THE ROLE OF STRUCTURAL PLASTICITY AND SYNAPTIC CONSOLIDATION FOR MEMORY AND AMNESIA IN A MODEL OF CORTICO-HIPPOCAMPAL INTERPLAY

By Andreas Knoblauch. 2009

14 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Statistical and Nonlinear Physics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Friedrich T. Sommer
2. Günther Palm

In Statistical and Nonlinear Physics

1. Friedrich T. Sommer
2. Günther Palm
3. Markus Diesmann
4. Moritz Helias

Citations in the corpus

• Structural Synaptic Plasticity Has High Memory Capacity and Can Explain Graded Amnesia, Catastrophic Forgetting, and the Spacing Effect (2014, background): “In face of these limitation, it might be beneficial that learning in brain circuits ‘‘moves’’ synapses to computationally useful locations [16,31,53,113].”
• Memory Capacities for Synaptic and Structural Plasticity (2010, background): “This process can lead to a sparsely connected neural network in which each synapse carries a large amount of information about previously learned patterns (Knoblauch, 2009).”
• Zip nets: Efficient associative computation with binary synapses (2010, unknown): “(7)”
• Efficient Associative Computation with Discrete Synapses (2016, background): “These arguments hold true for both computer implementations and variants modeling structural plasticity in the brain (Knoblauch, 2009b; Knoblauch et al., 2010, 2014) where energy consumption is dominated by the number of (nonzero) excitatory postsynaptic potentials corresponding to non-silent synaps”
• Spike-Timing Dependence of Structural Plasticity Explains Cooperative Synapse Formation in the Neocortex (2012, background): “A similar model has previously been proposed in the context of associative networks [44,45].”
• Optimal synaptic learning in non-linear associative memory (2010, background): “…in similar network models by structural plasticity (meaning the elimination and generation of synapses [60]) yields a much higher diverging synaptic capacity where the information that a binary synapse can store scales with the logarithm of the network size n [61], [62], [63], [64], [29], [65].”
• Neural Associative Memory with Optimal Bayesian Learning (2011, methodology): “Some preliminary work (Knoblauch, 2009c, 2010b) indicates that the Bayesian associative memory can be efficiently discretized such D ow nloaded from http://direct.m it.edu/neco/article-pdf/23/6/1393/854213/neco_a_00127.pdf by guest on 01 July 2021 that structurally compressed network…”