A Learning Theory for Reward-Modulated Spike-Timing-Dependent Plasticity with Application to Biofeedback
Figure 3
Setup of the model for the experiment by Fetz and Baker [17].
(A) Schema of the model: The activity of a single neuron in the circuit determines the amount of reward delivered to all synapses between excitatory neurons in the circuit. (B) The reward signal d(t) in response to a spike train (shown at the top) of the arbitrarily selected neuron (which was selected from a recurrently connected circuit consisting of 4000 neurons). The level of the reward signal d(t) follows the firing rate of the spike train. (C) The eligibility function fc(s) (black curve, left axis), the reward kernel εr(s) delayed by 200 ms (red curve, right axis), and the product of these two functions (blue curve, right axis) as used in our computer experiment. The integral of fc(s+dr)εr(s) is positive, as required according to Equation 10 in order to achieve a positive learning rate for the synapses to the selected neuron.