On striatum in silico

The basal ganglia are a collection of subcortical nuclei involved in movement and action selection. The striatum is the main input nucleus with extensive projections from the cortex and thalamus, and dopaminergic projections from SNc and VTA. The two main cell types are the striatal projection neurons (SPNs), which are divided into the direct (dSPN) and indirect (iSPN) pathways, based on the downstream projections and the expression of dopamine D1 and D2 receptors, respectively. The remaining 5% consists mainly of GABAergic interneurons, such as parvalbumin-expressing fastspiking interneurons (FS) and low threshold spiking interneurons (LTS). The cholinergic interneuron (ChIN) is spontaneously active and unlike the other interneurons releases acetylcholine. This thesis is focused on investigating the function of the striatum and the role of SPNs and the striatal interneurons. This is achieved by building a platform, tools, and a database of multi-compartmental models of SPN, FS, ChIN, and LTS; and through simulations systematically uncovering the roles of these striatal neuron types and external input and, more specifically, the role of neuromodulation and intrastriatal inhibition. In Paper I, Snudda, a platform for simulating large-scale networks, is developed and includes multicompartmental models of dSPN, iSPN, FS, LTS, and ChIN. The tools include methods to generate external input from the cortex and thalamus; and dopaminergic modulation from SNc. Paper II investigates the relationship between ChIN and LTS. The ChIN releases ACh, which activates both nicotinic and muscarinic receptors within the striatum. The dominating effect on LTS is inhibition caused by muscarinic M4 receptors. LTS, on the other hand, releases NO which excites ChINs. Paper II showed that the interaction between these neuromodulators could control the activity of ChIN and LTS, which are generally spontaneously active. In the subsequent Paper III, Snudda was complemented with the neuromodulation package called Neuromodcell, a Python Package, for creating models of neuromodulation, which can be included in large-scale network simulations in Snudda. The method of simulating neuromodulators in Snudda was expanded to include multiple simultaneously active modulators. This resulted in several simulations with simultaneous ACh pause with DA burst as well as an ACh burst with a DA burst. In Paper IV, the effect of intrastriatal surround inhibition on striatal activity was investigated by utilizing ablations, clustered input, dopaminergic modulation, and other features in Snudda. These simulations demonstrated that shunting inhibition could reduce the amplitude of corticostriatal input onto SPNs. The surround inhibition can further modulate the plateau potentials in SPNs, which is dependent on the GABA reversal. Lastly, the competition between populations of SPNs can be modified by varying the strength, size, and positions of populations. Furthermore, dopaminergic modulation can enhance the effect of dSPNs, while increasing the inhibition onto iSPNs. Overall, this thesis provides an analysis of the striatal microcircuit and a tool for further investigations of the striatum in silico; and demonstrates the importance to consider the different components of the striatal microcircuit and how neuromodulators can reshape microcircuits on both single neuron and network levels

Dopamine guides competition for cognitive control:Common effects of haloperidol on working memory and response conflict

Several lines of evidence suggest that dopamine modulates working memory (the ability to faithfully maintain and efficiently manipulate information over time) but its specific role has not been fully defined. Nor is it clear whether any effects of dopamine are specific to memory processes or whether they reflect more general cognitive mechanisms that extend beyond the working memory domain. Here, we examine the effect of haloperidol, principally a dopamine D2 receptor antagonist, on the ability of humans to ignore distracting information or update working memory contents. We compare these effects to performance on an independent measure of cognitive control (response conflict) which has minimal memory requirements. Haloperidol did not selectively affect the ability to ignore or update, but instead reduced the overall quality of recall. In addition, it impaired the ability to overcome response conflict. The deleterious effect of haloperidol on response conflict was selectively associated with the negative effect of the drug on ignoring - but not updating - suggesting that dopamine affects protection of working memory contents and inhibition in response conflict through a common mechanism. These findings provide new insights into the role of dopamine D2 receptors on human cognition. They suggest that D2 receptor effects on protecting the memory contents from distraction might be related to a more general process that supports inhibitory control in contexts that do not require working memory

