Model Of Synaptic Plasticity Research Articles

Averaging Principles for Markovian Models of Plasticity

In this paper we consider a stochastic system with two connected nodes, whose unidirectional connection is variable and depends on point processes associated to each node. The input node is represented by an homogeneous Poisson process, whereas the output node jumps with an intensity that depends on the jumps of the input nodes and the connection intensity. We study a scaling regime when the rate of both point processes is large compared to the dynamics of the connection. In neuroscience, this system corresponds to a neural network composed by two neurons, connected by a single synapse. The strength of this synapse depends on the past activity of both neurons, the notion of synaptic plasticity refers to the associated mechanism. A general class of such stochastic models has been introduced in Robert and Vignoud (Stochastic models of synaptic plasticity in neural networks, 2020, arxiv: 2010.08195) to describe most of the models of long-term synaptic plasticity investigated in the literature. The scaling regime corresponds to a classical assumption in computational neuroscience that cellular processes evolve much more rapidly than the synaptic strength. The central result of the paper is an averaging principle for the time evolution of the connection intensity. Mathematically, the key variable is the point process, associated to the output node, whose intensity depends on the past activity of the system. The proof of the result involves a detailed analysis of several of its unbounded additive functionals in the slow-fast limit, and technical results on interacting shot-noise processes.

A theoretical framework for the site-specific and frequency-dependent neuronal effects of deep brain stimulation

BackgroundDeep brain stimulation is an established therapy for several neurological disorders; however, its effects on neuronal activity vary across brain regions and depend on stimulation settings. Understanding these variable responses can aid in the development of physiologically-informed stimulation paradigms in existing or prospective indications. ObjectiveProvide experimental and computational insights into the brain-region-specific and frequency-dependent effects of extracellular stimulation on neuronal activity. MethodsIn patients with movement disorders, single-neuron recordings were acquired from the subthalamic nucleus, substantia nigra pars reticulata, ventral intermediate nucleus, or reticular thalamus during microstimulation across various frequencies (1–100 Hz) to assess single-pulse and frequency-response functions. Moreover, a biophysically-realistic computational framework was developed which generated postsynaptic responses under the assumption that electrical stimuli simultaneously activated all convergent presynaptic inputs to stimulation target neurons. The framework took into consideration the relative distributions of excitatory/inhibitory afferent inputs to model site-specific responses, which were in turn embedded within a model of short-term synaptic plasticity to account for stimulation frequency-dependence. ResultsWe demonstrated microstimulation-evoked excitatory neuronal responses in thalamic structures (which have predominantly excitatory inputs) and inhibitory responses in basal ganglia structures (predominantly inhibitory inputs); however, higher stimulation frequencies led to a loss of site-specificity and convergence towards neuronal suppression. The model confirmed that site-specific responses could be simulated by accounting for local neuroanatomical/microcircuit properties, while suppression of neuronal activity during high-frequency stimulation was mediated by short-term synaptic depression. ConclusionsBrain-region-specific and frequency-dependant neuronal responses could be simulated by considering neuroanatomical (local microcircuitry) and neurophysiological (short-term plasticity) properties.

Fast-Trk(B)ing the mechanism of antidepressants

The mechanism by which antidepressants elicit clinical improvements has proven elusive. In a recent publication in Cell, Casarotto etal. (2021) reveal a surprising direct interaction between antidepressants and TrkB. This link provides an important mechanistic insight into synaptic remodeling that may assist in the design of improved antidepressant therapeutics.

Behavioral Time Scale Plasticity of Place Fields: Mathematical Analysis.

