Department of Physiology and Biophysics, University of Washington, Seattle, WA 98195-7290, USA.
Curr Opin Neurobiol. 2012 Aug;22(4):653-9. doi: 10.1016/j.conb.2012.06.005. Epub 2012 Jul 12.
The analysis of stimulus/response patterns using information theoretic approaches requires the full probability distribution of stimuli and response. Recent progress in using information-based tools to understand circuit function has advanced understanding of neural coding at the single cell and population level. In advances over traditional reverse correlation approaches, the determination of receptive fields using information as a metric has allowed novel insights into stimulus representation and transformation. The application of maximum entropy methods to population codes has opened a rich exploration of the internal structure of these codes, revealing stimulus-driven functional connectivity. We speculate about the prospects and limitations of information as a general tool for dissecting neural circuits and relating their structure and function.
使用信息论方法分析刺激/反应模式需要刺激和反应的完整概率分布。最近，使用基于信息的工具来理解电路功能的进展提高了对单细胞和群体水平神经编码的理解。与传统的反向相关方法相比，使用信息作为度量来确定感受野使得对刺激表示和转换有了新的认识。最大熵方法在群体编码中的应用开辟了对这些编码内部结构的丰富探索，揭示了刺激驱动的功能连接。我们推测信息作为一种用于剖析神经电路并将其结构和功能联系起来的通用工具的前景和局限性。
