The Experts below are selected from a list of 315 Experts worldwide ranked by ideXlab platform
Geoffrey F Woodman - One of the best experts on this subject based on the ideXlab platform.
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visual working Memory buffers information retrieved from visual Long Term Memory
Proceedings of the National Academy of Sciences of the United States of America, 2017Co-Authors: Keisuke Fukuda, Geoffrey F WoodmanAbstract:Human Memory is thought to consist of Long-Term storage and short-Term storage mechanisms, the latter known as working Memory. Although it has Long been assumed that information retrieved from Long-Term Memory is represented in working Memory, we lack neural evidence for this and need neural measures that allow us to watch this retrieval into working Memory unfold with high temporal resolution. Here, we show that human electrophysiology can be used to track information as it is brought back into working Memory during retrieval from Long-Term Memory. Specifically, we found that the retrieval of information from Long-Term Memory was limited to just a few simple objects’ worth of information at once, and elicited a pattern of neurophysiological activity similar to that observed when people encode new information into working Memory. Our findings suggest that working Memory is where information is buffered when being retrieved from Long-Term Memory and reconcile current theories of Memory retrieval with classic notions about the Memory mechanisms involved.
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The role of working Memory and Long-Term Memory in visual search
Visual Cognition, 2006Co-Authors: Geoffrey F Woodman, Marvin M. ChunAbstract:Models of attentional deployment in visual search commonly specify that the short-Term, or working Memory, system plays a central role in biasing attention mechanisms to select task relevant information. In contrast, the role of Long-Term Memory in guiding search is rarely articulated. Our review of recent studies calls for the need to revisit how existing models explain the role of working Memory and Long-Term Memory in search. First, the role of working Memory in guiding attentional selection and search is much more complex than many current theories propose. Second, both explicit and implicit Long-Term Memory representations have such clear influences on visual search performance that they deserve more prominent treatment in theoretical models. These new findings in the literature should stir the conception of new models of visual search.
Bernd Girod - One of the best experts on this subject based on the ideXlab platform.
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Long Term Memory motion compensated prediction for robust video transmission
International Conference on Image Processing, 2000Co-Authors: Thomas Wiegand, N Farber, K Stuhlmuller, Bernd GirodAbstract:Long-Term Memory prediction extends the spatial displacement vector utilized in hybrid video coding by a variable time delay permitting the use of more than one reference frame for motion compensation. This extension provides improved rate-distortion performance. However, motion compensation in combination with transmission errors leads to temporal error propagation that occurs when the reference frames at encoder and decoder differ. In this paper, we present a framework that incorporates an error estimate into rate-constrained motion estimation and mode decision. Experimental results with a Rayleigh fading channel show that Long-Term Memory motion compensation significantly outperforms single-frame prediction.
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Long Term Memory motion compensated prediction
IEEE Transactions on Circuits and Systems for Video Technology, 1999Co-Authors: Thomas Wiegand, Xiaozheng Zhang, Bernd GirodAbstract:Long-Term Memory motion-compensated prediction extends the spatial displacement vector utilized in block-based hybrid video coding by a variable time delay permitting the use of more frames than the previously decoded one for motion compensated prediction. The Long-Term Memory covers several seconds of decoded frames at the encoder and decoder. The use of multiple frames for motion compensation in most cases provides significantly improved prediction gain. The variable time delay has to be transmitted as side information requiring an additional bit rate which may be prohibitive when the size of the Long-Term Memory becomes too large. Therefore, me control the bit rate of the motion information by employing rate constrained motion estimation. Simulation results are obtained by integrating Long-Term Memory prediction into an H.263 codec. Reconstruction PSNR improvements up to 2 dB for the Foreman sequence and 1.5 dB for the Mother-Daughter sequence are demonstrated in comparison to the TMN-2.0 H.263 coder. The PSNR improvements correspond to bit-rate savings up to 34 and 30%, respectively. Mathematical inequalities are used to speed up motion estimation while achieving full prediction gain.
