@article{ocker_kv7_2014,
abstract = {Low-threshold M currents are mediated by the Kv7 family of potassium channels. Kv7 channels are important regulators of spiking activity, having a direct influence on the firing rate, spike time variability, and filter properties of neurons. How Kv7 channels affect the joint spiking activity of populations of neurons is an important and open area of study. Using a combination of computational simulations and analytic calculations, we show that the activation of Kv7 conductances reduces the covariability between spike trains of pairs of neurons driven by common inputs. This reduction is beyond that explained by the lowering of firing rates and involves an active cancellation of common fluctuations in the membrane potentials of the cell pair. Our theory shows that the excess covariance reduction is due to a Kv7-induced shift from low-pass to band-pass filtering of the single neuron spike train response. Dysfunction of Kv7 conductances is related to a number of neurological diseases characterized by both elevated firing rates and increased network-wide correlations. We show how changes in the activation or strength of Kv7 conductances give rise to excess correlations that cannot be compensated for by synaptic scaling or homeostatic modulation of passive membrane properties. In contrast, modulation of Kv7 activation parameters consistent with pharmacological treatments for certain hyperactivity disorders can restore normal firing rates and spiking correlations. Our results provide key insights into how regulation of a ubiquitous potassium channel class can control the coordination of population spiking activity.},
author = {Ocker, Gabriel Koch and Doiron, Brent},
copyright = {Copyright {\copyright} 2014 the American Physiological Society},
doi = {10.1152/jn.00084.2014},
file = {Full Text PDF:/Users/gabeo/Zotero/storage/9CBFFUJS/Ocker and Doiron - 2014 - Kv7 channels regulate pairwise spiking covariabili.pdf:application/pdf;Snapshot:/Users/gabeo/Zotero/storage/542PE2JX/340.html:text/html},
issn = {0022-3077, 1522-1598},
journal = {Journal of Neurophysiology},
language = {en},
month = jul,
number = {2},
pages = {340--352},
title = {Kv7 channels regulate pairwise spiking covariability in health and disease},
url = {http://jn.physiology.org/content/112/2/340},
urldate = {2014-10-10},
volume = {112},
year = {2014},
bdsk-url-1 = {http://jn.physiology.org/content/112/2/340},
bdsk-url-2 = {https://doi.org/10.1152/jn.00084.2014}
}
@article{ocker_self-organization_2015,
abstract = {Author Summary The connectivity of mammalian brains exhibits structure at a wide variety of spatial scales, from the broad (which brain areas connect to which) to the extremely fine (where synapses form on the morphology of individual neurons). Recent experimental work in the neocortex has highlighted structure at the level of microcircuits: different patterns of connectivity between small groups of neurons are either more or less abundant than would be expected by chance. A central question in systems neuroscience is how this structure emerges. Attempts to answer this question are confounded by the mutual interaction of network structure and spiking activity. Synaptic connections influence spiking statistics, while individual synapses are highly plastic and become stronger or weaker depending on the activity of the pre- and postsynaptic neurons. We present a self-consistent theory for how activity-dependent synaptic plasticity leads to the emergence of neuronal microcircuits. We use this theory to show how the form of the plasticity rule can govern the promotion or suppression of different connectivity patterns. Our work provides a foundation for understanding how cortical circuits, and not just individual synapses, are malleable in response to inputs both external and internal to a network.},
author = {Ocker, Gabriel Koch and Litwin-Kumar, Ashok and Doiron, Brent},
doi = {10.1371/journal.pcbi.1004458},
file = {PLoS Full Text PDF:/Users/gabeo/Zotero/storage/VFE45JEZ/Ocker et al. - 2015 - Self-Organization of Microcircuits in Networks of .pdf:application/pdf},
journal = {PLoS Comput Biol},
month = aug,
number = {8},
pages = {e1004458},
title = {Self-{Organization} of {Microcircuits} in {Networks} of {Spiking} {Neurons} with {Plastic} {Synapses}},
url = {http://dx.doi.org/10.1371/journal.pcbi.1004458},
urldate = {2015-09-03},
volume = {11},
year = {2015},
