This study investigates the computational potential of single striatal projection neurons (SPNs), emphasizing dendritic nonlinearities and their crucial role in solving complex integration problems. Utilizing a biophysically detailed multicompartmental model of an SPN, we introduce a calcium-based, local synaptic learning rule dependent on dendritic plateau potentials. According to what is known about excitatory corticostriatal synapses, the learning rule is governed by local calcium dynamics from NMDA and L-type calcium channels and dopaminergic reward signals. In order to devise a self-adjusting learning rule, which ensures stability for individual synaptic weights, metaplasticity is also used. We demonstrate that this rule allows single neurons with sufficiently nonlinear dendrites to solve the nonlinear feature binding problem, a task traditionally attributed to neuronal networks. We also detail an inhibitory plasticity mechanism that contributes to dendritic compartmentalization, further enhancing computational efficiency in dendrites. This in silico study highlights the computational potential of single neurons, providing deeper insights into neuronal information processing and the mechanisms by which the brain executes complex computations.

This study explores the computational potential of single striatal projection neurons (SPN), emphasizing dendritic nonlinearities and their crucial role in solving complex integration problems. Utilizing a biophysically detailed multicompartmental model of an SPN, we introduce a calcium-based, local synaptic learning rule that leverages dendritic plateau potentials. According to what is known about excitatory corticostriatal synapses, the learning rule is governed by local calcium dynamics from NMDA and L-type calcium channels and dopaminergic reward signals. In addition, we incorporated metaplasticity in order to devise a self-adjusting learning rule which ensures stability for individual synaptic weights. We demonstrate that this rule allows single neurons to solve the nonlinear feature binding problem (NFBP), a task traditionally attributed to neuronal networks. We also detail an inhibitory plasticity mechanism, critical for dendritic compartmentalization, further enhancing computational efficiency in dendrites. This in silico study underscores the computational capacity of individual neurons, extending our understanding of neuronal processing and the brain’s ability to perform complex computations.

In this thesis we studied synaptic plasticity and neuronal computation in single striatal projection neurons (SPNs), which have a major role in goal-directed learning. Goal-directed or reward learning means to learn, based on sensory information from the body and the environment, to select actions out of all the behavioral repertoire that lead to obtaining a goal or reward (such as food or water). In mammals, all the behavioral motor repertoire is under constant, tonic inhibition, and the direct-pathway SPNs (dSPNs) select (disinhibit) goal-obtaining actions. The learning process is guided by the neuromodulator dopamine which signals the positive or negative out-come of an action. The synapses from cortical neurons on to the dSPNs, called corticostriatal synapses, are responsive to dopamine signals, and can strengthen and weaken based on the (positive or negative) action outcome. This promotes or discourages future actions in the same or similar sensory context.

Within a collaborative computational modeling effort, we studied the biochemical circuitry in the corticostriatal synapses with multiscale modeling and simulations. This circuitry in the corticostriatal synapses responds to neuromodulatory signals and controls the expression of synaptic plasticity. Multiscale modeling and simulations enable studying a system at multiple temporal and spatial scales, and integrating the results across the different scales. Based on molecular dynamics simulations of the enzyme which transduces extracellular neuromodulatory signals into an intracellular second messenger molecule, and Brownian dynamics simulations of regulator molecules binding to the enzyme, we constructed a kinetic model ofthe enzyme-based signal transduction network. The kinetic model showed that two co-occuring neuromodulatory signals, a dopamine peak and an acetylcholine pause, are required to produce the second messenger and thus enable strengthening of corticostriatal synapses onto dSPNs, and that only the dopamine signal is not enough.

Next, we developed a local, calcium- and reward-dependent learning rule based on what is known about the biochemical circuitry of corticostriatalsynapses onto dSPNs. We show that with this biologically-based learning rule, single SPNs can learn to solve the nonlinear feature binding problem(NFBP), a computationally hard problem representing the class of linearly nonseparable tasks. This result suggests that different, unrelated or partially related stimuli that require executing the same action to obtain a goal, canuse the same SPNs responsible for selecting that action, and that a single SPN can reliably distinguish between similar stimuli.

The solution of the NFBP with the aforementioned learning rule relieson supralinear dendritic voltage elevations called plateau potentials. Experimentally, plateau potentials are all-or-none events, a property crucial for performing nonlinear computations required to solve the NFBP. However, computational models of plateau potentials often produce graded voltage elevations. We analyzed and compared existing plateau potential models, and found that long-lasting glutamate spillover in the extrasynaptic space robustly produces all-or-none plateau potentials by activating extrasynaptic N-methyl-D-aspartate (NMDA) glutamate receptors. This suggests that glutamate spillover may be a mechanism for generating all-or-none plateau potentials in vivo, as well.

In summary, the findings presented in this thesis advance our understanding of the role of single dSPNs in goal-directed learning, the biophysical mechanisms involved in performing their nonlinear computations, and the neuromodulatory signals necessary to produce synaptic strengthening and thus implement goal-directed learning.

