Structurele en functionele hersenbeeldvorming bij suïcidaal gedrag

Suïcidaal gedrag is het gevolg van de interactie tussen ingrijpende levensgebeurtenissen en een voorbeschiktheid of kwetsbaarheid. In toenemende mate wordt duidelijk dat neuropsychologische veranderingen uiting kunnen zijn van deze kwetsbaarheid. Met behulp van functionele en structurele beeldvorming van de hersenen kan nu ook de neuroanatomische en neurobiologische basis van deze kwetsbaarheid in beeld worden gebracht. De laatste jaren is inderdaad een toenemend aantal studies gepubliceerd, waarin door middel van functionele en structurele beeldvorming van de hersenen onderzoek wordt verricht naar de kenmerken van suïcidale hersenen. In dit artikel worden de bevindingen uit deze onderzoeken besproken, waarop aansluitend wordt ingegaan op het belang voor de preventie van suïcide

Biologically Plausible Cortical Hierarchical-Classifier Circuit Extensions in Spiking Neurons

Hierarchical categorization inter-leaved with sequence recognition of incoming stimuli in the mammalian brain is theorized to be performed by circuits composed of the thalamus and the six-layer cortex. Using these circuits, the cortex is thought to learn a ‘brain grammar’ composed of recursive sequences of categories. A thalamo-cortical, hierarchical classification and sequence learning “Core” circuit implemented as a linear matrix simulation and was published by Rodriguez, Whitson & Granger in 2004. In the brain, these functions are implemented by cortical and thalamic circuits composed of recurrently-connected, spiking neurons. The Neural Engineering Framework (NEF) (Eliasmith & Anderson, 2003) allows for the construction of large-scale biologically plausible neural networks. Existing NEF models of the basal-ganglia and the thalamus exist but to the best of our knowledge there does not exist an integrated, spiking-neuron, cortical-thalamic-Core network model. We construct a more biologically-plausible version of the hierarchical-classification function of the Core circuit using leaky-integrate-and-fire neurons which performs progressive visual classification of static image sequences relying on the neural activity levels to trigger the progressive classification of the stimulus. We proceed by implementing a recurrent NEF model of the cortical-thalamic Core circuit and then test the resulting model on the hierarchical categorization of images

The evolution of brain architectures for predictive coding and active inference

This article considers the evolution of brain architectures for predictive processing. We argue that brain mechanisms for predictive perception and action are not late evolutionary additions of advanced creatures like us. Rather, they emerged gradually from simpler predictive loops (e.g. autonomic and motor reflexes) that were a legacy from our earlier evolutionary ancestors-and were key to solving their fundamental problems of adaptive regulation. We characterize simpler-to-more-complex brains formally, in terms of generative models that include predictive loops of increasing hierarchical breadth and depth. These may start from a simple homeostatic motif and be elaborated during evolution in four main ways: these include the multimodal expansion of predictive control into an allostatic loop; its duplication to form multiple sensorimotor loops that expand an animal's behavioural repertoire; and the gradual endowment of generative models with hierarchical depth (to deal with aspects of the world that unfold at different spatial scales) and temporal depth (to select plans in a future-oriented manner). In turn, these elaborations underwrite the solution to biological regulation problems faced by increasingly sophisticated animals. Our proposal aligns neuroscientific theorising-about predictive processing-with evolutionary and comparative data on brain architectures in different animal species. This article is part of the theme issue 'Systems neuroscience through the lens of evolutionary theory'

The role of dopaminergic and cholinergic modulation on the striatal network : a computational investigation

The famous words from the French philosopher René Descartes (1596-1650), “I think therefore I am”, proclaims that since we are thinking we must also exist. At the time when this was stated, very little was known about the main organ involved in thinking, the nervous system. Today we know that the nervous system consists of interconnected cells, so called neurons that communicate with each other through electro-chemical signals. This has been known for little over a century and during this time we have gathered an impressive amount of detailed data on neurons and the circuits they make up. Despite this, we still don’t have a detailed description of the overall computing mechanism of the central nervous system, the brain, or even single nuclei within the brain. One reason for this is the transient nature of the brain, continuously going in and out of operational modes, or so called brain states. The state of the brain is heavily influenced by neuromodulators – molecules changing the properties of neurons and the connections between them. One area strongly affected by neuromodulators is the striatum, the main input structure of the basal ganglia. The basal ganglia are an evolutionary conserved set of interconnected nuclei tightly connected to the cerebral cortex and thalamus, with which they form a loop. From pathological states like Parkinson’s disease we know that the basal ganglia are involved in motor control. More specifically they have been proposed to drive formation and control of automatic motor response sequences (including habits), but like in the rest of the brain, the modus operandi of the basal ganglia is not known. To bridge the gap between data and function we therefore need models and testable theories. In this thesis I have studied the role of neuromodulation in the striatal microcircuit, with the aim of understanding how subcellular changes affect cellular behavior. The technique used is biophysically detailed computational modelling. The essence of these models tries to mimic the electro-chemical signals within and between neurons using as detailed a description of individual neurons as possible. From this standpoint a good model minimizes the number of assumptions used in construction, by restricting the model to experimentally measured entities. Simulations of the striatal projection neurons in such models show that complex spikes – a particular type of neuronal signal associated with learning in other brain regions – may be triggered following manipulation of certain conductances in the cell membrane. In our simulations, the complex spikes were associated with large calcium signals in the dendrites, indicating a more robust form of crosstalk in the soma-to-dendrites direction than following regular action potentials. Together these simulations extend the theory of striatal function and learning

