Department of Computational Biology, School of Computer Science and Communication, Royal Institute of Technology Stockholm, Sweden ; Stockholm Brain Institute, Karolinska Institute Stockholm, Sweden.
Department of Computational Biology, School of Computer Science and Communication, Royal Institute of Technology Stockholm, Sweden ; Stockholm Brain Institute, Karolinska Institute Stockholm, Sweden ; Department of Numerical Analysis and Computer Science, Stockholm University Stockholm, Sweden.
Front Neural Circuits. 2014 Feb 7;8:5. doi: 10.3389/fncir.2014.00005. eCollection 2014.
Olfactory sensory information passes through several processing stages before an odor percept emerges. The question how the olfactory system learns to create odor representations linking those different levels and how it learns to connect and discriminate between them is largely unresolved. We present a large-scale network model with single and multi-compartmental Hodgkin-Huxley type model neurons representing olfactory receptor neurons (ORNs) in the epithelium, periglomerular cells, mitral/tufted cells and granule cells in the olfactory bulb (OB), and three types of cortical cells in the piriform cortex (PC). Odor patterns are calculated based on affinities between ORNs and odor stimuli derived from physico-chemical descriptors of behaviorally relevant real-world odorants. The properties of ORNs were tuned to show saturated response curves with increasing concentration as seen in experiments. On the level of the OB we explored the possibility of using a fuzzy concentration interval code, which was implemented through dendro-dendritic inhibition leading to winner-take-all like dynamics between mitral/tufted cells belonging to the same glomerulus. The connectivity from mitral/tufted cells to PC neurons was self-organized from a mutual information measure and by using a competitive Hebbian-Bayesian learning algorithm based on the response patterns of mitral/tufted cells to different odors yielding a distributed feed-forward projection to the PC. The PC was implemented as a modular attractor network with a recurrent connectivity that was likewise organized through Hebbian-Bayesian learning. We demonstrate the functionality of the model in a one-sniff-learning and recognition task on a set of 50 odorants. Furthermore, we study its robustness against noise on the receptor level and its ability to perform concentration invariant odor recognition. Moreover, we investigate the pattern completion capabilities of the system and rivalry dynamics for odor mixtures.
嗅觉感觉信息在产生气味知觉之前要经过几个处理阶段。嗅觉系统如何学习创建将不同层次联系起来的气味表示，以及如何学习连接和区分它们，这是一个很大的未解之谜。我们提出了一个大规模的网络模型，该模型使用单个和多室 Hodgkin-Huxley 型神经元来表示上皮细胞中的嗅觉受体神经元 (ORNs)、嗅小球 (OB) 中的近球细胞、僧帽细胞和颗粒细胞，以及梨状皮层 (PC) 中的三种皮质细胞。气味模式是根据 ORNs 与气味刺激物之间的亲和力计算的，这些气味刺激物源自行为相关真实气味的物理化学描述符。ORNs 的特性被调整为表现出随着浓度增加而饱和的响应曲线，这与实验中观察到的一致。在 OB 水平上，我们探索了使用模糊浓度间隔码的可能性，该代码通过树突-树突抑制来实现，导致属于同一肾小球的僧帽细胞/丛细胞产生类似于胜者全得的动力学。从僧帽细胞/丛细胞到 PC 神经元的连接是从互信息测度自组织的，并通过使用基于对不同气味的僧帽细胞/丛细胞反应模式的竞争Hebbian-Bayesian 学习算法来实现，从而产生到 PC 的分布式前馈投影。PC 作为一个模块化吸引器网络实现，具有通过Hebbian-Bayesian 学习组织的递归连接。我们在一组 50 种气味的单次嗅探学习和识别任务中展示了该模型的功能。此外，我们研究了其在受体水平上对噪声的鲁棒性及其执行浓度不变的气味识别的能力。此外，我们研究了系统的模式完成能力和气味混合物的竞争动态。
