<p><b>a.</b> The <i>effective</i> learning rate of a downstream synapse depends on the transfer process itself. Increasing the number of transfer repetitions increases this rate leading to faster learning and faster forgetting. Shown is SNR of each of the first two stages. Symbols are averages of ten simulations, lines are from the mean-field model, see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003146#s4" target="_blank"><i>Methods</i></a>. Here , , , and which gives and when . <b>b.</b> The neuronal model is well described by the mean-field synaptic model. There are 10 stages, each with all-to-all connected neurons. Parameters are chosen such that transfer rates are . The solid line is for a in the m...
Memories are stored, retained, and recollected through complex, coupled processes operating on multi...
<p><b>A,B.</b> Top: The – curves (green) for GS neurons ( pS/µm<sup>2</sup> and pS/µm<sup>2</sup>) ...
<p>(A) Raster plot showing network spiking during learning () and auto-associative recall (). The ve...
<p><b>a.</b> Upper left: In the model, the state of each synapse in stage one is updated stochastica...
<p><b>a.</b> The SNR for two values of for a fixed number of synapses (solid lines: consolidation m...
<p><b>a</b> A schematic representation of the neural network architecture. Here we show stage 1 and ...
<p>(<b>A</b>) The network consists of a square grid of units with periodic boundary conditions in b...
A growing body of research indicates that structural plasticity mechanisms are crucial for learning ...
Learning is not a isolated event, as nearly every encoding event occurs on a backdrop of previous kn...
Synaptic plasticity, a key process for memory formation, manifests itself across different time scal...
How humans are able to learn and memorize is a long-standing question in science. Much progress has ...
<p><b>A</b>, State/transition model of a single potential synapse (see text for details). <b>B</b>, ...
editorial reviewedNeurons adapt their connections with each other through synaptic plasticity, drive...
Long-term memories are likely stored in the synaptic weights of neuronal networks in the brain. The ...
New experiences can be memorized by modifying the synaptic efficacies. Old memories are partially ov...
Memories are stored, retained, and recollected through complex, coupled processes operating on multi...
<p><b>A,B.</b> Top: The – curves (green) for GS neurons ( pS/µm<sup>2</sup> and pS/µm<sup>2</sup>) ...
<p>(A) Raster plot showing network spiking during learning () and auto-associative recall (). The ve...
<p><b>a.</b> Upper left: In the model, the state of each synapse in stage one is updated stochastica...
<p><b>a.</b> The SNR for two values of for a fixed number of synapses (solid lines: consolidation m...
<p><b>a</b> A schematic representation of the neural network architecture. Here we show stage 1 and ...
<p>(<b>A</b>) The network consists of a square grid of units with periodic boundary conditions in b...
A growing body of research indicates that structural plasticity mechanisms are crucial for learning ...
Learning is not a isolated event, as nearly every encoding event occurs on a backdrop of previous kn...
Synaptic plasticity, a key process for memory formation, manifests itself across different time scal...
How humans are able to learn and memorize is a long-standing question in science. Much progress has ...
<p><b>A</b>, State/transition model of a single potential synapse (see text for details). <b>B</b>, ...
editorial reviewedNeurons adapt their connections with each other through synaptic plasticity, drive...
Long-term memories are likely stored in the synaptic weights of neuronal networks in the brain. The ...
New experiences can be memorized by modifying the synaptic efficacies. Old memories are partially ov...
Memories are stored, retained, and recollected through complex, coupled processes operating on multi...
<p><b>A,B.</b> Top: The – curves (green) for GS neurons ( pS/µm<sup>2</sup> and pS/µm<sup>2</sup>) ...
<p>(A) Raster plot showing network spiking during learning () and auto-associative recall (). The ve...