and in a sex-specific manner. To examine whether these neurons also possess sex-specific biophysical signatures, we conducted a multidimensional analysis of the intrinsic electrophysiological profiles of these transcription factor defined neurons in the male and female MeA. We observed striking differences in the action potential (AP) spiking patterns across lineages, and across sex within each lineage, properties known to be modified by different voltage-gated ion channels. To identify the potential mechanism underlying the observed lineage-specific and sex-specific differences in spiking adaptation, we conducted a phase plot analysis to narrow down putative ion channel candidates. Of these candidates, we found a subset expressed in a lineage-biased and/or sex-biased manner. Thus, our results uncover neuronal subpopulation and sex differences in the biophysical signatures of developmentally defined MeA output neurons, providing a potential physiological substrate for how the male and female MeA may process social and non-social cues that trigger innate behavioral responses.
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21
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Haumann NT, Hansen B, Huotilainen M, Vuust P, Brattico E. Applying stochastic spike train theory for high-accuracy human MEG/EEG. J Neurosci Methods 2020; 340:108743. [DOI: 10.1016/j.jneumeth.2020.108743] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Revised: 04/14/2020] [Accepted: 04/14/2020] [Indexed: 11/16/2022]
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22
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Höfling L, Oesterle J, Berens P, Zeck G. Probing and predicting ganglion cell responses to smooth electrical stimulation in healthy and blind mouse retina. Sci Rep 2020; 10:5248. [PMID: 32251331 PMCID: PMC7090015 DOI: 10.1038/s41598-020-61899-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Accepted: 02/19/2020] [Indexed: 11/14/2022] Open
Abstract
Retinal implants are used to replace lost photoreceptors in blind patients suffering from retinopathies such as retinitis pigmentosa. Patients wearing implants regain some rudimentary visual function. However, it is severely limited compared to normal vision because non-physiological stimulation strategies fail to selectively activate different retinal pathways at sufficient spatial and temporal resolution. The development of improved stimulation strategies is rendered difficult by the large space of potential stimuli. Here we systematically explore a subspace of potential stimuli by electrically stimulating healthy and blind mouse retina in epiretinal configuration using smooth Gaussian white noise delivered by a high-density CMOS-based microelectrode array. We identify linear filters of retinal ganglion cells (RGCs) by fitting a linear-nonlinear-Poisson (LNP) model. Our stimulus evokes spatially and temporally confined spiking responses in RGC which are accurately predicted by the LNP model. Furthermore, we find diverse shapes of linear filters in the linear stage of the model, suggesting diverse preferred electrical stimuli of RGCs. The linear filter base identified by our approach could provide a starting point of a model-guided search for improved stimuli for retinal prosthetics.
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Affiliation(s)
- Larissa Höfling
- Natural and Medical Sciences Institute at the University of Tübingen, Reutlingen, Germany
- Graduate Training Centre of Neuroscience, University of Tübingen, Tübingen, Germany
- Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany
- Centre for Integrative Neuroscience, University of Tübingen, Tübingen, Germany
| | - Jonathan Oesterle
- Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany
| | - Philipp Berens
- Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany
- Bernstein Center for Computational Neuroscience, University of Tübingen, Tübingen, Germany
- Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Günther Zeck
- Natural and Medical Sciences Institute at the University of Tübingen, Reutlingen, Germany.
- Bernstein Center for Computational Neuroscience, University of Tübingen, Tübingen, Germany.
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23
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Kastanenka KV, Moreno-Bote R, De Pittà M, Perea G, Eraso-Pichot A, Masgrau R, Poskanzer KE, Galea E. A roadmap to integrate astrocytes into Systems Neuroscience. Glia 2020; 68:5-26. [PMID: 31058383 PMCID: PMC6832773 DOI: 10.1002/glia.23632] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2018] [Revised: 04/08/2019] [Accepted: 04/09/2019] [Indexed: 12/14/2022]
Abstract
Systems neuroscience is still mainly a neuronal field, despite the plethora of evidence supporting the fact that astrocytes modulate local neural circuits, networks, and complex behaviors. In this article, we sought to identify which types of studies are necessary to establish whether astrocytes, beyond their well-documented homeostatic and metabolic functions, perform computations implementing mathematical algorithms that sub-serve coding and higher-brain functions. First, we reviewed Systems-like studies that include astrocytes in order to identify computational operations that these cells may perform, using Ca2+ transients as their encoding language. The analysis suggests that astrocytes may carry out canonical computations in a time scale of subseconds to seconds in sensory processing, neuromodulation, brain state, memory formation, fear, and complex homeostatic reflexes. Next, we propose a list of actions to gain insight into the outstanding question of which variables are encoded by such computations. The application of statistical analyses based on machine learning, such as dimensionality reduction and decoding in the context of complex behaviors, combined with connectomics of astrocyte-neuronal circuits, is, in our view, fundamental undertakings. We also discuss technical and analytical approaches to study neuronal and astrocytic populations simultaneously, and the inclusion of astrocytes in advanced modeling of neural circuits, as well as in theories currently under exploration such as predictive coding and energy-efficient coding. Clarifying the relationship between astrocytic Ca2+ and brain coding may represent a leap forward toward novel approaches in the study of astrocytes in health and disease.