Neurodemocracy: Self-Organization of the Embodied Mind

This thesis contributes to a better conceptual understanding of how self-organized control works. I begin by analyzing the control problem and its solution space. I argue that the two prominent solutions offered by classical cognitive science (centralized control with rich commands, e.g., the Fodorian central systems) and embodied cognitive science (distributed control with simple commands, such as the subsumption architecture by Rodney Brooks) are merely two positions in a two-dimensional solution space. I outline two alternative positions: one is distributed control with rich commands, defended by proponents of massive modularity hypothesis; the other is centralized control with simple commands. My goal is to develop a hybrid account that combines aspects of the second alternative position and that of the embodied cognitive science (i.e., centralized and distributed controls with simple commands). Before developing my account, I discuss the virtues and challenges of the first three. This discussion results in a set of criteria for successful neural control mechanisms. Then, I develop my account through analyzing neuroscientific models of decision-making and control with the theoretical lenses provided by formal decision and social choice theories. I contend that neural processes can be productively modeled as a collective of agents, and neural self-organization is analogous to democratic self-governance. In particular, I show that the basal ganglia, a set of subcortical structures, contribute to the production of coherent and intelligent behaviors through implementing “democratic" procedures. Unlike the Fodorian central system—which is a micro-managing “neural commander-in-chief”—the basal ganglia are a “central election commission.” They delegate control of habitual behaviors to other distributed control mechanisms. Yet, when novel problems arise, they engage and determine the result on the basis of simple information (the votes) from across the system with the principles of neurodemocracy, and control with simple commands of inhibition and disinhibition. By actively managing and taking advantage of the wisdom-of-the-crowd effect, these democratic processes enhance the intelligence and coherence of the mind’s final "collective" decisions. I end by defending this account from both philosophical and empirical criticisms and showing that it meets the criteria for successful solution

Entangled predictive brain: emotion, prediction and embodied cognition

How does the living body impact, and perhaps even help constitute, the thinking, reasoning, feeling agent? This is the guiding question that the following work seeks to answer. The subtitle of this project is emotion, prediction and embodied cognition for good reason: these are the three closely related themes that tie together the various chapters of the following thesis. The central claim is that a better understanding of the nature of emotion offers valuable insight for understanding the nature of the so called ‘predictive mind’, including a powerful new way to think about the mind as embodied Recently a new perspective has arguably taken the pole position in both philosophy of mind and the cognitive sciences when it comes to discussing the nature of mind. This framework takes the brain to be a probabilistic prediction engine. Such engines, so the framework proposes, are dedicated to the task of minimizing the disparity between how they expect the world to be and how the world actually is. Part of the power of the framework is the elegant suggestion that much of what we take to be central to human intelligence - perception, action, emotion, learning and language - can be understood within the framework of prediction and error reduction. In what follows I will refer to this general approach to understanding the mind and brain as 'predictive processing'. While the predictive processing framework is in many ways revolutionary, there is a tendency for researchers interested in this topic to assume a very traditional ‘neurocentric’ stance concerning the mind. I argue that this neurocentric stance is completely optional, and that a focus on emotional processing provides good reasons to think that the predictive mind is also a deeply embodied mind. The result is a way of understanding the predictive brain that allows the body and the surrounding environment to make a robust constitutive contribution to the predictive process. While it’s true that predictive models can get us a long way in making sense of what drives the neural-economy, I will argue that a complete picture of human intelligence requires us to also explore the many ways that a predictive brain is embodied in a living body and embedded in the social-cultural world in which it was born and lives