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ICIP (2) - Motion-compensating Long-Term Memory prediction
Proceedings of International Conference on Image Processing, 1Co-Authors: Thomas Wiegand, Xiaozheng Zhang, Bernd GirodAbstract:Motion-compensating Long-Term Memory prediction extends the spatial displacement utilized in block-based hybrid video coding by a variable time delay permitting the use of more frames than the previously decoded one for motion compensation. The Long-Term Memory covers the decoded frames of some seconds at encoder and decoder. We investigate the influence of Memory size in our motion compensation scheme and analyze the trade-off between the bit-rates spent for motion compensated prediction and residual coding. Simulation results are obtained by integrating Long-Term Memory prediction into an H.263 codec. PSNR improvements up to 2 dB for the Foreman sequence and 1.5 dB for the Mother-Daughter sequence are demonstrated in comparison to the TMN-2.0 H.263 coder.
Susumu Tonegawa - One of the best experts on this subject based on the ideXlab platform.
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A clustered plasticity model of Long-Term Memory engrams
Nature Reviews Neuroscience, 2006Co-Authors: Arvind Govindarajan, Raymond J. Kelleher, Susumu TonegawaAbstract:Long-Term Memory formation is associated with bidirectional changes in synaptic strength that require enhanced protein synthesis. Govindarajan, Kelleher and Tonegawa describe a model by which translation-dependent plasticity at synapses that are clustered in a dendritic branch facilitates the formation of Long-Term Memory engrams. Long-Term Memory and its putative synaptic correlates the late phases of both Long-Term potentiation and Long-Term depression require enhanced protein synthesis. On the basis of recent data on translation-dependent synaptic plasticity and on the supralinear effect of activation of nearby synapses on action potential generation, we propose a model for the formation of Long-Term Memory engrams at the single neuron level. In this model, which we call clustered plasticity, local translational enhancement, aLong with synaptic tagging and capture, facilitates the formation of Long-Term Memory engrams through bidirectional synaptic weight changes among synapses within a dendritic branch.
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A clustered plasticity model of Long-Term Memory engrams.
Nature Reviews Neuroscience, 2006Co-Authors: Arvind Govindarajan, Raymond J. Kelleher, Susumu TonegawaAbstract:Long-Term Memory and its putative synaptic correlates the late phases of both Long-Term potentiation and Long-Term depression require enhanced protein synthesis. On the basis of recent data on translation-dependent synaptic plasticity and on the supralinear effect of activation of nearby synapses on action potential generation, we propose a model for the formation of Long-Term Memory engrams at the single neuron level. In this model, which we call clustered plasticity, local translational enhancement, aLong with synaptic tagging and capture, facilitates the formation of Long-Term Memory engrams through bidirectional synaptic weight changes among synapses within a dendritic branch.
J.m. Wojtowicz - One of the best experts on this subject based on the ideXlab platform.
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A role for adult neurogenesis in spatial Long-Term Memory.
Neuroscience, 2005Co-Authors: Jason S. Snyder, Nancy S. Hong, Robert J. Mcdonald, J.m. WojtowiczAbstract:Adult hippocampal neurogenesis has been linked to learning but details of the relationship between neuronal production and Memory formation remain unknown. Using low dose irradiation to inhibit adult hippocampal neurogenesis we show that new neurons aged 4-28 days old at the time of training are required for Long-Term Memory in a spatial version of the water maze. This effect of irradiation was specific since Long-Term Memory for a visibly cued platform remained intact. Furthermore, irradiation just before or after water maze training had no effect on learning or Long-Term Memory. Relationships between learning and new neuron survival, as well as proliferation, were investigated but found non-significant. These results suggest a new role for adult neurogenesis in the formation and/or consolidation of Long-Term, hippocampus-dependent, spatial memories.
