[{"volume":11,"title":"Theta phase-specific codes for two-dimensional position, trajectory and heading in the hippocampus","year":"2008","day":"29","extern":1,"abstract":[{"text":"Temporal coding is a means of representing information by the time, as opposed to the rate, at which neurons fire. Evidence of temporal coding in the hippocampus comes from place cells, whose spike times relative to theta oscillations reflect a rat's position while running along stereotyped trajectories. This arises from the backwards shift in cell firing relative to local theta oscillations (phase precession). Here we demonstrate phase precession during place-field crossings in an open-field foraging task. This produced spike sequences in each theta cycle that disambiguate the rat's trajectory through two-dimensional space and can be used to predict movement direction. Furthermore, position and movement direction were maximally predicted from firing in the early and late portions of the theta cycle, respectively. This represents the first direct evidence of a combined representation of position, trajectory and heading in the hippocampus, organized on a fine temporal scale by theta oscillations.","lang":"eng"}],"publist_id":"2869","author":[{"first_name":"John","last_name":"Huxter","full_name":"Huxter,John R"},{"full_name":"Senior,Timothy J","first_name":"Timothy","last_name":"Senior"},{"full_name":"Allen, Kevin","first_name":"Kevin","last_name":"Allen"},{"first_name":"Jozsef L","last_name":"Csicsvari","orcid":"0000-0002-5193-4036","full_name":"Jozsef Csicsvari","id":"3FA14672-F248-11E8-B48F-1D18A9856A87"}],"publication_status":"published","doi":"10.1038/nn.2106","date_published":"2008-05-29T00:00:00Z","quality_controlled":0,"date_updated":"2021-01-12T07:44:00Z","type":"journal_article","publication":"Nature Neuroscience","intvolume":"        11","issue":"5","_id":"3516","citation":{"apa":"Huxter, J., Senior, T., Allen, K., &#38; Csicsvari, J. L. (2008). Theta phase-specific codes for two-dimensional position, trajectory and heading in the hippocampus. <i>Nature Neuroscience</i>. Nature Publishing Group. <a href=\"https://doi.org/10.1038/nn.2106\">https://doi.org/10.1038/nn.2106</a>","ama":"Huxter J, Senior T, Allen K, Csicsvari JL. Theta phase-specific codes for two-dimensional position, trajectory and heading in the hippocampus. <i>Nature Neuroscience</i>. 2008;11(5):587-594. doi:<a href=\"https://doi.org/10.1038/nn.2106\">10.1038/nn.2106</a>","ieee":"J. Huxter, T. Senior, K. Allen, and J. L. Csicsvari, “Theta phase-specific codes for two-dimensional position, trajectory and heading in the hippocampus,” <i>Nature Neuroscience</i>, vol. 11, no. 5. Nature Publishing Group, pp. 587–594, 2008.","chicago":"Huxter, John, Timothy Senior, Kevin Allen, and Jozsef L Csicsvari. “Theta Phase-Specific Codes for Two-Dimensional Position, Trajectory and Heading in the Hippocampus.” <i>Nature Neuroscience</i>. Nature Publishing Group, 2008. <a href=\"https://doi.org/10.1038/nn.2106\">https://doi.org/10.1038/nn.2106</a>.","ista":"Huxter J, Senior T, Allen K, Csicsvari JL. 2008. Theta phase-specific codes for two-dimensional position, trajectory and heading in the hippocampus. Nature Neuroscience. 11(5), 587–594.","mla":"Huxter, John, et al. “Theta Phase-Specific Codes for Two-Dimensional Position, Trajectory and Heading in the Hippocampus.” <i>Nature Neuroscience</i>, vol. 11, no. 5, Nature Publishing Group, 2008, pp. 587–94, doi:<a href=\"https://doi.org/10.1038/nn.2106\">10.1038/nn.2106</a>.","short":"J. Huxter, T. Senior, K. Allen, J.L. Csicsvari, Nature Neuroscience 11 (2008) 587–594."},"publisher":"Nature Publishing Group","date_created":"2018-12-11T12:03:44Z","page":"587 - 594","status":"public","month":"05"},{"publication_status":"published","doi":"10.1038/nn2037","author":[{"first_name":"Joseph","last_name":"O'Neill","id":"426376DC-F248-11E8-B48F-1D18A9856A87","full_name":"Joseph O'Neill"},{"first_name":"Timothy","last_name":"Senior","full_name":"Senior,Timothy J"},{"full_name":"Allen, Kevin","last_name":"Allen","first_name":"Kevin"},{"full_name":"Huxter,John R","first_name":"John","last_name":"Huxter"},{"first_name":"Jozsef L","last_name":"Csicsvari","full_name":"Jozsef Csicsvari","orcid":"0000-0002-5193-4036","id":"3FA14672-F248-11E8-B48F-1D18A9856A87"}],"abstract":[{"lang":"eng","text":"The hippocampus is thought to be involved in episodic memory formation by reactivating traces of waking experience during sleep. Indeed, the joint firing of spatially tuned pyramidal cells encoding nearby places recur during sleep. We found that the sleep cofiring of rat CA1 pyramidal cells encoding similar places increased relative to the sleep session before exploration. This cofiring increase depended on the number of times that cells fired together with short latencies ( &lt; 50 ms) during exploration, and was strongest between cells representing the most visited places. This is indicative of a Hebbian learning rule in which changes in firing associations between cells are determined by the number of waking coincident firing events. In contrast, cells encoding different locations reduced their cofiring in proportion to the number of times that they fired independently. Together these data indicate that reactivated patterns are shaped by both positive and negative changes in cofiring, which are determined by recent behavior."}],"publist_id":"2864","title":"Reactivation of experience-dependent cell assembly patterns in the hippocampus","volume":11,"year":"2008","day":"01","extern":1,"page":"209 - 215","status":"public","month":"02","publisher":"Nature Publishing Group","citation":{"ista":"O’Neill J, Senior T, Allen K, Huxter J, Csicsvari JL. 2008. Reactivation of experience-dependent cell assembly patterns in the hippocampus. Nature Neuroscience. 11(2), 209–215.","mla":"O’Neill, Joseph, et al. “Reactivation of Experience-Dependent Cell Assembly Patterns in the Hippocampus.” <i>Nature Neuroscience</i>, vol. 11, no. 2, Nature Publishing Group, 2008, pp. 209–15, doi:<a href=\"https://doi.org/10.1038/nn2037\">10.1038/nn2037</a>.","short":"J. O’Neill, T. Senior, K. Allen, J. Huxter, J.L. Csicsvari, Nature Neuroscience 11 (2008) 209–215.","chicago":"O’Neill, Joseph, Timothy Senior, Kevin Allen, John Huxter, and Jozsef L Csicsvari. “Reactivation of Experience-Dependent Cell Assembly Patterns in the Hippocampus.” <i>Nature Neuroscience</i>. Nature Publishing Group, 2008. <a href=\"https://doi.org/10.1038/nn2037\">https://doi.org/10.1038/nn2037</a>.","ama":"O’Neill J, Senior T, Allen K, Huxter J, Csicsvari JL. Reactivation of experience-dependent cell assembly patterns in the hippocampus. <i>Nature Neuroscience</i>. 2008;11(2):209-215. doi:<a href=\"https://doi.org/10.1038/nn2037\">10.1038/nn2037</a>","ieee":"J. O’Neill, T. Senior, K. Allen, J. Huxter, and J. L. Csicsvari, “Reactivation of experience-dependent cell assembly patterns in the hippocampus,” <i>Nature Neuroscience</i>, vol. 11, no. 2. Nature Publishing Group, pp. 209–215, 2008.","apa":"O’Neill, J., Senior, T., Allen, K., Huxter, J., &#38; Csicsvari, J. L. (2008). Reactivation of experience-dependent cell assembly patterns in the hippocampus. <i>Nature Neuroscience</i>. Nature Publishing Group. <a href=\"https://doi.org/10.1038/nn2037\">https://doi.org/10.1038/nn2037</a>"},"date_created":"2018-12-11T12:03:46Z","intvolume":"        11","issue":"2","_id":"3520","date_published":"2008-02-01T00:00:00Z","quality_controlled":0,"date_updated":"2021-01-12T07:44:02Z","type":"journal_article","publication":"Nature Neuroscience"},{"day":"27","extern":1,"title":"Ivy cells: A population of nitric-oxide-producing, slow-spiking GABAergic neurons and their involvement in hippocampal network activity","volume":57,"year":"2008","publist_id":"2855","abstract":[{"lang":"eng","text":"In the cerebral cortex, GABAergic interneurons are often regarded as fast-spiking cells. We have identified a type of slow-spiking interneuron that offers distinct contributions to network activity. “Ivy” cells, named after their dense and fine axons innervating mostly basal and oblique pyramidal cell dendrites, are more numerous than the parvalbumin-expressing basket, bistratified, or axo-axonic cells. Ivy cells express nitric oxide synthase, neuropeptide Y, and high levels of GABA(A) receptor alpha 1 subunit; they discharge at a low frequency with wide spikes in vivo, yet are distinctively phase-locked to behaviorally relevant network rhythms including theta, gamma, and ripple oscillations. Paired recordings in vitro showed that Ivy cells receive depressing EPSPs from pyramidal cells, which in turn receive slowly rising and decaying inhibitory input from Ivy cells. In contrast to fast-spiking interneurons operating with millisecond precision, the highly abundant Ivy cells express presynaptically acting neuromodulators and regulate the excitability of pyramidal cell dendrites through slowly rising and decaying GABAergic inputs."