Term Synaptic Depression Research Articles

Multivesicular release favors short term synaptic depression in hippocampal autapses.

Presynaptic terminals of the central nervous system can support univesicular and multivesicular synchronous release of neurotransmitters, however, the functional implications of the prevalence of one mechanism over the other are yet unresolved. Here, we took advantage of the expression of SF-iGluSnFR.S72A in the astrocytic feeder layer of autaptic hippocampal neuronal cultures to associate the liberation of glutamate to excitatory postsynaptic currents. The presence of the glutamate sensor in glial cells avoided any interference with the function of endogenous postsynaptic receptors. It was possible to optically detect changes in neurotransmitter release probability, which was heterogeneous among synaptic boutons studied. For each neuron investigated, the liberation of neurotransmitters occurred through a predominant mechanism. The prevalence of multivesicular over univesicular release increased synaptic strength and enhanced short-term synaptic depression. These results show that the preference of hippocampal boutons to synchronously release one or more vesicles determines the strength and low pass filtering properties of the synapses established.

Marco Capogna, a pioneering neuroscientist and true European

Marco Capogna, a pioneering neuroscientist and true European

Patterned stimulation leads in–vitro network into non-bursting state

Abstract Spontaneous synchronized neuronal activity is found in the neocortical areas of the developing as well as epileptic brain. Synchronized neuronal activity can also be found in dissociated neural networks in vitro. To study the mechanistic origin of this behavior, we built a simple and fast computational model of large network of approximately 80% excitatory and 20% inhibitory neurons. Our model precisely matched the spike times and spike frequency adaptation of cortical pyramidal neurons obtained via whole cell recordings in confined in vitro networks. High accuracy of our model was achieved by implementing an AHP current with sigmoid dynamics that approximated Ca2+ dependent spike frequency adaptation. Then, we implemented short term synaptic depression of the synaptic weights. Modeling of depression was verified by matching the experimental post synaptic responses with three different frequencies of spikes evocation in the presynaptic neuron using dual whole cell recording. Investigations with this computational network were then verified in an all-optical experimental system. This experimental setup was designed by confining a cortical network with polydimethylsiloxane (PDMS), and by co-transfecting the neurons with channel-rhodopsin 2 (ChR2) and jRgeco1a. Patterned stimulator with spatial resolution of somatic compartment (approximately 15 μm) was used to excite individual neuron in a 2D confined network consisting of approximately 300 neurons confined to an area of about 1 mm2. We explored different stimulation paradigms in silico and in vitro to achieve a burst free neuronal network of different size by activating different numbers and patterns of input neurons. Action potential adaptation of each spiking excitatory neuron and the short term synaptic depression of each activated synapses could lead the network into a non-bursting mode. Distributed stimulation method that was developed in this work could potentially be used in suppressing epileptic seizures. Keywords: burst free network, patterned stimulation, optogenetics, synaptic depression

A FeFET with a novel MFMFIS gate stack: towards energy-efficient and ultrafast NVMs for neuromorphic computing

The discovery of ferroelectricity in the fluorite structure based hafnium oxide (HfO2) material sparked major efforts for reviving the ferroelectric field effect transistor (FeFET) memory concept. A Novel metal-ferroelectric-metal-ferroelectric-insulator-semiconductor (MFMFIS) FeFET memory is reported based on dual ferroelectric integration as an MFM and MFIS in a single gate stack using Si-doped Hafnium oxide (HSO) ferroelectric (FE) material. The MFMFIS top and bottom electrode contacts, dual HSO based ferroelectric layers, and tailored MFM to MFIS area ratio (AR-TB) provide a flexible stack structure tuning for improving the FeFET performance. The AR-TB tuning shows a tradeoff between the MFM voltage increase and the weaker FET Si channel inversion, particularly notable in the drain saturation current I D(sat) when the AR-TB ratio decreases. Dual HSO ferroelectric layer integration enables a maximized memory window (MW) and dynamic control of its size by tuning the MFM to MFIS switching contribution through the AR-TB change. The stack structure control via the AR-TB tuning shows further merits in terms of a low voltage switching for a saturated MW size, an extremely linear at wide dynamic range of the current update, as well as high symmetry in the long term synaptic potentiation and depression. The MFMFIS stack reliability is reported in terms of the switching variability, temperature dependence, endurance, and retention. The MFMFIS concept is thoroughly discussed revealing profound insights on the optimal MFMFIS stack structure control for enhancing the FeFET memory performance.

