---
_id: '3516'
abstract:
- lang: eng
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.
author:
- first_name: John
full_name: Huxter,John R
last_name: Huxter
- first_name: Timothy
full_name: Senior,Timothy J
last_name: Senior
- first_name: Kevin
full_name: Allen, Kevin
last_name: Allen
- first_name: Jozsef L
full_name: Jozsef Csicsvari
id: 3FA14672-F248-11E8-B48F-1D18A9856A87
last_name: Csicsvari
orcid: 0000-0002-5193-4036
citation:
ama: Huxter J, Senior T, Allen K, Csicsvari JL. Theta phase-specific codes for two-dimensional
position, trajectory and heading in the hippocampus. Nature Neuroscience.
2008;11(5):587-594. doi:10.1038/nn.2106
apa: Huxter, J., Senior, T., Allen, K., & Csicsvari, J. L. (2008). Theta phase-specific
codes for two-dimensional position, trajectory and heading in the hippocampus.
Nature Neuroscience. Nature Publishing Group. https://doi.org/10.1038/nn.2106
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.” Nature Neuroscience. Nature Publishing Group, 2008. https://doi.org/10.1038/nn.2106.
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,”
Nature Neuroscience, vol. 11, no. 5. Nature Publishing Group, pp. 587–594,
2008.
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.” Nature Neuroscience, vol. 11,
no. 5, Nature Publishing Group, 2008, pp. 587–94, doi:10.1038/nn.2106.
short: J. Huxter, T. Senior, K. Allen, J.L. Csicsvari, Nature Neuroscience 11 (2008)
587–594.
date_created: 2018-12-11T12:03:44Z
date_published: 2008-05-29T00:00:00Z
date_updated: 2021-01-12T07:44:00Z
day: '29'
doi: 10.1038/nn.2106
extern: 1
intvolume: ' 11'
issue: '5'
month: '05'
page: 587 - 594
publication: Nature Neuroscience
publication_status: published
publisher: Nature Publishing Group
publist_id: '2869'
quality_controlled: 0
status: public
title: Theta phase-specific codes for two-dimensional position, trajectory and heading
in the hippocampus
type: journal_article
volume: 11
year: '2008'
...
---
_id: '3520'
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
( < 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.
author:
- first_name: Joseph
full_name: Joseph O'Neill
id: 426376DC-F248-11E8-B48F-1D18A9856A87
last_name: O'Neill
- first_name: Timothy
full_name: Senior,Timothy J
last_name: Senior
- first_name: Kevin
full_name: Allen, Kevin
last_name: Allen
- first_name: John
full_name: Huxter,John R
last_name: Huxter
- first_name: Jozsef L
full_name: Jozsef Csicsvari
id: 3FA14672-F248-11E8-B48F-1D18A9856A87
last_name: Csicsvari
orcid: 0000-0002-5193-4036
citation:
ama: O’Neill J, Senior T, Allen K, Huxter J, Csicsvari JL. Reactivation of experience-dependent
cell assembly patterns in the hippocampus. Nature Neuroscience. 2008;11(2):209-215.
doi:10.1038/nn2037
apa: O’Neill, J., Senior, T., Allen, K., Huxter, J., & Csicsvari, J. L. (2008).
Reactivation of experience-dependent cell assembly patterns in the hippocampus.
Nature Neuroscience. Nature Publishing Group. https://doi.org/10.1038/nn2037
chicago: O’Neill, Joseph, Timothy Senior, Kevin Allen, John Huxter, and Jozsef L
Csicsvari. “Reactivation of Experience-Dependent Cell Assembly Patterns in the
Hippocampus.” Nature Neuroscience. Nature Publishing Group, 2008. https://doi.org/10.1038/nn2037.
ieee: J. O’Neill, T. Senior, K. Allen, J. Huxter, and J. L. Csicsvari, “Reactivation
of experience-dependent cell assembly patterns in the hippocampus,” Nature
Neuroscience, vol. 11, no. 2. Nature Publishing Group, pp. 209–215, 2008.
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.” Nature Neuroscience, vol. 11, no. 2, Nature
Publishing Group, 2008, pp. 209–15, doi:10.1038/nn2037.
short: J. O’Neill, T. Senior, K. Allen, J. Huxter, J.L. Csicsvari, Nature Neuroscience
11 (2008) 209–215.
date_created: 2018-12-11T12:03:46Z
date_published: 2008-02-01T00:00:00Z
date_updated: 2021-01-12T07:44:02Z
day: '01'
doi: 10.1038/nn2037
extern: 1
intvolume: ' 11'
issue: '2'
month: '02'
page: 209 - 215
publication: Nature Neuroscience
publication_status: published
publisher: Nature Publishing Group
publist_id: '2864'
quality_controlled: 0
status: public
title: Reactivation of experience-dependent cell assembly patterns in the hippocampus
type: journal_article
volume: 11
year: '2008'
...
---
_id: '3530'
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:
- first_name: Pablo
full_name: Fuentealba,Pablo
last_name: Fuentealba
- first_name: Rahima
full_name: Begum,Rahima
last_name: Begum
- first_name: Marco
full_name: Capogna,Marco
last_name: Capogna
- first_name: Shozo
full_name: Jinno,Shozo
last_name: Jinno
- first_name: Laszlo
full_name: Marton,Laszlo F
last_name: Marton
- first_name: Jozsef L
full_name: Jozsef Csicsvari
id: 3FA14672-F248-11E8-B48F-1D18A9856A87
last_name: Csicsvari
orcid: 0000-0002-5193-4036
- first_name: Alex
full_name: Thomson,Alex
last_name: Thomson
- first_name: Péter
full_name: Somogyi, Péter
last_name: Somogyi
- first_name: Thomas
full_name: Klausberger,Thomas
last_name: Klausberger
citation:
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.
