Exact distribution of the quantal content in synaptic transmission

Rijal, Krishna; Müller, Nicolas I. C.; Friauf, Eckhard; Singh, Abhyudai; Prasad, Ashok; Das, Dibyendu

doi:10.1101/2022.12.28.522121

Cited by 3 publications

(7 citation statements)

References 54 publications

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“…We start by reviewing the SHS formalism for capturing the timing of AP-generation in the postsynaptic neuron [27], [29], [30]. Later in this section, we extend the model to implement negative feedback via an autapse.…”

Section: Formulating Neurotransmission As a Stochastic Hybrid Systemmentioning

confidence: 99%

“…Given that there are M − n ( t ) empty sites, the net replenishment rate is k ( M − n ( t )). This stochastic replenishment process can be probabilistically defined as Combining (3) with (1), the two resets driving the stochastic dynamics of n ( t ) can be represented together as Using the standard tools of moment dynamics [32], we had previously shown that the first and second-order statistical moments of the scalar integer-valued random process n ( t ) evolve as where the angular brackets ⟨ ⟩ denote the expected-value operation [27], [29]. Solving these equations at steady state yield Our prior work has investigated the steady-state noise levels in both n , and the number of released vesicles b , as a function of model parameters f, M, k, p r [27], [30], and also shown the utility of these results in the inference of parameters from experimental data [28].…”

Section: Formulating Neurotransmission As a Stochastic Hybrid Systemmentioning

confidence: 99%

“…where the angular brackets ⟨ ⟩ denote the expected-value operation [27], [29]. Solving these equations at steady state yield…”

Section: A Modeling Presynaptic Processesmentioning

confidence: 99%

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Characterizing the role of autaptic feedback in enhancing precision of neuronal firing times

Vahdat,

Gambrell,

Singh

2023

Preprint

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In a chemical synapse, information flow occurs via the release of neurotransmitters from a presynaptic neuron that triggers an Action potential (AP) in the postsynaptic neuron. At its core, this occurs via the postsynaptic membrane potential integrating neurotransmitter-induced synaptic currents, and AP generation occurs when potential reaches a critical threshold. This manuscript investigates feedback implementation via an autapse, where the axon from the postsynaptic neuron forms an inhibitory synapse onto itself. Using a stochastic model of neuronal synaptic transmission, we formulate AP generation as a first-passage time problem and derive expressions for both the mean and noise of AP-firing times. Our analytical results supported by stochastic simulations identify parameter regimes where autaptic feedback transmission enhances the precision of AP firing times consistent with experimental data. These noise attenuating regimes are intuitively based on two orthogonal mechanisms - either expanding the time window to integrate noisy upstream signals; or by linearizing the mean voltage increase over time. Interestingly, we find regimes for noise amplification that specifically occur when the inhibitory synapse has a low probability of release for synaptic vesicles. In summary, this work explores feedback modulation of the stochastic dynamics of autaptic neurotransmission and reveals its function of creating more regular AP firing patterns.

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Section: Formulating Neurotransmission As a Stochastic Hybrid Systemmentioning

confidence: 99%

Section: Formulating Neurotransmission As a Stochastic Hybrid Systemmentioning

confidence: 99%

See 1 more Smart Citation

Characterizing the role of autaptic feedback in enhancing precision of neuronal firing times

Vahdat,

Gambrell,

Singh

2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…3). This is in contrast to the case of deterministic arrivals (i.e., time between successive APs are fixed), and in this case Fano factors ≤ 1 are subPoissonian across parameter regimes [28], [40].…”

Section: Quantifying Statistics Of Neurotransmissionmentioning

confidence: 99%

“…no resets back to resting potential). Clearly, for (28) to be defined v s > v th which leads to result that if…”

Section: Connecting Neurotransmitter Activity To Postsynaptic Ap Firingmentioning

confidence: 99%

Feedback and Feedforward Regulation of Interneuronal Communication

Gambrell,

Vahdat,

Singh

2024

Preprint

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We formulate a mechanistic model capturing the dynamics of neurotransmitter release in a chemical synapse. The proposed modeling framework captures key aspects such as the random arrival of action potentials (AP) in the presynaptic (input) neuron, probabilistic docking and release of neurotransmitter-filled vesicles, and clearance of the released neurotransmitter from the synaptic cleft. Feedback regulation is implemented by having the released neurotransmitter impact the vesicle docking rate that occurs biologically through autoreceptors on the presynaptic membrane. Our analytical results show that these feedbacks can amplify or buffer fluctuations in neurotransmitter levels depending on the relative interplay of neurotransmitter clearance rate with the AP arrival rate and the vesicle replenishment rate, with faster clearance rates leading to noise amplification. We next consider a postsynaptic (output) neuron that fires an AP based on integrating upstream neurotransmitter activity. Investigating the postsynaptic AP firing times, we identify scenarios that lead to band-pass filtering, i.e., the output neuron frequency is maximized at intermediate input neuron frequencies. We extend these results to consider feedforward regulation where in addition to a direct excitatory synapse, the input neuron also impacts the output indirectly via an inhibitory interneuron, and we identify parameter regimes where feedforward neuronal networks result in band-pass filtering.

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Leveraging the transient statistics of quantal content to infer neuronal synaptic transmission

Vahdat,

Gambrell,

Fisch

et al. 2024

Preprint

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Quantal parameters of synapses are fundamental for the temporal dynamics of neurotransmitter release, forming the basis of interneuronal communication. We propose a class of models that capture the stochastic dynamics of quantal content (QC) - the number of SV fusion events per action potential (AP). Considering the probabilistic and time-varying nature of SV docking, undocking, and AP-triggered fusion, we derive anexactstatistical distribution for the QC over time. Analyzing this distribution at steady-state and its associated autocorrelation function, we show that QC fluctuation statistics can be leveraged for inferring key presynaptic parameters, such as the probability of SV fusion (release probability), and SV replenishment at empty docking sites (refilling probability). Our model predictions are tested with electrophysiological data obtained from 50-Hz stimulation of auditory MNTB-LSO synapses in brainstem slices of juvenile mice. Our results show that while synaptic depression can be explained by low and constant refilling/release probabilities, this scenario is inconsistent with the statistics of the electrophysiological data that show a low QC Fano factor and almost uncorrelated successive QCs. Our systematic analysis yields a model that couples a high release probability to a time-varying refilling probability to explain both the synaptic depression and its associated statistical fluctuations. In summary, we provide a general approach that exploits stochastic signatures in QCs to infer neurotransmission-regulating processes that are indistinguishable from just analyzing averaged synaptic responses.

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Exact distribution of the quantal content in synaptic transmission

Cited by 3 publications

References 54 publications

Characterizing the role of autaptic feedback in enhancing precision of neuronal firing times

Characterizing the role of autaptic feedback in enhancing precision of neuronal firing times

Feedback and Feedforward Regulation of Interneuronal Communication

Leveraging the transient statistics of quantal content to infer neuronal synaptic transmission

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