bdsk-url-1 = {http://dx.doi.org/10.1371/journal.pcbi.1004458}
}
@article{coggan_explaining_2011,
abstract = {Neurons rely on action potentials, or spikes, to relay information. Pathological changes in spike generation likely contribute to certain enigmatic features of neurological disease, like paroxysmal attacks of pain and muscle spasm. Paroxysmal symptoms are characterized by abrupt onset and short duration, and are associated with abnormal spiking although the exact pathophysiology remains unclear. To help decipher the biophysical basis for 'paroxysmal' spiking, we replicated afterdischarge (i.e. continued spiking after a brief stimulus) in a minimal conductance-based axon model. We then applied nonlinear dynamical analysis to explain the dynamical basis for initiation and termination of afterdischarge. A perturbation could abruptly switch the system between two (quasi-)stable attractor states: rest and repetitive spiking. This bistability was a consequence of slow positive feedback mediated by persistent inward current. Initiation of afterdischarge was explained by activation of the persistent inward current forcing the system to cross a saddle point that separates the basins of attraction associated with each attractor. Termination of afterdischarge was explained by the attractor associated with repetitive spiking being destroyed. This occurred when ultra-slow negative feedback, such as intracellular sodium accumulation, caused the saddle point and stable limit cycle to collide; in that regard, the active attractor is not truly stable when the slowest dynamics are taken into account. The model also explains other features of paroxysmal symptoms, including temporal summation and refractoriness.},
author = {Coggan, Jay S. and Ocker, Gabriel K. and Sejnowski, Terrence J. and Prescott, Steven A.},
doi = {10.1088/1741-2560/8/6/065002},
file = {Full Text PDF:/Users/gabeo/Zotero/storage/AGSMTWB2/Coggan et al. - 2011 - Explaining pathological changes in axonal excitabi.pdf:application/pdf;Snapshot:/Users/gabeo/Zotero/storage/TN7QRZFX/065002.html:text/html},
issn = {1741-2552},
journal = {Journal of Neural Engineering},
language = {en},
month = dec,
number = {6},
pages = {065002},
title = {Explaining pathological changes in axonal excitability through dynamical analysis of conductance-based models},
url = {http://iopscience.iop.org/1741-2552/8/6/065002},
urldate = {2015-02-17},
volume = {8},
year = {2011},
bdsk-url-1 = {http://iopscience.iop.org/1741-2552/8/6/065002},
bdsk-url-2 = {https://doi.org/10.1088/1741-2560/8/6/065002}
}
@article{doiron_mechanics_2016,
abstract = {Simultaneous recordings from large neural populations are becoming increasingly common. An important feature of population activity is the trial-to-trial correlated fluctuation of spike train outputs from recorded neuron pairs. Similar to the firing rate of single neurons, correlated activity can be modulated by a number of factors, from changes in arousal and attentional state to learning and task engagement. However, the physiological mechanisms that underlie these changes are not fully understood. We review recent theoretical results that identify three separate mechanisms that modulate spike train correlations: changes in input correlations, internal fluctuations and the transfer function of single neurons. We first examine these mechanisms in feedforward pathways and then show how the same approach can explain the modulation of correlations in recurrent networks. Such mechanistic constraints on the modulation of population activity will be important in statistical analyses of high-dimensional neural data.
View full text},
author = {Doiron, Brent and Litwin-Kumar, Ashok and Rosenbaum, Robert and Ocker, Gabriel K. and Josi{\'c}, Kre{\v s}imir},
copyright = {{\copyright} 2016 Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
doi = {10.1038/nn.4242},
file = {Full Text PDF:/Users/gabeo/Zotero/storage/RMXUXMDT/Doiron et al. - 2016 - The mechanics of state-dependent neural correlatio.pdf:application/pdf;Snapshot:/Users/gabeo/Zotero/storage/M7IZRAE3/nn.4242.html:text/html},
issn = {1097-6256},
journal = {Nature Neuroscience},
keywords = {biophysical models, computational neuroscience, Neural circuits},
language = {en},
month = mar,
number = {3},
pages = {383--393},
title = {The mechanics of state-dependent neural correlations},
url = {http://www.nature.com/neuro/journal/v19/n3/abs/nn.4242.html},
urldate = {2016-07-28},