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Affiliation(s)
- Ksenia V. Kastanenka
- Department of Neurology, MassGeneral Institute for Neurodegenerative Diseases, Massachusetts General Hospital and Harvard Medical School, Massachusetts 02129, USA
| | - Rubén Moreno-Bote
- Department of Information and Communications Technologies, Center for Brain and Cognition and Universitat Pompeu Fabra, 08018 Barcelona, Spain
- ICREA, 08010 Barcelona, Spain
| | | | | | - Abel Eraso-Pichot
- Departament de Bioquímica, Institut de Neurociències i Universitat Autònoma de Barcelona, Bellaterra, 08193 Barcelona, Spain
| | - Roser Masgrau
- Departament de Bioquímica, Institut de Neurociències i Universitat Autònoma de Barcelona, Bellaterra, 08193 Barcelona, Spain
| | - Kira E. Poskanzer
- Department of Biochemistry & Biophysics, Neuroscience Graduate Program, and Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, California 94143, USA
- Equally contributing authors
| | - Elena Galea
- ICREA, 08010 Barcelona, Spain
- Departament de Bioquímica, Institut de Neurociències i Universitat Autònoma de Barcelona, Bellaterra, 08193 Barcelona, Spain
- Equally contributing authors
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24
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Mano O, Creamer MS, Matulis CA, Salazar-Gatzimas E, Chen J, Zavatone-Veth JA, Clark DA. Using slow frame rate imaging to extract fast receptive fields. Nat Commun 2019; 10:4979. [PMID: 31672963 PMCID: PMC6823504 DOI: 10.1038/s41467-019-12974-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2019] [Accepted: 10/11/2019] [Indexed: 11/09/2022] Open
Abstract
In functional imaging, large numbers of neurons are measured during sensory stimulation or behavior. This data can be used to map receptive fields that describe neural associations with stimuli or with behavior. The temporal resolution of these receptive fields has traditionally been limited by image acquisition rates. However, even when acquisitions scan slowly across a population of neurons, individual neurons may be measured at precisely known times. Here, we apply a method that leverages the timing of neural measurements to find receptive fields with temporal resolutions higher than the image acquisition rate. We use this temporal super-resolution method to resolve fast voltage and glutamate responses in visual neurons in Drosophila and to extract calcium receptive fields from cortical neurons in mammals. We provide code to easily apply this method to existing datasets. This method requires no specialized hardware and can be used with any optical indicator of neural activity.
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Affiliation(s)
- Omer Mano
- Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT, 06511, USA
| | - Matthew S Creamer
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, 06511, USA
| | | | | | - Juyue Chen
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, 06511, USA
| | | | - Damon A Clark
- Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT, 06511, USA.
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, 06511, USA.
- Department of Physics, Yale University, New Haven, CT, 06511, USA.
- Department of Neuroscience, Yale University, New Haven, CT, 06511, USA.
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25
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Inferring and validating mechanistic models of neural microcircuits based on spike-train data. Nat Commun 2019; 10:4933. [PMID: 31666513 PMCID: PMC6821748 DOI: 10.1038/s41467-019-12572-0] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2018] [Accepted: 09/18/2019] [Indexed: 01/11/2023] Open
Abstract
The interpretation of neuronal spike train recordings often relies on abstract statistical models that allow for principled parameter estimation and model selection but provide only limited insights into underlying microcircuits. In contrast, mechanistic models are useful to interpret microcircuit dynamics, but are rarely quantitatively matched to experimental data due to methodological challenges. Here we present analytical methods to efficiently fit spiking circuit models to single-trial spike trains. Using derived likelihood functions, we statistically infer the mean and variance of hidden inputs, neuronal adaptation properties and connectivity for coupled integrate-and-fire neurons. Comprehensive evaluations on synthetic data, validations using ground truth in-vitro and in-vivo recordings, and comparisons with existing techniques demonstrate that parameter estimation is very accurate and efficient, even for highly subsampled networks. Our methods bridge statistical, data-driven and theoretical, model-based neurosciences at the level of spiking circuits, for the purpose of a quantitative, mechanistic interpretation of recorded neuronal population activity. It is difficult to fit mechanistic, biophysically constrained circuit models to spike train data from in vivo extracellular recordings. Here the authors present analytical methods that enable efficient parameter estimation for integrate-and-fire circuit models and inference of the underlying connectivity structure in subsampled networks.
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26
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Multiple Timescales Account for Adaptive Responses across Sensory Cortices. J Neurosci 2019; 39:10019-10033. [PMID: 31662427 DOI: 10.1523/jneurosci.1642-19.2019] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2019] [Revised: 10/01/2019] [Accepted: 10/01/2019] [Indexed: 11/21/2022] Open
Abstract
Sensory systems encounter remarkably diverse stimuli in the external environment. Natural stimuli exhibit timescales and amplitudes of variation that span a wide range. Mechanisms of adaptation, a ubiquitous feature of sensory systems, allow for the accommodation of this range of scales. Are there common rules of adaptation across different sensory modalities? We measured the membrane potential responses of individual neurons in the visual, somatosensory, and auditory cortices of male and female mice to discrete, punctate stimuli delivered at a wide range of fixed and nonfixed frequencies. We find that the adaptive profile of the response is largely preserved across these three areas, exhibiting attenuation and responses to the cessation of stimulation, which are signatures of response to changes in stimulus statistics. We demonstrate that these adaptive responses can emerge from a simple model based on the integration of fixed filters operating over multiple time scales.SIGNIFICANCE STATEMENT Our recent sensations affect our current expectations and perceptions of the environment. Neural correlates of this process exist throughout the brain and are loosely termed adaptation. Adaptive processes have been described across sensory cortices, but direct comparisons of these processes have not been possible because paradigms have been tailored specifically for each modality. We developed a common stimulus set that was used to characterize adaptation in somatosensory, visual, and auditory cortex. We describe here the similarities and differences in adaptation across these cortical areas and demonstrate that adaptive responses may emerge from a set of static filters that operate over a broad range of timescales.
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27
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Nadalin JK, Martinet LE, Blackwood EB, Lo MC, Widge AS, Cash SS, Eden UT, Kramer MA. A statistical framework to assess cross-frequency coupling while accounting for confounding analysis effects. eLife 2019; 8:44287. [PMID: 31617848 PMCID: PMC6821458 DOI: 10.7554/elife.44287] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2018] [Accepted: 10/06/2019] [Indexed: 01/14/2023] Open
Abstract
Cross frequency coupling (CFC) is emerging as a fundamental feature of brain activity, correlated with brain function and dysfunction. Many different types of CFC have been identified through application of numerous data analysis methods, each developed to characterize a specific CFC type. Choosing an inappropriate method weakens statistical power and introduces opportunities for confounding effects. To address this, we propose a statistical modeling framework to estimate high frequency amplitude as a function of both the low frequency amplitude and low frequency phase; the result is a measure of phase-amplitude coupling that accounts for changes in the low frequency amplitude. We show in simulations that the proposed method successfully detects CFC between the low frequency phase or amplitude and the high frequency amplitude, and outperforms an existing method in biologically-motivated examples. Applying the method to in vivo data, we illustrate examples of CFC during a seizure and in response to electrical stimuli.
