Evaluating which classifiers work best for decoding neural data
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Evaluating which classifiers work best for decoding neural data . Background: Neural decoding. neuron 1. neuron 2. neuron 3. Learning association between neural activity an image. neuron n. Pattern Classifier. Background.

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Background neural decoding
Background: Neural decoding data

neuron 1

neuron 2

neuron 3

Learning association between

neural activity an image

neuron n

Pattern Classifier


Background
Background data

  • A recent paper by Graf et al. (Nature Neuroscience 2011) showed that SVMs worked better than PNB classifiers for decoding information from simultaneously recorded populations of neural data from V1.

  • They claimed that SVMs performed better because they took into account correlated variability in neural responses

    • However, a more detailed examination of what led to the higher decoding accuracy was not done.


Purpose of this project
Purpose of this project data

  • Goals of this project are:

    • Try to replicate the finding that SVMs work better than PNB (and other) classifiers on simultaneously recorded neural data

    • Understand why SVMs are working better (is it really due to correlated variability? Can we say something more precise?).

      3. Examine other types of data (e.g., computer vision features). Do SVMs work better, and why?


Resources
Resources data

  • Paper:

    • Graf et al., Decoding the activity of neuronal populations in macaque primary visual cortex Nature Neuroscience, 2011 http://www.nature.com/neuro/journal/vaop/ncurrent/abs/nn.2733.html

  • I can supply additional code and data that can be used to test different population decoding algorithms


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