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Spike Train Decoding and Neural Information Processing

This chapter explores Spike Train Decoding and the role of Information Theory in understanding neural encoding. Topics include Mutual Information, Entropy, Bias, and Receptive Fields in neural responses.

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Spike Train Decoding and Neural Information Processing

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  1. Spike Train decoding

  2. Spike Train decoding

  3. Spike Train decoding

  4. Summary Decoding of stimulus from response Two choice case Discrimination ROC curves Population decoding MAP and ML estimators Bias and variance Fisher information, Cramer-Rao bound Spike train decoding

  5. Chapter 4

  6. Entropy

  7. Entropy

  8. Mutual information H_noise< H

  9. Mutual information

  10. KL divergence

  11. Continuous variables

  12. Entropy maximization

  13. Entropy maximization

  14. Population of neurons

  15. Retinal Ganglion Cell Receptive Fields

  16. Retinal Ganglion Cell Receptive Fields

  17. Retinal Ganglion Cell Receptive Fields

  18. Retinal Ganglion Cell Receptive Fields

  19. Retinal Ganglion Cell Receptive Fields

  20. Temporal processing in LGN

  21. Temporal processing in LGN

  22. Temporal processing in LGN

  23. Temporal vs spatial coding

  24. Entropy of spike trains

  25. Entropy of spike trains

  26. Entropy of spike trains

  27. Entropy of spike trains • Spike train mutual information measurements quantify stimulus specific aspects of neural encoding. • Mutual information of bullfrog peripheral auditory neurons was estimated • 1.4 bits/sec for broadband noise stimulus • 7.8 bits/sec for bullfrog call-like stimulus

  28. Summary • Information theory quantifies how much a response says about a stimulus • Stimulus, response entropy • Noise entropy • Mutual information, KL divergence • Maximizing information transfer yields biological receptive fields • Factorial codes • Equalization • Whitening • Spike train mutual information

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