I denna avhandling studerades synaptisk plasticitet samt förmågan till lokalberäkning i de striatala projektionsneuronens (SPNs) dendriter. Detta har direkt relevans för förståelsen av s.k. målstyrd inlärning. Målstyrd inlärning och belöningsinlärning innebär att man, baserat på sensorisk informationfrån kroppen och omgivningen, lär sig selektera hur man skall agera/handla så att man uppnår ett mål eller erhåller en belöning (t.ex. mat eller vatten). Hos alla däggdjur är motoriska/exekutiva centra i hjärnstammen och thalamus under konstant tonisk inhibition via basala ganglierna, men när manaktiverar SPNs i den s.k. direkta vägen genom basala ganglierna (dSPN) så disinhiberas de motoriska centra som behövs för att initiera specifika mål-styrda beteenden. Inlärningsprocessen för detta guidas av bl.a. dopamin, en neuromodulator som signalerar huruvida ett beteende ger positivt eller negativt resultat. Synapserna från kortikala projektionsneuron till dSPNs, s.k. kortikostriatala synapser, påverkas av dopamin och de kan förstärkas eller försvagas baserat på om dopaminsignalen signalerar ett positivt eller negativt utfall. Detta antingen främjar eller motverkar att man väljer samma beteende/handlingar under en liknande situation i framtiden.

Via samarbete med andra beräkningsbiologigrupper kunde vi studera de biokemiska signaleringsnätverken i de kortikostriatala synapserna med hjälp av multiskal modeller och simuleringar. Synapsens intracellulära biokemiska signaleringsnätverk kontrollerar synaptisk plasticitet och påverkas av neuro-modulering. Genom att använda multiskalsimuleringar kunde vi studera systemet över multipla temporala och spatiala skalor, och integrera resultaten över de olika skalorna. Baserat på molekylärdynamiska simuleringar av det enzym som överför de extracellulära neuromodulatoriska signalerna till en intracellulär s.k. 2nd messenger molekyl, och Brownianska dynamiksimuleringar av de regulatoriska molekyler som binder till enzymet, kunde vi konstruera en kinetisk modell av detta signaleringsnätverk. Denna kinetiska modell predicerade att två samtidigt förekommande neuromodulatoriska signalförändringar, nämligen en pik i dopamin och en paus i acetylkolin, behövs för att aktivera 2nd messengersignaleringen på ett optimalt sätt, vilket i sin tur leder till att synapsen på dSPN förstärks. Simuleringarna predicerade också att en förändring i dopaminsignalen inte var tillräckligt.

Vi utvecklade därefter en förenklad lokal synaptisk plasticitetsregel baserat på vad som är känt om hur plasticiteten styrs i de kortikostriatala synapserna på dSPNs. Vi kunde visa att med denna inlärningsmodell för synaptisk plasticitet så kan en enskild SPN lära sig att lösa problemet med olinjär funktionsbindning (NFBP), ett beräkningsmässigt svårt problem som representerar klassen av linjärt icke separerbara problem. Detta resultat pekar på att olika, orelaterade eller delvis relaterade stimuli som kräver att man utför samma handling för att uppnå ett mål, kan använda samma SPN för att välja den handlingen, och att en enstaka SPN kan på ett tillförlitligt sätt skilja mellan liknande stimuli.

Lösningen av NFBP med den ovan nämnda inlärningsregeln bygger på supralinjära dendritiska förändringar av membranspänningen vilket kallas platåpotentialer. Experimentellt uppvisar platåpotentialer ett allt-eller-inget beteende, en egenskap som är avgörande för att utföra icke-linjära beräkningar som krävs för att lösa NFBP. Dock producerar beräkningsmodeller av platåpotentialer ofta graderade spänningshöjningar. Vi analyserade och jämförde befintliga modeller för platåpotentialer och fann att glutamatspillover (läckage) i det extrasynaptiska utrymmet producerar, på ett robust sätt, allt-eller-inget platåpotentialer genom att aktivera extrasynaptiska N-methyl-D-aspartat (NMDA) glutamatreceptorer. Detta tyder på att glutamatspillover kan vara en mekanism för att generera dessa allt-eller-inget platåpotentialer även in vivo.

Sammanfattningsvis fördjupar de resultat som presenteras i denna avhandling vår förståelse av enskilda dSPNs roll i målinriktat lärande, de biofysiska mekanismer som är involverade i att utföra neuronens icke-linjära beräkningar, och de neuromodulerande signaler som krävs för att producera synaptisk plasticitet och därmed implementera målinriktat lärande.

Plateau potentials are a critical feature of neuronal excitability, but their all-or-none behavior is not easily captured in modeling. In this study, we investigated models of plateau potentials in multi-compartment neuron models and found that including glutamate spillover provides robust all-or-none behavior. This result arises due to the prolonged duration of extrasynaptic glutamate. When glutamate spillover is not included, the all-or-none behavior is very sensitive to the steepness of the Mg2+ block. These results suggest a potentially significant role of glutamate spillover in plateau potential generation, providing a mechanism for robust all-or-none behavior across a wide range of slopes of the Mg2+ block curve. We also illustrate the importance of the all-or-none plateau potential behavior for nonlinear computation with regard to the nonlinear feature binding problem.

