Biologically Plausible Dopamine-Modulated STDP Model of Pavlovian Learning in Spiking Neural Networks

Abstract

Spike-timing-dependent plasticity (STDP) and dopamine (DA) are fundamental to reward-based learning and memory formation. A widely used DA-modulated STDP model explains how neural networks associate stimuli with delayed dopaminergic rewards through an eligibility trace. However, we show that this model supports learning even at unrealistically high DA concentrations because DA simply scales the magnitude of STDP without changing its temporal profile. In contrast, experiments demonstrate that DA nonlinearly reshapes the STDP window, converting long-term depression (LTD) into long-term potentiation (LTP) at high DA levels. We therefore propose a DA-modulated STDP rule in which increasing DA progressively biases plasticity toward potentiation while receptor saturation limits further DA effects beyond a critical concentration. Simulations of recurrent networks of Izhikevich neurons show that the proposed rule supports robust conditioning only within a biologically realistic DA range (0.04–0.70 µM). Successful learning produces a hybrid network architecture consisting of a strong feedforward backbone embedded within recurrent circuitry and generates enhanced burst responses selectively to reward-associated stimuli. At the upper limit of the biologically plausible DA range, the network passes through a narrow bistable regime, converging to one of two distinct stable configurations. At higher DA concentrations, conditioning fails altogether. These results provide a biologically grounded model of DA-dependent plasticity and offer new insight into how abnormal dopamine signaling can impair learning in neurological disorders.