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稀疏嗅觉表示的最优性不受网络可塑性的影响。

Optimality of sparse olfactory representations is not affected by network plasticity.

机构信息

Division of Biology, Indian Institute of Science Education and Research, Pune, India.

NICHD, National Institutes of Health, Bethesda, Maryland, United States of America.

出版信息

PLoS Comput Biol. 2020 Feb 3;16(2):e1007461. doi: 10.1371/journal.pcbi.1007461. eCollection 2020 Feb.

Abstract

The neural representation of a stimulus is repeatedly transformed as it moves from the sensory periphery to deeper layers of the nervous system. Sparsening transformations are thought to increase the separation between similar representations, encode stimuli with great specificity, maximize storage capacity of associative memories, and provide an energy efficient instantiation of information in neural circuits. In the insect olfactory system, odors are initially represented in the periphery as a combinatorial code with relatively simple temporal dynamics. Subsequently, in the antennal lobe this representation is transformed into a dense and complex spatiotemporal activity pattern. Next, in the mushroom body Kenyon cells (KCs), the representation is dramatically sparsened. Finally, in mushroom body output neurons (MBONs), the representation takes on a new dense spatiotemporal format. Here, we develop a computational model to simulate this chain of olfactory processing from the receptor neurons to MBONs. We demonstrate that representations of similar odorants are maximally separated, measured by the distance between the corresponding MBON activity vectors, when KC responses are sparse. Sparseness is maintained across variations in odor concentration by adjusting the feedback inhibition that KCs receive from an inhibitory neuron, the Giant GABAergic neuron. Different odor concentrations require different strength and timing of feedback inhibition for optimal processing. Importantly, as observed in vivo, the KC-MBON synapse is highly plastic, and, therefore, changes in synaptic strength after learning can change the balance of excitation and inhibition, potentially leading to changes in the distance between MBON activity vectors of two odorants for the same level of KC population sparseness. Thus, what is an optimal degree of sparseness before odor learning, could be rendered sub-optimal post learning. Here, we show, however, that synaptic weight changes caused by spike timing dependent plasticity increase the distance between the odor representations from the perspective of MBONs. A level of sparseness that was optimal before learning remains optimal post-learning.

摘要

刺激的神经表示在从感觉外围向神经系统的更深层移动时会反复转换。稀疏转换被认为可以增加相似表示之间的分离,以极高的特异性编码刺激,最大化联想记忆的存储容量,并为神经回路中的信息提供节能的实例化。在昆虫嗅觉系统中,气味最初在外周作为具有相对简单时间动态的组合代码表示。随后,在触角叶中,这种表示被转换为密集而复杂的时空活动模式。接下来,在蘑菇体 Kenyon 细胞 (KC) 中,该表示急剧稀疏。最后,在蘑菇体输出神经元 (MBON) 中,该表示呈现出新的密集时空格式。在这里,我们开发了一个计算模型来模拟从受体神经元到 MBON 的这种嗅觉处理链。我们证明,当 KC 响应稀疏时,相似气味剂的表示通过对应 MBON 活动向量之间的距离来最大化分离。通过调整 KC 从抑制神经元(Giant GABAergic 神经元)接收的反馈抑制来维持稀疏性,该神经元可适应气味浓度的变化。不同的气味浓度需要不同的反馈抑制强度和时间来实现最佳处理。重要的是,如在体内观察到的那样,KC-MBON 突触具有高度的可塑性,因此,学习后突触强度的变化可以改变兴奋和抑制之间的平衡,这可能导致相同 KC 群体稀疏度下两种气味剂的 MBON 活动向量之间的距离发生变化。因此,在嗅觉学习之前,最佳的稀疏度可能会变得不理想。然而,我们在这里表明,由尖峰时间依赖性可塑性引起的突触权重变化从 MBON 的角度增加了气味表示之间的距离。学习前最佳的稀疏度在学习后仍然最佳。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3799/7028362/98cc5ad72830/pcbi.1007461.g001.jpg

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