Model Of Synaptic Plasticity Research Articles

Lacosamide Does Not Alter In Vitro Long-Term Potentiation in Mouse Hippocampal CA1 Area (P05.087)

Objective: To investigate the effects of lacosamide and topiramate in an in vitro model of long-term potentiation (LTP) induced by tetanic stimulation in the CA1 area of mouse hippocampal slices. Background Long-term potentiation (LTP) is believed to play an important role in encoding memories in neuronal networks and is a widely accepted model for synaptic plasticity, learning and memory. Several antiepileptic drugs (AEDs) induce memory deficits when tested in preclinical models at therapeutic doses. Lacosamide is an AED that differs from other sodium channel-blocking AEDs such as topiramate by selectively enhancing the slow inactivation of voltage-gated sodium (Nav) channels. Topiramate reportedly affects the cognitive performance of patients receiving chronic treatment. Design/Methods:In vitro LTP was recorded in CA1 area from adult C57 black mouse hippocampal slices. Drugs were tested at a final concentration of 100μM. LTP was studied from field excitatory post-synaptic potentials (fEPSPs) recorded in the stratum radiatum of the CA1 area and evoked by Schaeffer collaterals stimulation. LTP was induced with high frequency stimulation (tetanization; TET) at 100Hz applied once during 1s. Results: Lacosamide and topiramate do not affect basal fEPSP slope, thus indicating no effect of either drug on basal synaptic transmission. In the lacosamide- and topiramate-treated slices, TET-induced potentiation (lacosamide: 555%±65%; topiramate: 599%±53%, mean±SEM) was not different to that in the control group (546%±40%, mean±SEM) and was followed by a subsequent decay to a plateau level (fitted plateau value: lacosamide 377%±16%, topiramate 396%±13%; mean±SEM) also not different from control group (fitted plateau value 396%±8%; mean±SEM). Conclusions: This study showed that perfusion of 100μM lacosamide or of 100μM topiramate did not modify LTP recorded in CA1 from mice hippocampal slices. These results suggest that acute treatment with lacosamide or topiramate does not modify neuronal plasticity underlying memory formation in adult mice. Supported by: UCB Pharma. Disclosure: Dr. Niespodziany has received personal compensation for activities with UCB Pharma as an employee. Dr. Leclegre has received personal compensation for activities with UCB Pharmaceuticals as an employe. Dr. Wolff has received personal compensation for activities with Bial, Eisai Inc., Sepracor and UCB Pharma as a consultant.

Visualization of Subunit-Specific Delivery of Glutamate Receptors to Postsynaptic Membrane during Hippocampal Long-Term Potentiation

An increase in the number of AMPA-type glutamate receptors (AMPARs) is critical for long-term potentiation (LTP), synaptic plasticity regarded as a basal mechanism of learning and memory. However, when and how each type of AMPAR reaches the postsynaptic membrane remain unclear. We have developed experimental methods to form postsynaptic-like membrane (PSLM) on a glass surface to precisely visualize the location and movement of receptors. We observed fluorescence-labeled AMPAR subunits (GluA1-3) around PSLM with total internal reflection fluorescence microscopy. The increases of GluA1, 2, and 3 in PSLM showed different time courses after LTP induction. GluA1 increased first, and was exocytosed to the periphery of PSLM soon after LTP induction. GluA2 and GluA3 initially decreased, and then increased. Exocytosis of GluA2 and GluA3 occurred primarily in non-PSLM, and later than exocytosis of GluA1. Thus, GluA1-3 appear to increase in the postsynaptic membrane through distinct pathways during LTP.

Plasticity of motor threshold and motor-evoked potential amplitude – A model of intrinsic and synaptic plasticity in human motor cortex?