}],"author":[{"full_name":"Fuentealba,Pablo","first_name":"Pablo","last_name":"Fuentealba"},{"full_name":"Begum,Rahima","last_name":"Begum","first_name":"Rahima"},{"full_name":"Capogna,Marco","first_name":"Marco","last_name":"Capogna"},{"first_name":"Shozo","last_name":"Jinno","full_name":"Jinno,Shozo"},{"full_name":"Marton,Laszlo F","last_name":"Marton","first_name":"Laszlo"},{"last_name":"Csicsvari","first_name":"Jozsef L","id":"3FA14672-F248-11E8-B48F-1D18A9856A87","full_name":"Jozsef Csicsvari","orcid":"0000-0002-5193-4036"},{"first_name":"Alex","last_name":"Thomson","full_name":"Thomson,Alex"},{"full_name":"Somogyi, Péter","first_name":"Péter","last_name":"Somogyi"},{"full_name":"Klausberger,Thomas","last_name":"Klausberger","first_name":"Thomas"}],"doi":"10.1016/j.neuron.2008.01.034","publication_status":"published","date_updated":"2021-01-12T07:44:06Z","quality_controlled":0,"type":"journal_article","publication":"Neuron","date_published":"2008-03-27T00:00:00Z","intvolume":"        57","issue":"6","_id":"3530","date_created":"2018-12-11T12:03:49Z","citation":{"chicago":"Fuentealba, Pablo, Rahima Begum, Marco Capogna, Shozo Jinno, Laszlo Marton, Jozsef L Csicsvari, Alex Thomson, Péter Somogyi, and Thomas Klausberger. “Ivy Cells: A Population of Nitric-Oxide-Producing, Slow-Spiking GABAergic Neurons and Their Involvement in Hippocampal Network Activity.” <i>Neuron</i>. Elsevier, 2008. <a href=\"https://doi.org/10.1016/j.neuron.2008.01.034\">https://doi.org/10.1016/j.neuron.2008.01.034</a>.","ista":"Fuentealba P, Begum R, Capogna M, Jinno S, Marton L, Csicsvari JL, Thomson A, Somogyi P, Klausberger T. 2008. Ivy cells: A population of nitric-oxide-producing, slow-spiking GABAergic neurons and their involvement in hippocampal network activity. Neuron. 57(6), 917–929.","short":"P. Fuentealba, R. Begum, M. Capogna, S. Jinno, L. Marton, J.L. Csicsvari, A. Thomson, P. Somogyi, T. Klausberger, Neuron 57 (2008) 917–929.","mla":"Fuentealba, Pablo, et al. “Ivy Cells: A Population of Nitric-Oxide-Producing, Slow-Spiking GABAergic Neurons and Their Involvement in Hippocampal Network Activity.” <i>Neuron</i>, vol. 57, no. 6, Elsevier, 2008, pp. 917–29, doi:<a href=\"https://doi.org/10.1016/j.neuron.2008.01.034\">10.1016/j.neuron.2008.01.034</a>.","apa":"Fuentealba, P., Begum, R., Capogna, M., Jinno, S., Marton, L., Csicsvari, J. L., … Klausberger, T. (2008). Ivy cells: A population of nitric-oxide-producing, slow-spiking GABAergic neurons and their involvement in hippocampal network activity. <i>Neuron</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.neuron.2008.01.034\">https://doi.org/10.1016/j.neuron.2008.01.034</a>","ieee":"P. Fuentealba <i>et al.</i>, “Ivy cells: A population of nitric-oxide-producing, slow-spiking GABAergic neurons and their involvement in hippocampal network activity,” <i>Neuron</i>, vol. 57, no. 6. Elsevier, pp. 917–929, 2008.","ama":"Fuentealba P, Begum R, Capogna M, et al. Ivy cells: A population of nitric-oxide-producing, slow-spiking GABAergic neurons and their involvement in hippocampal network activity. <i>Neuron</i>. 2008;57(6):917-929. doi:<a href=\"https://doi.org/10.1016/j.neuron.2008.01.034\">10.1016/j.neuron.2008.01.034</a>"},"publisher":"Elsevier","status":"public","month":"03","page":"917 - 929"},{"date_created":"2018-12-11T12:03:50Z","citation":{"chicago":"Dupret, David, Barty Pleydell Bouverie, and Jozsef L Csicsvari. “Inhibitory Interneurons and Network Oscillations.” <i>PNAS</i>. National Academy of Sciences, 2008. <a href=\"https://doi.org/10.1073/pnas.0810064105\">https://doi.org/10.1073/pnas.0810064105</a>.","short":"D. Dupret, B. Pleydell Bouverie, J.L. Csicsvari, PNAS 105 (2008) 18079–18080.","mla":"Dupret, David, et al. “Inhibitory Interneurons and Network Oscillations.” <i>PNAS</i>, vol. 105, no. 47, National Academy of Sciences, 2008, pp. 18079–80, doi:<a href=\"https://doi.org/10.1073/pnas.0810064105\">10.1073/pnas.0810064105</a>.","ista":"Dupret D, Pleydell Bouverie B, Csicsvari JL. 2008. Inhibitory interneurons and network oscillations. PNAS. 105(47), 18079–18080.","apa":"Dupret, D., Pleydell Bouverie, B., &#38; Csicsvari, J. L. (2008). Inhibitory interneurons and network oscillations. <i>PNAS</i>. National Academy of Sciences. <a href=\"https://doi.org/10.1073/pnas.0810064105\">https://doi.org/10.1073/pnas.0810064105</a>","ieee":"D. Dupret, B. Pleydell Bouverie, and J. L. Csicsvari, “Inhibitory interneurons and network oscillations,” <i>PNAS</i>, vol. 105, no. 47. National Academy of Sciences, pp. 18079–18080, 2008.","ama":"Dupret D, Pleydell Bouverie B, Csicsvari JL. Inhibitory interneurons and network oscillations. <i>PNAS</i>. 2008;105(47):18079-18080. doi:<a href=\"https://doi.org/10.1073/pnas.0810064105\">10.1073/pnas.0810064105</a>"},"publisher":"National Academy of Sciences","month":"11","status":"public","page":"18079 - 18080","publication":"PNAS","type":"journal_article","quality_controlled":0,"date_updated":"2021-01-12T07:44:08Z","date_published":"2008-11-25T00:00:00Z","_id":"3534","intvolume":"       105","issue":"47","author":[{"first_name":"David","last_name":"Dupret","full_name":"Dupret, David"},{"first_name":"Barty","last_name":"Pleydell Bouverie","full_name":"Pleydell-Bouverie, Barty"},{"first_name":"Jozsef L","last_name":"Csicsvari","id":"3FA14672-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-5193-4036","full_name":"Jozsef Csicsvari"}],"doi":"10.1073/pnas.0810064105","publication_status":"published","extern":1,"day":"25","year":"2008","title":"Inhibitory interneurons and network oscillations","volume":105,"publist_id":"2852"},{"year":"2008","title":"Gamma oscillatory firing reveals distinct populations of pyramidal cells in the CA1 region of the hippocampus","volume":28,"extern":1,"day":"27","abstract":[{"lang":"eng","text":"Hippocampal place cells that fire together within the same cycle of theta oscillations represent the sequence of positions (movement trajectory) that a rat traverses on a linear track. Furthermore, it has been suggested that the encoding of these and other types of temporal memory sequences is organized by gamma oscillations nested within theta oscillations. Here, we examined whether gamma-related firing of place cells permits such discrete temporal coding. We found that gamma-modulated CA1 pyramidal cells separated into two classes on the basis of gamma firing phases during waking theta periods. These groups also differed in terms of their spike waveforms, firing rates, and burst firing tendency. During gamma oscillations one group's firing became restricted to theta phases associated with the highest gamma power. Consequently, on the linear track, cells in this group often failed to fire early in theta-phase precession (as the rat entered the place field) if gamma oscillations were present. The second group fired throughout the theta cycle during gamma oscillations, and maintained gamma-modulated firing at different stages of theta-phase precession. Our results suggest that the two different pyramidal cell classes may support different types of population codes within a theta cycle: one in which spike sequences representing movement trajectories occur across subsequent gamma cycles nested within each theta cycle, and another in which firing in synchronized gamma discharges without temporal sequences encode a representation of location. We propose that gamma oscillations during theta-phase precession organize the mnemonic recall of population patterns representing places and movement paths."}],"publist_id":"2847","author":[{"full_name":"Senior,Timothy J","first_name":"Timothy","last_name":"Senior"},{"last_name":"Huxter","first_name":"John","full_name":"Huxter,John R"},{"first_name":"Kevin","last_name":"Allen","full_name":"Allen, Kevin"},{"full_name":"Joseph O'Neill","id":"426376DC-F248-11E8-B48F-1D18A9856A87","first_name":"Joseph","last_name":"O'Neill"},{"id":"3FA14672-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-5193-4036","full_name":"Jozsef Csicsvari","first_name":"Jozsef L","last_name":"Csicsvari"}],"publication_status":"published","doi":"10.1523/JNEUROSCI.4669-07.2008","date_published":"2008-02-27T00:00:00Z","type":"journal_article","publication":"Journal of Neuroscience","quality_controlled":0,"date_updated":"2021-01-12T07:44:09Z","_id":"3537","issue":"9","intvolume":"        28","citation":{"chicago":"Senior, Timothy, John Huxter, Kevin Allen, Joseph O’Neill, and Jozsef L Csicsvari. “Gamma Oscillatory Firing Reveals Distinct Populations of Pyramidal Cells in the CA1 Region of the Hippocampus.” <i>Journal of Neuroscience</i>. Society for Neuroscience, 2008. <a href=\"https://doi.org/10.1523/JNEUROSCI.4669-07.2008\">https://doi.org/10.1523/JNEUROSCI.4669-07.2008</a>.","short":"T. Senior, J. Huxter, K. Allen, J. O’Neill, J.L. Csicsvari, Journal of Neuroscience 28 (2008) 2274–2286.","mla":"Senior, Timothy, et al. “Gamma Oscillatory Firing Reveals Distinct Populations of Pyramidal Cells in the CA1 Region of the Hippocampus.” <i>Journal of Neuroscience</i>, vol. 28, no. 9, Society for Neuroscience, 2008, pp. 2274–86, doi:<a href=\"https://doi.org/10.1523/JNEUROSCI.4669-07.2008\">10.1523/JNEUROSCI.4669-07.2008</a>.","ista":"Senior T, Huxter J, Allen K, O’Neill J, Csicsvari JL. 2008. Gamma oscillatory firing reveals distinct populations of pyramidal cells in the CA1 region of the hippocampus. Journal of Neuroscience. 