Estimating short-term synaptic plasticity from pre- and postsynaptic spiking

Ghanbari, Abed; Malyshev, Aleksey; Volgushev, Maxim; Stevenson, I. H.

doi:10.1371/journal.pcbi.1005738

Cited by 33 publications

(43 citation statements)

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“…Unlike intracellular PSP observations, where the amplitude of each individual 53 presynaptic spike can be measured (subject to noise), extracellular spike observations are sparse, 54 typically all-or-none binary events. Modeling dynamic, functional connections from spike 55 observations, especially in the presence of uncontrolled, ongoing neural activity, presents a major 56 statistical challenge (Ghanbari et al, 2017). Here we further develop a model-based approach that, 57…”

Section: Abstract (250 Words)mentioning

confidence: 99%

“…= 1.7, < = 0.02, =0.05 for the depressing synapse and =0.1, ! = 0.02, < = 357 1, =0.11 for the facilitating synapse (Ghanbari et al, 2017). We then convolve the PSCs (delta 358 function kernel) with a PSP kernel, exp(−t/ G ) − exp (−t/ H ), with G = .01 and H =.001 ms to 359 describe synaptic summation.…”

Section: A Simplified Rate Model To Simulate Effects Of Synaptic Summmentioning

confidence: 99%

“…The PSCs of the input then vary according to the Tsodyks-Markram model (parameters for 386 depression/facilitation are as in (Ghanbari et al, 2017)). 387…”

Section: Simulation Of Non-connections 374mentioning

confidence: 99%

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Modeling the short-term dynamics of in vivo excitatory spike transmission

Ghanbari

Ren

Keine

et al. 2018

Preprint

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250 words) 14 Information transmission in neural networks is influenced by both short-term synaptic plasticity 15(STP) as well as non-synaptic factors, such as after-hyperpolarization currents and changes in 16 excitability. Although these effects have been widely characterized in vitro using intracellular 17 recordings, how they interact in vivo is unclear. Here we develop a statistical model of the short-18 term dynamics of spike transmission that aims to disentangle the contributions of synaptic and 19 non-synaptic effects based only on observed pre-and postsynaptic spiking. The model includes a 20 dynamic functional connection with short-term plasticity as well as effects due to the recent history 21 of postsynaptic spiking and slow changes in postsynaptic excitability. Using paired spike 22recordings, we find that the model accurately describes the short-term dynamics of in vivo spike 23 transmission at a diverse set of identified and putative excitatory synapses, including a 24 thalamothalamic connection in mouse, a thalamocortical connection in a female rabbit, and an 25 auditory brainstem synapse in a female gerbil. We illustrate the utility of this modeling approach 26by showing how the spike transmission patterns captured by the model may be sufficient to account 27for stimulus-dependent differences in spike transmission in the auditory brainstem (endbulb of 28 Held). Finally, we apply this model to large-scale multi-electrode recordings to illustrate how such 29 an approach has the potential to reveal cell-type specific differences in spike transmission in vivo. 30Although short-term synaptic plasticity parameters estimated from ongoing pre-and postsynaptic 31 spiking are highly uncertain, our results are partially consistent with previous intracellular 32 observations in these synapses. 33Significance Statement (120 words) 34 Although synaptic dynamics have been extensively studied and modeled using intracellular 35 recordings of post-synaptic currents and potentials, inferring synaptic effects from extracellular 36 spiking is challenging. Whether or not a synaptic current contributes to postsynaptic spiking 37 depends not only on the amplitude of the current, but also on many other factors, including the 38 activity of other, typically unobserved, synapses, the overall excitability of the postsynaptic 39 neuron, and how recently the postsynaptic neuron has spiked. Here we developed a model that, 40using only observations of pre-and postsynaptic spiking, aims to describe the dynamics of in vivo 41 spike transmission by modeling both short-term synaptic plasticity and non-synaptic effects. This 42 approach may provide a novel description of fast, structured changes in spike transmission. 43

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Section: Abstract (250 Words)mentioning

confidence: 99%

Section: A Simplified Rate Model To Simulate Effects Of Synaptic Summmentioning

confidence: 99%

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Modeling the short-term dynamics of in vivo excitatory spike transmission

Ghanbari

Ren

Keine

et al. 2018

Preprint

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“…In vitro investigations have reported that STP has a deep influence on temporal filtering [ 18 ], network stability [ 18 ], and working memory [ 19 ]. Moreover, there is a bidirectional interplay between STP and long-term plasticity [ 20 , 21 ], and this interaction occurs on multiple timescales [ 22 ]. STP is critically important in development [ 23 ] and age-related disorders [ 24 ], including AD [ 25 , 26 ], but there is a lack of knowledge about its specific role, particularly in the context of local circuits, such as the cholinergic system affected in the AD brain.…”