BackgroundNeuronal plasticity is the physiological correlate of learning and memory. In animal experiments, synaptic (i.e. long-term potentiation (LTP) and depression (LTD)) and intrinsic plasticity are distinguished. In human motor cortex, cortical plasticity can be demonstrated using transcranial magnetic stimulation (TMS). Changes in motor-evoked potential (MEP) amplitudes most likely represent synaptic plasticity and are thus termed LTP-like and LTD-like plasticity. Objective/hypothesisWe investigated the role of changes of motor threshold and their relation to changes of MEP amplitudes. MethodsWe induced plasticity by paired associative stimulation (PAS) with 25 ms or 10 ms inter-stimulus interval or by motor practice (MP) in 64 healthy subjects aged 18–31 years (median 24.0). ResultsWe observed changes of MEP amplitudes and motor threshold after PAS[25], PAS[10] and MP. In all three protocols, long-term individual changes in MEP amplitude were inversely correlated to changes in motor threshold (PAS[25]: P = .003, n = 36; PAS[10]: P = .038, n = 19; MP: P = .041, n = 19). ConclusionWe conclude that changes of MEP amplitudes and MT represent two indices of motor cortex plasticity. Whereas increases and decreases in MEP amplitude are assumed to represent LTP-like or LTD-like synaptic plasticity of motor cortex output neurons, changes of MT may be considered as a correlate of intrinsic plasticity.

Peripheral and Central Inputs Shape Network Dynamics in the Developing Visual Cortex In Vivo

Spontaneous network activity constitutes a central theme during the development of neuronal circuitry [1, 2]. Before the onset of vision, retinal neurons generate waves of spontaneous activity that are relayed along the ascending visual pathway [3, 4] and shape activity patterns in these regions [5, 6]. The spatiotemporal nature of retinal waves is required to establish precise functional maps in higher visual areas, and their disruption results in enlarged axonal projection areas (e.g., [7-10]). However, how retinal inputs shape network dynamics in the visual cortex on the cellular level is unknown. Using in vivo two-photon calcium imaging, we identified two independently occurring patterns of network activity in the mouse primary visual cortex (V1) before and at the onset of vision. Acute manipulations of spontaneous retinal activity revealed that one type of network activity largely originated in the retina and was characterized by low synchronicity (L-) events. In addition, we identified a type of high synchronicity (H-) events that required gap junction signaling but were independent of retinal input. Moreover, the patterns differed in wave progression and developmental profile. Our data suggest that different activity patterns have complementary functions during the formation of synaptic circuits in the developing visual cortex.

Introduction to spiking neural networks: Information processing, learning and applications.

The concept that neural information is encoded in the firing rate of neurons has been the dominant paradigm in neurobiology for many years. This paradigm has also been adopted by the theory of artificial neural networks. Recent physiological experiments demonstrate, however, that in many parts of the nervous system, neural code is founded on the timing of individual action potentials. This finding has given rise to the emergence of a new class of neural models, called spiking neural networks. In this paper we summarize basic properties of spiking neurons and spiking networks. Our focus is, specifically, on models of spike-based information coding, synaptic plasticity and learning. We also survey real-life applications of spiking models. The paper is meant to be an introduction to spiking neural networks for scientists from various disciplines interested in spike-based neural processing.

A biophysically-based neuromorphic model of spike rate- and timing-dependent plasticity

Current advances in neuromorphic engineering have made it possible to emulate complex neuronal ion channel and intracellular ionic dynamics in real time using highly compact and power-efficient complementary metal-oxide-semiconductor (CMOS) analog very-large-scale-integrated circuit technology. Recently, there has been growing interest in the neuromorphic emulation of the spike-timing-dependent plasticity (STDP) Hebbian learning rule by phenomenological modeling using CMOS, memristor or other analog devices. Here, we propose a CMOS circuit implementation of a biophysically grounded neuromorphic (iono-neuromorphic) model of synaptic plasticity that is capable of capturing both the spike rate-dependent plasticity (SRDP, of the Bienenstock-Cooper-Munro or BCM type) and STDP rules. The iono-neuromorphic model reproduces bidirectional synaptic changes with NMDA receptor-dependent and intracellular calcium-mediated long-term potentiation or long-term depression assuming retrograde endocannabinoid signaling as a second coincidence detector. Changes in excitatory or inhibitory synaptic weights are registered and stored in a nonvolatile and compact digital format analogous to the discrete insertion and removal of AMPA or GABA receptor channels. The versatile Hebbian synapse device is applicable to a variety of neuroprosthesis, brain-machine interface, neurorobotics, neuromimetic computation, machine learning, and neural-inspired adaptive control problems.

