scholarly journals Impairment of Spike-Timing-Dependent Plasticity at Schaffer Collateral-CA1 Synapses in Adult APP/PS1 Mice Depends on Proximity of Aβ Plaques

2021 ◽  
Vol 22 (3) ◽  
pp. 1378
Author(s):  
Machhindra Garad ◽  
Elke Edelmann ◽  
Volkmar Leßmann
Keyword(s):  
Neurodegenerative Disorder ◽  
Hippocampal Slices ◽  
Long Term Potentiation ◽  
Spike Timing ◽  
Dependent Manner ◽  
Spike Timing Dependent Plasticity ◽  
Schaffer Collateral ◽  
Ca1 Neurons ◽  
Dependent Plasticity ◽  
The Impact

Alzheimer’s disease (AD) is a multifaceted neurodegenerative disorder characterized by progressive and irreversible cognitive decline, with no disease-modifying therapy until today. Spike timing-dependent plasticity (STDP) is a Hebbian form of synaptic plasticity, and a strong candidate to underlie learning and memory at the single neuron level. Although several studies reported impaired long-term potentiation (LTP) in the hippocampus in AD mouse models, the impact of amyloid-β (Aβ) pathology on STDP in the hippocampus is not known. Using whole cell patch clamp recordings in CA1 pyramidal neurons of acute transversal hippocampal slices, we investigated timing-dependent (t-) LTP induced by STDP paradigms at Schaffer collateral (SC)-CA1 synapses in slices of 6-month-old adult APP/PS1 AD model mice. Our results show that t-LTP can be induced even in fully developed adult mice with different and even low repeat STDP paradigms. Further, adult APP/PS1 mice displayed intact t-LTP induced by 1 presynaptic EPSP paired with 4 postsynaptic APs (6× 1:4) or 1 presynaptic EPSP paired with 1 postsynaptic AP (100× 1:1) STDP paradigms when the position of Aβ plaques relative to recorded CA1 neurons in the slice were not considered. However, when Aβ plaques were live stained with the fluorescent dye methoxy-X04, we observed that in CA1 neurons with their somata <200 µm away from the border of the nearest Aβ plaque, t-LTP induced by 6× 1:4 stimulation was significantly impaired, while t-LTP was unaltered in CA1 neurons >200 µm away from plaques. Treatment of APP/PS1 mice with the anti-inflammatory drug fingolimod that we previously showed to alleviate synaptic deficits in this AD mouse model did not rescue the impaired t-LTP. Our data reveal that overexpression of APP and PS1 mutations in AD model mice disrupts t-LTP in an Aβ plaque distance-dependent manner, but cannot be improved by fingolimod (FTY720) that has been shown to rescue conventional LTP in CA1 of APP/PS1 mice.

Discrete States of Synaptic Strength in a Stochastic Model of Spike-Timing-Dependent Plasticity

2010 ◽  
Vol 22 (1) ◽  
pp. 244-272 ◽  
Author(s):  
Terry Elliott
Keyword(s):  
Stochastic Model ◽  
Transition Probabilities ◽  
Long Term Potentiation ◽  
Synaptic Strength ◽  
Spike Timing ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Single Spike ◽  
The Impact

A stochastic model of spike-timing-dependent plasticity (STDP) postulates that single synapses presented with a single spike pair exhibit all-or-none quantal jumps in synaptic strength. The amplitudes of the jumps are independent of spiking timing, but their probabilities do depend on spiking timing. By making the amplitudes of both upward and downward transitions equal, synapses then occupy only a discrete set of states of synaptic strength. We explore the impact of a finite, discrete set of strength states on our model, finding three principal results. First, a finite set of strength states limits the capacity of a single synapse to express the standard, exponential STDP curve. We derive the expression for the expected change in synaptic strength in response to a standard, experimental spike pair protocol, finding a deviation from exponential behavior. We fit our prediction to recent data from single dendritic spine heads, finding results that are somewhat better than exponential fits. Second, we show that the fixed-point dynamics of our model regulate the upward and downward transition probabilities so that these are on average equal, leading to a uniform distribution of synaptic strength states. However, third, under long-term potentiation (LTP) and long-term depression (LTD) protocols, these probabilities are unequal, skewing the distribution away from uniformity. If the number of states of strength is at least of order 10, then we find that three effective states of synaptic strength appear, consistent with some experimental data on ternary-strength synapses. On this view, LTP and LTD protocols may therefore be saturating protocols.

