Learning to synchronize: How biological agents can couple neural task modules for dealing with the stability-plasticity dilemma
Fig 2
Detailed overview of the model.
A: The model. A detailed version of the model in Fig 1A is shown. The model consists of 3 units. A Processing unit is localized in posterior processing areas and contains a classic neural network. This network contains 3 layers (of nodes) for the BP model and 2 layers for the RW model. Layer 1 contains nodes that are activated by external input. At layer 2, modularity is implemented. This layer is divided in 3 task modules, one for each task the model has to execute. In the BP model, the nodes in these task modules represent hidden nodes; for the RW model these nodes represent response options. Layer 3 only occurs in the BP model and contains three response options. The Control unit consists of two parts. Here, the LFC contains 4 task neurons; 3 neurons point to a specific task module in the Processing unit that should be synchronized or desynchronized. A fourth neuron points to layer 1 and 3, to indicate that task modules should be (de)synchronized with these layers. The pMFC of the Control unit contains one single node that sends bursts in order to (de)synchronize modules in the Processing unit in line with the pointers sent by the LFC. The RL unit contains four neurons. One neuron (V) learns to assign a value to the task modules. Two other neurons (δ—, δ +) compare this value to external reward, in order to compute prediction errors. Negative prediction errors are accumulated in the Switch neuron in order to make a stay/switch decision, which it signals to the LFC. Additionally, the negative prediction error neuron signals to the pMFC (by giving bursts) that it should increase control. B: Neuronal triplet. Every square node in A consists of a triplet of neurons. Each such node consists of a phase-code pair (E, I) which, because of its excitatory (E)—inhibitory (I) coupling, oscillates at a certain frequency. These oscillations modulate the excitability of their rate code neuron (x) in line with the BBS hypothesis.