What is the appropriate description level for synaptic plasticity?

The hypothesis that learning memory and some aspects of development are mechanistically implemented by synaptic plasticity has gained significant experimental support (1, 2). At the cellular and molecular level synaptic plasticity is a very complex phenomenon, involving hundreds of molecular species, depending on the structure of dendrites and on ion channel concentrations. If we are to understand how high-level processes arise from synaptic plasticity, and not simply that they arise from synaptic plasticity, we must know how to best characterize plasticity theoretically. Such a characterization should account for key experimental results, yet at the same time it should be as simple as possible so that we can use it to explain how plasticity can lead to learning and memory. The article by Gjorgjieva et al. (3) in PNAS argues that a sufficient model of synaptic plasticity can depend only on spike pairs and triplets and that more-complex biophysical and molecular processes might not be needed. It also shows a correspondence between this triplet-based rule and the well-known phenomenological Bienenstock Copper Munro (BCM) learning rule (4).

Cross-Talk Induces Bifurcations in Nonlinear Models of Synaptic Plasticity

Linear models of synaptic plasticity provide a useful starting-point for examining the dynamics of neuronal development and learning, but their inherent problems are well known. Models of synaptic plasticity that embrace the demands of biological realism are therefore typically nonlinear. Viewed from a more abstract perspective, nonlinear models of synaptic plasticity are a subset of nonlinear dynamical systems. As such, they may therefore exhibit bifurcations under the variation of control parameters, including noise and errors in synaptic updates. One source of noise or error is the cross-talk that occurs during otherwise Hebbian plasticity. Under cross-talk, stimulation of a set of synapses can induce or modify plasticity in adjacent, unstimulated synapses. Here, we analyze two nonlinear models of developmental synaptic plasticity and a model of independent component analysis in the presence of a simple model of cross-talk. We show that cross-talk does indeed induce bifurcations in these models, entirely destroying their ability to acquire either developmentally or learning-related patterns of fixed points. Importantly, the critical level of cross-talk required to induce bifurcations in these models is very sensitive to the statistics of the afferents' activities and the number of afferents synapsing on a postsynaptic cell. In particular, the critical level can be made arbitrarily small. Because bifurcations are inevitable in nonlinear models, our results likely apply to many nonlinear models of synaptic plasticity, although the precise details vary by model. Hence, many nonlinear models of synaptic plasticity are potentially fatally compromised by the toxic influence of cross-talk and other sources of noise and errors more generally. We conclude by arguing that biologically realistic models of synaptic plasticity must be robust against noise-induced bifurcations and that biological systems may have evolved strategies to circumvent their possible dangers.

Computational Investigation of the Changing Patterns of Subtype Specific NMDA Receptor Activation during Physiological Glutamatergic Neurotransmission

NMDA receptors (NMDARs) are the major mediator of the postsynaptic response during synaptic neurotransmission. The diversity of roles for NMDARs in influencing synaptic plasticity and neuronal survival is often linked to selective activation of multiple NMDAR subtypes (NR1/NR2A-NMDARs, NR1/NR2B-NMDARs, and triheteromeric NR1/NR2A/NR2B-NMDARs). However, the lack of available pharmacological tools to block specific NMDAR populations leads to debates on the potential role for each NMDAR subtype in physiological signaling, including different models of synaptic plasticity. Here, we developed a computational model of glutamatergic signaling at a prototypical dendritic spine to examine the patterns of NMDAR subtype activation at temporal and spatial resolutions that are difficult to obtain experimentally. We demonstrate that NMDAR subtypes have different dynamic ranges of activation, with NR1/NR2A-NMDAR activation sensitive at univesicular glutamate release conditions, and NR2B containing NMDARs contributing at conditions of multivesicular release. We further show that NR1/NR2A-NMDAR signaling dominates in conditions simulating long-term depression (LTD), while the contribution of NR2B containing NMDAR significantly increases for stimulation frequencies that approximate long-term potentiation (LTP). Finally, we show that NR1/NR2A-NMDAR content significantly enhances response magnitude and fidelity at single synapses during chemical LTP and spike timed dependent plasticity induction, pointing out an important developmental switch in synaptic maturation. Together, our model suggests that NMDAR subtypes are differentially activated during different types of physiological glutamatergic signaling, enhancing the ability for individual spines to produce unique responses to these different inputs.

