Learning by structural remodeling in a class of single cell models
Learning by structural remodeling in a class of single cell models
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DOI：
10.1007/s10827-008-0078-6
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发表时间：
2008-10-01
影响因子：
1.2
通讯作者：
Josic, K.
中科院分区：
文献类型：
--
作者：
Kelleher, K. J.;Hajdik, V.;Josic, K.
神经连接的变化被认为是大脑中最持久的记忆形式的基础。我们考虑两个模型，来自clusteron（梅尔，先进的神经推理过程系统4：35-42，1992），研究这种方法的学习。这些模型显示了记忆获得的速度和形成适当突触连接的概率之间的直接关系。此外，学习联想的强度随着参与学习过程的纤维数量的增加而增加。我们通过分析突触激活的分布，对这两个结果提供了简单直观的解释。然后使用获得的见解来扩展模型以执行新的任务：特征检测和学习时空模式。我们还提供了一个易于分析的近似模型，把这些意见的坚实基础。的数值和分析模型的行为以及相关的学习任务，被认为是需要一个重组的神经网络的实验结果。
Changes in neural connectivity are thought to underlie the most permanent forms of memory in the brain. We consider two models, derived from the clusteron (Mel, Adv Neural Inf Process Syst 4:35-42, 1992), to study this method of learning. The models show a direct relationship between the speed of memory acquisition and the probability of forming appropriate synaptic connections. Moreover, the strength of learned associations grows with the number of fibers that have taken part in the learning process. We provide simple and intuitive explanations of these two results by analyzing the distribution of synaptic activations. The obtained insights are then used to extend the model to perform novel tasks: feature detection, and learning spatio-temporal patterns. We also provide an analytically tractable approximation to the model to put these observations on a firm basis. The behavior of both the numerical and analytical models correlate well with experimental results of learning tasks which are thought to require a reorganization of neuronal networks.