Estimating Fisher discriminant error in a linear integrator model of neural population activity

The Journal of Mathematical Neuroscience

Matias Calderini¹  Jean-Philippe Thivierge² 

[1] School of Psychology, University of Ottawa, 136 Jean Jacques Lussier, K1N 6N5, Ottawa, ON, Canada;School of Psychology, University of Ottawa, 136 Jean Jacques Lussier, K1N 6N5, Ottawa, ON, Canada;Brain and Mind Research Institute, University of Ottawa, K1N 6N5, Ottawa, ON, Canada;

关键词: Linear model;    Fisher linear discriminant analysis;    Noise correlation;   

DOI  :  10.1186/s13408-021-00104-4

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY