Sanger Terence D
Department of Neurology and Neurological Sciences, Pediatric Movement Disorders Clinic, Stanford University Medical Center, 300 Pasteur Drive, A345, Stanford, CA 94305-5235, USA.
Curr Opin Neurobiol. 2003 Apr;13(2):238-49. doi: 10.1016/s0959-4388(03)00034-5.
In many regions of the brain, information is represented by patterns of activity occurring over populations of neurons. Understanding the encoding of information in neural population activity is important both for grasping the fundamental computations underlying brain function, and for interpreting signals that may be useful for the control of prosthetic devices. We concentrate on the representation of information in neurons with Poisson spike statistics, in which information is contained in the average spike firing rate. We analyze the properties of population codes in terms of the tuning functions that describe individual neuron behavior. The discussion centers on three computational questions: first, what information is encoded in a population; second, how does the brain compute using populations; and third, when is a population optimal? To answer these questions, we discuss several methods for decoding population activity in an experimental setting. We also discuss how computation can be performed within the brain in networks of interconnected populations. Finally, we examine questions of optimal design of population codes that may help to explain their particular form and the set of variables that are best represented. We show that for population codes based on neurons that have a Poisson distribution of spike probabilities, the behavior and computational properties of the code can be understood in terms of the tuning properties of individual cells.
在大脑的许多区域，信息由神经元群体活动的模式来表征。理解神经群体活动中的信息编码，对于掌握大脑功能背后的基本计算，以及解读可能对控制假肢装置有用的信号都很重要。我们专注于具有泊松脉冲统计特性的神经元中的信息表征，其中信息包含在平均脉冲发放率中。我们根据描述单个神经元行为的调谐函数来分析群体编码的特性。讨论集中在三个计算问题上：第一，群体中编码了什么信息；第二，大脑如何利用群体进行计算；第三，群体何时是最优的？为了回答这些问题，我们讨论了在实验环境中解码群体活动的几种方法。我们还讨论了在相互连接的群体网络中大脑如何进行计算。最后，我们研究群体编码的最优设计问题，这些问题可能有助于解释其特定形式以及最能表征的变量集。我们表明，对于基于具有泊松脉冲概率分布的神经元的群体编码，编码的行为和计算特性可以根据单个细胞的调谐特性来理解。