volume = {19},
year = {2016},
bdsk-url-1 = {http://www.nature.com/neuro/journal/v19/n3/abs/nn.4242.html},
bdsk-url-2 = {https://doi.org/10.1038/nn.4242}
}
@article{ocker_linking_2017,
abstract = {Author summary Neuronal networks, like many biological systems, exhibit variable activity. This activity is shaped by both the underlying biology of the component neurons and the structure of their interactions. How can we combine knowledge of these two things---that is, models of individual neurons and of their interactions---to predict the statistics of single- and multi-neuron activity? Current approaches rely on linearizing neural activity around a stationary state. In the face of neural nonlinearities, however, these linear methods can fail to predict spiking statistics and even fail to correctly predict whether activity is stable or pathological. Here, we show how to calculate any spike train cumulant in a broad class of models, while systematically accounting for nonlinear effects. We then study a fundamental effect of nonlinear input-rate transfer--coupling between different orders of spiking statistic--and how this depends on single-neuron and network properties.},
author = {Ocker, Gabriel Koch and Josi{\'c}, Kre{\v s}imir and Shea-Brown, Eric and Buice, Michael A.},
doi = {10.1371/journal.pcbi.1005583},
file = {Full Text PDF:/Users/gabeo/Zotero/storage/C2WXB7IG/Ocker et al. - 2017 - Linking structure and activity in nonlinear spikin.pdf:application/pdf;Snapshot:/Users/gabeo/Zotero/storage/PAFNBXXK/article.html:text/html},
issn = {1553-7358},
journal = {PLOS Computational Biology},
keywords = {Neurons, Nonlinear Dynamics, Transfer functions, Neural networks, Graphs, Action potentials, Single neuron function, Operator theory},
month = jun,
number = {6},
pages = {e1005583},
title = {Linking structure and activity in nonlinear spiking networks},
url = {http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005583},
urldate = {2017-10-15},
volume = {13},
year = {2017},
bdsk-url-1 = {http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005583},
bdsk-url-2 = {https://doi.org/10.1371/journal.pcbi.1005583}
}
@article{ocker_training_2018,
abstract = {Abstract. The synaptic connectivity of cortex is plastic, with experience shaping the ongoing interactions between neurons. Theoretical studies of spike timing},
author = {Ocker, Gabriel Koch and Doiron, Brent},
doi = {10.1093/cercor/bhy001},
file = {Snapshot:/Users/gabeo/Zotero/storage/9FMXP2BS/4836778.html:text/html},
journal = {Cerebral Cortex},
language = {en},
month = feb,
title = {Training and {Spontaneous} {Reinforcement} of {Neuronal} {Assemblies} by {Spike} {Timing} {Plasticity}},
url = {https://academic.oup.com/cercor/advance-article/doi/10.1093/cercor/bhy001/4836778},
urldate = {2018-09-28},
year = {2018},
bdsk-url-1 = {https://academic.oup.com/cercor/advance-article/doi/10.1093/cercor/bhy001/4836778},
bdsk-url-2 = {https://doi.org/10.1093/cercor/bhy001}
}
@article{ocker_statistics_2017,
abstract = {An essential step toward understanding neural circuits is linking their structure and their dynamics. In general, this relationship can be almost arbitrarily complex. Recent theoretical work has, however, begun to identify some broad principles underlying collective spiking activity in neural circuits. The first is that local features of network connectivity can be surprisingly effective in predicting global statistics of activity across a network. The second is that, for the important case of large networks with excitatory-inhibitory balance, correlated spiking persists or vanishes depending on the spatial scales of recurrent and feedforward connectivity. We close by showing how these ideas, together with plasticity rules, can help to close the loop between network structure and activity statistics.},
author = {Ocker, Gabriel Koch and Hu, Yu and Buice, Michael A and Doiron, Brent and Josi{\'c}, Kre{\v s}imir and Rosenbaum, Robert and Shea-Brown, Eric},
doi = {10.1016/j.conb.2017.07.011},
file = {ScienceDirect Full Text PDF:/Users/gabeo/Zotero/storage/PQNET74B/Ocker et al. - 2017 - From the statistics of connectivity to the statist.pdf:application/pdf;ScienceDirect Snapshot:/Users/gabeo/Zotero/storage/ZK4I2N9D/S0959438817300740.html:text/html},
issn = {0959-4388},
journal = {Current Opinion in Neurobiology},
month = oct,
pages = {109--119},
series = {Computational {Neuroscience}},