Multiplicative Dynamics Underlie the Emergence of the Log-Normal Distribution of Spine Sizes in the Neocortex In Vivo

What fundamental properties of synaptic connectivity in the neocortex stem from the ongoing dynamics of synaptic changes? In this study, we seek to find the rules shaping the stationary distribution of synaptic efficacies in the cortex. To address this question, we combined chronic imaging of hundreds of spines in the auditory cortex of mice in vivo over weeks with modeling techniques to quantitatively study the dynamics of spines, the morphological correlates of excitatory synapses in the neocortex. We found that the stationary distribution of spine sizes of individual neurons can be exceptionally well described by a log-normal function. We furthermore show that spines exhibit substantial volatility in their sizes at timescales that range from days to months. Interestingly, the magnitude of changes in spine sizes is proportional to the size of the spine. Such multiplicative dynamics are in contrast with conventional models of synaptic plasticity, learning, and memory, which typically assume additive dynamics. Moreover, we show that the ongoing dynamics of spine sizes can be captured by a simple phenomenological model that operates at two timescales of days and months. This model converges to a log-normal distribution, bridging the gap between synaptic dynamics and the stationary distribution of synaptic efficacies.

Local establishment of repetitive long-term potentiation-induced synaptic enhancement in cultured hippocampal slices with divided input pathways

Long-term potentiation (LTP) in the rodent hippocampus is a popular model for synaptic plasticity, which is considered the cellular basis for brain memory. Because most LTP analysis involves acutely prepared brain slices, however, the longevity of single LTP has not been well documented. Using stable hippocampal slice cultures for long-term examination, we previously found that single LTP disappeared within 1 day. In contrast, repeated induction of LTP led to the development of a distinct type of plasticity that lasted for more than 3 weeks and was accompanied by the formation of new synapses. Naming this novel plastic phenomenon repetitive LTP-induced synaptic enhancement (RISE), we proposed it as a model for the cellular processes involved in long-term memory formation. However, because in those experiments LTP was induced pharmacologically in the whole slice, it is not known whether RISE has input-pathway specificity, an essential property for memory. In this study, we divided the input pathway of CA1 pyramidal neurons by a knife cut and induced LTP three times, the third by tetanic stimulation in one of the divided pathways to express RISE specifically. Voltage-sensitive dye imaging and Golgi-staining performed 2 weeks after the three LTP inductions revealed both enhanced synaptic strength and increased dendritic spine density confined to the tetanized region. These results demonstrate that RISE is a feasible cellular model for long-term memory.

Interactions between short-term and long-term plasticity: shooting for a moving target

AbstractFar from being static transmission units, synapses are highly dynamical elements that change over multiple time scales depending on the history of the neural activity of both the pre- and postsynaptic neuron. Moreover, synaptic changes on different time scales interact: long-term plasticity (LTP) can modify the properties of short-term plasticity (STP) in the same synapse. Most existing theories of synaptic plasticity focus on only one of these time scales (either STP or LTP or late-LTP) and the theoretical principles underlying their interactions are thus largely unknown. Here we develop a normative model of synaptic plasticity that combines both STP and LTP and predicts specific patterns for their interactions. Recently, it has been proposed that STP arranges for the local postsynaptic membrane potential at a synapse to behave as an optimal estimator of the presynaptic membrane potential based on the incoming spikes. Here we generalize this approach by considering an optimal estimator of a non-linear function of the membrane potential and the long-term synaptic efficacy -- which itself may be subject to change on a slower time scale. We find that an increase in the long-term synaptic efficacy necessitates changes in the dynamics of STP. More precisely, for a realistic non-linear function to be estimated, our model predicts that after the induction of LTP, causing long-term synaptic efficacy to increase, a depressing synapse should become even more depressing. That is, in a protocol using trains of presynaptic stimuli, as the initial EPSP becomes stronger due to LTP, subsequent EPSPs should become weakened and this weakening should be more pronounced with LTP. This form of redistribution of synaptic efficacies agrees well with electrophysiological data on synapses connecting layer 5 pyramidal neurons.

