期刊
FRONTIERS IN COMPUTATIONAL NEUROSCIENCE
卷 6, 期 -, 页码 -出版社
FRONTIERS MEDIA SA
DOI: 10.3389/fncom.2012.00036
关键词
plasticity; synaptic scaling; neural network; homeostasis; synapse; signal propagation
资金
- European Community [270273]
- Federal Ministry of Education and Research (BMBF)
- Bernstein Center for Computational Neuroscience (BCCN) - Gottingen [01GQ1005A, 01GQ1005B]
- Max Planck Research School for Physics of Biological and Complex Systems
Conventional synaptic plasticity in combination with synaptic scaling is a biologically plausible plasticity rule that guides the development of synapses toward stability. Here we analyze the development of synaptic connections and the resulting activity patterns in different feed-forward and recurrent neural networks, with plasticity and scaling. We show under which constraints an external input given to a feed-forward network forms an input trace similar to a cell assembly (Hebb, 1949) by enhancing synaptic weights to larger stable values as compared to the rest of the network. For instance, a weak input creates a less strong representation in the network than a strong input which produces a trace along large parts of the network. These processes are strongly influenced by the underlying connectivity. For example, when embedding recurrent structures (excitatory rings, etc.) into a feed-forward network, the input trace is extended into more distant layers, while inhibition shortens it. These findings provide a better understanding of the dynamics of generic network structures where plasticity is combined with scaling. This makes it also possible to use this rule for constructing an artificial network with certain desired storage properties.
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