title = {From the statistics of connectivity to the statistics of spike times in neuronal networks},
url = {http://www.sciencedirect.com/science/article/pii/S0959438817300740},
volume = {46},
year = {2017},
bdsk-url-1 = {http://www.sciencedirect.com/science/article/pii/S0959438817300740},
bdsk-url-2 = {https://doi.org/10.1016/j.conb.2017.07.011}
}
@article{kanashiro_attentional_2017,
abstract = {The circuit mechanisms behind shared neural variability (noise correlation) and its dependence on neural state are poorly understood. Visual attention is well-suited to constrain cortical models of response variability because attention both increases firing rates and their stimulus sensitivity, as well as decreases noise correlations. We provide a novel analysis of population recordings in rhesus primate visual area V4 showing that a single biophysical mechanism may underlie these diverse neural correlates of attention. We explore model cortical networks where top-down mediated increases in excitability, distributed across excitatory and inhibitory targets, capture the key neuronal correlates of attention. Our models predict that top-down signals primarily affect inhibitory neurons, whereas excitatory neurons are more sensitive to stimulus specific bottom-up inputs. Accounting for trial variability in models of state dependent modulation of neuronal activity is a critical step in building a mechanistic theory of neuronal cognition.},
author = {Kanashiro, Tatjana and Ocker, Gabriel Koch and Cohen, Marlene R and Doiron, Brent},
doi = {10.7554/eLife.23978},
editor = {Latham, Peter},
file = {Full Text PDF:/Users/gabeo/Zotero/storage/Z92X28MB/Kanashiro et al. - 2017 - Attentional modulation of neuronal variability in .pdf:application/pdf},
issn = {2050-084X},
journal = {eLife},
keywords = {noise correlations, inhibitory feedback, mean field model, neural correlates of attention},
month = jun,
pages = {e23978},
title = {Attentional modulation of neuronal variability in circuit models of cortex},
url = {https://doi.org/10.7554/eLife.23978},
urldate = {2019-09-29},
volume = {6},
year = {2017},
bdsk-url-1 = {https://doi.org/10.7554/eLife.23978}
}
@article{linaro_correlation_2019,
abstract = {Correlated electrical activity in neurons is a prominent characteristic of cortical microcircuits. Despite a growing amount of evidence concerning both spike-count and subthreshold membrane potential pairwise correlations, little is known about how different types of cortical neurons convert correlated inputs into correlated outputs. We studied pyramidal neurons and two classes of GABAergic interneurons of layer 5 in neocortical brain slices obtained from rats of both sexes, and we stimulated them with biophysically realistic correlated inputs, generated using dynamic clamp. We found that the physiological differences between cell types manifested unique features in their capacity to transfer correlated inputs. We used linear response theory and computational modeling to gain clear insights into how cellular properties determine both the gain and timescale of correlation transfer, thus tying single-cell features with network interactions. Our results provide further ground for the functionally distinct roles played by various types of neuronal cells in the cortical microcircuit.
SIGNIFICANCE STATEMENT No matter how we probe the brain, we find correlated neuronal activity over a variety of spatial and temporal scales. For the cerebral cortex, significant evidence has accumulated on trial-to-trial covariability in synaptic inputs activation, subthreshold membrane potential fluctuations, and output spike trains. Although we do not yet fully understand their origin and whether they are detrimental or beneficial for information processing, we believe that clarifying how correlations emerge is pivotal for understanding large-scale neuronal network dynamics and computation. Here, we report quantitative differences between excitatory and inhibitory cells, as they relay input correlations into output correlations. We explain this heterogeneity by simple biophysical models and provide the most experimentally validated test of a theory for the emergence of correlations.},
author = {Linaro, Daniele and Ocker, Gabriel K. and Doiron, Brent and Giugliano, Michele},
copyright = {Copyright {\copyright} 2019 the authors},
doi = {10.1523/JNEUROSCI.3169-18.2019},
file = {Snapshot:/Users/gabeo/Zotero/storage/JN28LUBX/7648.html:text/html},
