使用姐妹二尖瓣细胞的线性模型中MAP推断的稀疏连接性
Sparse connectivity for MAP inference in linear models using sister mitral cells
摘要：
感觉处理是困难的，因为感兴趣的变量以相对复杂的方式编码在脉冲序列中。感觉处理的一个主要目标是了解大脑如何提取这些变量。在这里，我们重新审视一个常见的编码模型，其中变量被线性编码。虽然变量通常比神经元多，但这个问题仍然是可解的，因为在任何一个时间只有少量变量出现（稀疏先验）。然而，以前的解决方案通常需要所有对所有的连接，与大脑中看到的稀疏连接不一致。在这里，我们提出了一个原则性的算法，可证明达到MAP推理解决方案，但使用稀疏连接。我们的算法的灵感来自于小鼠的嗅球，但我们的方法是通用的，足以适用于其他形式，此外，它应该是可能的，以扩展到非线性编码模型。
英文摘要：
Sensory processing is hard because the variables of interest are encoded in spike trains in a relatively complex way. A major goal in sensory processing is to understand how the brain extracts those variables. Here we revisit a common encoding model in which variables are encoded linearly. Although there are typically more variables than neurons, this problem is still solvable because only a small number of variables appear at any one time (sparse prior). However, previous solutions usually require all-to-all connectivity, inconsistent with the sparse connectivity seen in the brain. Here we propose a principled algorithm that provably reaches the MAP inference solution but using sparse connectivity. Our algorithm is inspired by the mouse olfactory bulb, but our approach is general enough to apply to other modalities; in addition, it should be possible to extend it to nonlinear encoding models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
