Model-Based Decoding, Information Estimation, and Change-Point Detection Techniques for Multineuron Spike Trains

@article{Pillow2011ModelBasedDI,
  title={Model-Based Decoding, Information Estimation, and Change-Point Detection Techniques for Multineuron Spike Trains},
  author={Jonathan W. Pillow and Yashar Ahmadian and Liam Paninski},
  journal={Neural Computation},
  year={2011},
  volume={23},
  pages={1-45}
}
One of the central problems in systems neuroscience is to understand how neural spike trains convey sensory information. Decoding methods, which provide an explicit means for reading out the information contained in neural spike responses, offer a powerful set of tools for studying the neural coding problem. Here we develop several decoding methods based on point-process neural encoding models, or forward models that predict spike responses to stimuli. These models have concave log-likelihood… CONTINUE READING
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