issn = {0270-6474, 1529-2401},
journal = {Journal of Neuroscience},
keywords = {noise correlations, cortical interneurons, dynamic clamp, neocortex, pyramidal cells},
language = {en},
month = sep,
number = {39},
pages = {7648--7663},
pmid = {31346031},
title = {Correlation {Transfer} by {Layer} 5 {Cortical} {Neurons} {Under} {Recreated} {Synaptic} {Inputs} {In} {Vitro}},
url = {https://www.jneurosci.org/content/39/39/7648},
urldate = {2019-10-06},
volume = {39},
year = {2019},
bdsk-url-1 = {https://www.jneurosci.org/content/39/39/7648},
bdsk-url-2 = {https://doi.org/10.1523/JNEUROSCI.3169-18.2019}
}
@article{siegle_survey_2019,
abstract = {{\textless}p{\textgreater}The mammalian visual system, from retina to neocortex, has been extensively studied at both anatomical and functional levels. Anatomy indicates the corticothalamic system is hierarchical, but characterization of cellular-level functional interactions across multiple levels of this hierarchy is lacking, partially due to the challenge of simultaneously recording activity across numerous regions. Here, we describe a large, open dataset (part of the \textit{Allen Brain Observatory}) that surveys spiking from units in six cortical and two thalamic regions responding to a battery of visual stimuli. Using spike cross-correlation analysis, we find that inter-area functional connectivity mirrors the anatomical hierarchy from the \textit{Allen Mouse Brain Connectivity Atlas}. Classical functional measures of hierarchy, including visual response latency, receptive field size, phase-locking to a drifting grating stimulus, and autocorrelation timescale are all correlated with the anatomical hierarchy. Moreover, recordings during a visual task support the behavioral relevance of hierarchical processing. Overall, this dataset and the hierarchy we describe provide a foundation for understanding coding and dynamics in the mouse corticothalamic visual system.{\textless}/p{\textgreater}},
author = {Siegle, Joshua H. and Jia, Xiaoxuan and Durand, Severine and Gale, Sam and Bennett, Corbett and Graddis, Nile and Heller, Greggory and Ramirez, Tamina K. and Choi, Hannah and Luviano, Jennifer A. and Groblewski, Peter A. and Arkhipov, Anton and Bernard, Amy and Billeh, Yazan N. and Brown, Dillan and Buice, Michael A. and Cain, Nicholas and Caldejon, Shiella and Casal, Linzy and Cho, Andrew and Chvilicek, Maggie and Cox, Timothy and Dai, Kael and Denman, Daniel J. and Vries, Saskia E. J. de and Esposito, Luke and Farrell, Colin and Feng, David and Galbraith, John and Garrett, Marina and Gelfand, Emily C. and Hancock, Nicole and Harris, Julie A. and Howard, Robert and Hu, Brian and Hytnen, Ross and Iyer, Ramakrishnan and Jessett, Erika and Kato, India and Kiggens, Justin and Lecoq, Jerome and Ledochowitsch, Peter and Lee, Jung Hoon and Leon, Arielle and Li, Yang and Liang, Elizabeth and Long, Fuhui and Mace, Kyla and Melchior, Jose and Millman, Daniel and Mollenkopf, Tyler and Nayan, Chelsea and Ng, Lydia and Ngo, Kiet and Nguyen, Thuyahn and Nicovich, Rusty and North, Kat and Ocker, Gabriel Koch and Ollerenshaw, Doug and Oliver, Michael and Pachitariu, Marius and Perkins, Jed and Reding, Melissa and Reid, David and Robertson, Miranda and Ronellenfitch, Kara and Seid, Sam and Slaughterbeck, Cliff and Stoecklin, Michelle and Sullivan, David and Sutton, Ben and Swapp, Jackie and Thompson, Carol and Wakeman, Wayne and Whitesell, Jennifer D. and Williams, Derric and Williford, Ali and Young, Rob and Zeng, Hongkui and Naylor, Sarah and Phillips, John P. and Reid, R. Clay and Mihalas, Stefan and Olsen, Shawn R. and Koch, Christof},
copyright = {{\copyright} 2019, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-NonCommercial-NoDerivs 4.0 International), CC BY-NC-ND 4.0, as described at http://creativecommons.org/licenses/by-nc-nd/4.0/},
doi = {10.1101/805010},
file = {Full Text PDF:/Users/gabeo/Zotero/storage/QXCFDWYT/Siegle et al. - 2019 - A survey of spiking activity reveals a functional .pdf:application/pdf;Snapshot:/Users/gabeo/Zotero/storage/QFXU9XWC/805010v1.html:text/html},
journal = {bioRxiv},
language = {en},
month = oct,
pages = {805010},
title = {A survey of spiking activity reveals a functional hierarchy of mouse corticothalamic visual areas},
url = {https://www.biorxiv.org/content/10.1101/805010v1},
urldate = {2019-10-17},
year = {2019},
bdsk-url-1 = {https://www.biorxiv.org/content/10.1101/805010v1},
bdsk-url-2 = {https://doi.org/10.1101/805010}
