A few strong connections: optimizing information retention in neuronal avalanches

<p>Abstract</p> <p>Background</p> <p>How living neural networks retain information is still incompletely understood. Two prominent ideas on this topic have developed in parallel, but have remained somewhat unconnected. The first of these, the "synaptic hypothesis," holds that information can be retained in synaptic connection strengths, or weights, between neurons. Recent work inspired by statistical mechanics has suggested that networks will retain the most information when their weights are distributed in a skewed manner, with many weak weights and only a few strong ones. The second of these ideas is that information can be represented by stable activity patterns. Multineuron recordings have shown that sequences of neural activity distributed over many neurons are repeated above chance levels when animals perform well-learned tasks. Although these two ideas are compelling, no one to our knowledge has yet linked the predicted optimum distribution of weights to stable activity patterns actually observed in living neural networks.</p> <p>Results</p> <p>Here, we explore this link by comparing stable activity patterns from cortical slice networks recorded with multielectrode arrays to stable patterns produced by a model with a tunable weight distribution. This model was previously shown to capture central features of the dynamics in these slice networks, including neuronal avalanche cascades. We find that when the model weight distribution is appropriately skewed, it correctly matches the distribution of repeating patterns observed in the data. In addition, this same distribution of weights maximizes the capacity of the network model to retain stable activity patterns. Thus, the distribution that best fits the data is also the distribution that maximizes the number of stable patterns.</p> <p>Conclusions</p> <p>We conclude that local cortical networks are very likely to use a highly skewed weight distribution to optimize information retention, as predicted by theory. Fixed distributions impose constraints on learning, however. The network must have mechanisms for preserving the overall weight distribution while allowing individual connection strengths to change with learning.</p

Action selection in the rhythmic brain: The role of the basal ganglia and tremor.

Low-frequency oscillatory activity has been the target of extensive research both in cortical structures and in the basal ganglia (BG), due to numerous reports of associations with brain disorders and the normal functioning of the brain. Additionally, a plethora of evidence and theoretical work indicates that the BG might be the locus where conflicts between prospective actions are being resolved. Whereas a number of computational models of the BG investigate these phenomena, these models tend to focus on intrinsic oscillatory mechanisms, neglecting evidence that points to the cortex as the origin of this oscillatory behaviour. In this thesis, we construct a detailed neural model of the complete BG circuit based on fine-tuned spiking neurons, with both electrical and chemical synapses as well as short-term plasticity between structures. To do so, we build a complete suite of computational tools for the design, optimization and simulation of spiking neural networks. Our model successfully reproduces firing and oscillatory behaviour found in both the healthy and Parkinsonian BG, and it was used to make a number of biologically-plausible predictions. First, we investigate the influence of various cortical frequency bands on the intrinsic effective connectivity of the BG, as well as the role of the latter in regulating cortical behaviour. We found that, indeed, effective connectivity changes dramatically for different cortical frequency bands and phase offsets, which are able to modulate (or even block) information flow in the three major BG pathways. Our results indicate the existence of a multimodal gating mechanism at the level of the BG that can be entirely controlled by cortical oscillations, and provide evidence for the hypothesis of cortically-entrained but locally-generated subthalamic beta activity. Next, we explore the relationship of wave properties of entrained cortical inputs, dopamine and the transient effectiveness of the BG, when viewed as an action selection device. We found that cortical frequency, phase, dopamine and the examined time scale, all have a very important impact on the ability of our model to select. Our simulations resulted in a canonical profile of selectivity, which we termed selectivity portraits. Taking together, our results suggest that the cortex is the structure that determines whether action selection will be performed and what strategy will be utilized while the role of the BG is to perform this selection. Some frequency ranges promote the exploitation of actions of whom the outcome is known, others promote the exploration of new actions with high uncertainty while the remaining frequencies simply deactivate selection. Based on this behaviour, we propose a metaphor according to which, the basal ganglia can be viewed as the ''gearbox" of the cortex. Coalitions of rhythmic cortical areas are able to switch between a repertoire of available BG modes which, in turn, change the course of information flow back to and within the cortex. In the same context, dopamine can be likened to the ''control pedals" of action selection that either stop or initiate a decision. Finally, the frequency of active cortical areas that project to the BG acts as a gear lever, that instead of controlling the type and direction of thrust that the throttle provides to an automobile, it dictates the extent to which dopamine can trigger a decision, as well as what type of decision this will be. Finally, we identify a selection cycle with a period of around 200 ms, which was used to assess the biological plausibility of the most popular architectures in cognitive science. Using extensions of the BG model, we further propose novel mechanisms that provide explanations for (1) the two distinctive dynamical behaviours of neurons in globus pallidus external, and (2) the generation of resting tremor in Parkinson's disease. Our findings agree well with experimental observations, suggest new insights into the pathophysiology of specific BG disorders, provide new justifications for oscillatory phenomena related to decision making and reaffirm the role of the BG as the selection centre of the brain.Open Acces

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

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

The connected brain: Causality, models and intrinsic dynamics

Recently, there have been several concerted international efforts - the BRAIN initiative, European Human Brain Project and the Human Connectome Project, to name a few - that hope to revolutionize our understanding of the connected brain. Over the past two decades, functional neuroimaging has emerged as the predominant technique in systems neuroscience. This is foreshadowed by an ever increasing number of publications on functional connectivity, causal modeling, connectomics, and multivariate analyses of distributed patterns of brain responses. In this article, we summarize pedagogically the (deep) history of brain mapping. We will highlight the theoretical advances made in the (dynamic) causal modelling of brain function - that may have escaped the wider audience of this article - and provide a brief overview of recent developments and interesting clinical applications. We hope that this article will engage the signal processing community by showcasing the inherently multidisciplinary nature of this important topic and the intriguing questions that are being addressed

Criticality in neural networks: a study of the interplay between experimental tools and theoretical models

In the human brain trillions of neurons transmit information “firing” electrical pulses called action potentials or spikes. Neurons are connected to each other and form highly complex networks in which a single neuron may be connected to thousands of other neurons. The activity of single neurons and, more recently, the activity of groups of neurons have been monitored extensively using intracellular and extracellular recordings. One of the most striking observations arisen from such recordings is the fact that neuronal activity seems to be characterized by “avalanches” whose size and lifetime distributions obey a power law, which is typical of self-organized critical systems. Such critical behavior has been confirmed also by theoretical models, but the way avalanches are defined and detected in the experimental analysis is very different from the way they are defined and detected in theoretical simulations. In this work, after a brief review of the concept of Self-Organized Criticality, we describe the experiment that led to the observation of neuronal avalanches. Then, we describe the Millman model, a neuronal network model that reproduces the critical behavior observed in real networks. Finally, we investigate the differences between theoretical and experimental avalanches. In particular, we analyze the data from numerical simulations with the methods used to detect avalanches in real networks. We show that if the methods of analysis change, the critical behavior is no longer observed