Computational principles of memory

被引:151
作者
Chaudhuri, Rishidev
Fiete, Ila [1 ]
机构
[1] Univ Texas Austin, Ctr Learning & Memory, Austin, TX 78712 USA
关键词
PERSISTENT NEURAL ACTIVITY; SPATIAL WORKING-MEMORY; SYNAPTIC PLASTICITY; ATTRACTOR DYNAMICS; NETWORK DYNAMICS; MODEL; MAINTENANCE; PROTEIN; INFORMATION; MECHANISMS;
D O I
10.1038/nn.4237
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
The ability to store and later use information is essential for a variety of adaptive behaviors, including integration, learning, generalization, prediction and inference. In this Review, we survey theoretical principles that can allow the brain to construct persistent states for memory. We identify requirements that a memory system must satisfy and analyze existing models and hypothesized biological substrates in light of these requirements. We also highlight open questions, theoretical puzzles and problems shared with computer science and information theory.
引用
收藏
页码:394 / 403
页数:10
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