Liu Yuxuan, Li Qianyi, Tang Chao, Qin Shanshan, Tu Yuhai
School of Physics, Peking University, Beijing, China.
Integrated Science Program, Yuanpei College, Peking University, Beijing, China.
Front Comput Neurosci. 2021 Oct 22;15:730431. doi: 10.3389/fncom.2021.730431. eCollection 2021.
In , olfactory information received by olfactory receptor neurons (ORNs) is first processed by an incoherent feed forward neural circuit in the antennal lobe (AL) that consists of ORNs (input), inhibitory local neurons (LNs), and projection neurons (PNs). This "early" olfactory information processing has two important characteristics. First, response of a PN to its cognate ORN is normalized by the overall activity of other ORNs, a phenomenon termed "divisive normalization." Second, PNs respond strongly to the onset of ORN activities, but they adapt to prolonged or continuously varying inputs. Despite the importance of these characteristics for learning and memory, their underlying mechanisms are not fully understood. Here, we develop a circuit model for describing the ORN-LN-PN dynamics by including key neuron-neuron interactions such as short-term plasticity (STP) and presynaptic inhibition (PI). By fitting our model to experimental data quantitatively, we show that a strong STP balanced between short-term facilitation (STF) and short-term depression (STD) is responsible for the observed nonlinear divisive normalization in . Our circuit model suggests that either STP or PI alone can lead to adaptive response. However, by comparing our model results with experimental data, we find that both STP and PI work together to achieve a strong and robust adaptive response. Our model not only helps reveal the mechanisms underlying two main characteristics of the early olfactory process, it can also be used to predict PN responses to arbitrary time-dependent signals and to infer microscopic properties of the circuit (such as the strengths of STF and STD) from the measured input-output relation. Our circuit model may be useful for understanding the role of STP in other sensory systems.
在[具体情境未提及]中，嗅觉受体神经元（ORN）接收到的嗅觉信息首先由触角叶（AL）中的非相干前馈神经回路进行处理，该回路由ORN（输入）、抑制性局部神经元（LN）和投射神经元（PN）组成。这种“早期”嗅觉信息处理具有两个重要特征。首先，PN对其同源ORN的反应会被其他ORN的整体活动归一化，这一现象被称为“分裂归一化”。其次，PN对ORN活动的起始有强烈反应，但它们会适应长时间或持续变化的输入。尽管这些特征对学习和记忆很重要，但其潜在机制尚未完全理解。在这里，我们通过纳入关键的神经元 - 神经元相互作用，如短期可塑性（STP）和突触前抑制（PI），开发了一个用于描述ORN - LN - PN动态的电路模型。通过将我们的模型与实验数据进行定量拟合，我们表明在[具体情境未提及]中观察到的非线性分裂归一化是由短期促进（STF）和短期抑制（STD）之间平衡的强STP所导致的。我们的电路模型表明，单独的STP或PI都可以导致适应性反应。然而，通过将我们的模型结果与实验数据进行比较，我们发现STP和PI共同作用以实现强烈而稳健的适应性反应。我们的模型不仅有助于揭示早期嗅觉过程两个主要特征的潜在机制，还可用于预测PN对任意时间依赖信号的反应，并从测量的输入 - 输出关系推断电路的微观特性（如STF和STD的强度）。我们的电路模型可能有助于理解STP在其他感觉系统中的作用。