The influence of dopamine on prediction, action and learning

In this thesis I explore functions of the neuromodulator dopamine in the context of autonomous learning and behaviour. I first investigate dopaminergic influence within a simulated agent-based model, demonstrating how modulation of synaptic plasticity can enable reward-mediated learning that is both adaptive and self-limiting. I describe how this mechanism is driven by the dynamics of agentenvironment interaction and consequently suggest roles for both complex spontaneous neuronal activity and specific neuroanatomy in the expression of early, exploratory behaviour. I then show how the observed response of dopamine neurons in the mammalian basal ganglia may also be modelled by similar processes involving dopaminergic neuromodulation and cortical spike-pattern representation within an architecture of counteracting excitatory and inhibitory neural pathways, reflecting gross mammalian neuroanatomy. Significantly, I demonstrate how combined modulation of synaptic plasticity and neuronal excitability enables specific (timely) spike-patterns to be recognised and selectively responded to by efferent neural populations, therefore providing a novel spike-timing based implementation of the hypothetical ‘serial-compound’ representation suggested by temporal difference learning. I subsequently discuss more recent work, focused upon modelling those complex spike-patterns observed in cortex. Here, I describe neural features likely to contribute to the expression of such activity and subsequently present novel simulation software allowing for interactive exploration of these factors, in a more comprehensive neural model that implements both dynamical synapses and dopaminergic neuromodulation. I conclude by describing how the work presented ultimately suggests an integrated theory of autonomous learning, in which direct coupling of agent and environment supports a predictive coding mechanism, bootstrapped in early development by a more fundamental process of trial-and-error learning

Sensory Processing and Associative Learning in Connectome-Based Neural Circuits

There has been a significant increase in the amount of connectomics data available at the level of single neurons and single synapses in the last few years. This increase enabled investigations into the structure and function of neural circuits in much greater detail than ever before. Thus, the next step in our quest to understand the brain's functional logic is the development of tools and methods to enable us to extract data from and model these new connectomics datasets, and their use to start to examine the brain computationally. Specifically, for Drosophila melanogaster, the fruit fly, a large amount of data on the connectome have become available in the last few years. In this dissertation, we start by introducing the tools we have built to extract information from the Drosophila connectome and to create spiking models of neuropils using this information to model sensory processing and associative learning circuits at single-synapse scale. We then use the toolkit we have introduced to explore sensory processing and associative learning in the brain. First, we introduce FlyBrainLab, an interactive computing environment designed to accelerate the discovery of functional logic of the Drosophila brain. Then, we propose a programmable ontology that expands the scope of the current Drosophila brain anatomy ontologies to encompass the functional logic of the fly brain, providing a language not only for modeling circuit motifs but also for programmatically exploring their functional logic; we introduce the FeedbackCircuits library for exploring the functional logic of the massive number of feedback loops (motifs) in the fruit fly brain, and NeuroNLP++, an application that supports free-form English queries for constructing functional brain circuits fully anchored on the available connectome/synaptome datasets. Thirdly, following up on the second, we explore the construction of antennal lobe circuits using models of glomeruli. We explore the composability of the connectivity of glomeruli with local neuron feedback loops, and quantitatively characterize the I/O of the AL as a function of feedback loop motifs in the one-glomerulus, two-glomerulus and 23-glomerulus scenarios. Lastly, in the final chapter, we consider the modeling of the mushroom body, a second order olfactory neuropil and a center of associative learning, to demonstrate how the architecture of the circuit interacts with the circuit mechanisms by which sensory inputs are represented and memories are updated. Thus, in this dissertation we introduce an approach for the analysis and modeling of neural circuits based on connectomics data, and apply this approach to neural circuits spanning multiple neuropils to extract and analyze the principles of computation in the brain. The methodology described here is designed to be applied to different sensory systems and organisms to infer the functional logic of connectome-based neural circuits