{1568} revision 2 modified: 05-05-2022 15:44 gmt

Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits

  • Roughly, single-events indicate the normal feature responses of neurons, while multiple-spike bursts indicate error signals.
  • Bursts are triggered by depolarizing currents to the apical dendrites, which can be uncoupled from bottom-up event rate, which arises from perisomatic inputs / basal dendrites.
  • The fact that the two are imperfectly multiplexed is OK, as in backprop the magnitude of the error signal is modulated by the activity of the feature detector.
  • "For credit assignment in hierarchical networks, connections should obey four constraints:
    • Feedback must steer the magnitude and sign of plasticity
    • Feedback signals from higher-order areas must be multipleed with feedforward signals from lower-order areas so that credit assignment can percolate down the hierarch with minimal effect on sensory information
    • There should be some form of alignment between feedforward and feedback connections
    • Integration of credit-carrying signals should be nearly linear to avoid saturation
      • Seems it's easy to saturate the burst probability within a window of background event rate, e.g. the window is all bursts to no bursts.
  • Perisomatic inputs were short-term depressing, whereas apical dendrite synapses were short-term facilitating.
    • This is a form of filtering on burst rates? E.g. the propagate better down than up?
  • They experiment with a series of models, one for solving the XOR task, and subsequent for MNIST and CIFAR.
  • The later, larger models are mean-field models, rather than biophysical neuron models, and have a few extra features:
    • Interneurons, presumably SOM neurons, are used to keep bursting within a linear regime via a 'simple' (supplementary) learning rule.
    • Feedback alignment occurs by adjusting both the feedforward and feedback weights with the same propagated error signal + weight decay.
  • The credit assignment problem, or in the case of unsupervised learning, the coordination problem, is very real: how do you change a middle-feature to improve representations in higher (and lower) levels of the hierarchy?
    • They mention that using REINFORCE on the same network was unable to find a solution.
    • Put another way: usually you need to coordinate the weight changes in a network; changing weights individually based on a global error signal (or objective function) does not readily work...
      • Though evolution seems to be quite productive at getting the settings of (very) large sets of interdependent coefficients all to be 'correct' and (sometimes) beautiful.
      • How? Why? Friston's free energy principle? Lol.