Neuron. 2008;57(6):917-929. doi:10.1016/j.neuron.2008.01.034'
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.
Neuron. Elsevier. https://doi.org/10.1016/j.neuron.2008.01.034'
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.” Neuron. Elsevier,
2008. https://doi.org/10.1016/j.neuron.2008.01.034.'
ieee: 'P. Fuentealba et al., “Ivy cells: A population of nitric-oxide-producing,
slow-spiking GABAergic neurons and their involvement in hippocampal network activity,”
Neuron, vol. 57, no. 6. Elsevier, pp. 917–929, 2008.'
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.'
mla: 'Fuentealba, Pablo, et al. “Ivy Cells: A Population of Nitric-Oxide-Producing,
Slow-Spiking GABAergic Neurons and Their Involvement in Hippocampal Network Activity.”
Neuron, vol. 57, no. 6, Elsevier, 2008, pp. 917–29, doi:10.1016/j.neuron.2008.01.034.'
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.
date_created: 2018-12-11T12:03:49Z
date_published: 2008-03-27T00:00:00Z
date_updated: 2021-01-12T07:44:06Z
day: '27'
doi: 10.1016/j.neuron.2008.01.034
extern: 1
intvolume: ' 57'
issue: '6'
month: '03'
page: 917 - 929
publication: Neuron
publication_status: published
publisher: Elsevier
publist_id: '2855'
quality_controlled: 0
status: public
title: 'Ivy cells: A population of nitric-oxide-producing, slow-spiking GABAergic
neurons and their involvement in hippocampal network activity'
type: journal_article
volume: 57
year: '2008'
...
---
_id: '3534'
author:
- first_name: David
full_name: Dupret, David
last_name: Dupret
- first_name: Barty
full_name: Pleydell-Bouverie, Barty
last_name: Pleydell Bouverie
- first_name: Jozsef L
full_name: Jozsef Csicsvari
id: 3FA14672-F248-11E8-B48F-1D18A9856A87
last_name: Csicsvari
orcid: 0000-0002-5193-4036
citation:
ama: Dupret D, Pleydell Bouverie B, Csicsvari JL. Inhibitory interneurons and network
oscillations. PNAS. 2008;105(47):18079-18080. doi:10.1073/pnas.0810064105
apa: Dupret, D., Pleydell Bouverie, B., & Csicsvari, J. L. (2008). Inhibitory
interneurons and network oscillations. PNAS. National Academy of Sciences.
https://doi.org/10.1073/pnas.0810064105
chicago: Dupret, David, Barty Pleydell Bouverie, and Jozsef L Csicsvari. “Inhibitory
Interneurons and Network Oscillations.” PNAS. National Academy of Sciences,
2008. https://doi.org/10.1073/pnas.0810064105.
ieee: D. Dupret, B. Pleydell Bouverie, and J. L. Csicsvari, “Inhibitory interneurons
and network oscillations,” PNAS, vol. 105, no. 47. National Academy of
Sciences, pp. 18079–18080, 2008.
ista: Dupret D, Pleydell Bouverie B, Csicsvari JL. 2008. Inhibitory interneurons
and network oscillations. PNAS. 105(47), 18079–18080.
mla: Dupret, David, et al. “Inhibitory Interneurons and Network Oscillations.” PNAS,
vol. 105, no. 47, National Academy of Sciences, 2008, pp. 18079–80, doi:10.1073/pnas.0810064105.
short: D. Dupret, B. Pleydell Bouverie, J.L. Csicsvari, PNAS 105 (2008) 18079–18080.
date_created: 2018-12-11T12:03:50Z
date_published: 2008-11-25T00:00:00Z
date_updated: 2021-01-12T07:44:08Z
day: '25'
doi: 10.1073/pnas.0810064105
extern: 1
intvolume: ' 105'
issue: '47'
month: '11'
page: 18079 - 18080
publication: PNAS
publication_status: published
publisher: National Academy of Sciences
publist_id: '2852'
quality_controlled: 0
status: public
title: Inhibitory interneurons and network oscillations
type: journal_article
volume: 105
year: '2008'
...
---
_id: '3537'
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.'
author:
- first_name: Timothy
full_name: Senior,Timothy J
last_name: Senior
- first_name: John
full_name: Huxter,John R
last_name: Huxter
- first_name: Kevin
full_name: Allen, Kevin
last_name: Allen
- first_name: Joseph
full_name: Joseph O'Neill
id: 426376DC-F248-11E8-B48F-1D18A9856A87
last_name: O'Neill
- first_name: Jozsef L
full_name: Jozsef Csicsvari
id: 3FA14672-F248-11E8-B48F-1D18A9856A87
last_name: Csicsvari
orcid: 0000-0002-5193-4036
citation:
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.
Journal of Neuroscience. 2008;28(9):2274-2286. doi:10.1523/JNEUROSCI.4669-07.2008
apa: Senior, T., Huxter, J., Allen, K., O’Neill, J., & Csicsvari, J. L. (2008).
