Deperrois, N. | bioRxiv Science

bioRxiv2019

Short-term depression and long-term plasticity together tune sensitive range of synaptic plasticity

Synaptic efficacy is subjected to activity-dependent changes on short- and long time scales. While short-term changes decay over minutes, long-term modifications last from hours up to a lifetime and are thought to constitute the basis of learning and memory. Both plasticity mechanisms have been studied extensively but how their interaction shapes synaptic dynamics is little known. To investigate how both short- and long-term plasticity together control the induction of synaptic depression and potentiation, we used numerical simulations and mathematical analysis of a calcium-based model, where pre- and postsynaptic activity induces calcium transients driving synaptic long-term plasticity. We found that the model implementing known synaptic short-term dynamics in the calcium transients can be successfully fitted to long-term plasticity data obtained in visual- and somatosensory cortex. Interestingly, the impact of spike-timing and firing rate changes on plasticity occurs in the prevalent firing rate range, which is different in both cortical areas considered here. Our findings suggest that short- and long-term plasticity are together tuned to adapt plasticity to area-specific activity statistics such as firing rates. Author summarySynaptic long-term plasticity, the long-lasting change in efficacy of connections between neurons, is believed to underlie learning and memory. Synapses furthermore change their efficacy reversibly in an activity-dependent manner on the subsecond time scale, referred to as short-term plasticity. It is not known how both synaptic plasticity mechanisms - long- and short-term - interact during activity epochs. To address this question, we used a biologically-inspired plasticity model in which calcium drives changes in synaptic efficacy. We applied the model to plasticity data from visual- and somatosensory cortex and found that synaptic changes occur in very different firing rate ranges, which correspond to the prevalent firing rates in both structures. Our results suggest that short- and long-term plasticity act in a well concerted fashion.

neuroscience↗

bioRxiv2018

Nicotinic and Cholinergic Modulation ofReward Prediction Error Computations in theVentral Tegmental Area: a Minimal CircuitModel.

Dopamine (DA) neurons in the ventral tegmental area (VTA) are thought to encode reward prediction errors (RPE) by comparing actual and expected rewards. In recent years, much work has been done to identify how the brain uses and computes this signal.While several lines of evidence suggest the the interplay of he DA and the inhibitory interneurons in the VTA implements the RPE computaiton, it still remains unclear how the DA neurons learn key quantities, for example the amplitude and the timing of primary rewards during conditioning tasks. Furthermore, exogenous nicotine and endogenous acetylcholine, acting on both VTA DA and GABA ({gamma} - aminobutyric acid) neurons via nicotinic-acetylcholine receptors (nAChRs), also likely affect these computations. To explore the potential circuit-level mechanisms for RPE computations during classical-conditioning tasks, we developed a minimal computational model of the VTA circuitry. The model was designed to account for several reward-related properties of VTA afferents and recent findings on VTA GABA neuron dynamics during conditioning.\n\nWith our minimal model, we showed that the RPE can be learned by a two-speed process computing reward timing and magnitude. Including a model of nAChR-mediated currents in the VTA DA-GABA circuit, we also showed that nicotine should reduce the acetylcholine action on the VTA GABA neurons by receptor desensitization and therefore potentially boost the DA responses to reward information. Together, our results delineate the mechanisms by which RPE are computed in the brain, and suggest a hypothesis on nicotine-mediated effects on reward-related perception and decision-making.

neuroscience↗