Optimal anticipatory control as a theory of motor preparation

Supported by a decade of primate electrophysiological experiments, the prevailing theory of neural motor control holds that movement generation is accomplished by a preparatory process that progressively steers the state of the motor cortex into a movement-specific optimal subspace prior to movement onset. The state of the cortex then evolves from these optimal subspaces, producing patterns of neural activity that serve as control inputs to the musculature. This theory, however, does not address the following questions: what characterizes the optimal subspace and what are the neural mechanisms that underlie the preparatory process? We address these questions with a circuit model of movement preparation and control. Specifically, we propose that preparation can be achieved by optimal feedback control (OFC) of the cortical state via a thalamo-cortical loop. Under OFC, the state of the cortex is selectively controlled along state-space directions that have future motor consequences, and not in other inconsequential ones. We show that OFC enables fast movement preparation and explains the observed orthogonality between preparatory and movement-related monkey motor cortex activity. This illustrates the importance of constraining new theories of neural function with experimental data. However, as recording technologies continue to improve, a key challenge is to extract meaningful insights from increasingly large-scale neural recordings. Latent variable models (LVMs) are powerful tools for addressing this challenge due to their ability to identify the low-dimensional latent variables that best explain these large data sets. One shortcoming of most LVMs, however, is that they assume a Euclidean latent space, while many kinematic variables, such as head rotations and the configuration of an arm, are naturally described by variables that live on non-Euclidean latent spaces (e.g., SO(3) and tori). To address this shortcoming, we propose the Manifold Gaussian Process Latent Variable Model, a method for simultaneously inferring nonparametric tuning curves and latent variables on non-Euclidean latent spaces. We show that our method is able to correctly infer the latent ring topology of the fly and mouse head direction circuits.This work was supported by a Trinity-Henry Barlow scholarship and a scholarship from the Ministry of Education, ROC Taiwan

Multiple value systems for adaptive decision-making

Values, rewards, uncertainty and risk play a central role in economic and psychological theories of decision-making. Over the past decade, numerous experiments have used neuroimaging techniques to uncover the neural realization of such decision variables while individuals engage in a range of tasks. These have led to a consensus that economic choice involves interplay between multiple systems that enjoy both cooperative and competitive relations. In this thesis, I utilize functional magnetic resonance imaging (fMRI) and computational formalizations of choice to explore how these different brain systems interact to support adaptive decision-making. In Chapters 4 and 5, I present data from a task in which the inclusion of a dynamic environment required subjects to sometimes approach an option they would normally avoid, or avoid an option they would normally approach. This allowed me to uncover brain systems that track time-varying components of the environment, or immediate reward information, as well as the mechanisms by which these components are integrated. I found that adaptive control in this context involves downstream integration, via functional coupling, of distinct decision components that are computed in separate, often widespread, networks. Yet, choice variables represented in the striatum may in some cases be resistant to modulation, contributing to maladaptive behaviour. In Chapter 6, I investigate whether task training alters the way in which these different value systems manifest in choice; or more broadly, whether value computations in the brain adapt as humans become more proficient at internalizing models of the world. To address this, I trained subjects on a value-guided decision-making task for 3 consecutive days. The data are suggestive of a shift in the implementation of value-guided planning with training, from a more cumbersome, resource-dependant mechanism, to a more efficient and robust process that remains resistant to attentional load

A Survey on Reservoir Computing and its Interdisciplinary Applications Beyond Traditional Machine Learning

Reservoir computing (RC), first applied to temporal signal processing, is a recurrent neural network in which neurons are randomly connected. Once initialized, the connection strengths remain unchanged. Such a simple structure turns RC into a non-linear dynamical system that maps low-dimensional inputs into a high-dimensional space. The model's rich dynamics, linear separability, and memory capacity then enable a simple linear readout to generate adequate responses for various applications. RC spans areas far beyond machine learning, since it has been shown that the complex dynamics can be realized in various physical hardware implementations and biological devices. This yields greater flexibility and shorter computation time. Moreover, the neuronal responses triggered by the model's dynamics shed light on understanding brain mechanisms that also exploit similar dynamical processes. While the literature on RC is vast and fragmented, here we conduct a unified review of RC's recent developments from machine learning to physics, biology, and neuroscience. We first review the early RC models, and then survey the state-of-the-art models and their applications. We further introduce studies on modeling the brain's mechanisms by RC. Finally, we offer new perspectives on RC development, including reservoir design, coding frameworks unification, physical RC implementations, and interaction between RC, cognitive neuroscience and evolution.Comment: 51 pages, 19 figures, IEEE Acces