The Experts below are selected from a list of 277218 Experts worldwide ranked by ideXlab platform

Karel Svoboda - One of the best experts on this subject based on the ideXlab platform.

  • locally dynamic synaptic learning rules in pyramidal neuron dendrites
    Nature, 2007
    Co-Authors: Christopher D Harvey, Karel Svoboda
    Abstract:

    Long-term potentiation (LTP) of synaptic transmission underlies aspects of learning and memory. LTP is input-specific at the level of individual synapses, but neural network models predict interactions between plasticity at nearby synapses. Here we show in mouse hippocampal pyramidal cells that LTP at individual synapses reduces the threshold for potentiation at neighbouring synapses. After input-specific LTP induction by two-photon glutamate uncaging or by synaptic stimulation, subthreshold stimuli, which by themselves were too weak to trigger LTP, caused robust LTP and spine enlargement at neighbouring spines. Furthermore, LTP induction broadened the presynaptic-postsynaptic spike interval for spike-timing-dependent LTP within a dendritic neighbourhood. The reduction in the threshold for LTP induction lasted approximately 10 min and spread over approximately 10 microm of dendrite. These local interactions between neighbouring synapses support clustered plasticity models of memory storage and could allow for the binding of behaviourally Linked Information on the same dendritic branch.

  • locally dynamic synaptic learning rules in pyramidal neuron dendrites
    Nature, 2007
    Co-Authors: Christopher D Harvey, Karel Svoboda
    Abstract:

    Long-term potentiation (LTP) of synaptic transmission underlies aspects of learning and memory. LTP is input-specific at the level of individual synapses, but neural network models predict interactions between plasticity at nearby synapses. Here we show in mouse hippocampal pyramidal cells that LTP at individual synapses reduces the threshold for potentiation at neighbouring synapses. After input-specific LTP induction by two-photon glutamate uncaging or by synaptic stimulation, subthreshold stimuli, which by themselves were too weak to trigger LTP, caused robust LTP and spine enlargement at neighbouring spines. Furthermore, LTP induction broadened the presynaptic–postsynaptic spike interval for spike-timing-dependent LTP within a dendritic neighbourhood. The reduction in the threshold for LTP induction lasted ∼10 min and spread over ∼10 µm of dendrite. These local interactions between neighbouring synapses support clustered plasticity models of memory storage and could allow for the binding of behaviourally Linked Information on the same dendritic branch. Use of neuronal imaging and photoactivation techniques on hippocampal pyramidal dendrites has shown that after long-term potentiation induction at individual synapses, neighbouring synapses become more easily potentiated, and across a broader time window. These results offer a new scale of Information integration available for those modelling the cellular processes underlying brain function.

Christopher D Harvey - One of the best experts on this subject based on the ideXlab platform.

  • locally dynamic synaptic learning rules in pyramidal neuron dendrites
    Nature, 2007
    Co-Authors: Christopher D Harvey, Karel Svoboda
    Abstract:

    Long-term potentiation (LTP) of synaptic transmission underlies aspects of learning and memory. LTP is input-specific at the level of individual synapses, but neural network models predict interactions between plasticity at nearby synapses. Here we show in mouse hippocampal pyramidal cells that LTP at individual synapses reduces the threshold for potentiation at neighbouring synapses. After input-specific LTP induction by two-photon glutamate uncaging or by synaptic stimulation, subthreshold stimuli, which by themselves were too weak to trigger LTP, caused robust LTP and spine enlargement at neighbouring spines. Furthermore, LTP induction broadened the presynaptic-postsynaptic spike interval for spike-timing-dependent LTP within a dendritic neighbourhood. The reduction in the threshold for LTP induction lasted approximately 10 min and spread over approximately 10 microm of dendrite. These local interactions between neighbouring synapses support clustered plasticity models of memory storage and could allow for the binding of behaviourally Linked Information on the same dendritic branch.

