Mechanisms of long-term presynaptic plasticity at Schaffer-collateral synapses

Autor: Padamsey, Z
Přispěvatelé: Emptage, N
Jazyk: angličtina
Rok vydání: 2017
Předmět:
Popis: Synaptic plasticity is thought to be integral to learning and memory. The two most common forms of plasticity are long-term potentiation (LTP) and long-term depression (LTD), both of which can be supported either by presynaptic changes in transmitter release probability (Pr), or by postsynaptic changes in AMPA receptor number. It is generally thought that the induction of LTP and LTD at Schaffer-collateral synapses in the hippocampus depends on the activation of NMDA receptors (GluN). Recent studies, however, have demonstrated that both increases and decreases in Pr can be induced under blockade of postsynaptic GluN receptors, suggesting that the activation of postsynaptic GluN receptors by glutamate is only a strict requirement for postsynaptic plasticity. In this thesis, I therefore re-examined the role of glutamate in presynaptic plasticity. I used single synapse imaging along with electrophysiological and pharmacological techniques to independently manipulate and monitor the levels of glutamatergic signalling during synaptic activity. I discovered that glutamate is inhibitory and unnecessary for the induction of LTP at the presynaptic locus. My findings support a novel model of presynaptic plasticity in which the net activity-dependent changes in Pr at an active presynaptic terminal is jointly determined by two opposing processes that can be simultaneously active: 1) postsynaptic depolarization, which, via the activation of L-type voltage-gated Ca2+ channels, increases Pr by driving the synthesis and release of nitric oxide from neuronal dendrites and 2) glutamate release, which through the activation of presynaptic GluN receptors, decreases Pr. Computationally, this model suggests that plasticity functions to reduce prediction-errors that arise during synaptic activity, and, thereby offers a biologically plausible mechanism by which neuronal networks may optimize learning at the level of single synapses.
Databáze: OpenAIRE