Biomedical Engineering Department and Neuroscience Graduate Program, University of Southern California, Los Angeles, CA 90089, United States.
Department of Physiology, The Rappaport Faculty of Medicine and Research Institute, Technion-Israel Institute of Technology, Haifa 35254, Israel.
Curr Opin Neurobiol. 2017 Apr;43:177-186. doi: 10.1016/j.conb.2017.03.012. Epub 2017 Apr 25.
The elaborate morphology, nonlinear membrane mechanisms and spatiotemporally varying synaptic activation patterns of dendrites complicate the expression, compartmentalization and modulation of synaptic plasticity. To grapple with this complexity, we start with the observation that neurons in different brain areas face markedly different learning problems, and dendrites of different neuron types contribute to the cell's input-output function in markedly different ways. By committing to specific assumptions regarding a neuron's learning problem and its input-output function, specific inferences can be drawn regarding the synaptic plasticity mechanisms and outcomes that we 'ought' to expect for that neuron. Exploiting this assumption-driven approach can help both in interpreting existing experimental data and designing future experiments aimed at understanding the brain's myriad learning processes.
树突精细的形态结构、非线性的膜机制和时变的突触激活模式使突触可塑性的表达、区室化和调制变得复杂。为了应对这种复杂性,我们首先观察到,不同脑区的神经元面临着明显不同的学习问题,而不同神经元类型的树突以明显不同的方式为细胞的输入-输出功能做出贡献。通过对神经元的学习问题及其输入-输出功能做出特定假设,可以针对我们应该预期该神经元具有的突触可塑性机制和结果做出具体推断。利用这种基于假设的方法,不仅有助于解释现有的实验数据,还可以设计未来的实验,以了解大脑的众多学习过程。