Gamma oscillatory firing reveals distinct populations of pyramidal cells in the
CA1 region of the hippocampus. Journal of Neuroscience. Society for Neuroscience.
https://doi.org/10.1523/JNEUROSCI.4669-07.2008
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.” Journal of Neuroscience. Society
for Neuroscience, 2008. https://doi.org/10.1523/JNEUROSCI.4669-07.2008.
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,” Journal of Neuroscience, vol. 28, no. 9. Society for Neuroscience,
pp. 2274–2286, 2008.
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.
mla: Senior, Timothy, et al. “Gamma Oscillatory Firing Reveals Distinct Populations
of Pyramidal Cells in the CA1 Region of the Hippocampus.” Journal of Neuroscience,
vol. 28, no. 9, Society for Neuroscience, 2008, pp. 2274–86, doi:10.1523/JNEUROSCI.4669-07.2008.
short: T. Senior, J. Huxter, K. Allen, J. O’Neill, J.L. Csicsvari, Journal of Neuroscience
28 (2008) 2274–2286.
date_created: 2018-12-11T12:03:51Z
date_published: 2008-02-27T00:00:00Z
date_updated: 2021-01-12T07:44:09Z
day: '27'
doi: 10.1523/JNEUROSCI.4669-07.2008
extern: 1
intvolume: ' 28'
issue: '9'
month: '02'
page: 2274 - 2286
publication: Journal of Neuroscience
publication_status: published
publisher: Society for Neuroscience
publist_id: '2847'
quality_controlled: 0
status: public
title: Gamma oscillatory firing reveals distinct populations of pyramidal cells in
the CA1 region of the hippocampus
type: journal_article
volume: 28
year: '2008'
...
---
_id: '3544'
abstract:
- lang: eng
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.
author:
- first_name: Nicolas
full_name: Mallet,Nicolas
last_name: Mallet
- first_name: Alek
full_name: Pogosyan,Alek
last_name: Pogosyan
- first_name: Andrew
full_name: Sharott,Andrew
last_name: Sharott
- first_name: Jozsef L
full_name: Jozsef Csicsvari
id: 3FA14672-F248-11E8-B48F-1D18A9856A87
last_name: Csicsvari
orcid: 0000-0002-5193-4036
- first_name: John
full_name: Bolam, John Paul
last_name: Bolam
- first_name: Peter
full_name: Brown,Peter
last_name: Brown
- first_name: Peter
full_name: Magill,Peter J
last_name: Magill
citation:
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. Journal of Neuroscience. 2008;28(18):4795-4806. doi:10.1523/JNEUROSCI.0123-08.2008
apa: Mallet, N., Pogosyan, A., Sharott, A., Csicsvari, J. L., 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. Society for Neuroscience. https://doi.org/10.1523/JNEUROSCI.0123-08.2008
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.” Journal of Neuroscience. Society for Neuroscience, 2008. https://doi.org/10.1523/JNEUROSCI.0123-08.2008.
ieee: N. Mallet et al., “Disrupted dopamine transmission and the emergence
of exaggerated beta oscillations in subthalamic nucleus and cerebral cortex,”
Journal of Neuroscience, vol. 28, no. 18. Society for Neuroscience, pp.
4795–4806, 2008.
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.
mla: Mallet, Nicolas, et al. “Disrupted Dopamine Transmission and the Emergence
of Exaggerated Beta Oscillations in Subthalamic Nucleus and Cerebral Cortex.”
Journal of Neuroscience, vol. 28, no. 18, Society for Neuroscience, 2008,
pp. 4795–806, doi:10.1523/JNEUROSCI.0123-08.2008.
short: N. Mallet, A. Pogosyan, A. Sharott, J.L. Csicsvari, J. Bolam, P. Brown, P.
Magill, Journal of Neuroscience 28 (2008) 4795–4806.
date_created: 2018-12-11T12:03:53Z
date_published: 2008-04-30T00:00:00Z
date_updated: 2021-01-12T07:44:12Z
day: '30'
doi: 10.1523/JNEUROSCI.0123-08.2008
extern: 1
intvolume: ' 28'
issue: '18'
month: '04'
page: 4795 - 4806
publication: Journal of Neuroscience
publication_status: published
publisher: Society for Neuroscience
publist_id: '2842'
quality_controlled: 0
status: public
title: Disrupted dopamine transmission and the emergence of exaggerated beta oscillations
in subthalamic nucleus and cerebral cortex
type: journal_article
volume: 28
year: '2008'
...
---
_id: '3577'
alternative_title:
- Mathematics and Visualization
author:
- first_name: Silvia
full_name: Biasotti, Silvia
last_name: Biasotti
- first_name: Dominique
full_name: Attali, Dominique
last_name: Attali
- first_name: Jean
full_name: Boissonnat, Jean-Daniel
last_name: Boissonnat
- first_name: Herbert
full_name: Herbert Edelsbrunner
id: 3FB178DA-F248-11E8-B48F-1D18A9856A87
last_name: Edelsbrunner
orcid: 0000-0002-9823-6833
- first_name: Gershon
full_name: Elber, Gershon
last_name: Elber
- first_name: Michela
full_name: Mortara, Michela
last_name: Mortara
- first_name: Gabriella
full_name: Sanniti di Baja, Gabriella
last_name: Sanniti Di Baja
- first_name: Michela
full_name: Spagnuolo, Michela
last_name: Spagnuolo
- first_name: Mirela
full_name: Tanase, Mirela
last_name: Tanase
- first_name: Remco
full_name: Veltkam, Remco
last_name: Veltkam
citation:
ama: 'Biasotti S, Attali D, Boissonnat J, et al. Skeletal structures. In: Shape
Analysis and Structuring. Springer; 2008:145-183. doi:10.1007/978-3-540-33265-7_5'
apa: Biasotti, S., Attali, D., Boissonnat, J., Edelsbrunner, H., Elber, G., Mortara,
M., … Veltkam, R. (2008). Skeletal structures. In Shape Analysis and Structuring
(pp. 145–183). Springer. https://doi.org/10.1007/978-3-540-33265-7_5
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 Shape Analysis
and Structuring, 145–83. Springer, 2008. https://doi.org/10.1007/978-3-540-33265-7_5.
