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.

Normalization is important for a large range of phenomena in biological neural systems such as light adaptation in the retina, context dependent decision making and probabilistic inference. In a normalizing circuit the activity of one neuron/-group of neurons is divisively rescaled in relation to the activity of other neurons/­­groups. This creates neural responses invariant to certain stimulus dimensions and dynamically adapts the range over which a neural system can respond discriminatively on stimuli. This thesis examines whether a biologically realistic normalizing circuit can be implemented by a spiking neural network model based on the columnar structure found in cortex. This was done by constructing and evaluating a highly structured spiking neural network model, modelling layer 2/3 of a cortical hypercolumn using a group of neurons as the basic computational unit. The results show that the structure of this hypercolumn module does not per se create a normalizing network. For most model versions the modulatory effect is better described as subtractive inhibition. However three mechanisms that shift the modulatory effect towards normalization were found: An increase in membrane variance for increased modulatory inputs; variability in neuron excitability and connections; and short-term depression on the driving synapses. Moreover it is shown that by combining those mechanisms it is possible to create a spiking neural network that implements approximate normalization over at least ten times increase in input magnitude. These results point towards possible normalizing mechanisms in a cortical hypercolumn; however more studies are needed to assess whether any of those could in fact be a viable explanation for normalization in the biological nervous system. / Normalisering är viktigt för en lång rad fenomen i biologiska nervsystem såsom näthinnans ljusanpassning, kontextberoende beslutsfattande och probabilistisk inferens. I en normaliserande krets skalas aktiviteten hos en nervcell/grupp av nervceller om i relation till aktiviteten hos andra nervceller/grupper. Detta ger neurala svar som är invarianta i förhållande till vissa dimensioner hos stimuli, och anpassar dynamiskt för vilka inputmagnituder ett system kan särskilja mellan stimuli. Den här uppsatsen undersöker huruvida en biologiskt realistisk normal­iserande krets kan implementeras av ett spikande neuronnätverk konstruerat med utgångspunkt från kolumnstrukturen i kortex. Detta gjordes genom att konstruera och utvärdera ett hårt strukturerat rekurrent spikande neuronnätverk, som modellerar lager 2/3 av en kortikal hyperkolumn med en grupp av neuroner som grundläggande beräkningsenhet. Resultaten visar att strukturen i hyperkolumn­modulen inte i sig skapar ett normaliserande nätverk. För de flesta nätverks­versioner implementerar nätverket en modulerande effekt som bättre beskrivs som subtraktiv inhibition. Dock hittades tre mekanismer som skapar ett mer normaliserande nätverk: Ökad membranvarians för större modulerande inputs; variabilitet i excitabilitet och inkommande kopplingar; och korttidsdepression på drivande synapser. Det visas också att genom att kombinera dessa mekanismer är det möjligt att skapa ett spikande neuronnät som approximerar normalisering över ett en åtminstone tio gångers ökning av storleken på input. Detta pekar på möjliga normaliserande mekanismer i en kortikal hyperkolumn, men ytterligare studier är nödvändiga för att avgöra om en eller flera av dessa kan vara en förklaring till hur normalisering är implementerat i biologiska nervsystem.