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Affiliation(s)
- Jessica K Nadalin
- Department of Mathematics and Statistics, Boston University, Boston, United States
| | | | - Ethan B Blackwood
- Department of Psychiatry, University of Minnesota, Minneapolis, United States
| | - Meng-Chen Lo
- Department of Psychiatry, University of Minnesota, Minneapolis, United States
| | - Alik S Widge
- Department of Psychiatry, University of Minnesota, Minneapolis, United States
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital, Boston, United States
| | - Uri T Eden
- Department of Mathematics and Statistics, Boston University, Boston, United States
| | - Mark A Kramer
- Department of Mathematics and Statistics, Boston University, Boston, United States
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28
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Weber AI, Fairhall AL. The role of adaptation in neural coding. Curr Opin Neurobiol 2019; 58:135-140. [DOI: 10.1016/j.conb.2019.09.013] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Revised: 08/30/2019] [Accepted: 09/12/2019] [Indexed: 10/25/2022]
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Ferraz MSA, Kihara AH. Hurst entropy: A method to determine predictability in a binary series based on a fractal-related process. Phys Rev E 2019; 99:062115. [PMID: 31330637 DOI: 10.1103/physreve.99.062115] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2018] [Indexed: 11/07/2022]
Abstract
Shannon's concept of information is related to predictability. In a binary series, the value of information relies on the frequency of 0's and 1's, or how it is expected to occur. However, information entropy does not consider the bias in randomness related to autocorrelation. In fact, it is possible for a binary temporal series to carry both short- and long-term memories related to the sequential distribution of 0's and 1's. Although the Hurst exponent measures the range of autocorrelation, there is a lack of mathematical connection between information entropy and autocorrelation present in the series. To fill this important gap, we combined numerical simulations and an analytical approach to determine how information entropy changes according to the frequency of 0's and 1's and the Hurst exponent. Indeed, we were able to determine how predictability depends on both parameters. Our findings are certainly useful to several fields when binary times series are applied, such as neuroscience to econophysics.
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Affiliation(s)
- Mariana Sacrini Ayres Ferraz
- Centro de Matemática, Computação e Cognição (CMCC), Universidade Federal do ABC (UFABC), São Bernardo do Campo, São Paulo 09606-045, Brasil
| | - Alexandre Hiroaki Kihara
- Centro de Matemática, Computação e Cognição (CMCC), Universidade Federal do ABC (UFABC), São Bernardo do Campo, São Paulo 09606-045, Brasil
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Abstract
Adaptation is a common principle that recurs throughout the nervous system at all stages of processing. This principle manifests in a variety of phenomena, from spike frequency adaptation, to apparent changes in receptive fields with changes in stimulus statistics, to enhanced responses to unexpected stimuli. The ubiquity of adaptation leads naturally to the question: What purpose do these different types of adaptation serve? A diverse set of theories, often highly overlapping, has been proposed to explain the functional role of adaptive phenomena. In this review, we discuss several of these theoretical frameworks, highlighting relationships among them and clarifying distinctions. We summarize observations of the varied manifestations of adaptation, particularly as they relate to these theoretical frameworks, focusing throughout on the visual system and making connections to other sensory systems.
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Affiliation(s)
- Alison I Weber
- Department of Physiology and Biophysics and Computational Neuroscience Center, University of Washington, Seattle, Washington 98195, USA; ,
| | - Kamesh Krishnamurthy
- Neuroscience Institute and Center for Physics of Biological Function, Department of Physics, Princeton University, Princeton, New Jersey 08544, USA;
| | - Adrienne L Fairhall
- Department of Physiology and Biophysics and Computational Neuroscience Center, University of Washington, Seattle, Washington 98195, USA; , .,UW Institute for Neuroengineering, University of Washington, Seattle, Washington 98195, USA
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31
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Geng K, Shin DC, Song D, Hampson RE, Deadwyler SA, Berger TW, Marmarelis VZ. Multi-Input, Multi-Output Neuronal Mode Network Approach to Modeling the Encoding Dynamics and Functional Connectivity of Neural Systems. Neural Comput 2019; 31:1327-1355. [PMID: 31113305 DOI: 10.1162/neco_a_01204] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
This letter proposes a novel method, multi-input, multi-output neuronal mode network (MIMO-NMN), for modeling encoding dynamics and functional connectivity in neural ensembles such as the hippocampus. Compared with conventional approaches such as the Volterra-Wiener model, linear-nonlinear-cascade (LNC) model, and generalized linear model (GLM), the NMN has several advantages in terms of estimation accuracy, model interpretation, and functional connectivity analysis. We point out the limitations of current neural spike modeling methods, especially the estimation biases caused by the imbalanced class problem when the number of zeros is significantly larger than ones in the spike data. We use synthetic data to test the performance of NMN with a comparison of the traditional methods, and the results indicate the NMN approach could reduce the imbalanced class problem and achieve better predictions. Subsequently, we apply the MIMO-NMN method to analyze data from the human hippocampus. The results indicate that the MIMO-NMN method is a promising approach to modeling neural dynamics and analyzing functional connectivity of multi-neuronal data.
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Affiliation(s)
- Kunling Geng
- Department of Biomedical Engineering and Biomedical Simulations Resource Center, University of Southern California, Los Angeles, CA, 90089, U.S.A.
| | - Dae C Shin
- Department of Biomedical Engineering and Biomedical Simulations Resource Center, University of Southern California, Los Angeles, CA, 90089, U.S.A.
| | - Dong Song
- Department of Biomedical Engineering and Biomedical Simulations Resource Center, University of Southern California, Los Angeles, CA, 90089, U.S.A.
| | - Robert E Hampson
- Department of Physiology and Pharmacology, Wake Forest School of Medicine, Winston-Salem, NC, 27157, U.S.A.
| | - Samuel A Deadwyler
- Department of Physiology and Pharmacology, Wake Forest School of Medicine, Winston-Salem, NC, 27157, U.S.A.
| | - Theodore W Berger
- Department of Biomedical Engineering and Biomedical Simulations Resource Center, University of Southern California, Los Angeles, CA, 90089, U.S.A.
| | - Vasilis Z Marmarelis
- Department of Biomedical Engineering and Biomedical Simulations Resource Center, University of Southern California, Los Angeles, CA, 90089, U.S.A.