ieee: S. Biasotti et al., “Skeletal structures,” in Shape Analysis and
Structuring, Springer, 2008, pp. 145–183.
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.'
mla: Biasotti, Silvia, et al. “Skeletal Structures.” Shape Analysis and Structuring,
Springer, 2008, pp. 145–83, doi:10.1007/978-3-540-33265-7_5.
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.
date_created: 2018-12-11T12:04:03Z
date_published: 2008-01-01T00:00:00Z
date_updated: 2021-01-12T07:44:25Z
day: '01'
doi: 10.1007/978-3-540-33265-7_5
extern: 1
month: '01'
page: 145 - 183
publication: Shape Analysis and Structuring
publication_status: published
publisher: Springer
publist_id: '2808'
quality_controlled: 0
status: public
title: Skeletal structures
type: book_chapter
year: '2008'
...
---
_id: '3591'
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
article_processing_charge: No
author:
- first_name: Florian
full_name: Ulrich, Florian
last_name: Ulrich
- first_name: Carl-Philipp J
full_name: Heisenberg, Carl-Philipp J
id: 39427864-F248-11E8-B48F-1D18A9856A87
last_name: Heisenberg
orcid: 0000-0002-0912-4566
citation:
ama: Ulrich F, Heisenberg C-PJ. Probing E-cadherin endocytosis by morpholino-mediated
Rab5 knock-down in zebrafish. Methods in Molecular Biology. 2008;440:371-387.
doi:10.1007/978-1-59745-178-9_27
apa: Ulrich, F., & Heisenberg, C.-P. J. (2008). Probing E-cadherin endocytosis
by morpholino-mediated Rab5 knock-down in zebrafish. Methods in Molecular Biology.
Springer. https://doi.org/10.1007/978-1-59745-178-9_27
chicago: Ulrich, Florian, and Carl-Philipp J Heisenberg. “Probing E-Cadherin Endocytosis
by Morpholino-Mediated Rab5 Knock-down in Zebrafish.” Methods in Molecular
Biology. Springer, 2008. https://doi.org/10.1007/978-1-59745-178-9_27.
ieee: F. Ulrich and C.-P. J. Heisenberg, “Probing E-cadherin endocytosis by morpholino-mediated
Rab5 knock-down in zebrafish.,” Methods in Molecular Biology, vol. 440.
Springer, pp. 371–387, 2008.
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.
mla: Ulrich, Florian, and Carl-Philipp J. Heisenberg. “Probing E-Cadherin Endocytosis
by Morpholino-Mediated Rab5 Knock-down in Zebrafish.” Methods in Molecular
Biology, vol. 440, Springer, 2008, pp. 371–87, doi:10.1007/978-1-59745-178-9_27.
short: F. Ulrich, C.-P.J. Heisenberg, Methods in Molecular Biology 440 (2008) 371–387.
date_created: 2018-12-11T12:04:08Z
date_published: 2008-01-01T00:00:00Z
date_updated: 2021-01-12T07:44:31Z
day: '01'
doi: 10.1007/978-1-59745-178-9_27
extern: '1'
intvolume: ' 440'
language:
- iso: eng
month: '01'
oa_version: None
page: 371 - 387
publication: Methods in Molecular Biology
publication_status: published
publisher: Springer
publist_id: '2792'
status: public
title: Probing E-cadherin endocytosis by morpholino-mediated Rab5 knock-down in zebrafish.
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 440
year: '2008'
...
---
_id: '3599'
abstract:
- lang: eng
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.
author:
- first_name: Erfu
full_name: Yang, Erfu
last_name: Yang
- first_name: Ahmet
full_name: Erdogan, Ahmet T
last_name: Erdogan
- first_name: Tughrul
full_name: Arslan, Tughrul
last_name: Arslan
- first_name: Nicholas H
full_name: Nicholas Barton
id: 4880FE40-F248-11E8-B48F-1D18A9856A87
last_name: Barton
orcid: 0000-0002-8548-5240
citation:
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:10.1109/AHS.2008.60'
apa: 'Yang, E., Erdogan, A., Arslan, T., & 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. https://doi.org/10.1109/AHS.2008.60'
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. https://doi.org/10.1109/AHS.2008.60.
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.'