Traditional synaptic plasticity experiments and models depend on tight temporal correlations between pre- and postsynaptic activity. These tight temporal correlations, on the order of tens of milliseconds, are incompatible with significantly longer behavioral time scales, and as such might not be able to account for plasticity induced by behavior. Indeed, recent findings in hippocampus suggest that rapid, bidirectional synaptic plasticity which modifies place fields in CA1 operates at behavioral time scales. These experimental results suggest that presynaptic activity generates synaptic eligibility traces both for potentiation and depression, which last on the order of seconds. These traces can be converted to changes in synaptic efficacies by the activation of an instructive signal that depends on naturally occurring or experimentally induced plateau potentials. We have developed a simple mathematical model that is consistent with these observations. This model can be fully analyzed to find the fixed points of induced place fields and how these fixed points depend on system parameters such as the size and shape of presynaptic place fields, the animal's velocity during induction, and the parameters of the plasticity rule. We also make predictions about the convergence time to these fixed points, both for induced and pre-existing place fields.

Vanillic acid attenuates amyloid β1-40-induced long-term potentiation deficit in male rats: an in vivo investigation

ABSTRACT Objectives: Alzheimer disease (AD) is a neurodegenerative disorderliness that involves deductible progressive cognition function caused by amyloid-beta (Aβ) peptide accumulation in the interstitial space. The increase of Aβ stimulates all kinds of active oxygen and causes oxidative stress and apoptosis. In this investigation, we researched the neuroprotective impacts of vanillic acid (VA) on the Aβ-induced (Aβ1-40) long-term potentiation (LTP) of the hippocampus – a commonly probed synaptic plasticity model that happens at the same time as memory and learning – in the AD rats. Methods: Forty-five male Wistar rats were categorized into five groups (n = 8 rats/group, 200–220 g), and studied as control (standard diet), sham (vehicle), VA (50 mg/kg), Aβ and Aβ + VA (50 mg/kg) groups. In vivo electrophysiological recordings were implemented after the stereotaxic surgery to gauge the excitatory postsynaptic potential (EPSP) slope and population spike (PS) amplitude in the dentate gyrus of the hippocampus. By the stimulation at high-frequency of the perforate pathway, long-term potentiation (LTP) was induced. To assess the plasma levels of malondialdehyde (MDA) and total thiol group (TTG), blood samples were garnered. Results: In the Aβ-injected rats, EPSP slope, and PS amplitude were significantly reduced after the induction of LTP. Thus, the findings demonstrate that VA decreases the impacts of Aβ on LTP; also, the treatments through VA neuroprotective against the negative effects of Aβ on the synaptic plasticity of the hippocampus can decrease the MDA levels and also increase the TTG levels significantly. Discussion: Therefore, based on this experiment on male rats, VA has neuroprotective effects and antioxidants benefits against the Aβ-mediated inhibition of long-term potentiation.

A Synaptic-plasticity Model Inspired by Metabolic Energy

Inspired by the study of metabolic energy, a new synaptic plasticity model was established based on postsynaptic membrane potential and membrane current density. In this model, the change of synaptic weights is expressed by the difference between the resting energy state and firing energy state. The simulation results in L5 pyramidal neurons show that the proposed model can reproduce the triplet and quadruplet experiments of synaptic plasticity, which indicates that our model is feasible. The results of this paper will help to expand the synaptic plasticity model and the understanding of learning and memory from the perspective of energy.

Stochastic Models of Neural Synaptic Plasticity

In neuroscience, learning and memory are usually associated with long-term changes in neuronal connectivity. In this context, synaptic plasticity refers to the set of mechanisms driving the dynamic...

Stochastic Models of Neural Synaptic Plasticity: A Scaling Approach

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 30 November 2020Accepted: 10 August 2021Published online: 08 November 2021Keywordssynaptic plasticity, averaging principles, stochastic modelsAMS Subject Headings60J75, 92C40, 92C20Publication DataISSN (print): 0036-1399ISSN (online): 1095-712XPublisher: Society for Industrial and Applied MathematicsCODEN: smjmap

Modeling on Heterosynaptic Plasticity Based on Postsynaptic Membrane Potential and Current Density