}
@article{de_vries_large-scale_2020,
abstract = {To understand how the brain processes sensory information to guide behavior, we must know how stimulus representations are transformed throughout the visual cortex. Here we report an open, large-scale physiological survey of activity in the awake mouse visual cortex: the Allen Brain Observatory Visual Coding dataset. This publicly available dataset includes the cortical activity of nearly 60,000 neurons from six visual areas, four layers, and 12 transgenic mouse lines in a total of 243 adult mice, in response to a systematic set of visual stimuli. We classify neurons on the basis of joint reliabilities to multiple stimuli and validate this functional classification with models of visual responses. While most classes are characterized by responses to specific subsets of the stimuli, the largest class is not reliably responsive to any of the stimuli and becomes progressively larger in higher visual areas. These classes reveal a functional organization wherein putative dorsal areas show specialization for visual motion signals.},
author = {de Vries, Saskia E. J. and Lecoq, Jerome A. and Buice, Michael A. and Groblewski, Peter A. and Ocker, Gabriel K. and Oliver, Michael and Feng, David and Cain, Nicholas and Ledochowitsch, Peter and Millman, Daniel and Roll, Kate and Garrett, Marina and Keenan, Tom and Kuan, Leonard and Mihalas, Stefan and Olsen, Shawn and Thompson, Carol and Wakeman, Wayne and Waters, Jack and Williams, Derric and Barber, Chris and Berbesque, Nathan and Blanchard, Brandon and Bowles, Nicholas and Caldejon, Shiella D. and Casal, Linzy and Cho, Andrew and Cross, Sissy and Dang, Chinh and Dolbeare, Tim and Edwards, Melise and Galbraith, John and Gaudreault, Nathalie and Gilbert, Terri L. and Griffin, Fiona and Hargrave, Perry and Howard, Robert and Huang, Lawrence and Jewell, Sean and Keller, Nika and Knoblich, Ulf and Larkin, Josh D. and Larsen, Rachael and Lau, Chris and Lee, Eric and Lee, Felix and Leon, Arielle and Li, Lu and Long, Fuhui and Luviano, Jennifer and Mace, Kyla and Nguyen, Thuyanh and Perkins, Jed and Robertson, Miranda and Seid, Sam and Shea-Brown, Eric and Shi, Jianghong and Sjoquist, Nathan and Slaughterbeck, Cliff and Sullivan, David and Valenza, Ryan and White, Casey and Williford, Ali and Witten, Daniela M. and Zhuang, Jun and Zeng, Hongkui and Farrell, Colin and Ng, Lydia and Bernard, Amy and Phillips, John W. and Reid, R. Clay and Koch, Christof},
copyright = {2019 The Author(s), under exclusive licence to Springer Nature America, Inc.},
doi = {10.1038/s41593-019-0550-9},
file = {Full Text PDF:/Users/gabeo/Zotero/storage/EGYIQA83/de Vries et al. - 2020 - A large-scale standardized physiological survey re.pdf:application/pdf;Snapshot:/Users/gabeo/Zotero/storage/SUY8G6RK/s41593-019-0550-9.html:text/html},
issn = {1546-1726},
journal = {Nature Neuroscience},
language = {en},
month = jan,
note = {Number: 1 Publisher: Nature Publishing Group},
number = {1},
pages = {138--151},
title = {A large-scale standardized physiological survey reveals functional organization of the mouse visual cortex},
url = {https://www.nature.com/articles/s41593-019-0550-9},
urldate = {2020-07-01},
volume = {23},
year = {2020},
bdsk-url-1 = {https://www.nature.com/articles/s41593-019-0550-9},
bdsk-url-2 = {https://doi.org/10.1038/s41593-019-0550-9}
}
@article{siegle_reconciling_2020,
abstract = {{\textless}p{\textgreater}Extracellular electrophysiology and two-photon calcium imaging are widely used methods for measuring physiological activity with single cell resolution across large populations of neurons in the brain. While these two modalities have distinct advantages and disadvantages, neither provides complete, unbiased information about the underlying neural population. Here, we compare evoked responses in visual cortex recorded in awake mice under highly standardized conditions using either imaging or electrophysiology. Across all stimulus conditions tested, we observe a larger fraction of responsive neurons in electrophysiology and higher stimulus selectivity in calcium imaging. This work explores which data transformations are most useful for explaining these modality specific discrepancies. We show that the higher selectivity in imaging can be partially reconciled by applying a spikes-to-calcium forward model to the electrophysiology data. However, the forward model could not reconcile differences in responsiveness without sub selecting neurons based on event rate or level of signal contamination. This suggests that differences in responsiveness more likely reflect neuronal sampling bias or cluster merging artifacts during spike sorting of electrophysiological recordings, rather than flaws in event detection from fluorescence time series. This work establishes the dominant impacts of the two modalities9 respective biases on a set of functional metrics that are fundamental for characterizing sensory-evoked responses.{\textless}/p{\textgreater}},
