Keemink Sander W, van Rossum Mark C W
Department of Machine Learning and Neural Computing, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands.
School of Psychology, University of Nottingham, Nottingham, United Kingdom.
PLoS Comput Biol. 2025 Apr 11;21(4):e1012969. doi: 10.1371/journal.pcbi.1012969. eCollection 2025 Apr.
Throughout the brain information is coded in the activity of multiple neurons at once, so called population codes. Population codes are a robust and accurate way of coding information. One can evaluate the quality of population coding by trying to read out the code with a decoder, and estimate the encoded stimulus. In particular when neurons are noisy, coding accuracy has extensively been evaluated in terms of the trial-to-trial variation in the estimate. While most decoders yield unbiased estimators if many neurons are actived, when only a few neurons are active, biases readily emerge. That is, even after averaging, a systematic difference between the true stimulus and its estimate remains. We characterize the shape of this bias for different encoding models (rectified cosine tuning and von Mises functions) and show that it can be both attractive or repulsive for different stimulus values. Biases appear for maximum likelihood and Bayesian decoders. The biases have a non-trivial dependence on noise. We also introduce a technique to estimate the bias and variance of Bayesian least square decoders. The work is of interest to those studying neural populations with a few active neurons.
在整个大脑中，信息是同时由多个神经元的活动进行编码的，即所谓的群体编码。群体编码是一种强大且准确的信息编码方式。人们可以通过尝试用解码器读出编码并估计编码的刺激来评估群体编码的质量。特别是当神经元存在噪声时，编码准确性已根据估计中的逐次试验变化进行了广泛评估。虽然如果许多神经元被激活，大多数解码器会产生无偏估计器，但当只有少数神经元活跃时，偏差很容易出现。也就是说，即使经过平均，真实刺激与其估计之间仍存在系统差异。我们描述了不同编码模型（整流余弦调谐和冯·米塞斯函数）下这种偏差的形状，并表明对于不同的刺激值，它可能是吸引性的或排斥性的。最大似然和贝叶斯解码器都会出现偏差。这些偏差对噪声有非平凡的依赖性。我们还介绍了一种估计贝叶斯最小二乘解码器偏差和方差的技术。这项工作对于研究少数活跃神经元的神经群体的人来说是有意义的。