It is still elusive whether different regions in pyramidal neurons have the same heterosynaptic plasticity. To explain the heterosynaptic plasticity, we created a model of synaptic plasticity in which synaptic changes depend on the postsynaptic membrane potential and current density. We found that, in a simulated L5 pyramidal neuron, LTP (long-term potentiation) of synapses in each region of the neuron leads to LTD (long-term depression) of synapses in other regions, which is consistent with the experimental results of heterosynaptic plasticity. However, the effects of heterosynaptic plasticity in different regions are different. Compared with the basal and apical regions, LTP of synapses in the tuft region can not induce significant LTD in other regions. Our research provides a new way to solve the runaway growth of synaptic weights in a neural network.

Noise suppression ability and its mechanism analysis of scale-free spiking neural network under white Gaussian noise.

With the continuous improvement of automation and informatization, the electromagnetic environment has become increasingly complex. Traditional protection methods for electronic systems are facing with serious challenges. Biological nervous system has the self-adaptive advantages under the regulation of the nervous system. It is necessary to explore a new thought on electromagnetic protection by drawing from the self-adaptive advantage of the biological nervous system. In this study, the scale-free spiking neural network (SFSNN) is constructed, in which the Izhikevich neuron model is employed as a node, and the synaptic plasticity model including excitatory and inhibitory synapses is employed as an edge. Under white Gaussian noise, the noise suppression abilities of the SFSNNs with the high average clustering coefficient (ACC) and the SFSNNs with the low ACC are studied comparatively. The noise suppression mechanism of the SFSNN is explored. The experiment results demonstrate that the following. (1) The SFSNN has a certain degree of noise suppression ability, and the SFSNNs with the high ACC have higher noise suppression performance than the SFSNNs with the low ACC. (2) The neural information processing of the SFSNN is the linkage effect of dynamic changes in neuron firing, synaptic weight and topological characteristics. (3) The synaptic plasticity is the intrinsic factor of the noise suppression ability of the SFSNN.

Description of amyloid beta influence on synaptic plasticity and tau hyperphosphorylation by mechanistic quantitative systems pharmacology model

AbstractBackgroundLong‐term potentiation (LTP) is a dependent increase in synaptic plasticity that may be an appropriate experimental and theoretical model for learning and memory. In animal models of Alzheimer’s disease (AD), amyloid beta (Aβ) impacts the LTP decline and tau hyperphosphorylation. Increase of the intracellular calcium level, mediated by NMDA and nACh receptors, is responsible for appearance of LTP, which depends on increased in AMPA phosphorylation. Quantitative systems pharmacology (QSP) model of synaptic plasticity was developed to investigate influence of Aβ on impairment of LTP and tau hyperphosphorylation.MethodThe model describes a calcium influx in postsynapse through acetylcholine and glutamate receptors, activation of efflux from intracellular stores, activation of kinase/phosphatase cascade, tau phosphorylation and level of phosphorylation of AMPA receptors, which is associated with LTP. AD pathology is modeled by Aβ impact on receptors (inhibits nACh) and glial glutamate uptake. Model is developed step by step from simpler variants by consecutive addition of new mechanistic details, using in vitro data. In vitro data from the literature on synaptic plasticity in presence of different effectors, are used for verification.ResultThe model qualitatively describes impairment of LTP and tau hyperphosphorylation by Aβ influence and effect of receptor blockers, kinase and phosphatase inhibitors. It demonstrates that: 1) cholinergic and glutamate stimulation leads to LTP, depending on the timing of cholinergic input relative to glutamate input; 2) impaired LTP may recover due to extrasynaptic NMDA receptor blockers (e.g. memantine) or agonists of nACh receptors (e.g. SSR180711); 3) inhibitors of calcineurin decreased tau phosphorylation and recover LTPConclusionThe QSP model describes an influence Aβ on different postsynaptic mechanisms (receptors activation and kinase/phosphatase cascade, phosphorylation and dephosphorylation of AMPA receptors), which seems to underlie LTP and tau hyperphosphorylation.