Adenylyl cyclases (ACs) play a key role in many signaling cascades. ACs catalyze the production of cyclic AMP from ATP and this function is stimulated or inhibited by the binding of their cognate stimulatory or inhibitory Gα subunits, respectively. Here we used simulation tools to uncover the molecular and subcellular mechanisms of AC function, with a focus on the AC5 isoform, extensively studied experimentally. First, quantum mechanical/molecular mechanical free energy simulations were used to investigate the enzymatic reaction and its changes upon point mutations. Next, molecular dynamics simulations were employed to assess the catalytic state in the presence or absence of Gα subunits. This led to the identification of an inactive state of the enzyme that is present whenever an inhibitory Gα is associated, independent of the presence of a stimulatory Gα. In addition, the use of coevolution-guided multiscale simulations revealed that the binding of Gα subunits reshapes the free-energy landscape of the AC5 enzyme by following the classical population-shift paradigm. Finally, Brownian dynamics simulations provided forward rate constants for the binding of Gα subunits to AC5, consistent with the ability of the protein to perform coincidence detection effectively. Our calculations also pointed to strong similarities between AC5 and other AC isoforms, including AC1 and AC6. Findings from the molecular simulations were used along with experimental data as constraints for systems biology modeling of a specific AC5-triggered neuronal cascade to investigate how the dynamics of downstream signaling depend on initial receptor activation.

Neuroscience incorporates knowledge from a range of scales, from single molecules to brain wide neural networks. Modeling is a valuable tool in understanding processes at a single scale or the interactions between two adjacent scales and researchers use a variety of different software tools in the model building and analysis process. Here we focus on the scale of biochemical pathways, which is one of the main objects of study in systems biology. While systems biology is among the more standardized fields, conversion between different model formats and interoperability between various tools is still somewhat problematic. To offer our take on tackling these shortcomings and by keeping in mind the FAIR (findability, accessibility, interoperability, reusability) data principles, we have developed a workflow for building and analyzing biochemical pathway models, using pre-existing tools that could be utilized for the storage and refinement of models in all phases of development. We have chosen the SBtab format which allows the storage of biochemical models and associated data in a single file and provides a human readable set of syntax rules. Next, we implemented custom-made MATLAB® scripts to perform parameter estimation and global sensitivity analysis used in model refinement. Additionally, we have developed a web-based application for biochemical models that allows simulations with either a network free solver or stochastic solvers and incorporating geometry. Finally, we illustrate convertibility and use of a biochemical model in a biophysically detailed single neuron model by running multiscale simulations in NEURON. Using this workflow, we can simulate the same model in three different simulators, with a smooth conversion between the different model formats, enhancing the characterization of different aspects of the model.

Author summary Adenylyl cyclases (ACs) are enzymes that can translate extracellular signals into the intracellular molecule cAMP, which is thus a 2nd messenger of extracellular events. The brain expresses nine membrane-bound AC variants, and AC5 is the dominant form in the striatum. The striatum is the input stage of the basal ganglia, a brain structure involved in reward learning, i.e. the learning of behaviors that lead to rewarding stimuli (such as food, water, sugar, etc). During reward learning, cAMP production is crucial for strengthening the synapses from cortical neurons onto the striatal principal neurons, and its formation is dependent on several neuromodulatory systems such as dopamine and acetylcholine. It is, however, not understood how AC5 is activated by transient (subsecond) changes in the neuromodulatory signals. Here we combine several computational tools, from molecular dynamics and Brownian dynamics simulations to bioinformatics approaches, to inform and constrain a kinetic model of the AC5-dependent signaling system. We use this model to show how the specific molecular properties of AC5 can detect particular combinations of co-occuring transient changes in the neuromodulatory signals which thus result in a supralinear/synergistic cAMP production. Our results also provide insights into the computational capabilities of the different AC isoforms. Long-term potentiation and depression of synaptic activity in response to stimuli is a key factor in reinforcement learning. Strengthening of the corticostriatal synapses depends on the second messenger cAMP, whose synthesis is catalysed by the enzyme adenylyl cyclase 5 (AC5), which is itself regulated by the stimulatory G alpha(olf) and inhibitory G alpha(i) proteins. AC isoforms have been suggested to act as coincidence detectors, promoting cellular responses only when convergent regulatory signals occur close in time. However, the mechanism for this is currently unclear, and seems to lie in their diverse regulation patterns. Despite attempts to isolate the ternary complex, it is not known if G alpha(olf) and G alpha(i) can bind to AC5 simultaneously, nor what activity the complex would have. Using protein structure-based molecular dynamics simulations, we show that this complex is stable and inactive. These simulations, along with Brownian dynamics simulations to estimate protein association rates constants, constrain a kinetic model that shows that the presence of this ternary inactive complex is crucial for AC5's ability to detect coincident signals, producing a synergistic increase in cAMP. These results reveal some of the prerequisites for corticostriatal synaptic plasticity, and explain recent experimental data on cAMP concentrations following receptor activation. Moreover, they provide insights into the regulatory mechanisms that control signal processing by different AC isoforms.