Low-dimensional chaos maps learning in a model neuropil (olfactory bulb).
Low-dimensional chaos maps learning in a model neuropil (olfactory bulb).
复制标题
低维混沌映射模型神经细胞（嗅球）中的学习。
DOI：
10.1007/bf02691166
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发表时间：
1992
期刊：
影响因子：
--
通讯作者：
Skinner,JE
中科院分区：
文献类型：
--
作者：
Mitra,M;Skinner,JE
混沌系统的量化可以通过计算系统生成的数据的关联维数（D2）来进行（Packard等人，1980年）。然而，D2算法需要发生器的平稳性，这是生物数据很少反映的特征（Mayer-Kress等人，1988年）。因此，我们开发了“点相关维度”（PD 2），这是一种在不同维度的关联数据中准确跟踪D2的算法（Carpeggiani等人，1991年）。我们现在提出一个数学论证，对于平稳数据，单个PD 2收敛到D2，并且我们证明该算法拒绝了噪声突发所做的贡献。在呈现新气味或习惯性气味之前和之后，从清醒兔子的嗅球表面获得数据（64个电极，每个640 Hz，1.3秒时期）。在10个新气味试验中只有1个可以计算出D2，而在所有试验中都可以计算出PD 2。这两种算法都表明，一种新的气味引起空间均匀的尺寸增加。PD 2独特地表现出吸气期间发生的尺寸减小和非刺激对照状态期间存在的平均尺寸梯度。这些控制梯度保持不变，没有气味的经验，但表现出空间特异性PD 2增加气味习惯。这是解释，1）PD 2是敏感的，准确的，和适当的维度评估的生物数据，2）在分析不熟悉的信息，一个单一的记忆过程是短暂的诱发在神经元，和3）经验后，多个空间特定的过程stonically地图的网站学习。
Quantification of a chaotic system can be made by calculating the correlation dimension (D2) of the data that the system generates (Packard et al., 1980). The D2 algorithm, however, requires stationarity of the generator, a feature that biological data rarely reflect (Mayer-Kress et al., 1988). So we developed the “point correlation dimension” (PD2), an algorithm that accurately tracks D2 in linked data of different dimensions (Carpeggiani et al., 1991). We now present a mathematical argument that, for stationary data, individual PD2s converge to D2 and we demonstrate that the algorithm rejects contributions made by bursts of noise. Data were obtained from the surface of the olfactory bulb of the conscious rabbit (64 electrodes, 640 Hz each, 1.3 sec epochs) before and after presentation of a novel or habituated odor. D2 could be calculated in only 1 of 10 novel-odor trials, whereas PD2 could be calculated in all. Both algorithms indicated that a novel odor evokes a spatially uniform dimensional increase. The PD2 uniquely exhibited the dimensional decreases that occur during inspiration and the gradients of mean dimension present during the nonstimulated control state. These control gradients remained unchanged without odor experience, but showed spatially specific PD2 increases following odor habituation. It is interpreted that, 1) the PD2 issensitive, accurate, and appropriatefor dimensional assessment of biological data, 2) that during analysis of unfamiliar information a singleglobal processis transiently evoked in the neuropil, and 3) after experience multiplespatially specific processestonically map the sites of learning.