Fig. 2.

Modulator-based neo-Hebbian local learning rules. (A) A conventional three-factor local learning rule models action of a “third,” TD GPCR-activating ligand (e.g., dopamine) that governs synapse reweighting ($\mathrm{\Delta }w$) in proportion to temporal coincidence of the two Hebbian factors (presynaptic and postsynaptic activity). Such models generally require a lingering ET to sustain information about Hebbian coincidence until arrival of the TD signal. (B) Embracing new genetic evidence for local GPCR-based modulatory machinery, the MDGL theory introduces additional factors that allow spike-dependent secretion of NP-like local modulators (${\text{LM} }_{\text{e}}$ from excitatory neurons and ${\text{LM}}_{\text{i}}$ from inhibitory neurons) to participate in governing synapse reweighting ($\mathrm{\Delta }w$) (35). As indicated here and in Fig. 1, the present MDGL model comprises both directly TD-recipient cells (types D–F; B, Left) and non–TD-recipient cells (types A–C; B, Right). Synapse reweighting requires combined GPCR activation with a persistent ET for all cell types, but GPCRs are activated on non–TD-recipient cells only by the local modulatory ligands. (C) Propagation of TD error/reward signal via spike-dependent secretion of local modulators from both excitatory and inhibitory cell types to cells lacking direct access to TD modulatory signal. For simplicity, this schema represents only the four subscripted synapses/weights, while the full model represents many more synaptic inputs per cell.