Dopaminergic Neuromodulation of Spike Timing Dependent Plasticity in Mature Adult Rodent and Human Cortical Neurons.

Synapses in the cerebral cortex constantly change and this dynamic property regulated by the action of neuromodulators such as dopamine (DA), is essential for reward learning and memory. DA modulates spike-timing-dependent plasticity (STDP), a cellular model of learning and memory, in juvenile rodent cortical neurons. However, it is unknown whether this neuromodulation also occurs at excitatory synapses of cortical neurons in mature adult mice or in humans. Cortical layer V pyramidal neurons were recorded with whole cell patch clamp electrophysiology and an extracellular stimulating electrode was used to induce STDP. DA was either bath-applied or optogenetically released in slices from mice. Classical STDP induction protocols triggered non-hebbian excitatory synaptic depression in the mouse or no plasticity at human cortical synapses. DA reverted long term synaptic depression to baseline in mouse via dopamine 2 type receptors or elicited long term synaptic potentiation in human cortical synapses. Furthermore, when DA was applied during an STDP protocol it depressed presynaptic inhibition in the mouse but not in the human cortex. Thus, DA modulates excitatory synaptic plasticity differently in human vs. mouse cortex. The data strengthens the importance of DA in gating cognition in humans, and may inform on therapeutic interventions to recover brain function from diseases.

SUMOylation of spastin promotes the internalization of GluA1 and regulates dendritic spine morphology by targeting microtubule dynamics

Dendritic spines are specialized structures involved in neuronal processes on which excitatory synaptic contact occurs. The microtubule cytoskeleton is vital for maintaining spine morphology and mature synapses. Spastin is related to microtubule-severing proteases and is involved in synaptic bouton formation. However, it is not yet known if spastin can be modified by Small Ubiquitin-like Modifier (SUMO) or how this modification regulates dendritic spines. Spastin was shown to be SUMOylated at K427, and its deSUMOylation promoted microtubule stability. In addition, SUMOylation of spastin was shown to affect signalling pathways associated with long term synaptic depression. SUMOylated spastin promoted the development of dendrites and dendritic spines. Moreover, SUMOylated spastin regulated endocytosis and affected the transport of the AMPA receptor, GluA1. Our findings suggest that SUMOylation of spastin promotes GluA1 internalization and regulates dendritic spine morphology through targeting of microtubule dynamics.

Systematic Analysis of tRNA-Derived Small RNAs Reveals Novel Potential Therapeutic Targets of Traditional Chinese Medicine (Buyang-Huanwu-Decoction) on Intracerebral Hemorrhage.

Although Buyang-Huanwu-Decoction (BYHWD), a famous traditional Chinese medicine, has been utilized to promote the recovery of neurological function in intracerebral hemorrhage (ICH) for centuries, its therapeutic mechanisms remain unclear. tRNA-derived small RNA (tsRNA) is a novel class of short non-coding RNA, possessing potential regulating functions. In the current study, we explored the novel therapeutic targets of BYHWD by tsRNA-sequencing. Rats were randomly divided into three groups: sham, ICH, and BYHWD-treated groups. The modified neurological severity score, corner turn test, foot-fault test, and weight change were used to assess neurological injury. After BYHWD treatment, these behavioral tests were obviously meliorated compared with ICH group in the recovery phase. In the rat brain tissues surrounding the hemorrhagic region, a total of 350 tsRNAs for exact match were identified. 12 of tRNAs (fold change >1.3 and P-value <0.05) were significantly changed in ICH group compared to sham group. Among them, 3 of tRNAs (rno-tRFi-Ser-25a, rno-tRF5-Ala-16a and rno-tRF5-Glu-29a) were markedly regulated by BYHWD treatment and validated with quantitative real-time PCR. Additionally, target prediction and bioinformatics analyses revealed that these tsRNAs could play therapeutic roles through FoxO signaling pathway, positive regulation of long term synaptic depression, autophagy - animal, IL-17 signaling pathway and regulation of cytoskeleton and transforming growth factor beta. In conclusion, tsRNAs are the potential therapeutic targets of BYHWD on ICH treatment. The present study provides novel insights for future investigations to explore the mechanisms, by which BYHWD promotes neurological function recovery after ICH.