author = {Siegle, Joshua H. and Ledochowitsch, Peter and Jia, Xiaoxuan and Millman, Daniel and Ocker, Gabriel K. and Caldejon, Shiella and Casal, Linzy and Cho, Andrew and Denman, Daniel J. and Durand, S{\'e}verine and Groblewski, Peter A. and Heller, Greggory and Kato, India and Kivikas, Sara and Lecoq, Jerome and Nayan, Chelsea and Ngo, Kiet and Nicovich, Philip R. and North, Kat R. and Ramirez, Tamina K. and Swapp, Jackie and Waughman, Xana and Williford, Ali and Olsen, Shawn R. and Koch, Christof and Buice, Michael A. and Vries, Saskia E. J. de},
copyright = {{\copyright} 2020, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-NonCommercial-NoDerivs 4.0 International), CC BY-NC-ND 4.0, as described at http://creativecommons.org/licenses/by-nc-nd/4.0/},
doi = {10.1101/2020.08.10.244723},
file = {Full Text PDF:/Users/gabeo/Zotero/storage/YHT8SPTU/Siegle et al. - 2020 - Reconciling functional differences in populations .pdf:application/pdf;Snapshot:/Users/gabeo/Zotero/storage/ENFI38RZ/2020.08.10.244723v1.html:text/html},
journal = {bioRxiv},
language = {en},
month = aug,
note = {Publisher: Cold Spring Harbor Laboratory Section: New Results},
pages = {2020.08.10.244723},
title = {Reconciling functional differences in populations of neurons recorded with two-photon imaging and electrophysiology},
url = {https://www.biorxiv.org/content/10.1101/2020.08.10.244723v1},
urldate = {2020-08-25},
year = {2020},
bdsk-url-1 = {https://www.biorxiv.org/content/10.1101/2020.08.10.244723v1},
bdsk-url-2 = {https://doi.org/10.1101/2020.08.10.244723}
}
@article{ocker_flexible_2020,
abstract = {Neural computation is determined by neurons' dynamics and circuit connectivity. Uncertain and dynamic environments may require neural hardware to adapt to different computational tasks, each requiring different connectivity configurations. At the same time, connectivity is subject to a variety of constraints, placing limits on the possible computations a given neural circuit can perform. Here we examine the hypothesis that the organization of neural circuitry favors computational flexibility: that it makes many computational solutions available, given physiological constraints. From this hypothesis, we develop models of connectivity degree distributions based on constraints on a neuron's total synaptic weight. To test these models, we examine reconstructions of the mushroom bodies from the first instar larva and adult Drosophila melanogaster. We perform a Bayesian model comparison for two constraint models and a random wiring null model. Overall, we find that flexibility under a homeostatically fixed total synaptic weight describes Kenyon cell connectivity better than other models, suggesting a principle shaping the apparently random structure of Kenyon cell wiring. Furthermore, we find evidence that larval Kenyon cells are more flexible earlier in development, suggesting a mechanism whereby neural circuits begin as flexible systems that develop into specialized computational circuits.},
author = {Ocker, Gabriel Koch and Buice, Michael A.},
doi = {10.1371/journal.pcbi.1008080},
file = {Full Text PDF:/Users/gabeo/Zotero/storage/BWYFK3JU/Ocker and Buice - 2020 - Flexible neural connectivity under constraints on .pdf:application/pdf;Snapshot:/Users/gabeo/Zotero/storage/ZURX5Z89/article.html:text/html},
issn = {1553-7358},
journal = {PLOS Computational Biology},
keywords = {Binomials, Body weight, Drosophila melanogaster, Larvae, Neural pathways, Neuronal dendrites, Neurons, Synapses},
language = {en},
month = aug,
note = {Publisher: Public Library of Science},
number = {8},
pages = {e1008080},
title = {Flexible neural connectivity under constraints on total connection strength},
url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008080},
urldate = {2020-08-25},
volume = {16},
year = {2020},
bdsk-url-1 = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008080},
