Biology subjects

Gambrell, O.

Publications and source records attributed to Gambrell, O..

4 recordsLinked to original sources

bioRxiv2024

Consequences of decoy site repair and feedback regulation on neurotransmission dynamics

Neurons form the fundamental unit of the central nervous system with the human brain containing close to 100 billion neurons. We present a systems-level model of a chemical synapse by which signals from a presynaptic neuron are transmitted to a postsynaptic neuron. In this model, neurotransmitter-filled synaptic vesicles (SVs) dock with a given rate at a fixed number of docking sites in the axon terminal of the presynaptic neuron. Upon the arrival of an action potential (AP), each docked SV has a certain probability to fuse with the presynaptic membrane and release neurotransmitters into the synaptic cleft. After the SV fusion event, the corresponding docking site undergoes repair before becoming available to be reoccupied by an SV. We develop a stochastic model of these coupled processes and derive exact analytical results quantifying the mean and the degree of random fluctuations (i.e., noise) in the levels of docked SVs and released neurotransmitters in response to a train of APs. Our results show that the repair of docking sites exacerbates synaptic depression, i.e., reduces the ability of the chemical synapse to release neurotransmitters in response to an AP. Moreover, repair amplifies statistical fluctuations in neurotransmission for fixed mean neurotransmitter levels. We next consider feedback regulation where the released neurotransmitters affect the rate of SV docking. Counterintuitively, our analysis reveals that for certain physiological parameter spaces, positive feedback loops can reduce noise levels in both the number of docked SVs and neurotransmitters in the cleft.

neuroscience↗

bioRxiv2024

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

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 an exact statistical 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.

neuroscience↗

bioRxiv2024

Feedback and Feedforward Regulation of Interneuronal Communication

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.

neuroscience↗

bioRxiv2023

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

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.

neuroscience↗