Flexible Motor Sequence Generation during Stereotyped Escape Responses

Complex animal behaviors arise from flexible combinations of stereotyped motor primitives. How do nervous systems generate dynamics to purposefully explore the action space? Here we study escape responses of the nematode C. elegans, in which the animal predictably moves away from a potential threat. However, the motor sequences and the timing that constitute the whole behavior are variable. We report that a rapid feedforward pathway embedded in a recurrent attractor network underlies robust motor sequence generation, and neurons in different functional modules exploit synaptic inhibitions to flexibly control the timing of motor sequences. We reveal that electrical synapses contribute to feedforward coupling while glutamatergic synaptic transmission contributes to selective inhibitions between motor programs. The rates of motor state transitions are consistent with a stochastic neural dynamic model with short term synaptic depression. Our findings identify a neural computation for robust and flexible motor sequence generation in a compact nervous system.

Statistical crossover and nonextensive behavior of neuronal short-term depression.

The theoretical basis of neuronal coding, associated with short-term degradation in synaptic transmission, is a matter of debate in the literature. In fact, electrophysiological signals are commonly characterized as inversely proportional to stimulus intensity. Among theoretical descriptions of this phenomenon, models based on 1/f-dependency are employed to investigate the biophysical properties of short-term synaptic depression. In this work, we formulate a model based on a paradigmatic q-differential equation to obtain a generalized formalism useful for investigation of nonextensivity in this specific type of synaptic plasticity. Our analysis reveals nonextensivity in data from electrophysiological recordings and also a statistical crossover in neurotransmission. In particular, statistical transitions provide additional support to the hypothesis of heterogeneous release probability of neurotransmitters. On the other hand, the simple vesicle model agrees with data only at low-frequency stimulations. Thus, the present work presents a method to demonstrate that short-term depression is not only governed by random mechanisms but also by nonextensive behavior. Our findings also conciliate morphological and electrophysiological investigations into a coherent biophysical scenario.

Latching dynamics in neural networks with synaptic depression.

Prediction is the ability of the brain to quickly activate a target concept in response to a related stimulus (prime). Experiments point to the existence of an overlap between the populations of the neurons coding for different stimuli, and other experiments show that prime-target relations arise in the process of long term memory formation. The classical modelling paradigm is that long term memories correspond to stable steady states of a Hopfield network with Hebbian connectivity. Experiments show that short term synaptic depression plays an important role in the processing of memories. This leads naturally to a computational model of priming, called latching dynamics; a stable state (prime) can become unstable and the system may converge to another transiently stable steady state (target). Hopfield network models of latching dynamics have been studied by means of numerical simulation, however the conditions for the existence of this dynamics have not been elucidated. In this work we use a combination of analytic and numerical approaches to confirm that latching dynamics can exist in the context of a symmetric Hebbian learning rule, however lacks robustness and imposes a number of biologically unrealistic restrictions on the model. In particular our work shows that the symmetry of the Hebbian rule is not an obstruction to the existence of latching dynamics, however fine tuning of the parameters of the model is needed.