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Gepner R, Wolk J, Wadekar DS, Dvali S, Gershow M. Variance adaptation in navigational decision making. eLife 2018; 7:37945. [PMID: 30480547 PMCID: PMC6257812 DOI: 10.7554/elife.37945] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2018] [Accepted: 10/29/2018] [Indexed: 11/13/2022] Open
Abstract
Sensory systems relay information about the world to the brain, which enacts behaviors through motor outputs. To maximize information transmission, sensory systems discard redundant information through adaptation to the mean and variance of the environment. The behavioral consequences of sensory adaptation to environmental variance have been largely unexplored. Here, we study how larval fruit flies adapt sensory-motor computations underlying navigation to changes in the variance of visual and olfactory inputs. We show that variance adaptation can be characterized by rescaling of the sensory input and that for both visual and olfactory inputs, the temporal dynamics of adaptation are consistent with optimal variance estimation. In multisensory contexts, larvae adapt independently to variance in each sense, and portions of the navigational pathway encoding mixed odor and light signals are also capable of variance adaptation. Our results suggest multiplication as a mechanism for odor-light integration.
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Affiliation(s)
- Ruben Gepner
- Department of Physics, New York University, New York, United States
| | - Jason Wolk
- Department of Physics, New York University, New York, United States
| | | | - Sophie Dvali
- Department of Physics, New York University, New York, United States
| | - Marc Gershow
- Department of Physics, New York University, New York, United States.,Center for Neural Science, New York University, New York, United States.,Neuroscience Institute, New York University, New York, United States
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Hale ME. Making sense of sparse data with neural encoding strategies. Proc Natl Acad Sci U S A 2018; 115:10545-10547. [PMID: 30279175 PMCID: PMC6196484 DOI: 10.1073/pnas.1814761115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Affiliation(s)
- Melina E Hale
- Department of Organismal Biology and Anatomy, The University of Chicago, Chicago, IL 60637
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Fischer BJ, Wydick JL, Köppl C, Peña JL. Multidimensional stimulus encoding in the auditory nerve of the barn owl. THE JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA 2018; 144:2116. [PMID: 30404459 PMCID: PMC6185867 DOI: 10.1121/1.5056171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2018] [Revised: 09/07/2018] [Accepted: 09/10/2018] [Indexed: 06/08/2023]
Abstract
Auditory perception depends on multi-dimensional information in acoustic signals that must be encoded by auditory nerve fibers (ANF). These dimensions are represented by filters with different frequency selectivities. Multiple models have been suggested; however, the identification of relevant filters and type of interactions has been elusive, limiting progress in modeling the cochlear output. Spike-triggered covariance analysis of barn owl ANF responses was used to determine the number of relevant stimulus filters and estimate the nonlinearity that produces responses from filter outputs. This confirmed that ANF responses depend on multiple filters. The first, most dominant filter was the spike-triggered average, which was excitatory for all neurons. The second and third filters could be either suppressive or excitatory with center frequencies above or below that of the first filter. The nonlinear function mapping the first two filter outputs to the spiking probability ranged from restricted to nearly circular-symmetric, reflecting different modes of interaction between stimulus dimensions across the sample. This shows that stimulus encoding in ANFs of the barn owl is multidimensional and exhibits diversity over the population, suggesting that models must allow for variable numbers of filters and types of interactions between filters to describe how sound is encoded in ANFs.
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Affiliation(s)
- Brian J Fischer
- Department of Mathematics, Seattle University, Seattle, Washington 98122, USA
| | - Jacob L Wydick
- Department of Mathematics, Seattle University, Seattle, Washington 98122, USA
| | - Christine Köppl
- Cluster of Excellence "Hearing4all" and Research Centre Neurosensory Science, Department of Neuroscience, School of Medicine and Health Science, Carl von Ossietzky University, Oldenburg, Germany
| | - José L Peña
- Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, New York, New York 10461, USA
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Neural-inspired sensors enable sparse, efficient classification of spatiotemporal data. Proc Natl Acad Sci U S A 2018; 115:10564-10569. [PMID: 30213850 PMCID: PMC6196534 DOI: 10.1073/pnas.1808909115] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Winged insects perform remarkable aerial feats in uncertain, complex fluid environments. This ability is enabled by sensation of mechanical forces to inform rapid corrections in body orientation. Curiously, mechanoreceptor neurons do not faithfully report forces; instead, they are activated by specific time histories of forcing. We find that, far from being a bug, neural encoding by biological sensors is a feature that acts as built-in temporal filtering superbly matched to detect body rotation. Indeed, this encoding further enables surprisingly efficient detection using only a small handful of neurons at key locations. Nature suggests smart data as an alternative strategy to big data, and neural-inspired sensors establish a paradigm in hyperefficient sensing of complex systems. Sparse sensor placement is a central challenge in the efficient characterization of complex systems when the cost of acquiring and processing data is high. Leading sparse sensing methods typically exploit either spatial or temporal correlations, but rarely both. This work introduces a sparse sensor optimization that is designed to leverage the rich spatiotemporal coherence exhibited by many systems. Our approach is inspired by the remarkable performance of flying insects, which use a few embedded strain-sensitive neurons to achieve rapid and robust flight control despite large gust disturbances. Specifically, we identify neural-inspired sensors at a few key locations on a flapping wing that are able to detect body rotation. This task is particularly challenging as the rotational twisting mode is three orders of magnitude smaller than the flapping modes. We show that nonlinear filtering in time, built to mimic strain-sensitive neurons, is essential to detect rotation, whereas instantaneous measurements fail. Optimized sparse sensor placement results in efficient classification with approximately 10 sensors, achieving the same accuracy and noise robustness as full measurements consisting of hundreds of sensors. Sparse sensing with neural-inspired encoding establishes an alternative paradigm in hyperefficient, embodied sensing of spatiotemporal data and sheds light on principles of biological sensing for agile flight control.