28(9), 2274–2286.","ama":"Senior T, Huxter J, Allen K, O’Neill J, Csicsvari JL. Gamma oscillatory firing reveals distinct populations of pyramidal cells in the CA1 region of the hippocampus. <i>Journal of Neuroscience</i>. 2008;28(9):2274-2286. doi:<a href=\"https://doi.org/10.1523/JNEUROSCI.4669-07.2008\">10.1523/JNEUROSCI.4669-07.2008</a>","apa":"Senior, T., Huxter, J., Allen, K., O’Neill, J., &#38; Csicsvari, J. L. (2008). Gamma oscillatory firing reveals distinct populations of pyramidal cells in the CA1 region of the hippocampus. <i>Journal of Neuroscience</i>. Society for Neuroscience. <a href=\"https://doi.org/10.1523/JNEUROSCI.4669-07.2008\">https://doi.org/10.1523/JNEUROSCI.4669-07.2008</a>","ieee":"T. Senior, J. Huxter, K. Allen, J. O’Neill, and J. L. Csicsvari, “Gamma oscillatory firing reveals distinct populations of pyramidal cells in the CA1 region of the hippocampus,” <i>Journal of Neuroscience</i>, vol. 28, no. 9. Society for Neuroscience, pp. 2274–2286, 2008."},"publisher":"Society for Neuroscience","date_created":"2018-12-11T12:03:51Z","page":"2274 - 2286","month":"02","status":"public"},{"month":"04","status":"public","page":"4795 - 4806","date_created":"2018-12-11T12:03:53Z","publisher":"Society for Neuroscience","citation":{"ieee":"N. Mallet <i>et al.</i>, “Disrupted dopamine transmission and the emergence of exaggerated beta oscillations in subthalamic nucleus and cerebral cortex,” <i>Journal of Neuroscience</i>, vol. 28, no. 18. Society for Neuroscience, pp. 4795–4806, 2008.","ama":"Mallet N, Pogosyan A, Sharott A, et al. Disrupted dopamine transmission and the emergence of exaggerated beta oscillations in subthalamic nucleus and cerebral cortex. <i>Journal of Neuroscience</i>. 2008;28(18):4795-4806. doi:<a href=\"https://doi.org/10.1523/JNEUROSCI.0123-08.2008\">10.1523/JNEUROSCI.0123-08.2008</a>","apa":"Mallet, N., Pogosyan, A., Sharott, A., Csicsvari, J. L., Bolam, J., Brown, P., &#38; Magill, P. (2008). Disrupted dopamine transmission and the emergence of exaggerated beta oscillations in subthalamic nucleus and cerebral cortex. <i>Journal of Neuroscience</i>. Society for Neuroscience. <a href=\"https://doi.org/10.1523/JNEUROSCI.0123-08.2008\">https://doi.org/10.1523/JNEUROSCI.0123-08.2008</a>","chicago":"Mallet, Nicolas, Alek Pogosyan, Andrew Sharott, Jozsef L Csicsvari, John Bolam, Peter Brown, and Peter Magill. “Disrupted Dopamine Transmission and the Emergence of Exaggerated Beta Oscillations in Subthalamic Nucleus and Cerebral Cortex.” <i>Journal of Neuroscience</i>. Society for Neuroscience, 2008. <a href=\"https://doi.org/10.1523/JNEUROSCI.0123-08.2008\">https://doi.org/10.1523/JNEUROSCI.0123-08.2008</a>.","mla":"Mallet, Nicolas, et al. “Disrupted Dopamine Transmission and the Emergence of Exaggerated Beta Oscillations in Subthalamic Nucleus and Cerebral Cortex.” <i>Journal of Neuroscience</i>, vol. 28, no. 18, Society for Neuroscience, 2008, pp. 4795–806, doi:<a href=\"https://doi.org/10.1523/JNEUROSCI.0123-08.2008\">10.1523/JNEUROSCI.0123-08.2008</a>.","short":"N. Mallet, A. Pogosyan, A. Sharott, J.L. Csicsvari, J. Bolam, P. Brown, P. Magill, Journal of Neuroscience 28 (2008) 4795–4806.","ista":"Mallet N, Pogosyan A, Sharott A, Csicsvari JL, Bolam J, Brown P, Magill P. 2008. Disrupted dopamine transmission and the emergence of exaggerated beta oscillations in subthalamic nucleus and cerebral cortex. Journal of Neuroscience. 28(18), 4795–4806."},"_id":"3544","issue":"18","intvolume":"        28","type":"journal_article","publication":"Journal of Neuroscience","quality_controlled":0,"date_updated":"2021-01-12T07:44:12Z","date_published":"2008-04-30T00:00:00Z","doi":"10.1523/JNEUROSCI.0123-08.2008","publication_status":"published","author":[{"full_name":"Mallet,Nicolas","first_name":"Nicolas","last_name":"Mallet"},{"first_name":"Alek","last_name":"Pogosyan","full_name":"Pogosyan,Alek"},{"first_name":"Andrew","last_name":"Sharott","full_name":"Sharott,Andrew"},{"id":"3FA14672-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-5193-4036","full_name":"Jozsef Csicsvari","last_name":"Csicsvari","first_name":"Jozsef L"},{"full_name":"Bolam, John Paul","last_name":"Bolam","first_name":"John"},{"last_name":"Brown","first_name":"Peter","full_name":"Brown,Peter"},{"first_name":"Peter","last_name":"Magill","full_name":"Magill,Peter J"}],"publist_id":"2842","abstract":[{"text":"In the subthalamic nucleus (STN) of Parkinson's disease (PD) patients, a pronounced synchronization of oscillatory activity at beta frequencies (15-30 Hz) accompanies movement difficulties. Abnormal beta oscillations and motor symptoms are concomitantly and acutely suppressed by dopaminergic therapies, suggesting that these inappropriate rhythms might also emerge acutely from disrupted dopamine transmission. The neural basis of these abnormal beta oscillations is unclear, and how they might compromise information processing, or how they arise, is unknown. Using a 6-hydroxydopamine-lesioned rodent model of PD, we demonstrate that beta oscillations are inappropriately exaggerated, compared with controls, in a brain-state-dependent manner after chronic dopamine loss. Exaggerated beta oscillations are expressed at the levels of single neurons and small neuronal ensembles, and are focally present and spatially distributed within STN. They are also expressed in synchronous population activities, as evinced by oscillatory local field potentials, in STN and cortex. Excessively synchronized beta oscillations reduce the information coding capacity of STN neuronal ensembles, which may contribute to parkinsonian motor impairment. Acute disruption of dopamine transmission in control animals with antagonists of D-1/D-2 receptors did not exaggerate STN or cortical beta oscillations. Moreover, beta oscillations were not exaggerated until several days after 6-hydroxydopamine injections. Thus, contrary to predictions, abnormally amplified beta oscillations in cortico-STN circuits do not result simply from an acute absence of dopamine receptor stimulation, but are instead delayed sequelae of chronic dopamine depletion. Targeting the plastic processes underlying the delayed emergence of pathological beta oscillations after continuing dopaminergic dysfunction may offer considerable therapeutic promise.","lang":"eng"}],"extern":1,"day":"30","year":"2008","title":"Disrupted dopamine transmission and the emergence of exaggerated beta oscillations in subthalamic nucleus and cerebral cortex","volume":28},{"publication_status":"published","doi":"10.1007/978-3-540-33265-7_5","author":[{"full_name":"Biasotti, Silvia","last_name":"Biasotti","first_name":"Silvia"},{"first_name":"Dominique","last_name":"Attali","full_name":"Attali, Dominique"},{"full_name":"Boissonnat, Jean-Daniel","last_name":"Boissonnat","first_name":"Jean"},{"id":"3FB178DA-F248-11E8-B48F-1D18A9856A87","full_name":"Herbert Edelsbrunner","orcid":"0000-0002-9823-6833","first_name":"Herbert","last_name":"Edelsbrunner"},{"full_name":"Elber, Gershon","last_name":"Elber","first_name":"Gershon"},{"first_name":"Michela","last_name":"Mortara","full_name":"Mortara, Michela"},{"first_name":"Gabriella","last_name":"Sanniti Di Baja","full_name":"Sanniti di Baja, Gabriella"},{"full_name":"Spagnuolo, Michela","last_name":"Spagnuolo","first_name":"Michela"},{"full_name":"Tanase, Mirela","first_name":"Mirela","last_name":"Tanase"},{"last_name":"Veltkam","first_name":"Remco","full_name":"Veltkam, Remco"}],"publist_id":"2808","title":"Skeletal structures","alternative_title":["Mathematics and Visualization"],"year":"2008","day":"01","extern":1,"page":"145 - 183","status":"public","month":"01","publisher":"Springer","citation":{"ieee":"S. Biasotti <i>et al.</i>, “Skeletal structures,” in <i>Shape Analysis and Structuring</i>, Springer, 2008, pp. 145–183.","apa":"Biasotti, S., Attali, D., Boissonnat, J., Edelsbrunner, H., Elber, G., Mortara, M., … Veltkam, R. (2008). Skeletal structures. In <i>Shape Analysis and Structuring</i> (pp. 145–183). Springer. <a href=\"https://doi.org/10.1007/978-3-540-33265-7_5\">https://doi.org/10.1007/978-3-540-33265-7_5</a>","ama":"Biasotti S, Attali D, Boissonnat J, et al. Skeletal structures. In: <i>Shape Analysis and Structuring</i>. Springer; 2008:145-183. doi:<a href=\"https://doi.org/10.1007/978-3-540-33265-7_5\">10.1007/978-3-540-33265-7_5</a>","short":"S. Biasotti, D. Attali, J. Boissonnat, H. Edelsbrunner, G. Elber, M. Mortara, G. Sanniti Di Baja, M. Spagnuolo, M. Tanase, R. Veltkam, in:, Shape Analysis and Structuring, Springer, 2008, pp. 145–183.","mla":"Biasotti, Silvia, et al. “Skeletal Structures.” <i>Shape Analysis and Structuring</i>, Springer, 2008, pp. 145–83, doi:<a href=\"https://doi.org/10.1007/978-3-540-33265-7_5\">10.1007/978-3-540-33265-7_5</a>.","ista":"Biasotti S, Attali D, Boissonnat J, Edelsbrunner H, Elber G, Mortara M, Sanniti Di Baja G, Spagnuolo M, Tanase M, Veltkam R. 2008.Skeletal structures. In: Shape Analysis and Structuring. Mathematics and Visualization, , 145–183.","chicago":"Biasotti, Silvia, Dominique Attali, Jean Boissonnat, Herbert Edelsbrunner, Gershon Elber, Michela Mortara, Gabriella Sanniti Di Baja, Michela Spagnuolo, Mirela Tanase, and Remco Veltkam. “Skeletal Structures.” In <i>Shape Analysis and Structuring</i>, 145–83. Springer, 2008. <a href=\"https://doi.org/10.1007/978-3-540-33265-7_5\">https://doi.org/10.1007/978-3-540-33265-7_5</a>."