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.'
mla: Yang, Erfu, et al. Adaptive Formation Control and Bio-Inspired Optimization
of a Cluster-Based Satellite Wireless Sensor Network . IEEE, 2008, pp. 432–39,
doi:10.1109/AHS.2008.60.
short: E. Yang, A. Erdogan, T. Arslan, N.H. Barton, in:, IEEE, 2008, pp. 432–439.
conference:
name: 'AHS: NASA/ESA Conference on Adaptive Hardware and Systems'
date_created: 2018-12-11T12:04:10Z
date_published: 2008-08-01T00:00:00Z
date_updated: 2021-01-12T07:44:34Z
day: '01'
doi: 10.1109/AHS.2008.60
extern: 1
month: '08'
page: 432 - 439
publication_status: published
publisher: IEEE
publist_id: '2784'
quality_controlled: 0
status: public
title: 'Adaptive formation control and bio-inspired optimization of a cluster-based
satellite wireless sensor network '
type: conference
year: '2008'
...
---
_id: '3600'
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.
alternative_title:
- LNCS
author:
- first_name: Erfu
full_name: Yang, Erfu
last_name: Yang
- first_name: Nicholas H
full_name: Nicholas Barton
id: 4880FE40-F248-11E8-B48F-1D18A9856A87
last_name: Barton
orcid: 0000-0002-8548-5240
- first_name: Tughrul
full_name: Arslan, Tughrul
last_name: Arslan
- first_name: Ahmet
full_name: Erdogan, Ahmet T
last_name: Erdogan
citation:
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:10.1007/978-3-540-85857-7_22'
apa: 'Yang, E., Barton, N. H., 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 (Vol. 5216, pp. 249–260). Presented
at the IECS: International Conference on Evolvable Systems, Springer. https://doi.org/10.1007/978-3-540-85857-7_22'
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.
https://doi.org/10.1007/978-3-540-85857-7_22.'
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.'
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.'
mla: 'Yang, Erfu, et al. Scalability of a Novel Shifting Balance Theory-Based
Optimization Algorithm: A Comparative Study on a Cluster-Based Wireless Sensor
Network. Vol. 5216, Springer, 2008, pp. 249–60, doi:10.1007/978-3-540-85857-7_22.'
short: E. Yang, N.H. Barton, T. Arslan, A. Erdogan, in:, Springer, 2008, pp. 249–260.
conference:
name: 'IECS: International Conference on Evolvable Systems'
date_created: 2018-12-11T12:04:10Z
date_published: 2008-09-08T00:00:00Z
date_updated: 2021-01-12T07:44:35Z
day: '08'
doi: 10.1007/978-3-540-85857-7_22
extern: 1
intvolume: ' 5216'
month: '09'
page: 249 - 260
publication_status: published
publisher: Springer
publist_id: '2783'
quality_controlled: 0
status: public
title: ' Scalability of a novel shifting balance theory-based optimization algorithm:
A comparative study on a cluster-based wireless sensor network'
type: conference
volume: 5216
year: '2008'
...
---
_id: '3605'
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.
author:
- first_name: Maria
full_name: De Cara, Maria A
last_name: De Cara
- first_name: Nicholas H
full_name: Nicholas Barton
id: 4880FE40-F248-11E8-B48F-1D18A9856A87
last_name: Barton
orcid: 0000-0002-8548-5240
- first_name: Mark
full_name: Kirkpatrick, Mark
last_name: Kirkpatrick
citation:
ama: De Cara M, Barton NH, Kirkpatrick M. A model for the evolution of assortative
mating. American Naturalist. 2008;171(5):580-596. doi:10.1086/587062
apa: De Cara, M., Barton, N. H., & Kirkpatrick, M. (2008). A model for the evolution
of assortative mating. American Naturalist. University of Chicago Press.
https://doi.org/10.1086/587062
chicago: De Cara, Maria, Nicholas H Barton, and Mark Kirkpatrick. “A Model for the
Evolution of Assortative Mating.” American Naturalist. University of Chicago
Press, 2008. https://doi.org/10.1086/587062.
ieee: M. De Cara, N. H. Barton, and M. Kirkpatrick, “A model for the evolution of
assortative mating,” American Naturalist, vol. 171, no. 5. University of
Chicago Press, pp. 580–596, 2008.
ista: De Cara M, Barton NH, Kirkpatrick M. 2008. A model for the evolution of assortative
mating. American Naturalist. 171(5), 580–596.
mla: De Cara, Maria, et al. “A Model for the Evolution of Assortative Mating.” American
Naturalist, vol. 171, no. 5, University of Chicago Press, 2008, pp. 580–96,
doi:10.1086/587062.
short: M. De Cara, N.H. Barton, M. Kirkpatrick, American Naturalist 171 (2008) 580–596.
date_created: 2018-12-11T12:04:12Z
date_published: 2008-05-01T00:00:00Z
date_updated: 2021-01-12T07:44:36Z
day: '01'
doi: 10.1086/587062
extern: 1
intvolume: ' 171'
issue: '5'
month: '05'
page: 580 - 596
publication: American Naturalist
publication_status: published
publisher: University of Chicago Press
publist_id: '2778'
quality_controlled: 0
status: public
title: A model for the evolution of assortative mating
type: journal_article
volume: 171
year: '2008'
...
---
_id: '3606'
abstract:
- lang: eng
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.'
author:
- first_name: Nicholas H
full_name: Nicholas Barton
id: 4880FE40-F248-11E8-B48F-1D18A9856A87
last_name: Barton
orcid: 0000-0002-8548-5240
citation:
ama: Barton NH. The effect of a barrier to gene flow on patterns of geographic variation.
