Hendin O, Horn D, Tsodyks M V
School of Physics and Astronomy, Raymond and Beverly Sackler Faculty of Exact Sciences, Tel-Aviv University, Israel.
J Comput Neurosci. 1998 May;5(2):157-69. doi: 10.1023/a:1008813915992.
We discuss the first few stages of olfactory processing in the framework of a layered neural network. Its central component is an oscillatory associative memory, describing the external plexiform layer, that consists of inhibitory and excitatory neurons with dendrodendritic interactions. We explore the computational properties of this neural network and point out its possible functional role in the olfactory bulb. When receiving a complex input that is composed of several odors, the network segments it into its components. This is done in two stages. First, multiple odor input is preprocessed in the glomerular layer via a decorrelation mechanism that relies on temporal independence of odor sources. Second, as the recall process of a pattern consists of associative convergence to an oscillatory attractor, multiple inputs are identified by alternate dominance of memory patterns during different sniff cycles. This could explain how quick analysis of mixed odors is subserved by the rapid sniffing behavior of highly olfactory animals. When one of the odors is much stronger than the rest, the network converges onto it, thus displaying odor masking.
我们在分层神经网络的框架下讨论嗅觉处理的最初几个阶段。其核心组件是一个振荡联想记忆，描述外部丛状层，它由具有树突 - 树突相互作用的抑制性和兴奋性神经元组成。我们探索了这个神经网络的计算特性，并指出其在嗅球中可能的功能作用。当接收到由几种气味组成的复杂输入时，该网络将其分解为各个成分。这分两个阶段完成。首先，多个气味输入在肾小球层通过一种去相关机制进行预处理，该机制依赖于气味源的时间独立性。其次，由于模式的召回过程包括向振荡吸引子的联想收敛，多个输入在不同的嗅吸周期中通过记忆模式的交替主导来识别。这可以解释高度嗅觉灵敏的动物的快速嗅吸行为是如何实现对混合气味的快速分析的。当其中一种气味比其他气味强烈得多时，网络会收敛到该气味上，从而表现出气味掩盖现象。