},"date_created":"2018-12-11T12:04:03Z","_id":"3577","date_published":"2008-01-01T00:00:00Z","date_updated":"2021-01-12T07:44:25Z","quality_controlled":0,"type":"book_chapter","publication":"Shape Analysis and Structuring"},{"title":"Probing E-cadherin endocytosis by morpholino-mediated Rab5 knock-down in zebrafish.","volume":440,"year":"2008","day":"01","extern":"1","abstract":[{"lang":"eng","text":"The controlled internalization of membrane receptors and lipids is crucial for cells to control signaling pathways and interact with their environment. During clathrin-mediated endocytosis, membrane constituents are transported via endocytic vesicles into early endosomes, from which they are further distributed within the cell. The small guanosine triphosphatase (GTPase) Rab5 is both required and sufficient for the formation of these early endosomes and can be used to experimentally address endocytic processes. Recent evidence shows that endocytic turnover of E-cadherin regulates the migration of mesendodermal cells during zebrafish gastrulation by modulating their adhesive interactions with neighboring cells. This in turn leads to effective and synchronized movement within the embryo. In this review, we discuss techniques to manipulate E-cadherin endocytosis by morpholino-mediated knockdown of rab5 during zebrafish gastrulation. We describe the use of antibodies specifically directed against zebrafish E-cadherin to detect its intracellular localization and of in situ hybridization and primary cell culture to reveal patterns of cell migration and adhesion, respectively"}],"language":[{"iso":"eng"}],"publist_id":"2792","author":[{"full_name":"Ulrich, Florian","last_name":"Ulrich","first_name":"Florian"},{"last_name":"Heisenberg","first_name":"Carl-Philipp J","id":"39427864-F248-11E8-B48F-1D18A9856A87","full_name":"Heisenberg, Carl-Philipp J","orcid":"0000-0002-0912-4566"}],"publication_status":"published","oa_version":"None","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","doi":"10.1007/978-1-59745-178-9_27","date_published":"2008-01-01T00:00:00Z","date_updated":"2021-01-12T07:44:31Z","type":"journal_article","publication":"Methods in Molecular Biology","intvolume":"       440","_id":"3591","publisher":"Springer","citation":{"mla":"Ulrich, Florian, and Carl-Philipp J. Heisenberg. “Probing E-Cadherin Endocytosis by Morpholino-Mediated Rab5 Knock-down in Zebrafish.” <i>Methods in Molecular Biology</i>, vol. 440, Springer, 2008, pp. 371–87, doi:<a href=\"https://doi.org/10.1007/978-1-59745-178-9_27\">10.1007/978-1-59745-178-9_27</a>.","short":"F. Ulrich, C.-P.J. Heisenberg, Methods in Molecular Biology 440 (2008) 371–387.","ista":"Ulrich F, Heisenberg C-PJ. 2008. Probing E-cadherin endocytosis by morpholino-mediated Rab5 knock-down in zebrafish. Methods in Molecular Biology. 440, 371–387.","chicago":"Ulrich, Florian, and Carl-Philipp J Heisenberg. “Probing E-Cadherin Endocytosis by Morpholino-Mediated Rab5 Knock-down in Zebrafish.” <i>Methods in Molecular Biology</i>. Springer, 2008. <a href=\"https://doi.org/10.1007/978-1-59745-178-9_27\">https://doi.org/10.1007/978-1-59745-178-9_27</a>.","ama":"Ulrich F, Heisenberg C-PJ. Probing E-cadherin endocytosis by morpholino-mediated Rab5 knock-down in zebrafish. <i>Methods in Molecular Biology</i>. 2008;440:371-387. doi:<a href=\"https://doi.org/10.1007/978-1-59745-178-9_27\">10.1007/978-1-59745-178-9_27</a>","apa":"Ulrich, F., &#38; Heisenberg, C.-P. J. (2008). Probing E-cadherin endocytosis by morpholino-mediated Rab5 knock-down in zebrafish. <i>Methods in Molecular Biology</i>. Springer. <a href=\"https://doi.org/10.1007/978-1-59745-178-9_27\">https://doi.org/10.1007/978-1-59745-178-9_27</a>","ieee":"F. Ulrich and C.-P. J. Heisenberg, “Probing E-cadherin endocytosis by morpholino-mediated Rab5 knock-down in zebrafish.,” <i>Methods in Molecular Biology</i>, vol. 440. Springer, pp. 371–387, 2008."},"date_created":"2018-12-11T12:04:08Z","page":"371 - 387","status":"public","article_processing_charge":"No","month":"01"},{"doi":"10.1109/AHS.2008.60","publication_status":"published","author":[{"first_name":"Erfu","last_name":"Yang","full_name":"Yang, Erfu"},{"first_name":"Ahmet","last_name":"Erdogan","full_name":"Erdogan, Ahmet T"},{"full_name":"Arslan, Tughrul","first_name":"Tughrul","last_name":"Arslan"},{"first_name":"Nicholas H","last_name":"Barton","full_name":"Nicholas Barton","id":"4880FE40-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-8548-5240"}],"publist_id":"2784","abstract":[{"text":"In this paper, adaptive formation control and bio-inspired optimization are jointly addressed for a cluster-based satellite wireless sensor network in which there are multiple satellites flying in formation (MSFF) in the presence of unknown disturbances. The full nonlinear dynamics model describing the relative positioning of the MSFF system is used to develop an adaptive formation controller. First, the original nonlinear system is transformed into a linear controllable system with aperturbation term by invoking the input-output feedback linearization technique. Second, by using the integral feedback design scheme, the adaptive formation controller is presented for improving the steady-state performance of the MSFF system in the presence of unknown disturbances. Third, as a currently popular bio-inspired algorithm, PSO (particle swarm optimizer) is employed to minimize the total energy consumption under the required quality of service by jointly optimizing the transmission power and rate for each satellite. Simulation results are provided to demonstrate the effectiveness of the adaptive formation controller and the PSO-based optimization for saving the total communication energy.","lang":"eng"}],"conference":{"name":"AHS: NASA/ESA Conference on Adaptive Hardware and Systems"},"day":"01","extern":1,"title":"Adaptive formation control and bio-inspired optimization of a cluster-based satellite wireless sensor network ","year":"2008","status":"public","month":"08","page":"432 - 439","date_created":"2018-12-11T12:04:10Z","publisher":"IEEE","citation":{"ieee":"E. Yang, A. Erdogan, T. Arslan, and N. H. Barton, “Adaptive formation control and bio-inspired optimization of a cluster-based satellite wireless sensor network ,” presented at the AHS: NASA/ESA Conference on Adaptive Hardware and Systems, 2008, pp. 432–439.","apa":"Yang, E., Erdogan, A., Arslan, T., &#38; Barton, N. H. (2008). Adaptive formation control and bio-inspired optimization of a cluster-based satellite wireless sensor network  (pp. 432–439). Presented at the AHS: NASA/ESA Conference on Adaptive Hardware and Systems, IEEE. <a href=\"https://doi.org/10.1109/AHS.2008.60\">https://doi.org/10.1109/AHS.2008.60</a>","ama":"Yang E, Erdogan A, Arslan T, Barton NH. Adaptive formation control and bio-inspired optimization of a cluster-based satellite wireless sensor network . In: IEEE; 2008:432-439. doi:<a href=\"https://doi.org/10.1109/AHS.2008.60\">10.1109/AHS.2008.60</a>","chicago":"Yang, Erfu, Ahmet Erdogan, Tughrul Arslan, and Nicholas H Barton. “Adaptive Formation Control and Bio-Inspired Optimization of a Cluster-Based Satellite Wireless Sensor Network ,” 432–39. IEEE, 2008. <a href=\"https://doi.org/10.1109/AHS.2008.60\">https://doi.org/10.1109/AHS.2008.60</a>.","mla":"Yang, Erfu, et al. <i>Adaptive Formation Control and Bio-Inspired Optimization of a Cluster-Based Satellite Wireless Sensor Network </i>. IEEE, 2008, pp. 432–39, doi:<a href=\"https://doi.org/10.1109/AHS.2008.60\">10.1109/AHS.2008.60</a>.","short":"E. Yang, A. Erdogan, T. Arslan, N.H. Barton, in:, IEEE, 2008, pp. 432–439.","ista":"Yang E, Erdogan A, Arslan T, Barton NH. 2008. Adaptive formation control and bio-inspired optimization of a cluster-based satellite wireless sensor network . AHS: NASA/ESA Conference on Adaptive Hardware and Systems, 432–439."},"_id":"3599","date_updated":"2021-01-12T07:44:34Z","quality_controlled":0,"type":"conference","date_published":"2008-08-01T00:00:00Z"},{"author":[{"first_name":"Erfu","last_name":"Yang","full_name":"Yang, Erfu"},{"orcid":"0000-0002-8548-5240","full_name":"Nicholas Barton","id":"4880FE40-F248-11E8-B48F-1D18A9856A87","first_name":"Nicholas H","last_name":"Barton"},{"last_name":"Arslan","first_name":"Tughrul","full_name":"Arslan, Tughrul"},{"full_name":"Erdogan, Ahmet T","last_name":"Erdogan","first_name":"Ahmet"}],"doi":"10.1007/978-3-540-85857-7_22","publication_status":"published","extern":1,"conference":{"name":"IECS: International Conference on Evolvable Systems"},"day":"08","year":"2008","alternative_title":["LNCS"],"volume":5216,"title":" Scalability of a novel shifting balance theory-based optimization algorithm: A comparative study on a cluster-based wireless sensor network","publist_id":"2783","abstract":[{"lang":"eng","text":"Scalability is one of the most important issues for optimization algorithms used in wireless sensor networks (WSNs) since there are often many parameters to be optimized at the same time. In this case it is very hard to ensure that an optimization algorithm can be smoothly scaled up from a low-dimensional optimization problem to the one with a high dimensionality. This paper addresses the scalability issue of a novel optimization algorithm inspired by the Shifting Balance Theory (SBT) of evolution in population genetics. Toward this end, a cluster-based WSN is employed in this paper as a benchmark to perform a comparative study. The total energy consumption is minimized under the required quality of service by jointly optimizing the transmission power and rate for each sensor node. The results obtained by the SBT-based algorithm are compared with the Metropolis algorithm (MA) and currently popular particle swarm optimizer (PSO) to assess the scaling performance of the three algorithms against the same WSN optimization problem."