The Neurexin/N-Ethylmaleimide-sensitive Factor (NSF) Interaction Regulates Short Term Synaptic Depression

Although Neurexins, which are cell adhesion molecules localized predominantly to the presynaptic terminals, are known to regulate synapse formation and synaptic transmission, their roles in the regulation of synaptic vesicle release during repetitive nerve stimulation are unknown. Here, we show that nrx mutant synapses exhibit rapid short term synaptic depression upon tetanic nerve stimulation. Moreover, we demonstrate that the intracellular region of NRX is essential for synaptic vesicle release upon tetanic nerve stimulation. Using a yeast two-hybrid screen, we find that the intracellular region of NRX interacts with N-ethylmaleimide-sensitive factor (NSF), an enzyme that mediates soluble NSF attachment protein receptor (SNARE) complex disassembly and plays an important role in synaptic vesicle release. We further map the binding sites of each molecule and demonstrate that the NRX/NSF interaction is critical for both the distribution of NSF at the presynaptic terminals and SNARE complex disassembly. Our results reveal a previously unknown role of NRX in the regulation of short term synaptic depression upon tetanic nerve stimulation and provide new mechanistic insights into the role of NRX in synaptic vesicle release.

Modeling the influence of short term depression in vesicle release and stochastic calcium channel gating on auditory nerve spontaneous firing statistics.

We propose several modifications to an existing computational model of stochastic vesicle release in inner hair cell ribbon synapses, with the aim of producing simulated auditory nerve fiber spiking data that more closely matches empirical data. Specifically, we studied the inter-spike-interval (ISI) distribution, and long and short term ISI correlations in spontaneous spiking in post-synaptic auditory nerve fibers. We introduced short term plasticity to the pre-synaptic release probability, in a manner analogous to standard stochastic models of cortical short term synaptic depression. This modification resulted in a similar distribution of vesicle release intervals to that estimated from empirical data. We also introduced a biophysical stochastic model of calcium channel opening and closing, but showed that this model is insufficient for generating a match with empirically observed spike correlations. However, by combining a phenomenological model of channel noise and our short term depression model, we generated short and long term correlations in auditory nerve spontaneous activity that qualitatively match empirical data.

Associative learning of classical conditioning as an emergent property of spatially extended spiking neural circuits with synaptic plasticity.

Associative learning of temporally disparate events is of fundamental importance for perceptual and cognitive functions. Previous studies of the neural mechanisms of such association have been mainly focused on individual neurons or synapses, often with an assumption that there is persistent neural firing activity that decays slowly. However, experimental evidence supporting such firing activity for associative learning is still inconclusive. Here we present a novel, alternative account of associative learning in the context of classical conditioning, demonstrating that it is an emergent property of a spatially extended, spiking neural circuit with spike-timing dependent plasticity and short term synaptic depression. We show that both the conditioned and unconditioned stimuli can be represented by spike sequences which are produced by wave patterns propagating through the network, and that the interactions of these sequences are timing-dependent. After training, the occurrence of the sequence encoding the conditioned stimulus (CS) naturally regenerates that encoding the unconditioned stimulus (US), therefore resulting in association between them. Such associative learning based on interactions of spike sequences can happen even when the timescale of their separation is significantly larger than that of individual neurons. In particular, our network model is able to account for the temporal contiguity property of classical conditioning, as observed in behavioral studies. We further show that this emergent associative learning in our network model is quite robust to noise perturbations. Our results therefore demonstrate that associative learning of temporally disparate events can happen in a distributed way at the level of neural circuits.

Connectivity, excitability and activity patterns in neuronal networks

Extremely synchronized firing patterns such as those observed in brain diseases like epilepsy may result from excessive network excitability. Although network excitability is closely related to (excitatory) connectivity, a direct measure for network excitability remains unavailable. Several methods currently exist for estimating network connectivity, most of which are related to cross-correlation. An example is the conditional firing probability (CFP) analysis which calculates the pairwise probability (CFPi,j) that electrode j records an action potential at time t = τ, given that electrode i recorded a spike at t = 0. However, electrode i often records multiple spikes within the analysis interval, and CFP values are biased by the on-going dynamic state of the network. Here we show that in a linear approximation this bias may be removed by deconvoluting CFPi,j with the autocorrelation of i (i.e. CFPi,i), to obtain the single pulse response (SPRi,j)—the average response at electrode j to a single spike at electrode i. Thus, in a linear system SPRs would be independent of the dynamic network state. Nonlinear components of synaptic transmission, such as facilitation and short term depression, will however still affect SPRs. Therefore SPRs provide a clean measure of network excitability. We used carbachol and ghrelin to moderately activate cultured cortical networks to affect their dynamic state. Both neuromodulators transformed the bursting firing patterns of the isolated networks into more dispersed firing. We show that the influence of the dynamic state on SPRs is much smaller than the effect on CFPs, but not zero. The remaining difference reflects the alteration in network excitability. We conclude that SPRs are less contaminated by the dynamic network state and that mild excitation may decrease network excitability, possibly through short term synaptic depression.