  • locally dynamic synaptic learning rules in pyramidal neuron dendrites
    Nature, 2007
    Co-Authors: Christopher D Harvey, Karel Svoboda
    Abstract:

    Long-term potentiation (LTP) of synaptic transmission underlies aspects of learning and memory. LTP is input-specific at the level of individual synapses, but neural network models predict interactions between plasticity at nearby synapses. Here we show in mouse hippocampal pyramidal cells that LTP at individual synapses reduces the threshold for potentiation at neighbouring synapses. After input-specific LTP induction by two-photon glutamate uncaging or by synaptic stimulation, subthreshold stimuli, which by themselves were too weak to trigger LTP, caused robust LTP and spine enlargement at neighbouring spines. Furthermore, LTP induction broadened the presynaptic–postsynaptic spike interval for spike-timing-dependent LTP within a dendritic neighbourhood. The reduction in the threshold for LTP induction lasted ∼10 min and spread over ∼10 µm of dendrite. These local interactions between neighbouring synapses support clustered plasticity models of memory storage and could allow for the binding of behaviourally Linked Information on the same dendritic branch. Use of neuronal imaging and photoactivation techniques on hippocampal pyramidal dendrites has shown that after long-term potentiation induction at individual synapses, neighbouring synapses become more easily potentiated, and across a broader time window. These results offer a new scale of Information integration available for those modelling the cellular processes underlying brain function.

J. Schneeberger - One of the best experts on this subject based on the ideXlab platform.

  • IV - Visualizing and exploring large networked Information spaces with matrix browser
    Proceedings Sixth International Conference on Information Visualisation, 2002
    Co-Authors: E. Ziegler, Christoph Kunz, Veit Botsch, J. Schneeberger
    Abstract:

    This paper presents a new approach for visualizing and exploring large networked Information structures which may represent, for instance, Linked Information resources or metadata structures such as ontologies. An interactive matrix display is used for showing relations between concepts and concept hierarchies displayed along the two axes of a matrix. The new approach also focuses on the engineering process to create these Information networks. Initial user testing shows performance advantages as well as reduced visual search in comparison to conventional graph representations.

E. Ziegler - One of the best experts on this subject based on the ideXlab platform.

  • IV - Visualizing and exploring large networked Information spaces with matrix browser
    Proceedings Sixth International Conference on Information Visualisation, 2002
    Co-Authors: E. Ziegler, Christoph Kunz, Veit Botsch, J. Schneeberger
    Abstract:

    This paper presents a new approach for visualizing and exploring large networked Information structures which may represent, for instance, Linked Information resources or metadata structures such as ontologies. An interactive matrix display is used for showing relations between concepts and concept hierarchies displayed along the two axes of a matrix. The new approach also focuses on the engineering process to create these Information networks. Initial user testing shows performance advantages as well as reduced visual search in comparison to conventional graph representations.

Veit Botsch - One of the best experts on this subject based on the ideXlab platform.

  • IV - Visualizing and exploring large networked Information spaces with matrix browser
    Proceedings Sixth International Conference on Information Visualisation, 2002
    Co-Authors: E. Ziegler, Christoph Kunz, Veit Botsch, J. Schneeberger
    Abstract:

    This paper presents a new approach for visualizing and exploring large networked Information structures which may represent, for instance, Linked Information resources or metadata structures such as ontologies. An interactive matrix display is used for showing relations between concepts and concept hierarchies displayed along the two axes of a matrix. The new approach also focuses on the engineering process to create these Information networks. Initial user testing shows performance advantages as well as reduced visual search in comparison to conventional graph representations.

  • CHI Extended Abstracts - Matrix browser: visualizing and exploring large networked Information spaces
    CHI '02 extended abstracts on Human factors in computing systems - CHI '02, 2002
    Co-Authors: Jürgen Ziegler, Christoph Kunz, Veit Botsch
    Abstract:

    We present a new approach for visualizing and exploring large networked Information structures which may represent, for instance, Linked Information resources or metadata structures such as ontologies. An interactive matrix display is used for showing relations between concepts and concept hierarchies displayed along the two axes of a matrix. Initial user testing shows performance advantages as well as reduced visual search in comparison to conventional graph representations.