Indices of cortical plasticity after therapeutic sleep deprivation in patients with major depressive disorder

BackgroundTherapeutic sleep deprivation (SD) presents a unique paradigm to study the neurobiology of major depressive disorder (MDD). However, the rapid antidepressant mechanism, which differs from today's slow first-line treatments, is not sufficiently understood. We recently integrated two prominent hypotheses of MDD and sleep, the synaptic plasticity hypothesis of MDD and the synaptic homeostasis hypothesis of sleep-wake regulation, into a synaptic plasticity model of therapeutic SD in MDD. Here, we further tested this model positing that homeostatically elevating net synaptic strength through therapeutic SD shifts the initially deficient inducibility of associative synaptic long-term potentiation (LTP)-like plasticity in patients with MDD into a more favorable window of associative plasticity. MethodsWe used paired associative stimulation (PAS), a transcranial magnetic stimulation protocol (TMS), to quantify cortical LTP-like plasticity after one night of therapeutic sleep deprivation in 28 patients with MDD. ResultsWe demonstrate a significantly different inducibility of associative plasticity in clinical responders to therapeutic SD (> 50% improvement on the 6-item Hamilton-Rating-Scale for Depression, n=13) compared to non-responders (n=15), which was driven by a long-term depression (LTD)-like response in SD-non-responders. Indices of global net synaptic strength (wake EEG theta activity, intracortical inhibition and BDNF serum levels) were increased after SD in both groups, with responders showing a generally lower intracortical inhibition than non-responders. LimitationsRepetitive assessments prior to and after treatment would be needed to further determine potential mechanisms. ConclusionAfter a night of therapeutic SD, clinical responders show a significantly higher inducibility of associative LTP-like plasticity than non-responders.

NitroSynapsin ameliorates hypersynchronous neural network activity in Alzheimer hiPSC models.

Beginning at early stages, human Alzheimer’s disease (AD) brains manifest hyperexcitability, contributing to subsequent extensive synapse loss, which has been linked to cognitive dysfunction. No current therapy for AD is disease-modifying. Part of the problem with AD drug discovery is that transgenic mouse models have been poor predictors of potential human treatment. While it is undoubtedly important to test drugs in these animal models, additional evidence for drug efficacy in a human context might improve our chances of success. Accordingly, in order to test drugs in a human context, we have developed a platform of physiological assays using patch-clamp electrophysiology, calcium imaging, and multielectrode array (MEA) experiments on human (h)iPSC-derived 2D cortical neuronal cultures and 3D cerebral organoids. We compare hiPSCs bearing familial AD mutations vs. their wild-type (WT) isogenic controls in order to characterize the aberrant electrical activity in such a human context. Here, we show that these AD neuronal cultures and organoids manifest increased spontaneous action potentials, slow oscillatory events (~1 Hz), and hypersynchronous network activity. Importantly, the dual-allosteric NMDAR antagonist NitroSynapsin, but not the FDA-approved drug memantine, abrogated this hyperactivity. We propose a novel model of synaptic plasticity in which aberrant neural networks are rebalanced by NitroSynapsin. We propose that hiPSC models may be useful for screening drugs to treat hyperexcitability and related synaptic damage in AD.

First Passage Time Memory Lifetimes for Multistate, Filter-Based Synapses.

Models of associative memory with discrete state synapses learn new memories by forgetting old ones. In contrast to non-integrative models of synaptic plasticity, models with integrative, filter-based synapses exhibit an initial rise in the fidelity of recall of stored memories. This rise to a peak is driven by a transient process and is then followed by a return to equilibrium. In a series of papers, we have employed a first passage time (FPT) approach to define and study memory lifetimes, incrementally developing our methods, from both simple and complex binary-strength synapses to simple multistate synapses. Here, we complete this work by analyzing FPT memory lifetimes in multistate, filter-based synapses. To achieve this, we integrate out the internal filter states so that we can work with transitions only in synaptic strength. We then generalize results on polysynaptic generating functions from binary strength to multistate synapses, allowing us to examine the dynamics of synaptic strength changes in an ensemble of synapses rather than just a single synapse. To derive analytical results for FPT memory lifetimes, we partition the synaptic dynamics into two distinct phases: the first, pre-peak phase studied with a drift-only approximation, and the second, post-peak phase studied with approximations to the full strength transition probabilities. These approximations capture the underlying dynamics very well, as demonstrated by the extremely good agreement between results obtained by simulating our model and results obtained from the Fokker-Planck or integral equation approaches to FPT processes.