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36
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Maheswaranathan N, Kastner DB, Baccus SA, Ganguli S. Inferring hidden structure in multilayered neural circuits. PLoS Comput Biol 2018; 14:e1006291. [PMID: 30138312 PMCID: PMC6124781 DOI: 10.1371/journal.pcbi.1006291] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2017] [Revised: 09/05/2018] [Accepted: 06/09/2018] [Indexed: 01/26/2023] Open
Abstract
A central challenge in sensory neuroscience involves understanding how neural circuits shape computations across cascaded cell layers. Here we attempt to reconstruct the response properties of experimentally unobserved neurons in the interior of a multilayered neural circuit, using cascaded linear-nonlinear (LN-LN) models. We combine non-smooth regularization with proximal consensus algorithms to overcome difficulties in fitting such models that arise from the high dimensionality of their parameter space. We apply this framework to retinal ganglion cell processing, learning LN-LN models of retinal circuitry consisting of thousands of parameters, using 40 minutes of responses to white noise. Our models demonstrate a 53% improvement in predicting ganglion cell spikes over classical linear-nonlinear (LN) models. Internal nonlinear subunits of the model match properties of retinal bipolar cells in both receptive field structure and number. Subunits have consistently high thresholds, supressing all but a small fraction of inputs, leading to sparse activity patterns in which only one subunit drives ganglion cell spiking at any time. From the model’s parameters, we predict that the removal of visual redundancies through stimulus decorrelation across space, a central tenet of efficient coding theory, originates primarily from bipolar cell synapses. Furthermore, the composite nonlinear computation performed by retinal circuitry corresponds to a boolean OR function applied to bipolar cell feature detectors. Our methods are statistically and computationally efficient, enabling us to rapidly learn hierarchical non-linear models as well as efficiently compute widely used descriptive statistics such as the spike triggered average (STA) and covariance (STC) for high dimensional stimuli. This general computational framework may aid in extracting principles of nonlinear hierarchical sensory processing across diverse modalities from limited data. Computation in neural circuits arises from the cascaded processing of inputs through multiple cell layers. Each of these cell layers performs operations such as filtering and thresholding in order to shape a circuit’s output. It remains a challenge to describe both the computations and the mechanisms that mediate them given limited data recorded from a neural circuit. A standard approach to describing circuit computation involves building quantitative encoding models that predict the circuit response given its input, but these often fail to map in an interpretable way onto mechanisms within the circuit. In this work, we build two layer linear-nonlinear cascade models (LN-LN) in order to describe how the retinal output is shaped by nonlinear mechanisms in the inner retina. We find that these LN-LN models, fit to ganglion cell recordings alone, identify filters and nonlinearities that are readily mapped onto individual circuit components inside the retina, namely bipolar cells and the bipolar-to-ganglion cell synaptic threshold. This work demonstrates how combining simple prior knowledge of circuit properties with partial experimental recordings of a neural circuit’s output can yield interpretable models of the entire circuit computation, including parts of the circuit that are hidden or not directly observed in neural recordings.
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Affiliation(s)
- Niru Maheswaranathan
- Neurosciences Graduate Program, Stanford University, Stanford, California, United States of America
| | - David B. Kastner
- Neurosciences Graduate Program, Stanford University, Stanford, California, United States of America
| | - Stephen A. Baccus
- Department of Neurobiology, Stanford University, Stanford, California, United States of America
| | - Surya Ganguli
- Department of Applied Physics, Stanford University, Stanford, California, United States of America
- * E-mail:
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37
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Benjamin AS, Fernandes HL, Tomlinson T, Ramkumar P, VerSteeg C, Chowdhury RH, Miller LE, Kording KP. Modern Machine Learning as a Benchmark for Fitting Neural Responses. Front Comput Neurosci 2018; 12:56. [PMID: 30072887 PMCID: PMC6060269 DOI: 10.3389/fncom.2018.00056] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2017] [Accepted: 06/29/2018] [Indexed: 11/13/2022] Open
Abstract
Neuroscience has long focused on finding encoding models that effectively ask "what predicts neural spiking?" and generalized linear models (GLMs) are a typical approach. It is often unknown how much of explainable neural activity is captured, or missed, when fitting a model. Here we compared the predictive performance of simple models to three leading machine learning methods: feedforward neural networks, gradient boosted trees (using XGBoost), and stacked ensembles that combine the predictions of several methods. We predicted spike counts in macaque motor (M1) and somatosensory (S1) cortices from standard representations of reaching kinematics, and in rat hippocampal cells from open field location and orientation. Of these methods, XGBoost and the ensemble consistently produced more accurate spike rate predictions and were less sensitive to the preprocessing of features. These methods can thus be applied quickly to detect if feature sets relate to neural activity in a manner not captured by simpler methods. Encoding models built with a machine learning approach accurately predict spike rates and can offer meaningful benchmarks for simpler models.
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Affiliation(s)
- Ari S. Benjamin
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States
| | - Hugo L. Fernandes
- Department of Physical Medicine and Rehabilitation, Rehabilitation Institute of Chicago, Northwestern University, Chicago, IL, United States
| | - Tucker Tomlinson
- Department of Physiology, Northwestern University, Chicago, IL, United States
| | - Pavan Ramkumar
- Department of Physical Medicine and Rehabilitation, Rehabilitation Institute of Chicago, Northwestern University, Chicago, IL, United States
- Department of Neurobiology, Northwestern University, Evanston, IL, United States
| | - Chris VerSteeg
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, United States
| | - Raeed H. Chowdhury
- Department of Physiology, Northwestern University, Chicago, IL, United States
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, United States
| | - Lee E. Miller
- Department of Physical Medicine and Rehabilitation, Rehabilitation Institute of Chicago, Northwestern University, Chicago, IL, United States
- Department of Physiology, Northwestern University, Chicago, IL, United States
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, United States
| | - Konrad P. Kording
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States
- Department of Neuroscience, University of Pennsylvania, Philadelphia, PA, United States
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38
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Wang Y, Chen K, Chan LLH. Responsive Neural Activities in the Primary Visual Cortex of Retina-Degenerated Rats. Neuroscience 2018; 383:84-97. [PMID: 29758253 DOI: 10.1016/j.neuroscience.2018.05.006] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2017] [Revised: 04/24/2018] [Accepted: 05/02/2018] [Indexed: 01/08/2023]
Abstract
To study the responsive neural activities in the primary visual cortex (V1) of retinal degeneration (RD) models, experiments involving the wild-type (WT) and RD rats were conducted. The neural responses in the V1 were recorded extracellularly, while a visual stimulus with varied light intensity was given to the subjects. First, the firing rate and its relationship with light intensity were compared between the WT and RD groups. Second, the mutual information (MI) between the visual stimulus and neural response was determined for every isolated unit to quantify the amount and efficiency of information transmission in the V1 for both the control and experimental groups. Third, the local field potential (LFP) signal was characterized and its power used to compute the MI and further evaluate the function change in the RD model regarding information transmission. Analysis of spiking activity showed that the RD group exhibited a relatively decreased firing rate, information amount and efficiency compared with the control group. However, the information transmission performance of the RD model was similar to that of the WT group in the context of LFP activity. Therefore, for the RD rats, the early stage of the visual system was impaired, while the later stage of the visual system, V1, was able to capture the information about the visual stimulus, especially at the population level. Thus, this pathway could be used to restore visual ability, such as by visual prostheses.
