Zanvyl Krieger Mind/Brain Institute, Johns Hopkins University, Baltimore, United States.
Division of Neurobiology and Zoology, Department of Biology, University of Kaiserslautern, Kaiserslautern, Germany.
Elife. 2023 Mar 14;12:e77578. doi: 10.7554/eLife.77578.
The ability to associate neutral stimuli with valence information and to store these associations as memories forms the basis for decision making. To determine the underlying computational principles, we build a realistic computational model of a central decision module within the mushroom body (MB), the fly's center for learning and memory. Our model combines the electron microscopy-based architecture of one MB output neuron (MBON-α3), the synaptic connectivity of its 948 presynaptic Kenyon cells (KCs), and its membrane properties obtained from patch-clamp recordings. We show that this neuron is electrotonically compact and that synaptic input corresponding to simulated odor input robustly drives its spiking behavior. Therefore, sparse innervation by KCs can efficiently control and modulate MBON activity in response to learning with minimal requirements on the specificity of synaptic localization. This architecture allows efficient storage of large numbers of memories using the flexible stochastic connectivity of the circuit.
将中性刺激与价值信息联系起来并将这些关联存储为记忆的能力是决策的基础。为了确定潜在的计算原理，我们构建了一个位于果蝇学习和记忆中心的蘑菇体（MB）内中央决策模块的现实计算模型。我们的模型结合了一个 MB 输出神经元（MBON-α3）的基于电子显微镜的结构、948 个突触前肯尼恩细胞（KCs）的突触连接以及从膜片钳记录获得的膜特性。我们表明，该神经元是电紧张的，并且对应于模拟气味输入的突触输入可以强有力地驱动其放电行为。因此，KCs 的稀疏神经支配可以以最小的突触定位特异性要求，有效地控制和调节 MBON 活性以进行学习。这种结构允许使用电路的灵活随机连接来高效地存储大量记忆。
