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

Abstract

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 life-time 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. * current address :Synaptic long-term plasticity, the long-lasting change in efficacy of connections between neurons, is believed to underlie learning and memory. Synapses furthermore change their transmission efficacy reversibly in an activity-dependent manner on the subsecond time scale, an phenomenon termed short-term plasticity. It is not known however both synaptic plasticity mechanisms -long-and short-term -interact during activity epochs. Using a biologically-inspired plasticity model in which calcium drives changes in synaptic transmission efficacy, we investigated how short-and long-term synaptic plasticity influence each other in response to activity patterns characterized by firing rate and precise timing of action potentials. We fitted the model to long-term plasticity data recorded in visual-and somatosensory cortex and found that synaptic changes occur in very different firing rate ranges. Importantly, these sensitive ranges of plasticity correspond to the prevalent firing rates in both cortical structures, which are markedly different. Our results lend support to the idea that short-and long-term plasticity dynamics act in a concerted fashion and that their dynamic ranges are well adjusted.

A stochastic model of hippocampal synaptic plasticity with geometrical readout of enzyme dynamics

“…Presynaptic vesicle release events were also stochastic: glutamate release was an all-or-none event, and the amplitude of each glutamate pulse was drawn randomly, modelling heterogeneity in vesicle size ( Liu et al, 1999 ). The inclusion of stochastic processes to account for an intrinsic noise in synaptic activation ( Deperrois and Graupner, 2020 ) contrasts with most previous models in the literature, which either represent all variables as continuous and deterministic or add an external generic noise source ( Bhalla, 2004; Antunes and De Schutter, 2012; Bartol et al, 2015 ).…”

“…Some of these variables have been modelled by other groups, e.g. stochasticity, BaP attenuation or pre-synaptic plasticity ( Cai et al, 2007; Shouval and Kalantzis, 2005; Zeng and Holmes, 2010; Miller et al, 2005; Yeung et al, 2004; Shah et al, 2006; Deperrois and Graupner, 2020; Costa et al, 2015 ), but generally independently from each other. To position the uniqueness of our model in this broader context, we also provide a direct comparison of our model with some of the most recent leading models of excitatory synapse plasticity and the experimental work they reproduce ( Table 1-Table Supplement 1 and Table 1-Table Supplement 2 ).…”

“…What do these results imply about the rules of plasticity in vivo? First, we noticed that successful LTP or LTD induction required a balance between two types of slow variables: those that attenuate, such as presynaptic vesicle pools and dendritic BaP, versus those that accumulate, such as slow enzymatic integration ( Cai et al, 2007; Mizusaki et al, 2018; Deperrois and Graupner, 2020 ). This balance is reflected in the inverted-U shaped magnitude of LTP seen as a function of post-synaptic firing rate ( Figure 7h ).…”

A Review on Plastic Artificial Neural Networks: Exploring the Intersection between Neural Architecture Search and Continual Learning

“…LTP is thought to have both Homo-and Hetero-synaptic components and is caused by persistent strengthening of the synapse over longer periods of time. While there has not been any conclusive proof that memory resides in the synapses and that it is possibly a result of non-synaptic mechanisms [64], numerous studies strongly support the theory that LTP within the hippocampus is at least partially responsible for learning and for the formation and retention of memory [61], [62], [80], [85], [87]- [89].…”