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Affiliation(s)
- Yi Wang
- Department of Electronic Engineering, City University of Hong Kong, Hong Kong
| | - Ke Chen
- Key Laboratory for Neuroinformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, China
| | - Leanne Lai Hang Chan
- Department of Electronic Engineering, City University of Hong Kong, Hong Kong; Center for Biosystems, Neuroscience, and Nanotechnology, City University of Hong Kong, Hong Kong.
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39
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The State of the NIH BRAIN Initiative. J Neurosci 2018; 38:6427-6438. [PMID: 29921715 DOI: 10.1523/jneurosci.3174-17.2018] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2018] [Revised: 06/01/2018] [Accepted: 06/04/2018] [Indexed: 12/30/2022] Open
Abstract
The BRAIN Initiative arose from a grand challenge to "accelerate the development and application of new technologies that will enable researchers to produce dynamic pictures of the brain that show how individual brain cells and complex neural circuits interact at the speed of thought." The BRAIN Initiative is a public-private effort focused on the development and use of powerful tools for acquiring fundamental insights about how information processing occurs in the central nervous system (CNS). As the Initiative enters its fifth year, NIH has supported >500 principal investigators, who have answered the Initiative's challenge via hundreds of publications describing novel tools, methods, and discoveries that address the Initiative's seven scientific priorities. We describe scientific advances produced by individual laboratories, multi-investigator teams, and entire consortia that, over the coming decades, will produce more comprehensive and dynamic maps of the brain, deepen our understanding of how circuit activity can produce a rich tapestry of behaviors, and lay the foundation for understanding how its circuitry is disrupted in brain disorders. Much more work remains to bring this vision to fruition, and the National Institutes of Health continues to look to the diverse scientific community, from mathematics, to physics, chemistry, engineering, neuroethics, and neuroscience, to ensure that the greatest scientific benefit arises from this unique research Initiative.
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40
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Kass RE, Amari SI, Arai K, Brown EN, Diekman CO, Diesmann M, Doiron B, Eden UT, Fairhall AL, Fiddyment GM, Fukai T, Grün S, Harrison MT, Helias M, Nakahara H, Teramae JN, Thomas PJ, Reimers M, Rodu J, Rotstein HG, Shea-Brown E, Shimazaki H, Shinomoto S, Yu BM, Kramer MA. Computational Neuroscience: Mathematical and Statistical Perspectives. ANNUAL REVIEW OF STATISTICS AND ITS APPLICATION 2018; 5:183-214. [PMID: 30976604 PMCID: PMC6454918 DOI: 10.1146/annurev-statistics-041715-033733] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
Mathematical and statistical models have played important roles in neuroscience, especially by describing the electrical activity of neurons recorded individually, or collectively across large networks. As the field moves forward rapidly, new challenges are emerging. For maximal effectiveness, those working to advance computational neuroscience will need to appreciate and exploit the complementary strengths of mechanistic theory and the statistical paradigm.
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Affiliation(s)
- Robert E Kass
- Carnegie Mellon University, Pittsburgh, PA, USA, 15213;
| | - Shun-Ichi Amari
- RIKEN Brain Science Institute, Wako, Saitama Prefecture, Japan, 351-0198
| | | | - Emery N Brown
- Massachusetts Institute of Technology, Cambridge, MA, USA, 02139
- Harvard Medical School, Boston, MA, USA, 02115
| | | | - Markus Diesmann
- Jülich Research Centre, Jülich, Germany, 52428
- RWTH Aachen University, Aachen, Germany, 52062
| | - Brent Doiron
- University of Pittsburgh, Pittsburgh, PA, USA, 15260
| | - Uri T Eden
- Boston University, Boston, MA, USA, 02215
| | | | | | - Tomoki Fukai
- RIKEN Brain Science Institute, Wako, Saitama Prefecture, Japan, 351-0198
| | - Sonja Grün
- Jülich Research Centre, Jülich, Germany, 52428
- RWTH Aachen University, Aachen, Germany, 52062
| | | | - Moritz Helias
- Jülich Research Centre, Jülich, Germany, 52428
- RWTH Aachen University, Aachen, Germany, 52062
| | - Hiroyuki Nakahara
- RIKEN Brain Science Institute, Wako, Saitama Prefecture, Japan, 351-0198
| | | | - Peter J Thomas
- Case Western Reserve University, Cleveland, OH, USA, 44106
| | - Mark Reimers
- Michigan State University, East Lansing, MI, USA, 48824
| | - Jordan Rodu
- Carnegie Mellon University, Pittsburgh, PA, USA, 15213;
| | | | | | - Hideaki Shimazaki
- Honda Research Institute Japan, Wako, Saitama Prefecture, Japan, 351-0188
- Kyoto University, Kyoto, Kyoto Prefecture, Japan, 606-8502
| | | | - Byron M Yu
- Carnegie Mellon University, Pittsburgh, PA, USA, 15213;
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41
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Pratt B, Deora T, Mohren T, Daniel T. Neural evidence supports a dual sensory-motor role for insect wings. Proc Biol Sci 2018; 284:rspb.2017.0969. [PMID: 28904136 PMCID: PMC5597827 DOI: 10.1098/rspb.2017.0969] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2017] [Accepted: 08/09/2017] [Indexed: 01/29/2023] Open
Abstract
Flying insects use feedback from various sensory modalities including vision and mechanosensation to navigate through their environment. The rapid speed of mechanosensory information acquisition and processing compensates for the slower processing times associated with vision, particularly under low light conditions. While halteres in dipteran species are well known to provide such information for flight control, less is understood about the mechanosensory roles of their evolutionary antecedent, wings. The features that wing mechanosensory neurons (campaniform sensilla) encode remains relatively unexplored. We hypothesized that the wing campaniform sensilla of the hawkmoth, Manduca sexta, rapidly and selectively extract mechanical stimulus features in a manner similar to halteres. We used electrophysiological and computational techniques to characterize the encoding properties of wing campaniform sensilla. To accomplish this, we developed a novel technique for localizing receptive fields using a focused IR laser that elicits changes in the neural activity of mechanoreceptors. We found that (i) most wing mechanosensors encoded mechanical stimulus features rapidly and precisely, (ii) they are selective for specific stimulus features, and (iii) there is diversity in the encoding properties of wing campaniform sensilla. We found that the encoding properties of wing campaniform sensilla are similar to those for haltere neurons. Therefore, it appears that the neural architecture that underlies the haltere sensory function is present in wings, which lends credence to the notion that wings themselves may serve a similar sensory function. Thus, wings may not only function as the primary actuator of the organism but also as sensors of the inertial dynamics of the animal.
