Gatsby Computational Neuroscience Unit, University College London, London, United Kingdom.
Sensory Circuits and Neurotechnology Laboratory, The Francis Crick Institute, London, United Kingdom.
PLoS Comput Biol. 2022 Jan 31;18(1):e1009808. doi: 10.1371/journal.pcbi.1009808. eCollection 2022 Jan.
Sensory processing is hard because the variables of interest are encoded in spike trains in a relatively complex way. A major goal in studies of 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 require all-to-all connectivity, inconsistent with the sparse connectivity seen in the brain. Here we propose an algorithm that provably reaches the MAP (maximum a posteriori) inference solution, but does so using sparse connectivity. Our algorithm is inspired by the circuit of 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.
感觉处理很困难，因为感兴趣的变量是以相对复杂的方式在尖峰火车中编码的。在感觉处理的研究中，一个主要目标是了解大脑如何提取这些变量。在这里，我们重新审视一种常见的编码模型，其中变量以线性方式编码。尽管通常变量的数量比神经元多，但这个问题仍然可以解决，因为只有一小部分变量在任何时候出现（稀疏先验）。然而，以前的解决方案需要全连接，这与大脑中看到的稀疏连接不一致。在这里，我们提出了一种算法，可以证明达到了 MAP（最大后验）推断的解决方案，但它使用的是稀疏连接。我们的算法受到老鼠嗅球电路的启发，但我们的方法足够通用，可以应用于其他模态。此外，应该可以将其扩展到非线性编码模型。