Genetical Research. 2008;90(1):139-149. doi:10.1017/S0016672307009081
apa: Barton, N. H. (2008). The effect of a barrier to gene flow on patterns of geographic
variation. Genetical Research. Cambridge University Press. https://doi.org/10.1017/S0016672307009081
chicago: Barton, Nicholas H. “The Effect of a Barrier to Gene Flow on Patterns of
Geographic Variation.” Genetical Research. Cambridge University Press,
2008. https://doi.org/10.1017/S0016672307009081.
ieee: N. H. Barton, “The effect of a barrier to gene flow on patterns of geographic
variation,” Genetical Research, vol. 90, no. 1. Cambridge University Press,
pp. 139–149, 2008.
ista: Barton NH. 2008. The effect of a barrier to gene flow on patterns of geographic
variation. Genetical Research. 90(1), 139–149.
mla: Barton, Nicholas H. “The Effect of a Barrier to Gene Flow on Patterns of Geographic
Variation.” Genetical Research, vol. 90, no. 1, Cambridge University Press,
2008, pp. 139–49, doi:10.1017/S0016672307009081.
short: N.H. Barton, Genetical Research 90 (2008) 139–149.
date_created: 2018-12-11T12:04:12Z
date_published: 2008-02-01T00:00:00Z
date_updated: 2021-01-12T07:44:37Z
day: '01'
doi: 10.1017/S0016672307009081
extern: 1
intvolume: ' 90'
issue: '1'
month: '02'
page: 139 - 149
publication: Genetical Research
publication_status: published
publisher: Cambridge University Press
publist_id: '2777'
quality_controlled: 0
status: public
title: The effect of a barrier to gene flow on patterns of geographic variation
type: journal_article
volume: 90
year: '2008'
...
---
_id: '3694'
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 "Adaptive History-Based
IP Filtering" (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.
author:
- first_name: Markus
full_name: Goldstein,Markus
last_name: Goldstein
- first_name: Christoph
full_name: Christoph Lampert
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
- first_name: Matthias
full_name: Reif,Matthias
last_name: Reif
- first_name: Armin
full_name: Stahl,Armin
last_name: Stahl
- first_name: Thomas
full_name: Breuel,Thomas M
last_name: Breuel
citation:
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:10.1109/ICN.2008.64'
apa: 'Goldstein, M., Lampert, C., Reif, M., Stahl, A., & 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. https://doi.org/10.1109/ICN.2008.64'
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. https://doi.org/10.1109/ICN.2008.64.
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.'
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.'
mla: Goldstein, Markus, et al. Bayes Optimal DDoS Mitigation by Adaptive History-Based
IP Filtering. IEEE, 2008, pp. 174–79, doi:10.1109/ICN.2008.64.
short: M. Goldstein, C. Lampert, M. Reif, A. Stahl, T. Breuel, in:, IEEE, 2008,
pp. 174–179.
conference:
name: 'ICN: International Conference on Networking'
date_created: 2018-12-11T12:04:39Z
date_published: 2008-04-13T00:00:00Z
date_updated: 2021-01-12T07:49:01Z
day: '13'
doi: 10.1109/ICN.2008.64
extern: 1
main_file_link:
- open_access: '0'
url: http://pub.ist.ac.at/~chl/papers/goldstein-icn2008.pdf
month: '04'
page: 174 - 179
publication_status: published
publisher: IEEE
publist_id: '2671'
quality_controlled: 0
status: public
title: Bayes optimal DDoS mitigation by adaptive history-based IP filtering
type: conference
year: '2008'
...
---
_id: '3698'
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.
alternative_title:
- LNCS
author:
- first_name: Matthew
full_name: Blaschko,Matthew B
last_name: Blaschko
- first_name: Christoph
full_name: Christoph Lampert
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
- first_name: Arthur
full_name: Gretton,Arthur
last_name: Gretton
citation:
ama: 'Blaschko M, Lampert C, Gretton A. Semi-supervised Laplacian regularization
of kernel canonical correlation analysis. In: Vol 5211. Springer; 2008:133-145.
doi:10.1007/978-3-540-87479-9_27'
apa: 'Blaschko, M., Lampert, C., & 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. https://doi.org/10.1007/978-3-540-87479-9_27'
chicago: Blaschko, Matthew, Christoph Lampert, and Arthur Gretton. “Semi-Supervised
Laplacian Regularization of Kernel Canonical Correlation Analysis,” 5211:133–45.
Springer, 2008. https://doi.org/10.1007/978-3-540-87479-9_27.
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.'
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.'
mla: Blaschko, Matthew, et al. Semi-Supervised Laplacian Regularization of Kernel
Canonical Correlation Analysis. Vol. 5211, no. Part 1, Springer, 2008, pp.
133–45, doi:10.1007/978-3-540-87479-9_27.
short: M. Blaschko, C. Lampert, A. Gretton, in:, Springer, 2008, pp. 133–145.
conference:
name: 'ECML: European Conference on Machine Learning'
date_created: 2018-12-11T12:04:41Z
date_published: 2008-10-21T00:00:00Z
date_updated: 2021-01-12T07:49:02Z
day: '21'
doi: 10.1007/978-3-540-87479-9_27
extern: 1
intvolume: ' 5211'
issue: Part 1
month: '10'
page: 133 - 145
publication_status: published
publisher: Springer
publist_id: '2662'
quality_controlled: 0
status: public
title: Semi-supervised Laplacian regularization of kernel canonical correlation analysis
type: conference
volume: 5211
year: '2008'
...
---
_id: '3700'
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;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.
acknowledgement: This work was funded in part by the EC project CLASS, IST 027978.
author:
- first_name: Christoph
full_name: Christoph Lampert
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Lampert C. Partitioning of image datasets using discriminative context information.