Influence of acetylcholine on spike timing-dependent plasticity in CA1 network of rat hippocampal slices

2011 ◽  
Vol 71 ◽  
pp. e111-e112
Author(s):  
Eriko Sugisaki ◽  
Yasuhiro Fukushima ◽  
Hirofumi Hayakawa ◽  
Minoru Tsukada ◽  
Takeshi Aihara
Keyword(s):  
Hippocampal Slices ◽  
Spike Timing ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity

Spike Timing-Dependent Plasticity: From Synapse to Perception

2006 ◽  
Vol 86 (3) ◽  
pp. 1033-1048 ◽  
Author(s):  
Yang Dan ◽  
Mu-Ming Poo
Keyword(s):  
Neuronal Excitability ◽  
Human Perception ◽  
Long Term Potentiation ◽  
Spike Timing ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Functional Changes

Information in the nervous system may be carried by both the rate and timing of neuronal spikes. Recent findings of spike timing-dependent plasticity (STDP) have fueled the interest in the potential roles of spike timing in processing and storage of information in neural circuits. Induction of long-term potentiation (LTP) and long-term depression (LTD) in a variety of in vitro and in vivo systems has been shown to depend on the temporal order of pre- and postsynaptic spiking. Spike timing-dependent modification of neuronal excitability and dendritic integration was also observed. Such STDP at the synaptic and cellular level is likely to play important roles in activity-induced functional changes in neuronal receptive fields and human perception.

scholarly journals Neurodegeneration exposes firing rate dependent effects on oscillation dynamics in computational neural networks

2019 ◽  
Author(s):  
D. Gabrieli ◽  
Samantha N. Schumm ◽  
B. Parvesse ◽  
D.F. Meaney
Keyword(s):  
Neural Networks ◽  
Firing Rate ◽  
Spike Timing ◽  
Network Activity ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Inhibitory Circuitry ◽  
Excitatory Neurons ◽  
Oscillation Dynamics ◽  
The Impact

AbstractTraumatic brain injury (TBI) can lead to neurodegeneration in the injured circuitry, either through primary structural damage to the neuron or secondary effects that disrupt key cellular processes. Moreover, traumatic injuries can preferentially impact subpopulations of neurons, but the functional network effects of these targeted degeneration profiles remain unclear. Although isolating the consequences of complex injury dynamics and long-term recovery of the circuit can be difficult to control experimentally, computational networks can be a powerful tool to analyze the consequences of injury. Here, we use the Izhikevich spiking neuron model to create networks representative of cortical tissue. After an initial settling period with spike-timing-dependent plasticity (STDP), networks developed rhythmic oscillations similar to those seenin vivo. As neurons were sequentially removed from the network, population activity rate and oscillation dynamics were significantly reduced. In a successive period of network restructuring with STDP, network activity levels were returned to baseline for some injury levels and oscillation dynamics significantly improved. We next explored the role that specific neurons have in the creation and termination of oscillation dynamics. We determined that oscillations initiate from activation of low firing rate neurons with limited structural inputs. To terminate oscillations, high activity excitatory neurons with strong input connectivity activate downstream inhibitory circuitry. Finally, we confirm the excitatory neuron population role through targeted neurodegeneration. These results suggest targeted neurodegeneration can play a key role in the oscillation dynamics after injury.Author SummaryIn this study, we study the impact of neuronal degeneration – a process that commonly occurs after traumatic injury and neurodegenerative disease – on the neuronal dynamics in a cortical network. We create computational models of neural networks and include spike timing plasticity to alter the synaptic strength among connections as networks remodel after simulated injury. We find that spike-timing dependent plasticity helps recover the neural dynamics of an injured microcircuit, but it frequently cannot recover the original oscillation dynamics in an uninjured network. In addition, we find that selectively injuring excitatory neurons with the highest firing rate reduced the neuronal oscillations in a circuit much more than either random deletion or the removing neurons with the lowest firing rate. In all, these data suggest (a) plasticity reduces the consequences of neurodegeneration and (b) losing the most active neurons in the network has the most adverse effect on neural oscillations.

scholarly journals Long-term population spike-timing-dependent plasticity promotes synaptic tagging but not cross-tagging in rat hippocampal area CA1

2019 ◽  
Vol 116 (12) ◽  
pp. 5737-5746 ◽  
Author(s):  
Karen Ka Lam Pang ◽  
Mahima Sharma ◽  
Kumar Krishna-K. ◽  
Thomas Behnisch ◽  
Sreedharan Sajikumar
Keyword(s):  
Synaptic Plasticity ◽  
Long Term Potentiation ◽  
Neuronal Population ◽  
Spike Timing ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Adult Rat ◽  
Term Potentiation ◽  
Hippocampal Ca3

