Parameter Estimation in Synaptic Coupling Model Using a Point Process Modeling Framework

Amidi, Yalda; Nazari, Behzad; Sadri, Saeed; Eden, Uri T.; Yousefi, Ali

doi:10.1109/embc.2018.8512815

Cited by 4 publications

(8 citation statements)

References 8 publications

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“…Although the TM model is biologically plausible, it only tracks average, deterministic dynamics of PSPs, while ignoring the stochasticity of synaptic release (Barri et al, 2016;Bird et al, 2016). Finally, there are many covariates that could be added to improve model performance, including local field potentials (Kelly et al, 2010), connections to other simultaneously observed presynaptic neurons (Harris et al, 2003), higher-order history or coupling terms (Robinson et al, 2016;Song et al, 2018), and covariates related to other types of plasticity (Stevenson and Koerding, 2011;Linderman et al, 2014;Robinson et al, 2016;Amidi et al, 2018;Bayat Mokhtari et al, 2018). Despite these simplifying assumptions and the fact that we only observe a fraction of inputs to the neuron, the TM-GLM captures a wide diversity of in vivo, excitatory spike transmission patterns.…”

Section: Discussionmentioning

confidence: 99%

Modeling the Short-Term Dynamics ofin VivoExcitatory Spike Transmission

Ghanbari¹,

Ren

Keine

et al. 2020

J. Neurosci.

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Information transmission in neural networks is influenced by both short-term synaptic plasticity (STP) as well as nonsynaptic factors, such as after-hyperpolarization currents and changes in excitability. Although these effects have been widely characterized in vitro using intracellular recordings, how they interact in vivo is unclear. Here, we develop a statistical model of the short-term dynamics of spike transmission that aims to disentangle the contributions of synaptic and nonsynaptic effects based only on observed presynaptic and postsynaptic spiking. The model includes a dynamic functional connection with short-term plasticity as well as effects due to the recent history of postsynaptic spiking and slow changes in postsynaptic excitability. Using paired spike recordings, we find that the model accurately describes the short-term dynamics of in vivo spike transmission at a diverse set of identified and putative excitatory synapses, including a pair of connected neurons within thalamus in mouse, a thalamocortical connection in a female rabbit, and an auditory brainstem synapse in a female gerbil. We illustrate the utility of this modeling approach by showing how the spike transmission patterns captured by the model may be sufficient to account for stimulus-dependent differences in spike transmission in the auditory brainstem (endbulb of Held). Finally, we apply this model to large-scale multielectrode recordings to illustrate how such an approach has the potential to reveal cell type-specific differences in spike transmission in vivo. Although STP parameters estimated from ongoing presynaptic and postsynaptic spiking are highly uncertain, our results are partially consistent with previous intracellular observations in these synapses.

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Section: Discussionmentioning

confidence: 99%

Modeling the Short-Term Dynamics ofin VivoExcitatory Spike Transmission

Ghanbari¹,

Ren

Keine

et al. 2020

J. Neurosci.

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“…To linearize the proposed synapse model, we use the extended Kalman filter (EKF) methodology to build an approximation gaussian linear state process (Amidi et al, 2018), and we linearize the nonlinear term around (F…”

Section: Mathematical Definitionmentioning

confidence: 99%

“…The PP-SS model provides a more flexible framework for the analysis of dynamical processes deriving the spiking activity of an individual or an ensemble of neurons (Smith & Brown, 2003;Wu, Kulkarni, Hatsopoulos, & Paninski, 2009;Paninski et al, 2010;Amidi, Nazari, Sadri, Eden, & Yousefi, 2018). Smith and Brown (2003) developed an approach to estimate state-space models through a point process.…”

Section: Introductionmentioning

confidence: 99%

“…In this letter, we use a PP-SS modeling framework to characterize the behavior of the hundreds of temporal dynamic connections deriving the spiking activity of a neuron and propose a computationally efficient parameter estimation technique to estimate the unknown free parameters. Previously we proposed a PP-SS technique to model the spiking activity of a neuron with a limited number of dynamical connections (Amidi et al, 2018). To develop this technique, an EM algorithm was used to estimate the free parameters of the model.…”

Section: Introductionmentioning

confidence: 99%

“…The solution we previously proposed (Amidi et al, 2018) is theoretically applicable to a high-dimensional stimulus; however, there are challenges like the high computational cost and overfitting problem due to many free parameters, which limit its applications. To solve these challenges in this study, we propose a Bayesian PP-SS modeling framework to model the neural activity of a neuron with multiple dynamical synaptic connections.…”

Section: Introductionmentioning

confidence: 99%

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Parameter Estimation in Multiple Dynamic Synaptic Coupling Model Using Bayesian Point Process State-Space Modeling Framework

et al. 2021

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It is of great interest to characterize the spiking activity of individual neurons in a cell ensemble. Many different mechanisms, such as synaptic coupling and the spiking activity of itself and its neighbors, drive a cell's firing properties. Though this is a widely studied modeling problem, there is still room to develop modeling solutions by simplifications embedded in previous models. The first shortcut is that synaptic coupling mechanisms in previous models do not replicate the complex dynamics of the synaptic response. The second is that the number of synaptic connections in these models is an order of magnitude smaller than in an actual neuron. In this research, we push this barrier by incorporating a more accurate model of the synapse and propose a system identification solution that can scale to a network incorporating hundreds of synaptic connections. Although a neuron has hundreds of synaptic connections, only a subset of these connections significantly contributes to its spiking activity. As a result, we assume the synaptic connections are sparse, and to characterize these dynamics, we propose a Bayesian point-process state-space model that lets us incorporate the sparsity of synaptic connections within the regularization technique into our framework. We develop an extended expectation-maximization. algorithm to estimate the free parameters of the proposed model and demonstrate the application of this methodology to the problem of estimating the parameters of many dynamic synaptic connections. We then go through a simulation example consisting of the dynamic synapses across a range of parameter values and show that the model parameters can be estimated using our method. We also show the application of the proposed algorithm in the intracellular data that contains 96 presynaptic connections and assess the estimation accuracy of our method using a combination of goodness-of-fit measures.

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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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Parameter Estimation in Synaptic Coupling Model Using a Point Process Modeling Framework

Cited by 4 publications

References 8 publications

Modeling the Short-Term Dynamics ofin VivoExcitatory Spike Transmission

Modeling the Short-Term Dynamics ofin VivoExcitatory Spike Transmission

Parameter Estimation in Multiple Dynamic Synaptic Coupling Model Using Bayesian Point Process State-Space Modeling Framework

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

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