In: IEEE; 2008:1-8. doi:10.1109/CVPR.2008.4587448'
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. https://doi.org/10.1109/CVPR.2008.4587448'
chicago: Lampert, Christoph. “Partitioning of Image Datasets Using Discriminative
Context Information,” 1–8. IEEE, 2008. https://doi.org/10.1109/CVPR.2008.4587448.
ieee: 'C. Lampert, “Partitioning of image datasets using discriminative context
information,” presented at the CVPR: Computer Vision and Pattern Recognition,
2008, pp. 1–8.'
ista: 'Lampert C. 2008. Partitioning of image datasets using discriminative context
information. CVPR: Computer Vision and Pattern Recognition, 1–8.'
mla: Lampert, Christoph. Partitioning of Image Datasets Using Discriminative
Context Information. IEEE, 2008, pp. 1–8, doi:10.1109/CVPR.2008.4587448.
short: C. Lampert, in:, IEEE, 2008, pp. 1–8.
conference:
name: 'CVPR: Computer Vision and Pattern Recognition'
date_created: 2018-12-11T12:04:41Z
date_published: 2008-09-18T00:00:00Z
date_updated: 2021-01-12T07:51:35Z
day: '18'
doi: 10.1109/CVPR.2008.4587448
extern: 1
main_file_link:
- open_access: '0'
url: http://pub.ist.ac.at/~chl/papers/lampert-cvpr2008b.pdf
month: '09'
page: 1 - 8
publication_status: published
publisher: IEEE
publist_id: '2657'
quality_controlled: 0
status: public
title: Partitioning of image datasets using discriminative context information
type: conference
year: '2008'
...
---
_id: '3705'
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.'
alternative_title:
- LNCS
author:
- first_name: Matthew
full_name: Blaschko,Matthew B
last_name: Blaschko
- first_name: Christoph
full_name: Christoph Lampert
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Blaschko M, Lampert C. Learning to localize objects with structured output
regression. In: Vol 5302. Springer; 2008:2-15. doi:10.1007/978-3-540-88682-2_2'
apa: 'Blaschko, M., & 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. https://doi.org/10.1007/978-3-540-88682-2_2'
chicago: Blaschko, Matthew, and Christoph Lampert. “Learning to Localize Objects
with Structured Output Regression,” 5302:2–15. Springer, 2008. https://doi.org/10.1007/978-3-540-88682-2_2.
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.'
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.'
mla: Blaschko, Matthew, and Christoph Lampert. Learning to Localize Objects with
Structured Output Regression. Vol. 5302, Springer, 2008, pp. 2–15, doi:10.1007/978-3-540-88682-2_2.
short: M. Blaschko, C. Lampert, in:, Springer, 2008, pp. 2–15.
conference:
name: 'ECCV: European Conference on Computer Vision'
date_created: 2018-12-11T12:04:43Z
date_published: 2008-11-17T00:00:00Z
date_updated: 2021-01-12T07:51:37Z
day: '17'
doi: 10.1007/978-3-540-88682-2_2
extern: 1
intvolume: ' 5302'
main_file_link:
- open_access: '0'
url: http://www.kyb.mpg.de/fileadmin/user_upload/files/publications/attachments/ECCV2008-Blaschko_5247%5b0%5d.pdf
month: '11'
page: 2 - 15
publication_status: published
publisher: Springer
publist_id: '2653'
quality_controlled: 0
status: public
title: Learning to localize objects with structured output regression
type: conference
volume: 5302
year: '2008'
...
---
_id: '3706'
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.
author:
- first_name: Christoph
full_name: Christoph Lampert
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
- first_name: Matthew
full_name: Blaschko,Matthew B
last_name: Blaschko
citation:
ama: 'Lampert C, Blaschko M. Joint kernel support estimation for structured prediction.
In: Curran Associates, Inc.; 2008:1-4.'
apa: 'Lampert, C., & 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.
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.'
ista: 'Lampert C, Blaschko M. 2008. Joint kernel support estimation for structured
prediction. NIPS SISO: NIPS Workshop on ‘Structured Input - Structured Output’,
1–4.'
mla: Lampert, Christoph, and Matthew Blaschko. Joint Kernel Support Estimation
for Structured Prediction. Curran Associates, Inc., 2008, pp. 1–4.
short: C. Lampert, M. Blaschko, in:, Curran Associates, Inc., 2008, pp. 1–4.
conference:
name: 'NIPS SISO: NIPS Workshop on "Structured Input - Structured Output"'
date_created: 2018-12-11T12:04:43Z
date_published: 2008-12-12T00:00:00Z
date_updated: 2021-01-12T07:51:37Z
day: '12'
extern: 1
main_file_link:
- open_access: '0'
url: http://agbs.kyb.tuebingen.mpg.de/wikis/bg/siso2008/Blaschkoetal.pdf
month: '12'
page: 1 - 4
publication_status: published
publisher: Curran Associates, Inc.
publist_id: '2650'
quality_controlled: 0
status: public
title: Joint kernel support estimation for structured prediction
type: conference
year: '2008'
...