Section: Introductionmentioning

confidence: 99%

Disruption of Cholinergic Circuits as an Area for Targeted Drug Treatment of Alzheimer’s Disease: In Vivo Assessment of Short-Term Plasticity in Rat Brain

et al. 2020

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The search for new therapeutics for the treatment of Alzheimer’s disease (AD) is still in progress. Aberrant pathways of synaptic transmission in basal forebrain cholinergic neural circuits are thought to be associated with the progression of AD. However, the effect of amyloid-beta (Aβ) on short-term plasticity (STP) of cholinergic circuits in the nucleus basalis magnocellularis (NBM) is largely unknown. STP assessment in rat brain cholinergic circuitry may indicate a new target for AD cholinergic therapeutics. Thus, we aimed to study in vivo electrophysiological patterns of synaptic activity in NBM-hippocampus and NBM-basolateral amygdala circuits associated with AD-like neurodegeneration. The extracellular single-unit recordings of responses from the hippocampal and basolateral amygdala neurons to high-frequency stimulation (HFS) of the NBM were performed after intracerebroventricular injection of Aβ 25–35. We found that after Aβ 25–35 exposure the number of hippocampal neurons exhibiting inhibitory responses to HFS of NBM is decreased. The reverse tendency was seen in the basolateral amygdala inhibitory neural populations, whereas the number of amygdala neurons with excitatory responses decreased. The low intensity of inhibitory and excitatory responses during HFS and post-stimulus period is probably due to the anomalous basal synaptic transmission and excitability of hippocampal and amygdala neurons. These functional changes were accompanied by structural alteration of hippocampal, amygdala, and NBM neurons. We have thus demonstrated that Aβ 25–35 induces STP disruption in NBM-hippocampus and NBM-basolateral amygdala circuits as manifested by unbalanced excitatory/inhibitory responses and their frequency. The results of this study may contribute to a better understanding of synaptic integrity. We believe that advancing our understanding of in vivo mechanisms of synaptic plasticity disruption in specific neural circuits could lead to effective drug searches for AD treatment.

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“…It is also possible to perform model-based inference of synaptic parameters based on post-synaptic spike trains instead of EPSCs, as inGhanbari et al (2017Ghanbari et al ( , 2020 Frontiers in Computational Neuroscience | www.frontiersin.org…”

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confidence: 99%

Identifiability of a Binomial Synapse

Gontier

Pfister

2020

Front. Comput. Neurosci.

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Synapses are highly stochastic transmission units. A classical model describing this stochastic transmission is called the binomial model, and its underlying parameters can be estimated from postsynaptic responses to evoked stimuli. The accuracy of parameter estimates obtained via such a model-based approach depends on the identifiability of the model. A model is said to be structurally identifiable if its parameters can be uniquely inferred from the distribution of its outputs. However, this theoretical property does not necessarily imply practical identifiability. For instance, if the number of observations is low or if the recording noise is high, the model's parameters can only be loosely estimated. Structural identifiability, which is an intrinsic property of a model, has been widely characterized; but practical identifiability, which is a property of both the model and the experimental protocol, is usually only qualitatively assessed. Here, we propose a formal definition for the practical identifiability domain of a statistical model. For a given experimental protocol, this domain corresponds to the set of parameters for which the model is correctly identified as the ground truth compared to a simpler alternative model. Considering a model selection problem instead of a parameter inference problem allows to derive a non-arbitrary criterion for practical identifiability. We apply our definition to the study of neurotransmitter release at a chemical synapse. Our contribution to the analysis of synaptic stochasticity is threefold: firstly, we propose a quantitative criterion for the practical identifiability of a statistical model, and compute the identifiability domains of different variants of the binomial release model (uni or multi-quantal, with or without short-term plasticity); secondly, we extend the Bayesian Information Criterion (BIC), a classically used tool for model selection, to models with correlated data (which is the case for most models of chemical synapses); finally, we show that our approach allows to perform data free model selection, i.e., to verify if a model used to fit data was indeed identifiable even without access to the data, but having only access to the fitted parameters.

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Estimating short-term synaptic plasticity from pre- and postsynaptic spiking

Cited by 33 publications

References 59 publications

Modeling the short-term dynamics of in vivo excitatory spike transmission

Modeling the short-term dynamics of in vivo excitatory spike transmission

Disruption of Cholinergic Circuits as an Area for Targeted Drug Treatment of Alzheimer’s Disease: In Vivo Assessment of Short-Term Plasticity in Rat Brain

Identifiability of a Binomial Synapse

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