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a role for adult neurogenesis in spatial Long Term Memory
Neuroscience, 2005Co-Authors: Jason S. Snyder, Nancy S. Hong, Robert J. Mcdonald, J.m. WojtowiczAbstract:Adult hippocampal neurogenesis has been linked to learning but details of the relationship between neuronal production and Memory formation remain unknown. Using low dose irradiation to inhibit adult hippocampal neurogen- esis we show that new neurons aged 4-28 days old at the time of training are required for Long-Term Memory in a spatial version of the water maze. This effect of irradiation was specific since Long-Term Memory for a visibly cued platform remained intact. Furthermore, irradiation just before or after water maze training had no effect on learning or Long-Term Memory. Relationships between learning and new neuron survival, as well as proliferation, were investigated but found non-significant. These results suggest a new role for adult neurogenesis in the formation and/or consolidation of Long- Term, hippocampus-dependent, spatial memories. © 2004 IBRO. Published by Elsevier Ltd. All rights reserved.
Thomas Wiegand - One of the best experts on this subject based on the ideXlab platform.
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Long Term Memory motion compensated prediction for robust video transmission
International Conference on Image Processing, 2000Co-Authors: Thomas Wiegand, N Farber, K Stuhlmuller, Bernd GirodAbstract:Long-Term Memory prediction extends the spatial displacement vector utilized in hybrid video coding by a variable time delay permitting the use of more than one reference frame for motion compensation. This extension provides improved rate-distortion performance. However, motion compensation in combination with transmission errors leads to temporal error propagation that occurs when the reference frames at encoder and decoder differ. In this paper, we present a framework that incorporates an error estimate into rate-constrained motion estimation and mode decision. Experimental results with a Rayleigh fading channel show that Long-Term Memory motion compensation significantly outperforms single-frame prediction.
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Long Term Memory motion compensated prediction
IEEE Transactions on Circuits and Systems for Video Technology, 1999Co-Authors: Thomas Wiegand, Xiaozheng Zhang, Bernd GirodAbstract:Long-Term Memory motion-compensated prediction extends the spatial displacement vector utilized in block-based hybrid video coding by a variable time delay permitting the use of more frames than the previously decoded one for motion compensated prediction. The Long-Term Memory covers several seconds of decoded frames at the encoder and decoder. The use of multiple frames for motion compensation in most cases provides significantly improved prediction gain. The variable time delay has to be transmitted as side information requiring an additional bit rate which may be prohibitive when the size of the Long-Term Memory becomes too large. Therefore, me control the bit rate of the motion information by employing rate constrained motion estimation. Simulation results are obtained by integrating Long-Term Memory prediction into an H.263 codec. Reconstruction PSNR improvements up to 2 dB for the Foreman sequence and 1.5 dB for the Mother-Daughter sequence are demonstrated in comparison to the TMN-2.0 H.263 coder. The PSNR improvements correspond to bit-rate savings up to 34 and 30%, respectively. Mathematical inequalities are used to speed up motion estimation while achieving full prediction gain.
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ICIP (2) - Motion-compensating Long-Term Memory prediction
Proceedings of International Conference on Image Processing, 1Co-Authors: Thomas Wiegand, Xiaozheng Zhang, Bernd GirodAbstract:Motion-compensating Long-Term Memory prediction extends the spatial displacement utilized in block-based hybrid video coding by a variable time delay permitting the use of more frames than the previously decoded one for motion compensation. The Long-Term Memory covers the decoded frames of some seconds at encoder and decoder. We investigate the influence of Memory size in our motion compensation scheme and analyze the trade-off between the bit-rates spent for motion compensated prediction and residual coding. Simulation results are obtained by integrating Long-Term Memory prediction into an H.263 codec. PSNR improvements up to 2 dB for the Foreman sequence and 1.5 dB for the Mother-Daughter sequence are demonstrated in comparison to the TMN-2.0 H.263 coder.