bdsk-url-2 = {https://doi.org/10.1371/journal.pcbi.1008080}
}
@article{millman_vip_2019,
abstract = {{\textless}h3{\textgreater}Abstract{\textless}/h3{\textgreater} {\textless}p{\textgreater}Vasoactive intestinal peptide-expressing (VIP) interneurons in cortex regulate feedback inhibition of pyramidal neurons through suppression of somatostatin-expressing (SST) interneurons and, reciprocally, SST neurons inhibit VIP neurons. Here, we show that VIP neurons in mouse primary visual cortex have complementary contrast tuning to SST neurons and respond synergistically to front-to-back visual motion and locomotion. Network modeling indicates that this VIP-SST mutual antagonism regulates the gain of cortex to achieve both sensitivity to behaviorally-relevant stimuli and network stability.{\textless}/p{\textgreater}},
author = {Millman, Daniel J. and Ocker, Gabriel Koch and Caldejon, Shiella and Kato, India and Larkin, Josh D. and Lee, Eric Kenji and Luviano, Jennifer and Nayan, Chelsea and Nguyen, Thuyanh V. and North, Kat and Seid, Sam and White, Cassandra and Lecoq, Jerome A. and Reid, R. Clay and Buice, Michael A. and Vries, Saskia E. J. de},
copyright = {{\copyright} 2019, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-NonCommercial-NoDerivs 4.0 International), CC BY-NC-ND 4.0, as described at http://creativecommons.org/licenses/by-nc-nd/4.0/},
doi = {10.1101/858001},
file = {Full Text PDF:/Users/gabeo/Zotero/storage/U4ZMIVBG/Millman et al. - 2019 - VIP interneurons selectively enhance weak but beha.pdf:application/pdf;Snapshot:/Users/gabeo/Zotero/storage/W3JTFNCZ/858001v1.html:text/html},
journal = {bioRxiv},
language = {en},
month = nov,
note = {Publisher: Cold Spring Harbor Laboratory Section: New Results},
pages = {858001},
title = {{VIP} interneurons selectively enhance weak but behaviorally-relevant stimuli},
url = {https://www.biorxiv.org/content/10.1101/858001v1},
urldate = {2020-08-25},
year = {2019},
bdsk-url-1 = {https://www.biorxiv.org/content/10.1101/858001v1},
bdsk-url-2 = {https://doi.org/10.1101/858001}
}
@article{recanatesi_dimensionality_2019,
abstract = {The dimensionality of a network's collective activity is of increasing interest in neuroscience. This is because dimensionality provides a compact measure of how coordinated network-wide activity is, in terms of the number of modes (or degrees of freedom) that it can independently explore. A low number of modes suggests a compressed low dimensional neural code and reveals interpretable dynamics [1], while findings of high dimension may suggest flexible computations [2, 3]. Here, we address the fundamental question of how dimensionality is related to connectivity, in both autonomous and stimulus-driven networks. Working with a simple spiking network model, we derive three main findings. First, the dimensionality of global activity patterns can be strongly, and systematically, regulated by local connectivity structures. Second, the dimensionality is a better indicator than average correlations in determining how constrained neural activity is. Third, stimulus evoked neural activity interacts systematically with neural connectivity patterns, leading to network responses of either greater or lesser dimensionality than the stimulus.},
author = {Recanatesi, Stefano and Ocker, Gabriel Koch and Buice, Michael A. and Shea-Brown, Eric},
doi = {10.1371/journal.pcbi.1006446},
file = {Full Text PDF:/Users/gabeo/Zotero/storage/ESRDU6PM/Recanatesi et al. - 2019 - Dimensionality in recurrent spiking networks Glob.pdf:application/pdf;Snapshot:/Users/gabeo/Zotero/storage/64VMBU7V/article.html:text/html},
issn = {1553-7358},
journal = {PLOS Computational Biology},
keywords = {Action potentials, Covariance, Network analysis, Network motifs, Network reciprocity, Neural networks, Neurons, Scale-free networks},
language = {en},
month = jul,
note = {Publisher: Public Library of Science},
number = {7},
pages = {e1006446},
shorttitle = {Dimensionality in recurrent spiking networks},
title = {Dimensionality in recurrent spiking networks: {Global} trends in activity and local origins in connectivity},
url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006446},
urldate = {2020-08-25},
volume = {15},
year = {2019},
bdsk-url-1 = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006446},
bdsk-url-2 = {https://doi.org/10.1371/journal.pcbi.1006446}
}
This file was generated by bibtex2html 1.96.