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Affiliation(s)
- Brandon Pratt
- Department of Biology, University of Washington, Seattle, WA 98105, USA
| | - Tanvi Deora
- Department of Biology, University of Washington, Seattle, WA 98105, USA
| | - Thomas Mohren
- Department of Mechanical Engineering, University of Washington, Seattle, WA 98105, USA
| | - Thomas Daniel
- Department of Biology, University of Washington, Seattle, WA 98105, USA
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Abstract
The nervous system extracts information from its environment and distributes and processes that information to inform and drive behaviour. In this task, the nervous system faces a type of data analysis problem, for, while a visual scene may be overflowing with information, reaching for the television remote before us requires extraction of only a relatively small fraction of that information. We could care about an almost infinite number of visual stimulus patterns, but we don't: we distinguish two actors' faces with ease but two different images of television static with significant difficulty. Equally, we could respond with an almost infinite number of movements, but we don't: the motions executed to pick up the remote are highly stereotyped and related to many other grasping motions. If we were to look at what was going on inside the brain during this task, we would find populations of neurons whose electrical activity was highly structured and correlated with the images on the screen and the action of localizing and picking up the remote.
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Affiliation(s)
- Rich Pang
- Neuroscience Graduate Program, University of Washington, Box 357270, T-471 Health Sciences Ctr, Seattle, WA 98195, USA
| | - Benjamin J Lansdell
- Department of Applied Mathematics, University of Washington, Lewis Hall #202, Box 353925, Seattle, WA 98195, USA
| | - Adrienne L Fairhall
- Department of Physiology and Biophysics, University of Washington, 1705 NE Pacific Street, Box 357290, Seattle, WA 98195, USA; WRF UW Institute for Neuroengineering, University of Washington, Box Seattle, WA 98195, USA; Center for Sensorimotor Neural Engineering, University of Washington, Box 37, 1414 NE 42nd St., Suite 204, Seattle, WA 98105, USA.
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Chan LLH. Information transmission in the primary visual cortex of retinal degenerated rats. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2017; 2017:3632-3635. [PMID: 29060685 DOI: 10.1109/embc.2017.8037644] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
To study the information transmission in the primary visual cortex (V1) of retinal degenerated (RD) models, wild type (WT) and RD rats were used in the experiments. The neural response in V1 was recorded extracellularly while the flicker with varied intensity levels was given as the visual stimulus. The mutual information (MI) and normalized mutual information (NMI) were determined for every isolated neuron, in order to quantify the amount and efficiency of information transmission in V1 of both control and experimental groups. The results showed that, on one hand, the RD group manifested relatively decreased information transmission amount and efficiency, comparing to the control group; On the other hand, it also implied that even for the RD rat, whose early stage of visual system was impaired, the later parts of visual system, especially the primary visual cortex, were still able to capture the information on visual stimulation, thus they can be utilized for restoring the visual ability, for example, via the visual prosthesis. In addition, it certainly requires more experiments for testifying and extending those results and implications.
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Barack DL, Chang SWC, Platt ML. Posterior Cingulate Neurons Dynamically Signal Decisions to Disengage during Foraging. Neuron 2017; 96:339-347.e5. [PMID: 29024659 DOI: 10.1016/j.neuron.2017.09.048] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2017] [Revised: 08/30/2017] [Accepted: 09/26/2017] [Indexed: 01/27/2023]
Abstract
Foraging for resources is a fundamental behavior balancing systematic search and strategic disengagement. The foraging behavior of primates is especially complex and requires long-term memory, value comparison, strategic planning, and decision-making. Here we provide evidence from two different foraging tasks that neurons in primate posterior cingulate cortex (PCC) signal decision salience during foraging to motivate disengagement from the current strategy. In our foraging tasks, salience refers to the difference between decision thresholds and the net harvested reward. Salience signals were stronger in poor foraging contexts than rich ones, suggesting low harvest rates recruit mechanisms in PCC that regulate strategic disengagement and exploration during foraging.
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Affiliation(s)
- David L Barack
- Department of Philosophy and Center for Cognitive Neuroscience, Duke University, Durham, NC 27701, USA.
| | - Steve W C Chang
- Department of Psychology, Yale University, New Haven, CT 06520, USA; Department of Neuroscience, Yale University School of Medicine, New Haven, CT 06510, USA
| | - Michael L Platt
- Departments of Neuroscience, Psychology, and Marketing, University of Pennsylvania, Philadelphia, PA 19104, USA
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How linear response shaped models of neural circuits and the quest for alternatives. Curr Opin Neurobiol 2017; 46:234-240. [DOI: 10.1016/j.conb.2017.09.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2017] [Accepted: 09/07/2017] [Indexed: 11/23/2022]
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Bale MR, Maravall M. Organization of sensory feature selectivity in the whisker system. Neuroscience 2017; 368:70-80. [PMID: 28918260 PMCID: PMC5798594 DOI: 10.1016/j.neuroscience.2017.09.014] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2017] [Revised: 08/25/2017] [Accepted: 09/05/2017] [Indexed: 12/14/2022]
Abstract
Neurons in the whisker system are selective to spatial and dynamical properties – features – of sensory stimuli. At each stage of the pathway, different neurons encode distinct features, generating a rich population representation. Whisker touch is robustly represented; neurons respond to touch-driven fast fluctuations in forces at the whisker base. Cortical neurons have more complex and context-dependent selectivity than subcortical, e.g., to collective whisker motion. Understanding how these signals are integrated to construct whisker-mediated percepts requires further research.
