Abstract
During the past 50 years, the cellular and molecular mechanisms of synaptic plasticity have been studied in great detail. A plethora of signaling pathways have been identified that account for synaptic changes based on positive and negative feedback mechanisms. Yet, the biological significance of Hebbian synaptic plasticity (= positive feedback) and homeostatic synaptic plasticity (= negative feedback) remains a matter of debate. Specifically, it is unclear how these opposing forms of plasticity, which share common downstream mechanisms, operate in the same networks, neurons, and synapses. Based on the observation that rapid and input-specific homeostatic mechanisms exist, we here discuss a model that is based on signaling pathways that may adjust a balance between Hebbian and homeostatic synaptic plasticity. Hence, “alterations” in Hebbian plasticity may, in fact, resemble “enhanced” homeostasis, which rapidly returns synaptic strength to baseline. In turn, long-lasting experience-dependent synaptic changes may require attenuation of homeostatic mechanisms or the adjustment of homeostatic setpoints at the single-synapse level. In this context, we propose a role for the proteolytic processing of the amyloid precursor protein (APP) in setting a balance between the ability of neurons to express Hebbian and homeostatic synaptic plasticity.
Highlights
The ability of neural tissue to adapt to specific stimuli through structural, functional and molecular changes plays a fundamental role in complex brain functions such as perception, decision-making, learning and memory (Citri and Malenka, 2008; Bailey et al, 2015)
Solid evidence suggests that homeostatic synaptic adaptation can occur locally, in subsets of synapses (e.g., Desai et al, 2002; Kim and Tsien, 2008; Vlachos et al, 2013). These findings indicate that Hebbian and homeostatic synaptic mechanisms may operate in parallel and could interfere with each other in the same subset of synapses
Central mechanisms that regulate the activity-dependent strengthening of excitatory neurotransmission are modification, trafficking and synthesis of α-amino3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (AMPA-Rs) at excitatory postsynapses (Malinow and Malenka, 2002; Diering and Huganir, 2018). Both Hebbian and homeostatic synaptic plasticity recruit Ca2+-dependent signaling pathways which lead to characteristic changes in synaptic AMPA-R content and function (Malinow and Malenka, 2002; Song and Huganir, 2002; Derkach et al, 2007; Turrigiano, 2008)
Summary
The ability of neural tissue to adapt to specific stimuli through structural, functional and molecular changes plays a fundamental role in complex brain functions such as perception, decision-making, learning and memory (Citri and Malenka, 2008; Bailey et al, 2015). Central mechanisms that regulate the activity-dependent strengthening (or dampening) of excitatory neurotransmission are modification, trafficking and synthesis of α-amino3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (AMPA-Rs) at excitatory postsynapses (Malinow and Malenka, 2002; Diering and Huganir, 2018) Both Hebbian and homeostatic synaptic plasticity recruit Ca2+-dependent signaling pathways which lead to characteristic changes in synaptic AMPA-R content and function (Malinow and Malenka, 2002; Song and Huganir, 2002; Derkach et al, 2007; Turrigiano, 2008). Increased intracellular Ca2+ levels are expected to trigger homeostatic synaptic down-scaling, which returns AMPA-R content to baseline (= negative feedback mechanism) Considering such rapid interactions between Hebbian and homeostatic plasticity mechanisms (Figure 1), a widely used interpretation of ‘‘alterations’’ in Hebbian plasticity—i.e., failure to persistently change the amplitude or the slope of evoked field excitatory postsynaptic potentials (fEPSPs)—may, resemble ‘‘enhanced’’ homeostasis, which effectively returns fEPSPs to baseline after the LTP- or LTD-inducing ‘‘network perturbation’’ (see Figures 1A,C). A recent study properties of firing-rate homeostasis in recurrent neuronal employed computational modeling to demonstrate associative networks (Gallinaro and Rotter, 2018)
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