Learning and executing goal-directed choices by internally generated sequences in spiking neural circuits

Recent neural ensemble recordings have established a link between goal-directed spatial decision making and internally generated neural sequences in the hippocampus of rats. To elucidate the synaptic mechanisms of these sequences underlying spatial decision making processes, we develop and investigate a spiking neural circuit model endowed with a combination of two synaptic plasticity mechanisms including spike-timing dependent plasticity (STDP) and synaptic scaling. In this model, the interplay of the combined synaptic plasticity mechanisms and network dynamics gives rise to neural sequences which propagate ahead of the animals’ decision point to reach goal locations. The dynamical properties of these forward-sweeping sequences and the rates of correct binary choices executed by these sequences are quantitatively consistent with experimental observations; this consistency, however, is lost in our model when only one of STDP or synaptic scaling is included. We further demonstrate that such sequence-based decision making in our network model can adaptively respond to time-varying and probabilistic associations of cues and goal locations, and that our model performs as well as an optimal Kalman filter model. Our results thus suggest that the combination of plasticity phenomena on different timescales provides a candidate mechanism for forming internally generated neural sequences and for implementing adaptive spatial decision making.Author summary: Adaptive goal-directed decision making is critical for animals, robots and humans to navigate through space. In this study, we propose a novel neural mechanism for implementing spatial decision making in cued-choice tasks. We show that in a spiking neural circuit model, the interplay of network dynamics and a combination of two synaptic plasticity rules, STDP and synaptic scaling, gives rise to neural sequences. When a model rat pauses around a decision point, these sequences propagate ahead of the animal’s current location and travel towards a goal location. The dynamical properties of these forward-sweeping sequences and the rate of correct responses made by them are consistent with experimental data. In addition, we demonstrate that STDP when complemented by slower synaptic scaling enables neural sequences to make adaptive choices under probabilistic and time-varying cue-goal associations. The adaptive performance of our sequence-based network is comparable to a mathematical model, namely the Kalman filter, which is optimal for this adaptive task. Our results thus shed new light on our understanding of neural mechanisms underlying goal-directed decision making.