}],"date_created":"2018-12-11T12:04:10Z","citation":{"ista":"Yang E, Barton NH, Arslan T, Erdogan A. 2008.  Scalability of a novel shifting balance theory-based optimization algorithm: A comparative study on a cluster-based wireless sensor network. IECS: International Conference on Evolvable Systems, LNCS, vol. 5216, 249–260.","short":"E. Yang, N.H. Barton, T. Arslan, A. Erdogan, in:, Springer, 2008, pp. 249–260.","mla":"Yang, Erfu, et al. <i> Scalability of a Novel Shifting Balance Theory-Based Optimization Algorithm: A Comparative Study on a Cluster-Based Wireless Sensor Network</i>. Vol. 5216, Springer, 2008, pp. 249–60, doi:<a href=\"https://doi.org/10.1007/978-3-540-85857-7_22\">10.1007/978-3-540-85857-7_22</a>.","chicago":"Yang, Erfu, Nicholas H Barton, Tughrul Arslan, and Ahmet Erdogan. “ Scalability of a Novel Shifting Balance Theory-Based Optimization Algorithm: A Comparative Study on a Cluster-Based Wireless Sensor Network,” 5216:249–60. Springer, 2008. <a href=\"https://doi.org/10.1007/978-3-540-85857-7_22\">https://doi.org/10.1007/978-3-540-85857-7_22</a>.","apa":"Yang, E., Barton, N. H., Arslan, T., &#38; Erdogan, A. (2008).  Scalability of a novel shifting balance theory-based optimization algorithm: A comparative study on a cluster-based wireless sensor network (Vol. 5216, pp. 249–260). Presented at the IECS: International Conference on Evolvable Systems, Springer. <a href=\"https://doi.org/10.1007/978-3-540-85857-7_22\">https://doi.org/10.1007/978-3-540-85857-7_22</a>","ieee":"E. Yang, N. H. Barton, T. Arslan, and A. Erdogan, “ Scalability of a novel shifting balance theory-based optimization algorithm: A comparative study on a cluster-based wireless sensor network,” presented at the IECS: International Conference on Evolvable Systems, 2008, vol. 5216, pp. 249–260.","ama":"Yang E, Barton NH, Arslan T, Erdogan A.  Scalability of a novel shifting balance theory-based optimization algorithm: A comparative study on a cluster-based wireless sensor network. In: Vol 5216. Springer; 2008:249-260. doi:<a href=\"https://doi.org/10.1007/978-3-540-85857-7_22\">10.1007/978-3-540-85857-7_22</a>"},"publisher":"Springer","month":"09","status":"public","page":"249 - 260","type":"conference","date_updated":"2021-01-12T07:44:35Z","quality_controlled":0,"date_published":"2008-09-08T00:00:00Z","_id":"3600","intvolume":"      5216"},{"publist_id":"2778","abstract":[{"lang":"eng","text":"Many animals and plants show a correlation between the traits of the individuals in the mating pair, implying assortative mating. Given the ubiquity of assortative mating in nature, why and how it has evolved remain open questions. Here we attempt to answer these questions in those cases where the trait under assortment is the same in males and females. We consider the most favorable scenario for assortment to evolve, where the same trait is under assortment and viability selection. We find conditions for assortment to evolve using a multilocus formalism in a haploid population. Our results show how epistasis in fitness between the loci that control the focal trait is crucial for assortment to evolve. We then assume specific forms of assortment in haploids and diploids and study the limiting cases of selective and nonselective mating. We find that selection for increased assortment is weak and that where increased assortment is costly, it does not invade."}],"day":"01","extern":1,"volume":171,"title":"A model for the evolution of assortative mating","year":"2008","doi":"10.1086/587062","publication_status":"published","author":[{"full_name":"De Cara, Maria A","first_name":"Maria","last_name":"De Cara"},{"first_name":"Nicholas H","last_name":"Barton","id":"4880FE40-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-8548-5240","full_name":"Nicholas Barton"},{"full_name":"Kirkpatrick, Mark","first_name":"Mark","last_name":"Kirkpatrick"}],"issue":"5","intvolume":"       171","_id":"3605","date_updated":"2021-01-12T07:44:36Z","quality_controlled":0,"type":"journal_article","publication":"American Naturalist","date_published":"2008-05-01T00:00:00Z","status":"public","month":"05","page":"580 - 596","date_created":"2018-12-11T12:04:12Z","citation":{"chicago":"De Cara, Maria, Nicholas H Barton, and Mark Kirkpatrick. “A Model for the Evolution of Assortative Mating.” <i>American Naturalist</i>. University of Chicago Press, 2008. <a href=\"https://doi.org/10.1086/587062\">https://doi.org/10.1086/587062</a>.","short":"M. De Cara, N.H. Barton, M. Kirkpatrick, American Naturalist 171 (2008) 580–596.","mla":"De Cara, Maria, et al. “A Model for the Evolution of Assortative Mating.” <i>American Naturalist</i>, vol. 171, no. 5, University of Chicago Press, 2008, pp. 580–96, doi:<a href=\"https://doi.org/10.1086/587062\">10.1086/587062</a>.","ista":"De Cara M, Barton NH, Kirkpatrick M. 2008. A model for the evolution of assortative mating. American Naturalist. 171(5), 580–596.","ieee":"M. De Cara, N. H. Barton, and M. Kirkpatrick, “A model for the evolution of assortative mating,” <i>American Naturalist</i>, vol. 171, no. 5. University of Chicago Press, pp. 580–596, 2008.","apa":"De Cara, M., Barton, N. H., &#38; Kirkpatrick, M. (2008). A model for the evolution of assortative mating. <i>American Naturalist</i>. University of Chicago Press. <a href=\"https://doi.org/10.1086/587062\">https://doi.org/10.1086/587062</a>","ama":"De Cara M, Barton NH, Kirkpatrick M. A model for the evolution of assortative mating. <i>American Naturalist</i>. 2008;171(5):580-596. doi:<a href=\"https://doi.org/10.1086/587062\">10.1086/587062</a>"},"publisher":"University of Chicago Press"},{"intvolume":"        90","issue":"1","_id":"3606","date_updated":"2021-01-12T07:44:37Z","quality_controlled":0,"type":"journal_article","publication":"Genetical Research","date_published":"2008-02-01T00:00:00Z","status":"public","month":"02","page":"139 - 149","date_created":"2018-12-11T12:04:12Z","publisher":"Cambridge University Press","citation":{"short":"N.H. Barton, Genetical Research 90 (2008) 139–149.","mla":"Barton, Nicholas H. “The Effect of a Barrier to Gene Flow on Patterns of Geographic Variation.” <i>Genetical Research</i>, vol. 90, no. 1, Cambridge University Press, 2008, pp. 139–49, doi:<a href=\"https://doi.org/10.1017/S0016672307009081\">10.1017/S0016672307009081</a>.","ista":"Barton NH. 2008. The effect of a barrier to gene flow on patterns of geographic variation. Genetical Research. 90(1), 139–149.","chicago":"Barton, Nicholas H. “The Effect of a Barrier to Gene Flow on Patterns of Geographic Variation.” <i>Genetical Research</i>. Cambridge University Press, 2008. <a href=\"https://doi.org/10.1017/S0016672307009081\">https://doi.org/10.1017/S0016672307009081</a>.","ieee":"N. H. Barton, “The effect of a barrier to gene flow on patterns of geographic variation,” <i>Genetical Research</i>, vol. 90, no. 1. Cambridge University Press, pp. 139–149, 2008.","apa":"Barton, N. H. (2008). The effect of a barrier to gene flow on patterns of geographic variation. <i>Genetical Research</i>. Cambridge University Press. <a href=\"https://doi.org/10.1017/S0016672307009081\">https://doi.org/10.1017/S0016672307009081</a>","ama":"Barton NH. The effect of a barrier to gene flow on patterns of geographic variation. <i>Genetical Research</i>. 