The ‘Ideal Homunculus’: Statistical Inference from Neural Population Responses
The ‘Ideal Homunculus’: Statistical Inference from Neural Population Responses
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“理想的小人”：神经群体反应的统计推断
DOI：
10.1007/978-1-4615-3254-5_9
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发表时间：
1993
期刊：
影响因子：
64.5
通讯作者：
Peter Földiák
中科院分区：
文献类型：
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作者：
Peter Földiák
一个神经元或一组神经元的反应意味着什么？它对经济刺激计划有什么看法？人口反应中的信息编码如何分布和有效？这里建议贝叶斯统计推断可以帮助回答这些问题，因为它允许我们不仅在时域中“阅读神经代码”[2，5]，而且还可以跨越神经元群体。基于对一组已知刺激的神经反应的重复记录，我们可以估计给定刺激的反应的条件概率分布，P（响应|刺激）。行为相关分布，即给定来自一个细胞或一组细胞的观察到的响应的刺激的条件概率分布，P（刺激|响应）可以使用贝叶斯规则导出。这个分布包含了所有关于刺激的信息，并给出了一个上限和一个有用的比较，以进一步的神经处理阶段接收来自这些神经元的输入的性能。由于“理想观察者”的概念使心理物理效率的定义成为可能[1]，这种“理想侏儒”（观察神经反应而不是刺激）可以用来测试神经表征的效率。贝叶斯规则是：P（s| r）= P（r| s）P（s）/P（r）= P（r| s）P（s）/s P（r| s）P（s），这里s代表刺激，r代表反应，S是可能刺激的集合。
What does the response of a neuron, or of a group of neurons mean? What does it say about the stimulus? How distributed and efficient is the encoding of information in population responses? It is suggested here that Bayesian statistical inference can help answer these questions by allowing us to ‘read the neural code’ not only in the time domain[2, 5] but also across a population of neurons. Based on repeated recordings of neural responses to a known set of stimuli, we can estimate the conditional probability distribution of the responses given the stimulus, P(response|stimulus). The behaviourally relevant distribution, i.e. the conditional probability distribution of the stimuli given an observed response from a cell or a group of cells, P(stimulus|response) can be derived using the Bayes rule. This distribution contains all the information present in the response about the stimulus, and gives an upper limit and a useful comparison to the performance of further neural processing stages receiving input from these neurons. As the notion of an ‘ideal observer’ makes the definition of psychophysical efficiency possible[1], this ‘ideal homunculus’ (looking at the neural response instead of the stimulus) can be used to test the efficiency of neural representation. The Bayes rule is: P(s|r) = P(r|s)P(s)/P(r) = P(r|s)P(s)/Σ s P(r|s)P(s), where in this case s stands for stimulus, r for response, and S is the set of possible stimuli.