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Information Theoretic Characterization of Stimulus Relationships Encoded by Sensory Neurons

Information Theoretic Characterization of Stimulus Relationships Encoded by Sensory Neurons. Robin Ince MSc Computational Neuroscience and Neuroinformatics University of Manchester. Output (spike or no spike). Inputs (stimulus features). Black Box System (neuron). Introduction.

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Information Theoretic Characterization of Stimulus Relationships Encoded by Sensory Neurons

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  1. Information Theoretic Characterization of Stimulus Relationships Encoded by Sensory Neurons Robin Ince MSc Computational Neuroscience and Neuroinformatics University of Manchester

  2. Output (spike or no spike) Inputs (stimulus features) Black Box System (neuron) Introduction

  3. Subspace of distributions with no higher order interactions Subset of distributions sharing same pairwise marginals True Distribution Projection of true distribution: Maximum Entropy Solution Maximum Entropy Method • a principled way to investigate interactions • same marginals as true distribution but no higher order effects • solution via Information Geometry (Amari, 2001)

  4. Information Decomposition I(k;R) = IS(2)(k) + I(1)(k;R) + I(=2)(k;R) + IHO(k;R) • IS(2)(k) = H(k) – H(2)(k) • I(1)(k;R) = H(1)(k) – H(1)(k|R) • I(=2)(k;R) = I(2)(k;R) – I(1)(k;R) = H(2)(k) – H(1)(k) + H(1)(k|R) – H(2)(k|R) • IHO(k;R) = H(2)(k|R) – H(k|R)

  5. Results – Artificial Examples

  6. Results – Experimental Data

  7. Results – Experimental Data

  8. Results – Experimental Data

  9. Output Features processed separately Conclusions • few underlying assumptions; widely applicable • accurate predictions; good temporal precision • can be difficult to interpret

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