Optimal storage and recall with biologically plausible synapses

AbstractSynaptic plasticity is widely accepted to underlie learning and memory. Yet, models of associative networks with biologically plausible synapses fail to match brain performance: memories stored in such networks are quickly overwritten by ongoing plasticity (Amit & Fusi 1996, Fusi et al 2007). Metaplasticity - the process by which neural activity changes the ability of synapses to exhibit further plasticity - is believed to increase memory capacity (Fusi et al 2005). However, it remains unclear if neurons can make use of this additional information during recall. In particular, previous attempts at reading out information in metaplastic synapses using heuristic recall dynamics led to rather poor performance (Huang & Amit 2010).Here, we developed a theoretical framework for storage and recall with finite-state synapses that allowed us to find neural and synaptic dynamics that maximize the efficiency of autoassociative recall.Since information storage by synaptic plasticity is lossy, we formulated the problem of recalling a previously stored pattern from a noisy cue as probabilistic inference (Lengyel et al 2005) and derived neural dynamics efficiently implementing such inferences. Our approach is general and can be applied to any synaptic plasticity model which involves stochastic transitions between a finite set of states.We show how synaptic plasticity rules need to be matched to the statistics of stored patterns, and how recall dynamics need to be matched both to input statistics and to the plasticity rule itself in order to achieve optimal performance. In particular, for binary synapses with metastates we demonstrate for the first time that memories can be efficiently read out with biologically plausible network dynamics that we derive directly from the synaptic metaplasticity rule with virtually no free parameters.

Synaptic Plasticity: A Unifying Model to Address Some Persisting Questions

ABSTRACTSince it was first observed, synaptic plasticity has been considered as the experimental paradigm most likely to provide us with an understanding of how information is stored in the vertebrate brain. Various types have been demonstrated over these past 45 years, most notably long-term potentiation and long-term depression, and their established characteristics as well as their induction and consolidation requirements are highly indicative of this plasticity being the substrate for skills acquisition and mnemonic engraving. The molecular, biochemical, and structural models that have been proposed in the past, although most accommodate some aspect of synaptic plasticity observations, admittedly cannot offer a universally functional connection between all the phenomena that surround and result in the different modifications of synaptic efficacy. As a result, there are a number of persisting questions. In an attempt toward synthesis, we reviewed the most important studies in the field and believe that we can now propose a unifying Model for synaptic plasticity that can accommodate the experimental evidence and reconcile most of the contradictions. Moreover, from this model emerge potential answers to several unyielding questions, namely, accounting for the induction and expression of long-term depression, identifying the plasticity switch, offering a possible explanation for the sliding modification threshold, and proposing a new mechanism for synaptic tagging.

Nuclear Translocation of Jacob in Hippocampal Neurons after Stimuli Inducing Long-Term Potentiation but Not Long-Term Depression