2008;90(1):139-149. doi:<a href=\"https://doi.org/10.1017/S0016672307009081\">10.1017/S0016672307009081</a>"},"publist_id":"2777","abstract":[{"text":"Explicit formulae are given for the effects of a barrier to gene flow on random fluctuations in allele frequency; these formulae can also be seen as generating functions for the distribution of coalescence times. The formulae are derived using a continuous diffusion approximation, which is accurate over all but very small spatial scales. The continuous approximation is confirmed by comparison with the exact solution to the stepping stone model. In both one and two spatial dimensions, the variance of fluctuations in allele frequencies increases near the barrier; when the barrier is very strong, the variance doubles. However, the effect on fluctuations close to the barrier is much greater when the population is spread over two spatial dimensions than when it occupies a linear, one-dimensional habitat: barriers of strength comparable with the dispersal range (B≈σ) can have an appreciable effect in two dimensions, whereas only barriers with strength comparable with the characteristic scale (B\\! \\approx\\! L \\equals \\sigma \\sol \\sqrt {2 \\mu}\\hskip2) are significant in one dimension (μ is the rate of mutation or long-range dispersal). Thus, in a two-dimensional population, barriers to gene flow can be detected through their effect on the spatial pattern of genetic marker alleles.","lang":"eng"}],"day":"01","extern":1,"volume":90,"title":"The effect of a barrier to gene flow on patterns of geographic variation","year":"2008","doi":"10.1017/S0016672307009081","publication_status":"published","author":[{"first_name":"Nicholas H","last_name":"Barton","full_name":"Nicholas Barton","orcid":"0000-0002-8548-5240","id":"4880FE40-F248-11E8-B48F-1D18A9856A87"}]},{"month":"04","status":"public","page":"174 - 179","date_created":"2018-12-11T12:04:39Z","publisher":"IEEE","citation":{"apa":"Goldstein, M., Lampert, C., Reif, M., Stahl, A., &#38; Breuel, T. (2008). Bayes optimal DDoS mitigation by adaptive history-based IP filtering (pp. 174–179). Presented at the ICN: International Conference on Networking, IEEE. <a href=\"https://doi.org/10.1109/ICN.2008.64\">https://doi.org/10.1109/ICN.2008.64</a>","ieee":"M. Goldstein, C. Lampert, M. Reif, A. Stahl, and T. Breuel, “Bayes optimal DDoS mitigation by adaptive history-based IP filtering,” presented at the ICN: International Conference on Networking, 2008, pp. 174–179.","ama":"Goldstein M, Lampert C, Reif M, Stahl A, Breuel T. Bayes optimal DDoS mitigation by adaptive history-based IP filtering. In: IEEE; 2008:174-179. doi:<a href=\"https://doi.org/10.1109/ICN.2008.64\">10.1109/ICN.2008.64</a>","chicago":"Goldstein, Markus, Christoph Lampert, Matthias Reif, Armin Stahl, and Thomas Breuel. “Bayes Optimal DDoS Mitigation by Adaptive History-Based IP Filtering,” 174–79. IEEE, 2008. <a href=\"https://doi.org/10.1109/ICN.2008.64\">https://doi.org/10.1109/ICN.2008.64</a>.","short":"M. Goldstein, C. Lampert, M. Reif, A. Stahl, T. Breuel, in:, IEEE, 2008, pp. 174–179.","mla":"Goldstein, Markus, et al. <i>Bayes Optimal DDoS Mitigation by Adaptive History-Based IP Filtering</i>. IEEE, 2008, pp. 174–79, doi:<a href=\"https://doi.org/10.1109/ICN.2008.64\">10.1109/ICN.2008.64</a>.","ista":"Goldstein M, Lampert C, Reif M, Stahl A, Breuel T. 2008. Bayes optimal DDoS mitigation by adaptive history-based IP filtering. ICN: International Conference on Networking, 174–179."},"_id":"3694","type":"conference","quality_controlled":0,"date_updated":"2021-01-12T07:49:01Z","date_published":"2008-04-13T00:00:00Z","doi":"10.1109/ICN.2008.64","publication_status":"published","author":[{"full_name":"Goldstein,Markus","first_name":"Markus","last_name":"Goldstein"},{"first_name":"Christoph","last_name":"Lampert","full_name":"Christoph Lampert","orcid":"0000-0001-8622-7887","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Reif,Matthias","last_name":"Reif","first_name":"Matthias"},{"last_name":"Stahl","first_name":"Armin","full_name":"Stahl,Armin"},{"first_name":"Thomas","last_name":"Breuel","full_name":"Breuel,Thomas M"}],"main_file_link":[{"open_access":"0","url":"http://pub.ist.ac.at/~chl/papers/goldstein-icn2008.pdf"}],"publist_id":"2671","abstract":[{"lang":"eng","text":"Distributed Denial of Service (DDoS) attacks are today the most destabilizing factor in the global internet and there is a strong need for sophisticated solutions. We introduce a formal statistical framework and derive a Bayes optimal packet classifier from it. Our proposed practical algorithm &quot;Adaptive History-Based IP Filtering&quot; (AHIF) mitigates DDoS attacks near the victim and outperforms existing methods by at least 32% in terms of collateral damage. Furthermore, it adjusts to the strength of an ongoing attack and ensures availability of the attacked server. In contrast to other adaptive solutions, firewall rulesets used to resist an attack can be precalculated before an attack takes place. This ensures an immediate response in a DDoS emergency. For evaluation, simulated DDoS attacks and two real-world user traffic datasets are used."}],"extern":1,"day":"13","conference":{"name":"ICN: International Conference on Networking"},"year":"2008","title":"Bayes optimal DDoS mitigation by adaptive history-based IP filtering"},{"publication_status":"published","doi":"10.1007/978-3-540-87479-9_27","author":[{"last_name":"Blaschko","first_name":"Matthew","full_name":"Blaschko,Matthew B"},{"last_name":"Lampert","first_name":"Christoph","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-8622-7887","full_name":"Christoph Lampert"},{"last_name":"Gretton","first_name":"Arthur","full_name":"Gretton,Arthur"}],"abstract":[{"lang":"eng","text":"Kernel canonical correlation analysis (KCCA) is a dimensionality reduction technique for paired data. By finding directions that maximize correlation, KCCA learns representations that are more closely tied to the underlying semantics of the data rather than noise. However, meaningful directions are not only those that have high correlation to another modality, but also those that capture the manifold structure of the data. We propose a method that is simultaneously able to find highly correlated directions that are also located on high variance directions along the data manifold. This is achieved by the use of semi-supervised Laplacian regularization of KCCA. We show experimentally that Laplacian regularized training improves class separation over KCCA with only Tikhonov regularization, while causing no degradation in the correlation between modalities. We propose a model selection criterion based on the Hilbert-Schmidt norm of the semi-supervised Laplacian regularized cross-covariance operator, which we compute in closed form."}],"publist_id":"2662","year":"2008","alternative_title":["LNCS"],"title":"Semi-supervised Laplacian regularization of kernel canonical correlation analysis","volume":5211,"extern":1,"day":"21","conference":{"name":"ECML: European Conference on Machine Learning"},"page":"133 - 145","month":"10","status":"public","publisher":"Springer","citation":{"chicago":"Blaschko, Matthew, Christoph Lampert, and Arthur Gretton. “Semi-Supervised Laplacian Regularization of Kernel Canonical Correlation Analysis,” 5211:133–45. Springer, 2008. <a href=\"https://doi.org/10.1007/978-3-540-87479-9_27\">https://doi.org/10.1007/978-3-540-87479-9_27</a>.","short":"M. Blaschko, C. Lampert, A. Gretton, in:, Springer, 2008, pp. 133–145.","mla":"Blaschko, Matthew, et al. <i>Semi-Supervised Laplacian Regularization of Kernel Canonical Correlation Analysis</i>. Vol. 5211, no. Part 1, Springer, 2008, pp. 133–45, doi:<a href=\"https://doi.org/10.1007/978-3-540-87479-9_27\">10.1007/978-3-540-87479-9_27</a>.","ista":"Blaschko M, Lampert C, Gretton A. 2008. Semi-supervised Laplacian regularization of kernel canonical correlation analysis. ECML: European Conference on Machine Learning, LNCS, vol. 5211, 133–145.","apa":"Blaschko, M., Lampert, C., &#38; Gretton, A. (2008). Semi-supervised Laplacian regularization of kernel canonical correlation analysis (Vol. 5211, pp. 133–145). Presented at the ECML: European Conference on Machine Learning, Springer. <a href=\"https://doi.org/10.1007/978-3-540-87479-9_27\">https://doi.org/10.1007/978-3-540-87479-9_27</a>","ama":"Blaschko M, Lampert C, Gretton A. Semi-supervised Laplacian regularization of kernel canonical correlation analysis. In: Vol 5211. Springer; 2008:133-145. doi:<a href=\"https://doi.org/10.1007/978-3-540-87479-9_27\">10.1007/978-3-540-87479-9_27</a>","ieee":"M. Blaschko, C. Lampert, and A. Gretton, “Semi-supervised Laplacian regularization of kernel canonical correlation analysis,” presented at the ECML: European Conference on Machine Learning, 2008, vol. 5211, no. Part 1, pp. 133–145."},"date_created":"2018-12-11T12:04:41Z","_id":"3698","intvolume":"      5211","issue":"Part 1","date_published":"2008-10-21T00:00:00Z","type":"conference","quality_controlled":0,"date_updated":"2021-01-12T07:49:02Z"},{"quality_controlled":0,"date_updated":"2021-01-12T07:51:35Z","type":"conference","date_published":"2008-09-18T00:00:00Z","_id":"3700","date_created":"2018-12-11T12:04:41Z","publisher":"IEEE","citation":{"ista":"Lampert C. 2008. Partitioning of image datasets using discriminative context information. CVPR: Computer Vision and Pattern Recognition, 1–8.","short":"C. Lampert, in:, IEEE, 2008, pp. 1–8.","mla":"Lampert, Christoph. <i>Partitioning of Image Datasets Using Discriminative Context Information</i>. IEEE, 2008, pp. 1–8, doi:<a href=\"https://doi.org/10.1109/CVPR.2008.4587448\">10.1109/CVPR.2008.4587448</a>.","chicago":"Lampert, Christoph. “Partitioning of Image Datasets Using Discriminative Context Information,” 1–8. IEEE, 2008. <a href=\"https://doi.org/10.1109/CVPR.2008.4587448\">https://doi.org/10.1109/CVPR.2008.4587448</a>.","ieee":"C. Lampert, “Partitioning of image datasets using discriminative context information,” presented at the CVPR: Computer Vision and Pattern Recognition, 2008, pp. 1–8.","ama":"Lampert C. Partitioning of image datasets using discriminative context information. In: IEEE; 2008:1-8. doi:<a href=\"https://doi.org/10.1109/CVPR.2008.4587448\">10.1109/CVPR.2008.4587448</a>","apa":"Lampert, C. (2008). Partitioning of image datasets using discriminative context information (pp. 1–8). Presented at the CVPR: Computer