scholarly journals Effect of Inhibitory Spike-Timing-Dependent Plasticity on Fast Sparsely Synchronized Rhythms in A Small-World Neuronal Network

2018 ◽  
Author(s):  
Sang-Yoon Kim ◽  
Woochang Lim
Keyword(s):  
Synaptic Plasticity ◽  
Network Architecture ◽  
Time Window ◽  
Small World ◽  
Long Term Potentiation ◽  
Spike Timing ◽  
Synaptic Inhibition ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity

We consider the Watts-Strogatz small-world network (SWN) consisting of inhibitory fast spiking Izhikevich interneurons. This inhibitory neuronal population has adaptive dynamic synaptic strengths governed by the inhibitory spike-timing-dependent plasticity (iSTDP). In previous works without iSTDP, fast sparsely synchronized rhythms, associated with diverse cognitive functions, were found to appear in a range of large noise intensities for fixed strong synaptic inhibition strengths. Here, we investigate the effect of iSTDP on fast sparse synchronization (FSS) by varying the noise intensity D. We employ an asymmetric anti-Hebbian time window for the iSTDP update rule [which is in contrast to the Hebbian time window for the excitatory STDP (eSTDP)]. Depending on values of D, population-averaged values of saturated synaptic inhibition strengths are potentiated [long-term potentiation (LTP)] or depressed [long-term depression (LTD)] in comparison with the initial mean value, and dispersions from the mean values of LTP/LTD are much increased when compared with the initial dispersion, independently of D. In most cases of LTD where the effect of mean LTD is dominant in comparison with the effect of dispersion, good synchronization (with higher spiking measure) is found to get better via LTD, while bad synchronization (with lower spiking measure) is found to get worse via LTP. This kind of Matthew effect in inhibitory synaptic plasticity is in contrast to that in excitatory synaptic plasticity where good (bad) synchronization gets better (worse) via LTP (LTD). Emergences of LTD and LTP of synaptic inhibition strengths are intensively investigated via a microscopic method based on the distributions of time delays between the pre- and the post-synaptic spike times. Furthermore, we also investigate the effects of network architecture on FSS by changing the rewiring probability p of the SWN in the presence of iSTDP.

scholarly journals Burst Synchronization in A Scale-Free Neuronal Network with Inhibitory Spike-Timing-Dependent Plasticity

2018 ◽  
Author(s):  
Sang-Yoon Kim ◽  
Woochang Lim
Keyword(s):  
Synaptic Plasticity ◽  
Network Architecture ◽  
Neuronal Population ◽  
Matthew Effect ◽  
Spike Timing ◽  
Synaptic Inhibition ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Scale Free ◽  
Burst Synchronization

We are concerned about burst synchronization (BS), related to neural information processes in health and disease, in the Barabasi-Albert scale-free network (SFN) composed of inhibitory bursting Hindmarsh-Rose neurons. This inhibitory neuronal population has adaptive dynamic synaptic strengths governed by the inhibitory spike-timing-dependent plasticity (iSTDP). In previous works without considering iSTDP, BS was found to appear in a range of noise intensities for fixed synaptic inhibition strengths. In contrast, in our present work, we take into consideration iSTDP and investigate its effect on BS by varying the noise intensity. Our new main result is to find occurrence of a Matthew effect in inhibitory synaptic plasticity: good BS gets better via LTD, while bad BS get worse via LTP. This kind of Matthew effect in inhibitory synaptic plasticity is in contrast to that in excitatory synaptic plasticity where good (bad) synchronization gets better (worse) via LTP (LTD). We note that, due to inhibition, the roles of LTD and LTP in inhibitory synaptic plasticity are reversed in comparison with those in excitatory synaptic plasticity. Moreover, emergences of LTD and LTP of synaptic inhibition strengths are intensively investigated via a microscopic method based on the distributions of time delays between the preand the post-synaptic burst onset times. Finally, in the presence of iSTDP we investigate the effects of network architecture on BS by varying the symmetric attachment degree l* and the asymmetry parameter Δl in the SFN.

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.

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 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.

