Redistribution of synaptic efficacy supports stable pattern learning in neural networks
Redistribution of synaptic efficacy supports stable pattern learning in neural networks
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DOI：
10.1162/089976602317318992
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发表时间：
2002-04-01
影响因子：
2.9
通讯作者：
Milenova, BL
中科院分区：
文献类型：
--
作者：
Carpenter, GA;Milenova, BL
Markram和Tsodyks证明，通过单脉冲长时程增强(LTP)测量观察到的突触效率升高随着更高频率的测试脉冲而消失，他们对LTP反映一般增益增加的传统假设提出了严重挑战。在突触增强过程中观察到的这种频率依赖性的变化称为突触效能的重新分配(RSE)。RSE在这里被视为用于模式编码的神经网络中全局设计原则的局部实现。基本的计算模型设定了一个自适应阈值，而不是乘性权重作为长期记忆的基本单位。分布式的龄期学习规律只允许阈值单调增加，但适应对模型突触后潜力有双向影响。在每个突触，阈值增加通过频率相关的信号成分实现模式选择性，而互补的频率无关成分非特异性地加强路径。这种突触平衡产生频率依赖性的变化，这与Markram和Tsodyks观察到的变化非常相似。因此，网络设计建议了RSE的功能目的，它通过帮助限制总的存储器变化，支持稳定的、具有快学习和慢学习的分布式编码方案。几十年来，乘性权重一直是生理数据和神经系统模型的基石。尽管这里讨论的模型没有实现突触传递的详细生理学，但它的新学习法则在一个网络体系结构中运行，该体系结构表明最近发现的突触计算(如RSE)可能有助于产生新的网络能力，如快速、稳定和分布式的学习。
Markram and Tsodyks, by showing that the elevated synaptic efficacy observed with single-pulse long-term potentiation (LTP) measurements disappears with higher-frequency test pulses, have critically challenged the conventional assumption that LTP reflects a general gain increase. This observed change in frequency dependence during synaptic potentiation is called redistribution of synaptic efficacy (RSE). RSE is here seen as the local realization of a global design principle in a neural network for pattern coding. The underlying computational model posits an adaptive threshold rather than a multiplicative weight as the elementary unit of long-term memory. A distributed instar learning law allows thresholds to increase only monotonically, but adaptation has a bidirectional effect on the model postsynaptic potential. At each synapse, threshold increases implement pattern selectivity via a frequency-dependent signal component, while a complementary frequency-independent component nonspecifically strengthens the path. This synaptic balance produces changes in frequency dependence that are robustly similar to those observed by Markram and Tsodyks. The network design therefore suggests a functional purpose for RSE, which, by helping to bound total memory change, supports a distributed coding scheme that is stable with fast as well as slow learning. Multiplicative weights have served as a cornerstone for models of physiological data and neural systems for decades. Although the model discussed here does not implement detailed physiology of synaptic transmission, its new learning laws operate in a network architecture that suggests how recently discovered synaptic computations such as RSE may help produce new network capabilities such as learning that is fast, stable, and distributed.