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Neural Encoding - Coggle Diagram

- - - - Kullback Leiber Divergence
        
        We find f that maximises the difference between the two distributions (that is Dkl) - aka maximising the mutual information between the spike and the prior
        
        \[ D_{KL} (P(s), Q(s)) = \int ds P(s) log_2 \frac{P(s)}{Q(s)} \]
  - - - We want the P(s1|spike) to be as different as it can be from P(s1) meaning that the filter is encoding some feature of the stimulus
  - - - We project an arbitrary stimulus onto the sta to filter it.
      - the spike triggered average is the single feature that captures the relevant components of the system - a linear filter
      - To reduce dimensionality, find the spike triggered average - the vector running along it can give a good idea of structure of data
      - Sample responses of the system to Gaussian white noise to characterize what it is about the input that triggers a response