The impact of short term synaptic depression and stochastic vesicle dynamics on neuronal variability

Neuronal variability plays a central role in neural coding and impacts the dynamics of neuronal networks. Unreliability of synaptic transmission is a major source of neural variability: synaptic neurotransmitter vesicles are released probabilistically in response to presynaptic action potentials and are recovered stochastically in time. The dynamics of this process of vesicle release and recovery interacts with variability in the arrival times of presynaptic spikes to shape the variability of the postsynaptic response. We use continuous time Markov chain methods to analyze a model of short term synaptic depression with stochastic vesicle dynamics coupled with three different models of presynaptic spiking: one model in which the timing of presynaptic action potentials are modeled as a Poisson process, one in which action potentials occur more regularly than a Poisson process (sub-Poisson) and one in which action potentials occur more irregularly (super-Poisson). We use this analysis to investigate how variability in a presynaptic spike train is transformed by short term depression and stochastic vesicle dynamics to determine the variability of the postsynaptic response. We find that sub-Poisson presynaptic spiking increases the average rate at which vesicles are released, that the number of vesicles released over a time window is more variable for smaller time windows than larger time windows and that fast presynaptic spiking gives rise to Poisson-like variability of the postsynaptic response even when presynaptic spike times are non-Poisson. Our results complement and extend previously reported theoretical results and provide possible explanations for some trends observed in recorded data.

Short term synaptic depression improves information transfer in perceptual multistability

Competitive neural networks are often used to model the dynamics of perceptual bistability. Switching between percepts can occur through fluctuations and/or a slow adaptive process. Here, we analyze switching statistics in competitive networks with short term synaptic depression and noise. We start by analyzing a ring model that yields spatially structured solutions and complement this with a study of a space-free network whose populations are coupled with mutual inhibition. Dominance times arising from depression driven switching can be approximated using a separation of timescales in the ring and space-free model. For purely noise-driven switching, we derive approximate energy functions to justify how dominance times are exponentially related to input strength. We also show that a combination of depression and noise generates realistic distributions of dominance times. Unimodal functions of dominance times are more easily told apart by sampling, so switches induced by synaptic depression induced provide more information about stimuli than noise-driven switching. Finally, we analyze a competitive network model of perceptual tristability, showing depression generates a history-dependence in dominance switching.

Proteomic Analysis of α-Amino-3-hydroxy-5-methyl-4-isoxazole Propionate Receptor Complexes

The AMPA receptor (AMPA-R) is a major excitatory neurotransmitter receptor in the brain. Identifying and characterizing the neuronal proteins interacting with AMPA-Rs have provided important information about the molecular mechanisms underlying synaptic transmission and plasticity. In this study, to identify more AMPA-R interactors in vivo, we performed proteomic analyses of AMPA-R complexes from the brain. AMPA-R complexes were isolated from the brain through various combinations of biochemical techniques for solubilization, enrichment, and immunoprecipitation. Mass spectrometry analyses of these isolated complexes identified several novel components of the AMPA-R complexes as well as some previously identified components. The identification of these novel components helps to further define the complex mechanisms involved in the regulation of AMPA receptor function and synaptic plasticity.