In spike-timing-dependent plasticity (STDP), the direction and degree of synaptic modification are determined by the coherence of pre- and postsynaptic activities within a neuron. However, in the adult rat hippocampus, it remains unclear whether STDP-like mechanisms in a neuronal population induce synaptic potentiation of a long duration. Thus, we asked whether the magnitude and maintenance of synaptic plasticity in a population of CA1 neurons differ as a function of the temporal order and interval between pre- and postsynaptic activities. Modulation of the relative timing of Schaffer collateral fibers (presynaptic component) and CA1 axons (postsynaptic component) stimulations resulted in an asymmetric population STDP (pSTDP). The resulting potentiation in response to 20 pairings at 1 Hz was largest in magnitude and most persistent (4 h) when presynaptic activity coincided with or preceded postsynaptic activity. Interestingly, when postsynaptic activation preceded presynaptic stimulation by 20 ms, an immediate increase in field excitatory postsynaptic potentials was observed, but it eventually transformed into a synaptic depression. Furthermore, pSTDP engaged in selective forms of late-associative activity: It facilitated the maintenance of tetanization-induced early long-term potentiation (LTP) in neighboring synapses but not early long-term depression, reflecting possible mechanistic differences with classical tetanization-induced LTP. The data demonstrate that a pairing of pre- and postsynaptic activities in a neuronal population can greatly reduce the required number of synaptic plasticity-evoking events and induce a potentiation of a degree and duration similar to that with repeated tetanization. Thus, pSTDP determines synaptic efficacy in the hippocampal CA3–CA1 circuit and could bias the CA1 neuronal population toward potentiation in future events.

scholarly journals Learning complex temporal patterns with resource-dependent spike timing-dependent plasticity

2012 ◽  
Vol 108 (2) ◽  
pp. 551-566 ◽  
Author(s):  
Jason F. Hunzinger ◽  
Victor H. Chan ◽  
Robert C. Froemke
Keyword(s):  
Long Term Potentiation ◽  
Spike Timing ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Rate Dependent ◽  
Temporal Relationships ◽  
Term Potentiation ◽  
Functional Mechanisms

Studies of spike timing-dependent plasticity (STDP) have revealed that long-term changes in the strength of a synapse may be modulated substantially by temporal relationships between multiple presynaptic and postsynaptic spikes. Whereas long-term potentiation (LTP) and long-term depression (LTD) of synaptic strength have been modeled as distinct or separate functional mechanisms, here, we propose a new shared resource model. A functional consequence of our model is fast, stable, and diverse unsupervised learning of temporal multispike patterns with a biologically consistent spiking neural network. Due to interdependencies between LTP and LTD, dendritic delays, and proactive homeostatic aspects of the model, neurons are equipped to learn to decode temporally coded information within spike bursts. Moreover, neurons learn spike timing with few exposures in substantial noise and jitter. Surprisingly, despite having only one parameter, the model also accurately predicts in vitro observations of STDP in more complex multispike trains, as well as rate-dependent effects. We discuss candidate commonalities in natural long-term plasticity mechanisms.

scholarly journals Interaction between theta-phase and spike-timing dependent plasticity simulates theta induced memory effects

2021 ◽  
Author(s):  
Danying Wang ◽  
George Michael Parish ◽  
Kimron L Shapiro ◽  
Simon Hanslmayr
Keyword(s):  
Episodic Memory ◽  
Theta Rhythm ◽  
Long Term Potentiation ◽  
Spike Timing ◽  
Spike Timing Dependent Plasticity ◽  
Cell Culture Study ◽  
Dependent Plasticity ◽  
Hippocampal Cell Culture ◽  
Theta Phase

Rodent studies suggest that spike timing relative to hippocampal theta activity determines whether potentiation or depression of synapses arise. Such changes also depend on spike timing between pre- and post-synaptic neurons, known as spike-timing-dependent plasticity (STDP). STDP, together with theta-phase-dependent learning, has inspired several computational models of learning and memory. However, evidence to elucidate how these mechanisms directly link to human episodic memory is lacking. In a computational model, we modulate long-term potentiation (LTP) and long-term depression (LTD) of STDP, by opposing phases of a simulated theta rhythm. We fit parameters to a hippocampal cell culture study in which LTP and LTD were observed to occur in opposing phases of a theta rhythm. Further, we modulated two inputs by cosine waves with synchronous and asynchronous phase offsets and replicate key findings in human episodic memory. Learning advantage was found for the synchronous condition, as compared to the asynchronous conditions, and was specific to theta modulated inputs. Importantly, simulations with and without each mechanism suggest that both STDP and theta-phase-dependent plasticity are necessary to replicate the findings. Together, the results indicate a role for circuit-level mechanisms, which bridges the gap between slice preparation studies and human memory.

Sign in / Sign up

Export Citation Format

Share Document