Kirchner Jan H, Gjorgjieva Julijana
Computation in Neural Circuits Group, Max Planck Institute for Brain Research, Max-von-Laue-Str. 4, 60438 Frankfurt, Germany.
Technical University of Munich, School of Life Sciences, 85354 Freising, Germany.
Neuroforum. 2022 Feb 23;28(1):21-30. doi: 10.1515/nf-2021-0031. Epub 2021 Dec 31.
Single neurons in the brain exhibit astounding computational capabilities, which gradually emerge throughout development and enable them to become integrated into complex neural circuits. These capabilities derive in part from the precise arrangement of synaptic inputs on the neurons' dendrites. While the full computational benefits of this arrangement are still unknown, a picture emerges in which synapses organize according to their functional properties across multiple spatial scales. In particular, on the local scale (tens of microns), excitatory synaptic inputs tend to form clusters according to their functional similarity, whereas on the scale of individual dendrites or the entire tree, synaptic inputs exhibit dendritic maps where excitatory synapse function varies smoothly with location on the tree. The development of this organization is supported by inhibitory synapses, which are carefully interleaved with excitatory synapses and can flexibly modulate activity and plasticity of excitatory synapses. Here, we summarize recent experimental and theoretical research on the developmental emergence of this synaptic organization and its impact on neural computations.
大脑中的单个神经元展现出惊人的计算能力,这些能力在整个发育过程中逐渐显现,并使它们能够融入复杂的神经回路。这些能力部分源于神经元树突上突触输入的精确排列。虽然这种排列的全部计算益处仍不明确,但一幅图景浮现出来,即突触根据其功能特性在多个空间尺度上进行组织。特别是在局部尺度(几十微米)上,兴奋性突触输入倾向于根据其功能相似性形成簇,而在单个树突或整个树突树的尺度上,突触输入呈现出树突图谱,其中兴奋性突触功能随树突上的位置而平滑变化。这种组织的发育得到抑制性突触的支持,抑制性突触与兴奋性突触仔细交错,并能灵活调节兴奋性突触的活性和可塑性。在这里,我们总结了关于这种突触组织发育出现及其对神经计算影响的近期实验和理论研究。