Memory Capacities for Synaptic and Structural Plasticity

By Andreas Knoblauch, Günther Palm, Friedrich T. Sommer. 2010

144 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Electrical and Electronic Engineering

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Citations in the corpus

Structural Synaptic Plasticity Has High Memory Capacity and Can Explain Graded Amnesia, Catastrophic Forgetting, and the Spacing Effect (2014, methodology): “7) and receiving v̂j (for details see appendix A in [16]).”
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].”
Spike-Timing Dependence of Structural Plasticity Explains Cooperative Synapse Formation in the Neocortex (2012, background): “According to theoretical studies on associative networks, structural plasticity enhances the memory capacity of a network substantially [14,15], and has been shown to be related to motor learning in the brain [16].”
Does Spike-Timing-Dependent Synaptic Plasticity Couple or Decouple Neurons Firing in Synchrony? (2012, background): “As long as connectivity analyses do not include individual synaptic strengths, remaining differences in connectivity patterns may also attributable to differences in the dynamics of structural plasticity (Holtmaat and Svoboda, 2009; Knoblauch, 2009; Knoblauch et al., 2010; Fu and Zuo, 2011), i.”
Cliques of Neurons Bound into Cavities Provide a Missing Link between Structure and Function (2016, background): “We conjecture that a stimulus may be processed by binding neurons into cliques of increasingly higher dimension, as a specific class of cell assemblies, possibly to represent features of the stimulus (Hebb, 1949; Braitenberg, 1978), and by binding these cliques into cavities of increasing complexity”
Neural Associative Memory with Optimal Bayesian Learning (2011, methodology): “…the computational resources required to represent the resulting network, the Willshaw model outperforms all other models due to the binary weights (Knoblauch et al., 2010): For implementations on digital hardware, the Willshaw model can reach the theoretical maximum of C I = 1 bit per computer…”