Our sensory receptors are faced with an onslaught of different environmental inputs. Each sensory event or encounter with an object involves a distinct combination of physical energy sources impinging upon receptors. In the rodent whisker system, each primary afferent neuron located in the trigeminal ganglion innervates and responds to a single whisker and encodes a distinct set of physical stimulus properties – features – corresponding to changes in whisker angle and shape and the consequent forces acting on the whisker follicle. Here we review the nature of the features encoded by successive stages of processing along the whisker pathway. At each stage different neurons respond to distinct features, such that the population as a whole represents diverse properties. Different neuronal types also have distinct feature selectivity. Thus, neurons at the same stage of processing and responding to the same whisker nevertheless play different roles in representing objects contacted by the whisker. This diversity, combined with the precise timing and high reliability of responses, enables populations at each stage to represent a wide range of stimuli. Cortical neurons respond to more complex stimulus properties – such as correlated motion across whiskers – than those at early subcortical stages. Temporal integration along the pathway is comparatively weak: neurons up to barrel cortex (BC) are sensitive mainly to fast (tens of milliseconds) fluctuations in whisker motion. The topographic organization of whisker sensitivity is paralleled by systematic organization of neuronal selectivity to certain other physical features, but selectivity to touch and to dynamic stimulus properties is distributed in “salt-and-pepper” fashion.
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Affiliation(s)
- Michael R Bale
- Sussex Neuroscience, School of Life Sciences, University of Sussex, Brighton BN1 9QG, United Kingdom
| | - Miguel Maravall
- Sussex Neuroscience, School of Life Sciences, University of Sussex, Brighton BN1 9QG, United Kingdom.
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Xiao D, Vanni MP, Mitelut CC, Chan AW, LeDue JM, Xie Y, Chen AC, Swindale NV, Murphy TH. Mapping cortical mesoscopic networks of single spiking cortical or sub-cortical neurons. eLife 2017; 6. [PMID: 28160463 PMCID: PMC5328594 DOI: 10.7554/elife.19976] [Citation(s) in RCA: 78] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2016] [Accepted: 02/02/2017] [Indexed: 12/13/2022] Open
Abstract
Understanding the basis of brain function requires knowledge of cortical operations over wide-spatial scales, but also within the context of single neurons. In vivo, wide-field GCaMP imaging and sub-cortical/cortical cellular electrophysiology were used in mice to investigate relationships between spontaneous single neuron spiking and mesoscopic cortical activity. We make use of a rich set of cortical activity motifs that are present in spontaneous activity in anesthetized and awake animals. A mesoscale spike-triggered averaging procedure allowed the identification of motifs that are preferentially linked to individual spiking neurons by employing genetically targeted indicators of neuronal activity. Thalamic neurons predicted and reported specific cycles of wide-scale cortical inhibition/excitation. In contrast, spike-triggered maps derived from single cortical neurons yielded spatio-temporal maps expected for regional cortical consensus function. This approach can define network relationships between any point source of neuronal spiking and mesoscale cortical maps. DOI:http://dx.doi.org/10.7554/eLife.19976.001
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Affiliation(s)
- Dongsheng Xiao
- Department of Psychiatry, Kinsmen Laboratory of Neurological Research, Vancouver, Canada.,Beijing Institute for Brain Disorders, Capital Medical University, Beijing, China.,Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, Canada
| | - Matthieu P Vanni
- Department of Psychiatry, Kinsmen Laboratory of Neurological Research, Vancouver, Canada.,Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, Canada
| | - Catalin C Mitelut
- Department of Psychiatry, Kinsmen Laboratory of Neurological Research, Vancouver, Canada.,Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, Canada
| | - Allen W Chan
- Department of Psychiatry, Kinsmen Laboratory of Neurological Research, Vancouver, Canada.,Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, Canada
| | - Jeffrey M LeDue
- Department of Psychiatry, Kinsmen Laboratory of Neurological Research, Vancouver, Canada.,Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, Canada
| | - Yicheng Xie
- Department of Psychiatry, Kinsmen Laboratory of Neurological Research, Vancouver, Canada.,Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, Canada
| | - Andrew Cn Chen
- Beijing Institute for Brain Disorders, Capital Medical University, Beijing, China
| | - Nicholas V Swindale
- Department of Ophthalmology and Visual Sciences, University of British Columbia, Vancouver, Canada
| | - Timothy H Murphy
- Department of Psychiatry, Kinsmen Laboratory of Neurological Research, Vancouver, Canada.,Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, Canada
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Mano O, Clark DA. Graphics Processing Unit-Accelerated Code for Computing Second-Order Wiener Kernels and Spike-Triggered Covariance. PLoS One 2017; 12:e0169842. [PMID: 28068420 PMCID: PMC5222505 DOI: 10.1371/journal.pone.0169842] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2016] [Accepted: 12/22/2016] [Indexed: 11/18/2022] Open
Abstract
Sensory neuroscience seeks to understand and predict how sensory neurons respond to stimuli. Nonlinear components of neural responses are frequently characterized by the second-order Wiener kernel and the closely-related spike-triggered covariance (STC). Recent advances in data acquisition have made it increasingly common and computationally intensive to compute second-order Wiener kernels/STC matrices. In order to speed up this sort of analysis, we developed a graphics processing unit (GPU)-accelerated module that computes the second-order Wiener kernel of a system's response to a stimulus. The generated kernel can be easily transformed for use in standard STC analyses. Our code speeds up such analyses by factors of over 100 relative to current methods that utilize central processing units (CPUs). It works on any modern GPU and may be integrated into many data analysis workflows. This module accelerates data analysis so that more time can be spent exploring parameter space and interpreting data.
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Affiliation(s)
- Omer Mano
- Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, Connecticut, United States of America
| | - Damon A. Clark
- Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, Connecticut, United States of America
- Department of Physics, Yale University, New Haven, Connecticut, United States of America
- Interdepartmental Neuroscience Program, Yale University, New Haven, Connecticut, United States of America
- * E-mail:
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