Short term synaptic depression with stochastic vesicle dynamics imposes a high-pass filter on presynaptic information

The filtering properties of synapses are modulated by a form of short term depression arising from the depletion of neurotransmitter vesicles. The uptake and release of these vesicles is stochastic in nature, but a widely used model of synaptic depression does not take this stochasticity into account. While this model of synaptic depression accurately captures the trial-averaged synaptic response to a presynaptic spike train [1], it fails to capture variability introduced by stochastic vesicle dynamics [2]. Our goal is to understand the impact of stochastic vesicle dynamics on filtering and information transfer in depressing synapses. We derive compact, closed-form expressions for the synaptic filter induced by short term synaptic depression when stochastic vesicle dynamics are taken into account and when they are not. We find that stochasticity in vesicle uptake and release fundamentally alters the way in which a synapse filters presynaptic information. Predictably, the variability introduced by this stochasticity reduces the rate at which information is transmitted through a synapse. Additionally, this variability introduces frequency-dependence to the transfer of information through a synapse: a model that ignores synaptic variability transmits slowly varying signals with the same fidelity as faster varying signals [3,4], but a model that takes this variability into account transmits faster varying signals with higher fidelity than slower signals (Figure ​(Figure1).1). Differences between the models persist even when the presynaptic cell makes many contacts onto the postsynaptic cell. We extend our analysis to the population level and conclude that a slowly varying signal must be encoded by a large presynaptic population if it is to be reliably transmitted through depressing synapses, but faster varying signals can be reliably encoded by smaller populations. Our results provide useful analytical tools for understanding the filtering properties of depressing synapses and have important consequences for neural coding in the presence of short term synaptic depression. Figure 1 The linear information rate, IL(g;s), which represents the information per unit time available to an optimal linear decoder that estimates a rate-coded presynaptic signal, s(t), by observing a postsynaptic conductance, g(t). The linear information rate ...

Short term synaptic depression imposes a frequency dependent filter on synaptic information transfer.

Depletion of synaptic neurotransmitter vesicles induces a form of short term depression in synapses throughout the nervous system. This plasticity affects how synapses filter presynaptic spike trains. The filtering properties of short term depression are often studied using a deterministic synapse model that predicts the mean synaptic response to a presynaptic spike train, but ignores variability introduced by the probabilistic nature of vesicle release and stochasticity in synaptic recovery time. We show that this additional variability has important consequences for the synaptic filtering of presynaptic information. In particular, a synapse model with stochastic vesicle dynamics suppresses information encoded at lower frequencies more than information encoded at higher frequencies, while a model that ignores this stochasticity transfers information encoded at any frequency equally well. This distinction between the two models persists even when large numbers of synaptic contacts are considered. Our study provides strong evidence that the stochastic nature neurotransmitter vesicle dynamics must be considered when analyzing the information flow across a synapse.

Calcium Signaling in Induction of Synaptic Plasticity in Cortical Pyramidal Neurons

Pairing of pre‐ and postsynaptic activity in layer 2/3 pyramidal neurons in visual cortex at a fixed temporal delay results in variable plasticity outcomes (long term synaptic potentiation ‐ LTP, long term synaptic depression ‐ LTD or no significant change ‐ NC). In order to understand the role that intracellular calcium signaling may play in the different outcomes, we measured [Ca2+]i transients during pairing while pharmacologically inhibiting different calcium sources. In control experiments, pre‐postsynaptic pairing resulted in a net LTP but individual cells in the sample underwent either LTD or LTP or NC. Selective inhibition of individual intracellular calcium sources such as IP3 receptors or ryanodine receptors changed the plasticity outcome to a net LTD when RyRs were blocked with 200μM ryanodine while inhibition of IP3 receptors with 1.0 uM xestospongin resulted in a net LTP. Individual [Ca2+]i. Controls had peak transients =430±87 nM and an average decay time constant = 323±44 msec. However, ryanodine treatment reduced the calcium transient peaks to 310±56 nM and reduced the decay time constant to 239±38 msec. Inhibition of IP3 receptors with xestospongin c reduced the peak transient level to 380±62 nM and the decay time constant to 417±55). Thus, the specific and selective mobilization of different intracellular calcium signaling pathways may be one source of the variable plasticity outcomes between cells.Supported by NIH grant EY‐12782 to MJF