Numerous experiments have demonstrated that the activity of neurons can alter the strength of excitatory synapses. This synaptic plasticity is bidirectional and synapses can be strengthened (potentiation) or weakened (depression). Synaptic plasticity offers a mechanism that links the ongoing activity of the brain with persistent physical changes to its structure. For this reason it is widely believed that synaptic plasticity mediates learning and memory. The hypothesis that synapses store memories by modifying their strengths raises an important issue. There should be a balance between the necessity that synapses change frequently, allowing new memories to be stored with high fidelity, and the necessity that synapses retain previously stored information. This is the plasticity stability dilemma. In this thesis the plasticity stability dilemma is studied in the context of the two dominant paradigms of activity dependent synaptic plasticity: Spike timing dependent plasticity (STDP) and long term potentiation and depression (LTP/D). Models of biological synapses are analysed and processes that might ameliorate the plasticity stability dilemma are identified. Two popular existing models of STDP are compared. Through this comparison it is demonstrated that the synaptic weight dynamics of STDP has a large impact upon the retention time of correlation between the weights of a single neuron and a memory. In networks it is shown that lateral inhibition stabilises the synaptic weights and receptive fields. To analyse LTP a novel model of LTP/D is proposed. The model centres on the distinction between early LTP/D, when synaptic modifications are persistent on a short timescale, and late LTP/D when synaptic modifications are persistent on a long timescale. In the context of the hippocampus it is proposed that early LTP/D allows the rapid and continuous storage of short lasting memory traces over a long lasting trace established with late LTP/D. It is shown that this might confer a longer memory retention time than in a system with only one phase of LTP/D. Experimental predictions about the dynamics of amnesia based upon this model are proposed. Synaptic tagging is a phenomenon whereby early LTP can be converted into late LTP, by subsequent induction of late LTP in a separate but nearby input. Synaptic tagging is incorporated into the LTP/D framework. Using this model it is demonstrated that synaptic tagging could lead to the conversion of a short lasting memory trace into a longer lasting trace. It is proposed that this allows the rescue of memory traces that were initially destined for complete decay. When combined with early and late LTP/D iii synaptic tagging might allow the management of hippocampal memory traces, such that not all memories must be stored on the longest, most stable late phase timescale. This lessens the plasticity stability dilemma in the hippocampus, where it has been hypothesised that memory traces must be frequently and vividly formed, but that not all traces demand eventual consolidation at the systems level.

The α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (AMPARs) are ligand-gated glutamatergic ion channels that mediate most excitatory neurotransmission in the brain. Alterations in AMPAR synaptic accumulation mediate synaptic plasticity, including long-term potentiation, long-term depression and homeostatic synaptic plasticity. AMPAR abundance in neurons is determined by balanced processes of protein translation and degradation. Changes in AMPAR function and trafficking have direct impacts on synaptic transmission and cognitive functions. However, the molecular mechanisms regulating AMPAR expression and dynamics in neurons remain largely unknown. In this thesis, two molecular mechanisms that regulate AMPAR translation and protein stability through two different signaling pathways, 5' adenosine monophosphate-activated protein kinase (AMPK) and sirtuin 2 (SIRT2), are described. It is shown that SIRT2, a NAD+-dependent protein deacetylase, directly controls AMPAR stability by regulating AMPAR acetylation. For the first time, we discovered that AMPARs are subject to lysine acetylation, a novel form of post-translational modification for glutamate receptors. Under basal conditions, AMPARs are highly acetylated at their intracellular C termini, which protects against ubiquitination to antagonize AMPAR endocytosis and degradation, leading to prolonged receptor half-life. SIRT2 is also identified as the enzyme responsible for AMPAR deacetylation. Knockdown of SIRT2 led to elevated AMPAR acetylation and reduced ubiquitination, and consequently, increased AMPAR levels and synaptic transmission. SIRT2 knockout mice displayed weakened synaptic plasticity and impaired learning and memory. Resveratrol is a phytoalexin that has been shown to increase AMPAR expression and synaptic accumulation in neurons. The resveratrol effect on AMPAR expression is independent of sirtuin 1, the conventional target of resveratrol, but rather is mediated by AMPK and its downstream phosphoinositide 3-kinase (PI3K)/Akt pathway. Application of the AMPK activator, 5-aminoimidazole-4-carboxamide 1-β-D-ribofuranoside (AICAR), to neurons mimics the effects of resveratrol on both signaling and AMPAR expression. The resveratrol-induced increase in AMPAR expression results from elevated protein synthesis through the AMPK-PI3K pathway activation. These studies describe novel regulatory mechanisms responsible for the control of AMPAR protein amount and subcellular distribution in neurons, providing insights into our understanding of synaptic plasticity, brain function and neurological disorders. / 2017-12-06T00:00:00Z

Cortical and subcortical microcircuits are continuously modified throughout life. Despite ongoing changes these networks stubbornly maintain their functions, which persist although destabilizing synaptic and nonsynaptic mechanisms should ostensibly propel them towards runaway excitation or quiescence. What dynamical phenomena exist to act together to balance such learning with information processing? What types of activity patterns do they underpin, and how do these patterns relate to our perceptual experiences? What enables learning and memory operations to occur despite such massive and constant neural reorganization? Progress towards answering many of these questions can be pursued through large-scale neuronal simulations.    In this thesis, a Hebbian learning rule for spiking neurons inspired by statistical inference is introduced. The spike-based version of the Bayesian Confidence Propagation Neural Network (BCPNN) learning rule involves changes in both synaptic strengths and intrinsic neuronal currents. The model is motivated by molecular cascades whose functional outcomes are mapped onto biological mechanisms such as Hebbian and homeostatic plasticity, neuromodulation, and intrinsic excitability. Temporally interacting memory traces enable spike-timing dependence, a stable learning regime that remains competitive, postsynaptic activity regulation, spike-based reinforcement learning and intrinsic graded persistent firing levels.    The thesis seeks to demonstrate how multiple interacting plasticity mechanisms can coordinate reinforcement, auto- and hetero-associative learning within large-scale, spiking, plastic neuronal networks. Spiking neural networks can represent information in the form of probability distributions, and a biophysical realization of Bayesian computation can help reconcile disparate experimental observations. / <p>QC 20170421</p>