Towards the interpretation of complex visual hallucinations in terms of self-reorganization of neural networks

Patients suffering from dementia with Lewy body (DLB) often see complex visual hallucinations (CVH). Despite many pathological, clinical, and neuroimaging studies, the mechanism of CVH remains unknown. One possible scenario is that top-down information is being used to compensate for the lack of bottom-up information. To investigate this possibility and understand the underlying mathematical structure of the CVH mechanism, we propose a simple computational model of synaptic plasticity with particular focus on the effect of selective damage to the bottom-up network on self-reorganization. We show neurons that undergo a change in activity from a bottom-up to a top-down network framework during the reorganization process, which can be understood in terms of state transitions. Assuming that the pre-reorganization representation of this neuron remains after reorganization, it is possible to interpret neural response induced by top-down information as the sensation of bottom-up information. This situation might correspond to a hallucinatory situation in DLB patients. Our results agree with existing experimental evidence and provide new insights into data that have hitherto not been experimentally validated on patients with DLB.

Anti-Injury Function of Complex Spiking Neural Networks Under Random Attack and Its Mechanism Analysis

The biological brain has self-adaptive ability through neural information processing and regulation. Drawing from the advantage of the biological brain, it is significant to research the robustness of artificial neural network (ANN) based on brain-like intelligence. In this study, based on the Izhikevich neuron model and the synaptic plasticity model which contains excitatory and inhibitory synapses, a spiking neural network (SNN) with small-world topology and a SNN with scale-free topology are constructed. The anti-injury function of two complex SNNs (CSNN) under random attack is comparatively analyzed. On this basis, the information processing of CSNN under attack is further discussed, and the anti-injury mechanism of CSNN is explored based on the synaptic plasticity. The experimental results show that: (1) scale-free SNN (SFSNN) has better performance than small-world SNN (SWSNN) in the anti-injury ability under random attacks. (2) The information processing of CSNN under random attacks is clarified by the linkage effects of dynamic changes in neuron firing, synaptic weight, and topological characteristics. (3) The anti-injury ability of CSNNs is closely related to the dynamic evolution of synaptic weight, which implies the dynamic regulation of synaptic plasticity is the intrinsic factor of the anti-injury function of CSNNs. This study lays a theoretical foundation for the application of brain-like intelligence with adaptive fault-tolerance.

Frequency-dependent synaptic plasticity model for neurocomputing applications

In neuroscience, there is substantial evidence that suggests temporal filtering of stimulus by synaptic connections. In this paper, a novel frequency-dependent plasticity mechanism (FDSP) for neurocomputing applications is presented. It is proposed that synaptic junctions could be used to perform bandpass filtering on the input stimulus. The unique transfer function of a bandpass filter replaces the conventional weight value associated with synaptic connections. The proposed model has been simulated and rigorously tested with standard machine learning benchmarks such as XOR and multivariate IRIS dataset while utilising minimum resources. The proposed model offers a unique advantage and has the potential to overcome the burden of hidden layer neurons from the network. Exclusion of hidden layer from the network significantly reduces the size of the network and hence the computational effort required for classification tasks. The proposed FDSP mechanism allows for complete analogue system design with a frequency multiplexed communication scheme. The main goal of this study is to establish frequency-dependent plasticity as an alternative to existing time-domain-based techniques. The proposed method has a number of applications in neurocomputing, low power IoT devices and compute-efficient deep convolutional neural networks (DCNNs).