BackgroundIn recent years a number of potential synapto-nuclear protein messengers have been characterized that are thought to be involved in plasticity-related gene expression, and that have the capacity of importin- mediated and activity-dependent nuclear import. However, there is a surprising paucity of data showing the nuclear import of such proteins in cellular models of learning and memory. Only recently it was found that the transcription factor cyclic AMP response element binding protein 2 (CREB2) transits to the nucleus during long-term depression (LTD), but not during long-term potentiation (LTP) of synaptic transmission in hippocampal primary neurons. Jacob is another messenger that couples NMDA-receptor-activity to nuclear gene expression. We therefore aimed to study whether Jacob accumulates in the nucleus in physiological relevant models of activity-dependent synaptic plasticity.Methodology/Principal FindingsWe have analyzed the dynamics of Jacob's nuclear import following induction of NMDA-receptor dependent LTP or LTD at Schaffer collateral-CA1 synapses in rat hippocampal slices. Using time-lapse imaging of neurons expressing a Jacob-Green-Fluorescent-Protein we found that Jacob rapidly translocates from dendrites to the nucleus already during the tetanization period of LTP, but not after induction of LTD. Immunocytochemical stainings confirmed the nuclear accumulation of endogenous Jacob in comparison to apical dendrites after induction of LTP but not LTD. Complementary findings were obtained after induction of NMDA-receptor dependent chemical LTP and LTD in hippocampal primary neurons. However, in accordance with previous studies, high concentrations of NMDA and glycine as well as specific activation of extrasynaptic NMDA-receptors resembling pathological conditions induce an even more profound increase of nuclear Jacob levels.Conclusions/SignificanceTaken together, these findings suggest that the two major forms of NMDA-receptor dependent synaptic plasticity, LTP and LTD, elicit the transition of different synapto-nuclear messengers albeit in both cases importin-mediated retrograde transport and NMDA-receptor activation is required.

Modeling Signal Transduction Leading to Synaptic Plasticity: Evaluation and Comparison of Five Models

An essential phenomenon of the functional brain is synaptic plasticity which is associated with changes in the strength of synapses between neurons. These changes are affected by both extracellular and intracellular mechanisms. For example, intracellular phosphorylation-dephosphorylation cycles have been shown to possess a special role in synaptic plasticity. We, here, provide the first computational comparison of models for synaptic plasticity by evaluating five models describing postsynaptic signal transduction networks. Our simulation results show that some of the models change their behavior completely due to varying total concentrations of protein kinase and phosphatase. Furthermore, the responses of the models vary when models are compared to each other. Based on our study, we conclude that there is a need for a general setup to objectively compare the models and an urgent demand for the minimum criteria that a computational model for synaptic plasticity needs to meet.

Dopamine Modulates Spike Timing-Dependent Plasticity and Action Potential Properties in CA1 Pyramidal Neurons of Acute Rat Hippocampal Slices

Spike timing-dependent plasticity (STDP) is a cellular model of Hebbian synaptic plasticity which is believed to underlie memory formation. In an attempt to establish a STDP paradigm in CA1 of acute hippocampal slices from juvenile rats (P15–20), we found that changes in excitability resulting from different slice preparation protocols correlate with the success of STDP induction. Slice preparation with sucrose containing ACSF prolonged rise time, reduced frequency adaptation, and decreased latency of action potentials in CA1 pyramidal neurons compared to preparation in conventional ASCF, while other basal electrophysiological parameters remained unaffected. Whereas we observed prominent timing-dependent long-term potentiation (t-LTP) to 171 ± 10% of controls in conventional ACSF, STDP was absent in sucrose prepared slices. This sucrose-induced STDP deficit could not be rescued by stronger STDP paradigms, applying either more pre- and/or postsynaptic stimuli, or by a higher stimulation frequency. Importantly, slice preparation with sucrose containing ACSF did not eliminate theta-burst stimulation induced LTP in CA1 in field potential recordings in our rat hippocampal slices. Application of dopamine (for 10–20 min) to sucrose prepared slices completely rescued t-LTP and recovered action potential properties back to levels observed in ACSF prepared slices. Conversely, acute inhibition of D1 receptor signaling impaired t-LTP in ACSF prepared slices. No similar restoring effect for STDP as seen with dopamine was observed in response to the β-adrenergic agonist isoproterenol. ELISA measurements demonstrated a significant reduction of endogenous dopamine levels (to 61.9 ± 6.9% of ACSF values) in sucrose prepared slices. These results suggest that dopamine signaling is involved in regulating the efficiency to elicit STDP in CA1 pyramidal neurons.