---
_id: '3712'
abstract:
- lang: eng
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.
author:
- first_name: Matthew
full_name: Blaschko,Matthew B
last_name: Blaschko
- first_name: Christoph
full_name: Christoph Lampert
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Blaschko M, Lampert C. Correlational spectral clustering. In: IEEE; 2008:1-8.
doi:10.1109/CVPR.2008.4587353'
apa: 'Blaschko, M., & Lampert, C. (2008). Correlational spectral clustering
(pp. 1–8). Presented at the CVPR: Computer Vision and Pattern Recognition, IEEE.
https://doi.org/10.1109/CVPR.2008.4587353'
chicago: Blaschko, Matthew, and Christoph Lampert. “Correlational Spectral Clustering,”
1–8. IEEE, 2008. https://doi.org/10.1109/CVPR.2008.4587353.
ieee: 'M. Blaschko and C. Lampert, “Correlational spectral clustering,” presented
at the CVPR: Computer Vision and Pattern Recognition, 2008, pp. 1–8.'
ista: 'Blaschko M, Lampert C. 2008. Correlational spectral clustering. CVPR: Computer
Vision and Pattern Recognition, 1–8.'
mla: Blaschko, Matthew, and Christoph Lampert. Correlational Spectral Clustering.
IEEE, 2008, pp. 1–8, doi:10.1109/CVPR.2008.4587353.
short: M. Blaschko, C. Lampert, in:, IEEE, 2008, pp. 1–8.
conference:
name: 'CVPR: Computer Vision and Pattern Recognition'
date_created: 2018-12-11T12:04:45Z
date_published: 2008-09-18T00:00:00Z
date_updated: 2021-01-12T07:51:40Z
day: '18'
doi: 10.1109/CVPR.2008.4587353
extern: 1
month: '09'
page: 1 - 8
publication_status: published
publisher: IEEE
publist_id: '2646'
quality_controlled: 0
status: public
title: Correlational spectral clustering
type: conference
year: '2008'
...
---
_id: '3714'
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.
author:
- first_name: Christoph
full_name: Christoph Lampert
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
- first_name: Matthew
full_name: Blaschko,Matthew B
last_name: Blaschko
- first_name: Thomas
full_name: Hofmann,Thomas
last_name: Hofmann
citation:
ama: 'Lampert C, Blaschko M, Hofmann T. Beyond sliding windows: Object localization
by efficient subwindow search. In: IEEE; 2008:1-8. doi:10.1109/CVPR.2008.4587586'
apa: 'Lampert, C., Blaschko, M., & 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. https://doi.org/10.1109/CVPR.2008.4587586'
chicago: 'Lampert, Christoph, Matthew Blaschko, and Thomas Hofmann. “Beyond Sliding
Windows: Object Localization by Efficient Subwindow Search,” 1–8. IEEE, 2008.
https://doi.org/10.1109/CVPR.2008.4587586.'
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.'
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. Beyond Sliding Windows: Object Localization
by Efficient Subwindow Search. IEEE, 2008, pp. 1–8, doi:10.1109/CVPR.2008.4587586.'
short: C. Lampert, M. Blaschko, T. Hofmann, in:, IEEE, 2008, pp. 1–8.
conference:
name: 'CVPR: Computer Vision and Pattern Recognition'
date_created: 2018-12-11T12:04:46Z
date_published: 2008-09-18T00:00:00Z
date_updated: 2021-01-12T07:51:40Z
day: '18'
doi: 10.1109/CVPR.2008.4587586
extern: 1
main_file_link:
- open_access: '0'
url: http://www.kyb.mpg.de/fileadmin/user_upload/files/publications/pdfs/pdf5070.pdf
month: '09'
page: 1 - 8
publication_status: published
publisher: IEEE
publist_id: '2644'
quality_controlled: 0
status: public
title: 'Beyond sliding windows: Object localization by efficient subwindow search'
type: conference
year: '2008'
...
---
_id: '3716'
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.
In 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.
alternative_title:
- LNCS
author:
- first_name: Christoph
full_name: Christoph Lampert
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
- first_name: Matthew
full_name: Blaschko,Matthew B
last_name: Blaschko
citation:
ama: 'Lampert C, Blaschko M. A multiple kernel learning approach to joint multi-class
object detection. In: Vol 5096. Springer; 2008:31-40. doi:10.1007/978-3-540-69321-5_4'
apa: 'Lampert, C., & 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. https://doi.org/10.1007/978-3-540-69321-5_4'
chicago: Lampert, Christoph, and Matthew Blaschko. “A Multiple Kernel Learning Approach
to Joint Multi-Class Object Detection,” 5096:31–40. Springer, 2008. https://doi.org/10.1007/978-3-540-69321-5_4.
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.'
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.'
mla: Lampert, Christoph, and Matthew Blaschko. A Multiple Kernel Learning Approach
to Joint Multi-Class Object Detection. Vol. 5096, Springer, 2008, pp. 31–40,
doi:10.1007/978-3-540-69321-5_4.
short: C. Lampert, M. Blaschko, in:, Springer, 2008, pp. 31–40.
conference:
name: 'DAGM: German Association For Pattern Recognition'
date_created: 2018-12-11T12:04:46Z
date_published: 2008-07-07T00:00:00Z
date_updated: 2021-01-12T07:51:41Z
day: '07'
doi: 10.1007/978-3-540-69321-5_4
extern: 1
intvolume: ' 5096'
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
month: '07'
page: 31 - 40
publication_status: published
publisher: Springer
publist_id: '2641'
quality_controlled: 0
status: public
title: A multiple kernel learning approach to joint multi-class object detection
type: conference
volume: 5096
year: '2008'
...