Synapses are a fundamental unit of computation in the brain. Far from being passive connections between spiking neurons, synapses display striking short-term dynamics, undergo long-term changes in strength, and sculpt network-level processes in a complex manner. These synaptic dynamics, both in time and across space, may be a fundamental determinant of population-level computations and behavioral output of the brain, yet their role in neuromodulatory circuits is relatively under-explored. First, I developed and validated a set of likelihood-based inference tools to quantify the dynamics of synaptic ensemble composition throughout development. Second, I examined network computations in the serotonergic dorsal raphe nucleus through a dynamical lens, exploring the role of short-term synaptic dynamics at sparse recurrent connections, and of distinct long-range synaptic inputs, in shaping the output of spiking populations. 1. Simulation-based inference of synaptic ensembles. Functional features of synapses are typically inferred by sampling small ensembles of synapses, yet it is unclear if such subsamples exhibit biases. I developed a statistical framework to address this question, using it to demonstrate that common bulk electrical stimulation methods for characterizing the fraction of silent synapses exhibit high bias and variance, and using typical sample sizes, possess insufficient statistical power for accurate inference. I developed and validated a novel synthetic likelihood-based inference approach based on a simulator of the underlying experimental methodology. This new estimator, made available in an object-oriented Python toolbox, reduces bias and variance compared to previously reported methods, and provides a scalable method for examining synaptic dynamics throughout development. These tools were validated by targeted recording from hippocampal CA1 neurons in juvenile mice, where they reveal fundamental tradeoffs between release probability, number of synapses sampled, and statistical power. 2. Synaptic dynamics and population computations in the serotonin system. This part is comprised of two manuscripts. First, in the dorsal raphe nucleus, I uncovered slow, inhibitory recurrent interactions between serotonin neurons that are generated by local serotonin release. These connections were probabilistic, displayed striking short-term facilitation, gated the spiking output of serotonin neurons, and could be activated by long-range excitatory input from lateral habenula, representing threat signals. Targeted physiology and modeling revealed that these recurrent short-term facilitation features generated paradoxical excitation-driven inhibition in response to high-frequency habenula input. These facilitation rules additionally supported winner-take-all dynamics at the population level, providing a contrastive operation between functionally distinct serotonergic ensembles. Behaviorally, activating long-range lateral habenula input to dorsal raphe nucleus generated a transient, frequency-dependent suppression of reward anticipation consistent with these recurrent dynamics, without modulating the underlying reward association itself. These dynamics, we suggest, support sharp behavioral state transitions in changing environments. In a second manuscript, I explored the multiplexing of distinct long-range inputs in serotonergic circuits through spike synchrony. I demonstrated that a population of serotonergic neurons receives input from both lateral habenula and prefrontal cortex. These inputs produced similar subthreshold events, but prefrontal cortex triggered spikes with much higher latencies, supporting a population synchrony code for input identity. These input-specific spike timing patterns could be read out by simple linear decoders with high accuracy, suggesting they could be demultiplexed by downstream circuits receiving sparse innervation by serotonergic axons. We uncovered a novel intracellular calcium conductance in serotonergic neurons that altered the spectral characteristics of membrane voltage in a manner sufficient to generate long-latency, power law-distributed spike times, suggesting a simple dynamical origin for the production of synchronous or asynchronous spiking. This work indicates that serotonergic circuits can multiplex distinct informational streams through population spike synchrony mechanisms. Together, these investigations reveal that the dynamics of short-term facilitation and synaptic ensemble composition can act as the fundamental substrate for flexible computation by spiking networks across the brain.
| Identifer | oai:union.ndltd.org:uottawa.ca/oai:ruor.uottawa.ca:10393/45261 |
| Date | 14 August 2023 |
| Creators | Lynn, Michael Benjamin Fernando |
| Contributors | Béïque, Jean-Claude, Maler, Leonard |
| Publisher | Université d'Ottawa / University of Ottawa |
| Source Sets | Université d’Ottawa |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
| Rights | Attribution-NonCommercial-NoDerivatives 4.0 International, http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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