Vision and Pattern Recognition, IEEE. <a href=\"https://doi.org/10.1109/CVPR.2008.4587448\">https://doi.org/10.1109/CVPR.2008.4587448</a>"},"status":"public","month":"09","page":"1 - 8","day":"18","conference":{"name":"CVPR: Computer Vision and Pattern Recognition"},"extern":1,"title":"Partitioning of image datasets using discriminative context information","year":"2008","publist_id":"2657","acknowledgement":"This work was funded in part by the EC project CLASS, IST 027978.","abstract":[{"lang":"eng","text":"We propose a new method to partition an unlabeled dataset, called Discriminative Context Partitioning (DCP). It is motivated by the idea of splitting the dataset based only on how well the resulting parts can be separated from a context class of disjoint data points. This is in contrast to typical clustering techniques like K-means that are based on a generative model by implicitly or explicitly searching for modes in the distribution of samples. The discriminative criterion in DCP avoids the problems that density based methods have when the a priori assumption of multimodality is violated, when the number of samples becomes small in relation to the dimensionality of the feature space, or if the cluster sizes are strongly unbalanced. We formulate DCP&amp;amp;amp;amp;amp;amp;amp;amp;amp;lsquo;s separation property as a large-margin criterion, and show how the resulting optimization problem can be solved efficiently. Experiments on the MNIST and USPS datasets of handwritten digits and on a subset of the Caltech256 dataset show that, given a suitable context, DCP can achieve good results even in situation where density-based clustering techniques fail."}],"author":[{"first_name":"Christoph","last_name":"Lampert","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","full_name":"Christoph Lampert","orcid":"0000-0001-8622-7887"}],"main_file_link":[{"open_access":"0","url":"http://pub.ist.ac.at/~chl/papers/lampert-cvpr2008b.pdf"}],"doi":"10.1109/CVPR.2008.4587448","publication_status":"published"},{"quality_controlled":0,"date_updated":"2021-01-12T07:51:37Z","type":"conference","date_published":"2008-11-17T00:00:00Z","intvolume":"      5302","_id":"3705","date_created":"2018-12-11T12:04:43Z","citation":{"ieee":"M. Blaschko and C. Lampert, “Learning to localize objects with structured output regression,” presented at the ECCV: European Conference on Computer Vision, 2008, vol. 5302, pp. 2–15.","ama":"Blaschko M, Lampert C. Learning to localize objects with structured output regression. In: Vol 5302. Springer; 2008:2-15. doi:<a href=\"https://doi.org/10.1007/978-3-540-88682-2_2\">10.1007/978-3-540-88682-2_2</a>","apa":"Blaschko, M., &#38; Lampert, C. (2008). Learning to localize objects with structured output regression (Vol. 5302, pp. 2–15). Presented at the ECCV: European Conference on Computer Vision, Springer. <a href=\"https://doi.org/10.1007/978-3-540-88682-2_2\">https://doi.org/10.1007/978-3-540-88682-2_2</a>","short":"M. Blaschko, C. Lampert, in:, Springer, 2008, pp. 2–15.","mla":"Blaschko, Matthew, and Christoph Lampert. <i>Learning to Localize Objects with Structured Output Regression</i>. Vol. 5302, Springer, 2008, pp. 2–15, doi:<a href=\"https://doi.org/10.1007/978-3-540-88682-2_2\">10.1007/978-3-540-88682-2_2</a>.","ista":"Blaschko M, Lampert C. 2008. Learning to localize objects with structured output regression. ECCV: European Conference on Computer Vision, LNCS, vol. 5302, 2–15.","chicago":"Blaschko, Matthew, and Christoph Lampert. “Learning to Localize Objects with Structured Output Regression,” 5302:2–15. Springer, 2008. <a href=\"https://doi.org/10.1007/978-3-540-88682-2_2\">https://doi.org/10.1007/978-3-540-88682-2_2</a>."},"publisher":"Springer","status":"public","month":"11","page":"2 - 15","day":"17","conference":{"name":"ECCV: European Conference on Computer Vision"},"extern":1,"title":"Learning to localize objects with structured output regression","volume":5302,"year":"2008","alternative_title":["LNCS"],"publist_id":"2653","abstract":[{"lang":"eng","text":"Sliding window classifiers are among the most successful and widely applied techniques for object localization. However, training is typically done in a way that is not specific to the localization task. First a binary classifier is trained using a sample of positive and negative examples, and this classifier is subsequently applied to multiple regions within test images. We propose instead to treat object localization in a principled way by posing it as a problem of predicting structured data: we model the problem not as binary classification, but as the prediction of the bounding box of objects located in images. The use of a joint-kernel framework allows us to formulate the training procedure as a generalization of an SVM, which can be solved efficiently. We further improve computational efficiency by using a branch-and-bound strategy for localization during both training and testing. Experimental evaluation on the PASCAL VOC and TU Darmstadt datasets show that the structured training procedure improves pe rformance over binary training as well as the best previously published scores."}],"main_file_link":[{"url":"http://www.kyb.mpg.de/fileadmin/user_upload/files/publications/attachments/ECCV2008-Blaschko_5247%5b0%5d.pdf","open_access":"0"}],"author":[{"full_name":"Blaschko,Matthew B","first_name":"Matthew","last_name":"Blaschko"},{"id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-8622-7887","full_name":"Christoph Lampert","first_name":"Christoph","last_name":"Lampert"}],"doi":"10.1007/978-3-540-88682-2_2","publication_status":"published"},{"publication_status":"published","author":[{"full_name":"Christoph Lampert","orcid":"0000-0001-8622-7887","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","last_name":"Lampert","first_name":"Christoph"},{"first_name":"Matthew","last_name":"Blaschko","full_name":"Blaschko,Matthew B"}],"main_file_link":[{"url":"http://agbs.kyb.tuebingen.mpg.de/wikis/bg/siso2008/Blaschkoetal.pdf","open_access":"0"}],"publist_id":"2650","abstract":[{"lang":"eng","text":"We present a new technique for structured prediction that works in a hybrid generative/discriminative way, using a one-class support vector machine to model the joint probability of (input, output)-pairs in a joint reproducing kernel Hilbert space. Compared to discriminative techniques, like conditional random fields or structured output SVMs?, the proposed method has the advantage that its training time depends only on the number of training examples, not on the size of the label space. Due to its generative aspect, it is also very tolerant against ambiguous, incomplete or incorrect labels. Experiments on realistic data show that our method works efficiently and robustly in situations that discriminative techniques have problems with or that are computationally infeasible for them."}],"day":"12","conference":{"name":"NIPS SISO: NIPS Workshop on \"Structured Input - Structured Output\""},"extern":1,"title":"Joint kernel support estimation for structured prediction","year":"2008","status":"public","month":"12","page":"1 - 4","date_created":"2018-12-11T12:04:43Z","citation":{"ieee":"C. Lampert and M. Blaschko, “Joint kernel support estimation for structured prediction,” presented at the NIPS SISO: NIPS Workshop on “Structured Input - Structured Output,” 2008, pp. 1–4.","ama":"Lampert C, Blaschko M. Joint kernel support estimation for structured prediction. In: Curran Associates, Inc.; 2008:1-4.","apa":"Lampert, C., &#38; Blaschko, M. (2008). Joint kernel support estimation for structured prediction (pp. 1–4). Presented at the NIPS SISO: NIPS Workshop on “Structured Input - Structured Output,” Curran Associates, Inc.","chicago":"Lampert, Christoph, and Matthew Blaschko. “Joint Kernel Support Estimation for Structured Prediction,” 1–4. Curran Associates, Inc., 2008.","mla":"Lampert, Christoph, and Matthew Blaschko. <i>Joint Kernel Support Estimation for Structured Prediction</i>. Curran Associates, Inc., 2008, pp. 1–4.","short":"C. Lampert, M. Blaschko, in:, Curran Associates, Inc., 2008, pp. 1–4.","ista":"Lampert C, Blaschko M. 2008. Joint kernel support estimation for structured prediction. NIPS SISO: NIPS Workshop on ‘Structured Input - Structured Output’, 1–4."