A Cognitive Model for Generalization during Sequential Learning

Traditional artificial neural network models of learning suffer fromcatastrophic interference. They are commonly trained to perform only one specific task, and, when trained on a new task, they forget the original task completely. It has been shown that the foundational neurocomputational principles embodied by the Leabra cognitive modeling framework, specifically fast lateral inhibition and a local synaptic plasticity model that incorporates both correlational and error-based components, are sufficient to largely overcome this limitation during the sequential learning of multiple motor skills. Evidence has also provided that Leabra is able to generalize the subsequences of motor skills, when doing so is appropriate. In this paper, we provide a detailed analysis of the extent of generalization possible with Leabra during sequential learning of multiple tasks. For comparison, we measure the generalization exhibited by the backpropagation of error learning algorithm. Furthermore, we demonstrate the applicability of sequential learning to a pair of movement tasks using a simulated robotic arm.

Stability Against Fluctuations: Scaling, Bifurcations, and Spontaneous Symmetry Breaking in Stochastic Models of Synaptic Plasticity

In stochastic models of synaptic plasticity based on a random walk, the control of fluctuations is imperative. We have argued that synapses could act as low-pass filters, filtering plasticity induction steps before expressing a step change in synaptic strength. Earlier work showed, in simulation, that such a synaptic filter tames fluctuations very well, leading to patterns of synaptic connectivity that are stable for long periods of time. Here, we approach this problem analytically. We explicitly calculate the lifetime of meta-stable states of synaptic connectivity using a Fokker-Planck formalism in order to understand the dependence of this lifetime on both the plasticity step size and the filtering mechanism. We find that our analytical results agree very well with simulation results, despite having to make two approximations. Our analysis reveals, however, a deeper significance to the filtering mechanism and the plasticity step size. We show that a filter scales the step size into a smaller, effective step size. This scaling suggests that the step size may itself play the role of a temperature parameter, so that a filter cools the dynamics, thereby reducing the influence of fluctuations. Using the master equation, we explicitly demonstrate a bifurcation at a critical step size, confirming this interpretation. At this critical point, spontaneous symmetry breaking occurs in the class of stochastic models of synaptic plasticity that we consider.

Synaptotagmin1 synthesis induced by synaptic plasticity in mouse hippocampus through activation of nicotinic acetylcholine receptors

We have reported that systemic application of nicotinic agonists expresses a long-term potentiation (LTP)-like facilitation, a model of synaptic plasticity, in vivo in the mouse hippocampus. The present study conducted to clarify the involvement of synaptotagmin1 in synaptic plasticity by investigating the time-dependent change of the mRNA and protein levels of synaptotagmin1 during LTP-like facilitation in the mouse hippocampus. The mRNA expression of synaptotagmin1 increased during 2- to 8-h period by intraperitoneal application of nicotine (3 mg/kg), returning to the basal level in 12-h. Also, the protein level of synaptotagmin1, but not synaptophysin, in a total fraction from hippocampus increased during 4- to 12-h period by the same treatment, returning to the basal level in 24-h. The protein level of synaptotagmin1 in a membrane fraction from hippocampus also increased during 4- to 8-h period by nicotine, returning to the basal level in 12-h. This nicotine-enhanced synaptotagmin1 protein in a membrane fraction was inhibited by pretreatment of mecamylamine (0.3 mg/kg, i.p.), a nonselective nicotinic acetylcholine receptors (nAChRs) antagonist. Furthermore, choline (30 mg/kg, i.p.), a selective α7 nAChR agonist, or ABT-418 (10 mg/kg, i.p.), a selective α4β2 nAChR agonist, enhanced the level of synaptotagmin1 in a membrane fraction. Our findings demonstrate that synaptotagmin1 protein following mRNA which is enhanced without increasing the number of synapse gathers around pre-synaptic membrane during hippocampal LTP-like facilitation through activation of α7 and/or α4β2 nAChRs in the brain. These results suggest that new-synthesized synaptotagmin1 following synaptic plasticity may contribute to long-lasting synaptic plasticity via positive, feedfoward mechanisms.