Inhibition of the slow afterhyperpolarization restores the classical spike timing-dependent plasticity rule obeyed in layer 2/3 pyramidal cells of the prefrontal cortex

2012 ◽  
Vol 107 (1) ◽  
pp. 205-215 ◽  
Author(s):  
Aleksey V. Zaitsev ◽  
Roger Anwyl
Keyword(s):  
Prefrontal Cortex ◽  
Pyramidal Cells ◽  
Long Term Potentiation ◽  
Spike Timing ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Layer 2 ◽  
Stdp Rule ◽  
Stimulation Of

The induction of long-term potentiation (LTP) and long-term depression (LTD) of excitatory postsynaptic currents was investigated in proximal synapses of layer 2/3 pyramidal cells of the rat medial prefrontal cortex. The spike timing-dependent plasticity (STDP) induction protocol of negative timing, with postsynaptic leading presynaptic stimulation of action potentials (APs), induced LTD as expected from the classical STDP rule. However, the positive STDP protocol of presynaptic leading postsynaptic stimulation of APs predominantly induced a presynaptically expressed LTD rather than the expected postsynaptically expressed LTP. Thus the induction of plasticity in layer 2/3 pyramidal cells does not obey the classical STDP rule for positive timing. This unusual STDP switched to a classical timing rule if the slow Ca2+-dependent, K+-mediated afterhyperpolarization (sAHP) was inhibited by the selective blocker N-trityl-3-pyridinemethanamine (UCL2077), by the β-adrenergic receptor agonist isoproterenol, or by the cholinergic agonist carbachol. Thus we demonstrate that neuromodulators can affect synaptic plasticity by inhibition of the sAHP. These findings shed light on a fundamental question in the field of memory research regarding how environmental and behavioral stimuli influence LTP, thereby contributing to the modulation of memory.

scholarly journals Reinforced Random Walker meets Spike Timing Dependent Plasticity

2017 ◽  
Author(s):  
Mohammadreza Soltanipour ◽  
Hamed Seyed-allaei
Keyword(s):  
Complex Adaptive System ◽  
Long Term Potentiation ◽  
Spike Timing ◽  
The Other ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Scale Free ◽  
Random Walker ◽  
Complex Adaptive

AbstractWe blended Reinforced Random Walker (RRW) and Spike Timing Dependent Plasticity (STDP) as a minimalistic model to study plasticity of neural network. The model includes walkers which randomly wander on a weighted network. A walker selects a link with a probability proportional to its weight. If the other side of the link is empty, the move succeeds and link’s weight is strengthened (Long Term Potentiation). If the other side is occupied, then the move fails and the weight of the link is weakened (Long Term Depression). Depending on the number of walkers, we observed two phases: ordered (a few strong loops) and disordered (all links are alike). We detected a phase transition from disorder to order depending on the number of walkers. At the transition point, where there was a balance between potentiation and depression, the system became scale-free and histogram of weights was a power law. This work demonstrate how dynamic of a complex adaptive system can lead to critical behavior in its structure via a STDP-like rule.

scholarly journals Pre- and postsynaptically expressed spike-timing-dependent plasticity contribute differentially to neuronal learning

2021 ◽  
Author(s):  
Beatriz Eymi Pimentel Mizusaki ◽  
Sally Si Ying Li ◽  
Rui Ponte Costa ◽  
Jesper Sjöström
Keyword(s):  
Synaptic Plasticity ◽  
Activity Patterns ◽  
Experimental Studies ◽  
Spike Timing ◽  
Response Amplitude ◽  
Synaptic Response ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Response Range

A plethora of experimental studies have shown that long-term synaptic plasticity can be expressed pre- or postsynaptically depending on a range of factors such as developmental stage, synapse type, and activity patterns. The functional consequences of this diversity are not clear, although it is understood that whereas postsynaptic expression of plasticity predominantly affects synaptic response amplitude, presynaptic expression alters both synaptic response amplitude and short-term dynamics. In most models of neuronal learning, long-term synaptic plasticity is implemented as changes in connective weights. The consideration of long-term plasticity as a fixed change in amplitude corresponds more closely to post- than to presynaptic expression, which means theoretical outcomes based on this choice of implementation may have a postsynaptic bias. To explore the functional implications of the diversity of expression of long-term synaptic plasticity, we adapted a model of long-term plasticity, more specifically spike-timing-dependent plasticity (STDP), such that it was expressed either independently pre- or postsynaptically, or in a mixture of both ways. We compared pair-based standard STDP models and a biologically tuned triplet STDP model, and investigated the outcomes in a minimal setting, using two different learning schemes: in the first, inputs were triggered at different latencies, and in the second a subset of inputs were temporally correlated. We found that presynaptic changes adjusted the speed of learning, while postsynaptic expression was more efficient at regulating spike timing and frequency. When combining both expression loci, postsynaptic changes amplified the response range, while presynaptic plasticity allowed control over postsynaptic firing rates, potentially providing a form of activity homeostasis. Our findings highlight how the seemingly innocuous choice of implementing synaptic plasticity by single weight modification may unwittingly introduce a postsynaptic bias in modelling outcomes. We conclude that pre- and postsynaptically expressed plasticity are not interchangeable, but enable complimentary functions.

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