Learning with sparse reward in a gap junction network inspired by the insect mushroom body
Fig 14
(A) A minimal state network: state node i and state node j have different potentials (note ‘potential’ here refers to potential in a resistive network, not the membrane potential of a neuron). The potential difference Vi,j can cause current Ii,j from i to j if there is a connection from i to j and i has a higher potential than j. The weight of the connection wi,j is the conductance and the current Ii,j follows Ohm’s law. (B) A minimal circuit including an action node: an action node k can be influenced by the state node connection it is attached to. The influence can be described as a function of a potential difference Vi,j, current Ii,j, and weight from the connection to the action node wi,j,k. See text for details.