Learning poly-synaptic paths with traveling waves
Fig 2
Upstate propagation improves the reinforcement task of poly-synaptic paths.
(A) An example of a successful trial. The initial synaptic weights are represented in color (Left). All neurons are aligned in a grid with 100 μm spacing, and the adjacent neurons within 200√2 μm are randomly connected with a probability of 0.5. Synaptic connections from stimulated neuron S are all outward, while the synaptic connections to target neuron T and false-target neurons F are all inward. The path from S to T is selectively strengthened at the end of the learning (Middle). The difference between the initial synaptic weight and the final synaptic weight (Right). (B) A successful example of this task. The firing rate of the target neuron selectively increases. (C) The averaged synaptic weight difference from the initial condition to the 10th, 25th, 40th trials is plotted. The averaged synaptic weights are calculated, including the direction of synaptic weights (so that the opposite direction has a minus sign). (D) The averaged synaptic weight difference between the initial trial and the last trial (the 80th trial) is plotted. (E) The success rate of each condition (50 simulations averaged). The shaded area indicates the standard error of the mean. A combination of wave and tonic dopamine signal Dt (red line) shows the best task performance, while the conventional model (black line) fails to complete this task.