Decoding approaches provide a useful means of estimating the information contained in neuronal circuits. In this work, we analyze the expected classification error of a decoder based on Fisher linear discriminant analysis. We provide expressions that relate decoding error to the specific parameters of a population model that performs linear integration of sensory input. Results show conditions that lead to beneficial and detrimental effects of noise correlation on decoding. Further, the proposed framework sheds light on the contribution of neuronal noise, highlighting cases where, counter-intuitively, increased noise may lead to improved decoding performance. Finally, we examined the impact of dynamical parameters, including neuronal leak and integration time constant, on decoding. Overall, this work presents a fruitful approach to the study of decoding using a comprehensive theoretical framework that merges dynamical parameters with estimates of readout error.

中文翻译：

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估计神经群体活动线性积分器模型中的 Fisher 判别误差


解码方法提供了一种估计神经元回路中包含的信息的有用方法。在这项工作中，我们基于费舍尔线性判别分析来分析解码器的预期分类误差。我们提供了将解码误差与执行感觉输入线性积分的群体模型的特定参数相关联的表达式。结果显示了噪声相关性对解码产生有益和有害影响的条件。此外，所提出的框架揭示了神经元噪声的贡献，强调了与直觉相反的情况，增加的噪声可能会导致解码性能的提高。最后，我们检查了动态参数（包括神经元泄漏和积分时间常数）对解码的影响。总的来说，这项工作提出了一种利用综合理论框架来研究解码的富有成效的方法，该框架将动态参数与读出误差的估计相结合。