},"publisher":"Curran Associates, Inc.","_id":"3706","date_updated":"2021-01-12T07:51:37Z","quality_controlled":0,"type":"conference","date_published":"2008-12-12T00:00:00Z"},{"_id":"3712","date_published":"2008-09-18T00:00:00Z","quality_controlled":0,"date_updated":"2021-01-12T07:51:40Z","type":"conference","page":"1 - 8","status":"public","month":"09","citation":{"chicago":"Blaschko, Matthew, and Christoph Lampert. “Correlational Spectral Clustering,” 1–8. IEEE, 2008. <a href=\"https://doi.org/10.1109/CVPR.2008.4587353\">https://doi.org/10.1109/CVPR.2008.4587353</a>.","mla":"Blaschko, Matthew, and Christoph Lampert. <i>Correlational Spectral Clustering</i>. IEEE, 2008, pp. 1–8, doi:<a href=\"https://doi.org/10.1109/CVPR.2008.4587353\">10.1109/CVPR.2008.4587353</a>.","short":"M. Blaschko, C. Lampert, in:, IEEE, 2008, pp. 1–8.","ista":"Blaschko M, Lampert C. 2008. Correlational spectral clustering. CVPR: Computer Vision and Pattern Recognition, 1–8.","ieee":"M. Blaschko and C. Lampert, “Correlational spectral clustering,” presented at the CVPR: Computer Vision and Pattern Recognition, 2008, pp. 1–8.","ama":"Blaschko M, Lampert C. Correlational spectral clustering. In: IEEE; 2008:1-8. doi:<a href=\"https://doi.org/10.1109/CVPR.2008.4587353\">10.1109/CVPR.2008.4587353</a>","apa":"Blaschko, M., &#38; Lampert, C. (2008). Correlational spectral clustering (pp. 1–8). Presented at the CVPR: Computer Vision and Pattern Recognition, IEEE. <a href=\"https://doi.org/10.1109/CVPR.2008.4587353\">https://doi.org/10.1109/CVPR.2008.4587353</a>"},"publisher":"IEEE","date_created":"2018-12-11T12:04:45Z","abstract":[{"text":"We present a new method for spectral clustering with paired data based on kernel canonical correlation analysis, called correlational spectral clustering. Paired data are common in real world data sources, such as images with text captions. Traditional spectral clustering algorithms either assume that data can be represented by a single similarity measure, or by co-occurrence matrices that are then used in biclustering. In contrast, the proposed method uses separate similarity measures for each data representation, and allows for projection of previously unseen data that are only observed in one representation (e.g. images but not text). We show that this algorithm generalizes traditional spectral clustering algorithms and show consistent empirical improvement over spectral clustering on a variety of datasets of images with associated text.","lang":"eng"}],"publist_id":"2646","title":"Correlational spectral clustering","year":"2008","day":"18","conference":{"name":"CVPR: Computer Vision and Pattern Recognition"},"extern":1,"publication_status":"published","doi":"10.1109/CVPR.2008.4587353","author":[{"full_name":"Blaschko,Matthew B","first_name":"Matthew","last_name":"Blaschko"},{"last_name":"Lampert","first_name":"Christoph","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-8622-7887","full_name":"Christoph Lampert"}]},{"page":"1 - 8","status":"public","month":"09","publisher":"IEEE","citation":{"ista":"Lampert C, Blaschko M, Hofmann T. 2008. Beyond sliding windows: Object localization by efficient subwindow search. CVPR: Computer Vision and Pattern Recognition, 1–8.","mla":"Lampert, Christoph, et al. <i>Beyond Sliding Windows: Object Localization by Efficient Subwindow Search</i>. IEEE, 2008, pp. 1–8, doi:<a href=\"https://doi.org/10.1109/CVPR.2008.4587586\">10.1109/CVPR.2008.4587586</a>.","short":"C. Lampert, M. Blaschko, T. Hofmann, in:, IEEE, 2008, pp. 1–8.","chicago":"Lampert, Christoph, Matthew Blaschko, and Thomas Hofmann. “Beyond Sliding Windows: Object Localization by Efficient Subwindow Search,” 1–8. IEEE, 2008. <a href=\"https://doi.org/10.1109/CVPR.2008.4587586\">https://doi.org/10.1109/CVPR.2008.4587586</a>.","ama":"Lampert C, Blaschko M, Hofmann T. Beyond sliding windows: Object localization by efficient subwindow search. In: IEEE; 2008:1-8. doi:<a href=\"https://doi.org/10.1109/CVPR.2008.4587586\">10.1109/CVPR.2008.4587586</a>","ieee":"C. Lampert, M. Blaschko, and T. Hofmann, “Beyond sliding windows: Object localization by efficient subwindow search,” presented at the CVPR: Computer Vision and Pattern Recognition, 2008, pp. 1–8.","apa":"Lampert, C., Blaschko, M., &#38; Hofmann, T. (2008). Beyond sliding windows: Object localization by efficient subwindow search (pp. 1–8). Presented at the CVPR: Computer Vision and Pattern Recognition, IEEE. <a href=\"https://doi.org/10.1109/CVPR.2008.4587586\">https://doi.org/10.1109/CVPR.2008.4587586</a>"},"date_created":"2018-12-11T12:04:46Z","_id":"3714","date_published":"2008-09-18T00:00:00Z","date_updated":"2021-01-12T07:51:40Z","quality_controlled":0,"type":"conference","publication_status":"published","doi":"10.1109/CVPR.2008.4587586","main_file_link":[{"open_access":"0","url":"http://www.kyb.mpg.de/fileadmin/user_upload/files/publications/pdfs/pdf5070.pdf"}],"author":[{"last_name":"Lampert","first_name":"Christoph","full_name":"Christoph Lampert","orcid":"0000-0001-8622-7887","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Blaschko,Matthew B","first_name":"Matthew","last_name":"Blaschko"},{"full_name":"Hofmann,Thomas","first_name":"Thomas","last_name":"Hofmann"}],"abstract":[{"lang":"eng","text":"Most successful object recognition systems rely on binary classification, deciding only if an object is present or not, but not providing information on the actual object location. To perform localization, one can take a sliding window approach, but this strongly increases the computational cost, because the classifier function has to be evaluated over a large set of candidate subwindows. In this paper, we propose a simple yet powerful branchand- bound scheme that allows efficient maximization of a large class of classifier functions over all possible subimages. It converges to a globally optimal solution typically in sublinear time. We show how our method is applicable to different object detection and retrieval scenarios. The achieved speedup allows the use of classifiers for localization that formerly were considered too slow for this task, such as SVMs with a spatial pyramid kernel or nearest neighbor classifiers based on the 2-distance. We demonstrate state-of-the-art performance of the resulting systems on the UIUC Cars dataset, the PASCAL VOC 2006 dataset and in the PASCAL VOC 2007 competition."}],"publist_id":"2644","title":"Beyond sliding windows: Object localization by efficient subwindow search","year":"2008","conference":{"name":"CVPR: Computer Vision and Pattern Recognition"},"day":"18","extern":1},{"citation":{"ista":"Lampert C, Blaschko M. 2008. A multiple kernel learning approach to joint multi-class object detection. DAGM: German Association For Pattern Recognition, LNCS, vol. 5096, 31–40.","short":"C. Lampert, M. Blaschko, in:, Springer, 2008, pp. 31–40.","mla":"Lampert, Christoph, and Matthew Blaschko. <i>A Multiple Kernel Learning Approach to Joint Multi-Class Object Detection</i>. Vol. 5096, Springer, 2008, pp. 31–40, doi:<a href=\"https://doi.org/10.1007/978-3-540-69321-5_4\">10.1007/978-3-540-69321-5_4</a>.","chicago":"Lampert, Christoph, and Matthew Blaschko. “A Multiple Kernel Learning Approach to Joint Multi-Class Object Detection,” 5096:31–40. Springer, 2008. <a href=\"https://doi.org/10.1007/978-3-540-69321-5_4\">https://doi.org/10.1007/978-3-540-69321-5_4</a>.","apa":"Lampert, C., &#38; Blaschko, M. (2008). A multiple kernel learning approach to joint multi-class object detection (Vol. 5096, pp. 31–40). Presented at the DAGM: German Association For Pattern Recognition, Springer. <a href=\"https://doi.org/10.1007/978-3-540-69321-5_4\">https://doi.org/10.1007/978-3-540-69321-5_4</a>","ieee":"C. Lampert and M. Blaschko, “A multiple kernel learning approach to joint multi-class object detection,” presented at the DAGM: German Association For Pattern Recognition, 2008, vol. 5096, pp. 31–40.","ama":"Lampert C, Blaschko M. A multiple kernel learning approach to joint multi-class object detection. In: Vol 5096. Springer; 2008:31-40. doi:<a href=\"https://doi.org/10.1007/978-3-540-69321-5_4\">10.1007/978-3-540-69321-5_4</a>"},"publisher":"Springer","date_created":"2018-12-11T12:04:46Z","page":"31 - 40","status":"public","month":"07","date_published":"2008-07-07T00:00:00Z","date_updated":"2021-01-12T07:51:41Z","quality_controlled":0,"type":"conference","intvolume":"      5096","_id":"3716","main_file_link":[{"open_access":"0","url":"http://www.kyb.mpg.de/fileadmin/user_upload/files/publications/attachments/DAGM2008-Lampert-Blaschko_5072%5b0%5d.pdf"}],"author":[{"last_name":"Lampert","first_name":"Christoph","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-8622-7887","full_name":"Christoph Lampert"},{"full_name":"Blaschko,Matthew B","last_name":"Blaschko","first_name":"Matthew"}],"publication_status":"published","doi":"10.1007/978-3-540-69321-5_4","title":"A multiple kernel learning approach to joint multi-class object detection","volume":5096,"year":"2008","alternative_title":["LNCS"],"conference":{"name":"DAGM: German Association For Pattern Recognition"},"day":"07","extern":1,"abstract":[{"lang":"eng","text":"Most current methods for multi-class object classification and localization work as independent 1-vs-rest classifiers. They decide whether and where an object is visible in an image purely on a per-class basis. Joint learning of more than one object class would generally be preferable, since this would allow the use of contextual information such as co-occurrence between classes. However, this approach is usually not employed because of its computational cost.\n\nIn this paper we propose a method to combine the efficiency of single class localization with a subsequent decision process that works jointly for all given object classes. By following a multiple kernel learning (MKL) approach, we automatically obtain a sparse dependency graph of relevant object classes on which to base the decision. Experiments on the PASCAL VOC 2006 and 2007 datasets show that the subsequent joint decision step clearly improves the accuracy compared to single class detection.\n"}],"publist_id":"2641"}]