Frequency-dependent synaptic plasticity model for neurocomputing applications

In neuroscience, there is substantial evidence that suggests temporal filtering of stimulus by synaptic connections. In this paper, a novel frequency-dependent plasticity mechanism (FDSP) for neurocomputing applications is presented. It is proposed that synaptic junctions could be used to perform bandpass filtering on the input stimulus. The unique transfer function of a bandpass filter replaces the conventional weight value associated with synaptic connections. The proposed model has been simulated and rigorously tested with standard machine learning benchmarks such as XOR and multivariate IRIS dataset while utilising minimum resources. The proposed model offers a unique advantage and has the potential to overcome the burden of hidden layer neurons from the network. Exclusion of hidden layer from the network significantly reduces the size of the network and hence the computational effort required for classification tasks. The proposed FDSP mechanism allows for complete analogue system design with a frequency multiplexed communication scheme. The main goal of this study is to establish frequency-dependent plasticity as an alternative to existing time-domain-based techniques. The proposed method has a number of applications in neurocomputing, low power IoT devices and compute-efficient deep convolutional neural networks (DCNNs).

Simplified calcium signaling cascade for synaptic plasticity

We propose a model for synaptic plasticity based on a calcium signaling cascade. The model simplifies the full signaling pathways from a calcium influx to the phosphorylation (potentiation) and dephosphorylation (depression) of glutamate receptors that are gated by fictive C1 and C2 catalysts, respectively. This model is based on tangible chemical reactions, including fictive catalysts, for long-term plasticity rather than the conceptual theories commonplace in various models, such as preset thresholds of calcium concentration. Our simplified model successfully reproduced the experimental synaptic plasticity induced by different protocols such as (i) a synchronous pairing protocol and (ii) correlated presynaptic and postsynaptic action potentials (APs). Further, the ocular dominance plasticity (or the experimental verification of the celebrated Bienenstock—Cooper—Munro theory) was reproduced by two model synapses that compete by means of back-propagating APs (bAPs). The key to this competition is synapse-specific bAPs with reference to bAP-boosting on the physiological grounds.

Metabolic constraints on synaptic learning and memory.

Dendritic spines, the carriers of long-term memory, occupy a small fraction of cortical space, and yet they are the major consumers of brain metabolic energy. What fraction of this energy goes for synaptic plasticity, correlated with learning and memory? It is estimated here based on neurophysiological and proteomic data for rat brain that, depending on the level of protein phosphorylation, the energy cost of synaptic plasticity constitutes a small fraction of the energy used for fast excitatory synaptic transmission, typically 4.0-11.2%. Next, this study analyzes a metabolic cost of new learning and its memory trace in relation to the cost of prior memories, using a class of cascade models of synaptic plasticity. It is argued that these models must contain bidirectional cyclic motifs, related to protein phosphorylation, to be compatible with basic thermodynamic principles. For most investigated parameters longer memories generally require proportionally more energy to store. The exceptions are the parameters controlling the speed of molecular transitions (e.g., ATP-driven phosphorylation rate), for which memory lifetime per invested energy can increase progressively for longer memories. Furthermore, in general, a memory trace decouples dynamically from a corresponding synaptic metabolic rate such that the energy expended on new learning and its memory trace constitutes in most cases only a small fraction of the baseline energy associated with prior memories. Taken together, these empirical and theoretical results suggest a metabolic efficiency of synaptically stored information.NEW & NOTEWORTHY Learning and memory involve a sequence of molecular events in dendritic spines called synaptic plasticity. These events are physical in nature and require energy, which has to be supplied by ATP molecules. However, our knowledge of the energetics of these processes is very poor. This study estimates the empirical energy cost of synaptic plasticity and considers theoretically a metabolic rate of learning and its memory trace in a class of cascade models of synaptic plasticity.